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| # Solve.mjs Log - 2026-01-18T20:49:30.900Z | |
| [2026-01-18T20:49:30.901Z] [INFO] 📁 Log file: /home/hive/solve-2026-01-18T20-49-30-899Z.log | |
| [2026-01-18T20:49:30.902Z] [INFO] (All output will be logged here) | |
| [2026-01-18T20:49:31.376Z] [INFO] | |
| [2026-01-18T20:49:31.377Z] [INFO] 🚀 solve v1.4.0 | |
| [2026-01-18T20:49:31.377Z] [INFO] 🔧 Raw command executed: | |
| [2026-01-18T20:49:31.378Z] [INFO] /home/hive/.nvm/versions/node/v20.19.6/bin/node /home/hive/.bun/bin/solve https://github.com/suenot/machine-learning-for-trading/issues/83 --model opus --attach-logs --verbose --no-tool-check --auto-resume-on-limit-reset --tokens-budget-stats | |
| [2026-01-18T20:49:31.378Z] [INFO] | |
| [2026-01-18T20:49:31.392Z] [INFO] | |
| [2026-01-18T20:49:31.393Z] [WARNING] ⚠️ SECURITY WARNING: --attach-logs is ENABLED | |
| [2026-01-18T20:49:31.394Z] [INFO] | |
| [2026-01-18T20:49:31.394Z] [INFO] This option will upload the complete solution draft log file to the Pull Request. | |
| [2026-01-18T20:49:31.394Z] [INFO] The log may contain sensitive information such as: | |
| [2026-01-18T20:49:31.395Z] [INFO] • API keys, tokens, or secrets | |
| [2026-01-18T20:49:31.395Z] [INFO] • File paths and directory structures | |
| [2026-01-18T20:49:31.395Z] [INFO] • Command outputs and error messages | |
| [2026-01-18T20:49:31.395Z] [INFO] • Internal system information | |
| [2026-01-18T20:49:31.396Z] [INFO] | |
| [2026-01-18T20:49:31.396Z] [INFO] ⚠️ DO NOT use this option with public repositories or if the log | |
| [2026-01-18T20:49:31.396Z] [INFO] might contain sensitive data that should not be shared publicly. | |
| [2026-01-18T20:49:31.396Z] [INFO] | |
| [2026-01-18T20:49:31.397Z] [INFO] Continuing in 5 seconds... (Press Ctrl+C to abort) | |
| [2026-01-18T20:49:31.397Z] [INFO] | |
| [2026-01-18T20:49:36.401Z] [INFO] | |
| [2026-01-18T20:49:36.424Z] [INFO] 💾 Disk space check: 52845MB available (2048MB required) ✅ | |
| [2026-01-18T20:49:36.426Z] [INFO] 🧠 Memory check: 9586MB available, swap: 4095MB (0MB used), total: 13681MB (256MB required) ✅ | |
| [2026-01-18T20:49:36.448Z] [INFO] ⏩ Skipping tool connection validation (dry-run mode or skip-tool-connection-check enabled) | |
| [2026-01-18T20:49:36.449Z] [INFO] ⏩ Skipping GitHub authentication check (dry-run mode or skip-tool-connection-check enabled) | |
| [2026-01-18T20:49:36.450Z] [INFO] 📋 URL validation: | |
| [2026-01-18T20:49:36.450Z] [INFO] Input URL: https://github.com/suenot/machine-learning-for-trading/issues/83 | |
| [2026-01-18T20:49:36.451Z] [INFO] Is Issue URL: true | |
| [2026-01-18T20:49:36.451Z] [INFO] Is PR URL: false | |
| [2026-01-18T20:49:36.451Z] [INFO] 🔍 Checking repository access for auto-fork... | |
| [2026-01-18T20:49:37.112Z] [INFO] Repository visibility: public | |
| [2026-01-18T20:49:37.112Z] [INFO] ✅ Auto-fork: No write access detected, enabling fork mode | |
| [2026-01-18T20:49:37.113Z] [INFO] ✅ Repository access check: Skipped (fork mode enabled) | |
| [2026-01-18T20:49:37.469Z] [INFO] Repository visibility: public | |
| [2026-01-18T20:49:37.470Z] [INFO] Auto-cleanup default: false (repository is public) | |
| [2026-01-18T20:49:37.471Z] [INFO] 🔍 Auto-continue enabled: Checking for existing PRs for issue #83... | |
| [2026-01-18T20:49:38.372Z] [INFO] 🔍 Fork mode: Checking for existing branches in konard/suenot-machine-learning-for-trading... | |
| [2026-01-18T20:49:39.404Z] [INFO] 📋 Found 1 existing PR(s) linked to issue #83 | |
| [2026-01-18T20:49:39.405Z] [INFO] PR #375: created 0h ago (OPEN, draft) | |
| [2026-01-18T20:49:39.406Z] [INFO] PR #375: Branch 'issue-81-916ad28305f5' doesn't match expected pattern 'issue-83-*' - skipping | |
| [2026-01-18T20:49:39.406Z] [INFO] ⏭️ No suitable PRs found (missing CLAUDE.md/.gitkeep or older than 24h) - creating new PR as usual | |
| [2026-01-18T20:49:39.406Z] [INFO] 📝 Issue mode: Working with issue #83 | |
| [2026-01-18T20:49:39.408Z] [INFO] | |
| Creating temporary directory: /tmp/gh-issue-solver-1768769379407 | |
| [2026-01-18T20:49:39.410Z] [INFO] | |
| 🍴 Fork mode: ENABLED | |
| [2026-01-18T20:49:39.410Z] [INFO] Checking fork status... | |
| [2026-01-18T20:49:39.745Z] [INFO] 🔍 Detecting fork conflicts... | |
| [2026-01-18T20:49:40.777Z] [INFO] ✅ No fork conflict: Safe to proceed | |
| [2026-01-18T20:49:41.186Z] [INFO] ✅ Fork exists: konard/suenot-machine-learning-for-trading | |
| [2026-01-18T20:49:41.187Z] [INFO] 🔍 Validating fork parent... | |
| [2026-01-18T20:49:41.561Z] [INFO] ✅ Fork parent validated: suenot/machine-learning-for-trading | |
| [2026-01-18T20:49:41.563Z] [INFO] | |
| 📥 Cloning repository: konard/suenot-machine-learning-for-trading | |
| [2026-01-18T20:51:01.596Z] [INFO] ✅ Cloned to: /tmp/gh-issue-solver-1768769379407 | |
| [2026-01-18T20:51:01.652Z] [INFO] 🔗 Setting upstream: suenot/machine-learning-for-trading | |
| [2026-01-18T20:51:01.704Z] [INFO] ℹ️ Upstream exists: Using existing upstream remote | |
| [2026-01-18T20:51:01.705Z] [INFO] 🔄 Fetching upstream... | |
| [2026-01-18T20:51:02.148Z] [INFO] ✅ Upstream fetched: Successfully | |
| [2026-01-18T20:51:02.148Z] [INFO] 🔄 Syncing default branch... | |
| [2026-01-18T20:51:02.635Z] [INFO] ℹ️ Default branch: main | |
| [2026-01-18T20:51:02.985Z] [INFO] ✅ Default branch synced: with upstream/main | |
| [2026-01-18T20:51:02.986Z] [INFO] 🔄 Pushing to fork: main branch | |
| [2026-01-18T20:51:03.532Z] [INFO] ✅ Fork updated: Default branch pushed to fork | |
| [2026-01-18T20:51:03.722Z] [INFO] | |
| 📌 Default branch: main | |
| [2026-01-18T20:51:03.812Z] [INFO] | |
| 🌿 Creating branch: issue-83-e34794bc7991 from main (default) | |
| [2026-01-18T20:51:03.880Z] [INFO] 🔍 Verifying: Branch creation... | |
| [2026-01-18T20:51:03.929Z] [INFO] ✅ Branch created: issue-83-e34794bc7991 | |
| [2026-01-18T20:51:03.930Z] [INFO] ✅ Current branch: issue-83-e34794bc7991 | |
| [2026-01-18T20:51:03.931Z] [INFO] Branch operation: Create new branch | |
| [2026-01-18T20:51:03.932Z] [INFO] Branch verification: Matches expected | |
| [2026-01-18T20:51:03.935Z] [INFO] | |
| 🚀 Auto PR creation: ENABLED | |
| [2026-01-18T20:51:03.936Z] [INFO] Creating: Initial commit and draft PR... | |
| [2026-01-18T20:51:03.936Z] [INFO] | |
| [2026-01-18T20:51:04.004Z] [INFO] Using CLAUDE.md mode (--claude-file=true, --gitkeep-file=false, --auto-gitkeep-file=true) | |
| [2026-01-18T20:51:04.005Z] [INFO] 📝 Creating: CLAUDE.md with task details | |
| [2026-01-18T20:51:04.006Z] [INFO] Issue URL from argv['issue-url']: https://github.com/suenot/machine-learning-for-trading/issues/83 | |
| [2026-01-18T20:51:04.007Z] [INFO] Issue URL from argv._[0]: undefined | |
| [2026-01-18T20:51:04.010Z] [INFO] Final issue URL: https://github.com/suenot/machine-learning-for-trading/issues/83 | |
| [2026-01-18T20:51:04.011Z] [INFO] ✅ File created: CLAUDE.md | |
| [2026-01-18T20:51:04.012Z] [INFO] 📦 Adding file: To git staging | |
| [2026-01-18T20:51:04.130Z] [INFO] Git status after add: A CLAUDE.md | |
| [2026-01-18T20:51:04.132Z] [INFO] 📝 Creating commit: With CLAUDE.md file | |
| [2026-01-18T20:51:04.192Z] [INFO] ✅ Commit created: Successfully with CLAUDE.md | |
| [2026-01-18T20:51:04.193Z] [INFO] Commit output: [issue-83-e34794bc7991 f41555e] Initial commit with task details | |
| 1 file changed, 7 insertions(+) | |
| create mode 100644 CLAUDE.md | |
| [2026-01-18T20:51:04.231Z] [INFO] Commit hash: f41555e... | |
| [2026-01-18T20:51:04.276Z] [INFO] Latest commit: f41555e Initial commit with task details | |
| [2026-01-18T20:51:04.338Z] [INFO] Git status: clean | |
| [2026-01-18T20:51:04.377Z] [INFO] Remotes: origin https://github.com/konard/suenot-machine-learning-for-trading.git (fetch) | |
| [2026-01-18T20:51:04.419Z] [INFO] Branch info: * issue-83-e34794bc7991 f41555e [origin/main: ahead 1] Initial commit with task details | |
| main 73ccc8a [origin/main] Merge pull request #374 from konard/issue-80-14783cab8a5d | |
| [2026-01-18T20:51:04.419Z] [INFO] 📤 Pushing branch: To remote repository... | |
| [2026-01-18T20:51:04.419Z] [INFO] Push command: git push -u origin issue-83-e34794bc7991 | |
| [2026-01-18T20:51:05.204Z] [INFO] Push exit code: 0 | |
| [2026-01-18T20:51:05.205Z] [INFO] Push output: remote: | |
| remote: Create a pull request for 'issue-83-e34794bc7991' on GitHub by visiting: | |
| remote: https://github.com/konard/suenot-machine-learning-for-trading/pull/new/issue-83-e34794bc7991 | |
| remote: | |
| To https://github.com/konard/suenot-machine-learning-for-trading.git | |
| * [new branch] issue-83-e34794bc7991 -> issue-83-e34794bc7991 | |
| branch 'issue-83-e34794bc7991' set up to track 'origin/issue-83-e34794bc7991'. | |
| [2026-01-18T20:51:05.205Z] [INFO] ✅ Branch pushed: Successfully to remote | |
| [2026-01-18T20:51:05.205Z] [INFO] Push output: remote: | |
| remote: Create a pull request for 'issue-83-e34794bc7991' on GitHub by visiting: | |
| remote: https://github.com/konard/suenot-machine-learning-for-trading/pull/new/issue-83-e34794bc7991 | |
| remote: | |
| To https://github.com/konard/suenot-machine-learning-for-trading.git | |
| * [new branch] issue-83-e34794bc7991 -> issue-83-e34794bc7991 | |
| branch 'issue-83-e34794bc7991' set up to track 'origin/issue-83-e34794bc7991'. | |
| [2026-01-18T20:51:05.205Z] [INFO] Waiting for GitHub to sync... | |
| [2026-01-18T20:51:07.681Z] [INFO] Compare API check: 1 commit(s) ahead of main | |
| [2026-01-18T20:51:07.682Z] [INFO] GitHub compare API ready: 1 commit(s) found | |
| [2026-01-18T20:51:08.007Z] [INFO] Branch verified on GitHub: issue-83-e34794bc7991 | |
| [2026-01-18T20:51:08.313Z] [INFO] Remote commit SHA: f41555e... | |
| [2026-01-18T20:51:08.314Z] [INFO] 📋 Getting issue: Title from GitHub... | |
| [2026-01-18T20:51:08.648Z] [INFO] Issue title: "Chapter 46: temporal attention networks" | |
| [2026-01-18T20:51:08.648Z] [INFO] 👤 Getting user: Current GitHub account... | |
| [2026-01-18T20:51:08.971Z] [INFO] Current user: konard | |
| [2026-01-18T20:51:09.212Z] [INFO] User is not a collaborator (will skip assignment) | |
| [2026-01-18T20:51:09.213Z] [INFO] User is not a collaborator (will skip assignment) | |
| [2026-01-18T20:51:09.214Z] [INFO] 🔄 Fetching: Latest main branch... | |
| [2026-01-18T20:51:09.542Z] [INFO] ✅ Base updated: Fetched latest main | |
| [2026-01-18T20:51:09.543Z] [INFO] 🔍 Checking: Commits between branches... | |
| [2026-01-18T20:51:09.589Z] [INFO] Commits ahead of origin/main: 1 | |
| [2026-01-18T20:51:09.590Z] [INFO] ✅ Commits found: 1 commit(s) ahead | |
| [2026-01-18T20:51:09.590Z] [INFO] 🔀 Creating PR: Draft pull request... | |
| [2026-01-18T20:51:09.591Z] [INFO] 🎯 Target branch: main (default) | |
| [2026-01-18T20:51:09.591Z] [INFO] PR Title: [WIP] Chapter 46: temporal attention networks | |
| [2026-01-18T20:51:09.591Z] [INFO] Base branch: main | |
| [2026-01-18T20:51:09.591Z] [INFO] Head branch: issue-83-e34794bc7991 | |
| [2026-01-18T20:51:09.591Z] [INFO] Assignee: konard | |
| [2026-01-18T20:51:09.592Z] [INFO] PR Body: | |
| ## 🤖 AI-Powered Solution Draft | |
| This pull request is being automatically generated to solve issue suenot/machine-learning-for-trading#83. | |
| ### 📋 Issue Reference | |
| Fixes suenot/machine-learning-for-trading#83 | |
| ### 🚧 Status | |
| **Work in Progress** - The AI assistant is currently analyzing and implementing the solution draft. | |
| ### 📝 Implementation Details | |
| _Details will be added as the solution draft is developed..._ | |
| --- | |
| *This PR was created automatically by the AI issue solver* | |
| [2026-01-18T20:51:09.593Z] [INFO] Command: cd "/tmp/gh-issue-solver-1768769379407" && gh pr create --draft --title "$(cat '/tmp/pr-title-1768769469593.txt')" --body-file "/tmp/pr-body-1768769469593.md" --base main --head konard:issue-83-e34794bc7991 --repo suenot/machine-learning-for-trading | |
| [2026-01-18T20:51:11.243Z] [INFO] 🔍 Verifying: PR creation... | |
| [2026-01-18T20:51:11.563Z] [INFO] ✅ Verification: PR exists on GitHub | |
| [2026-01-18T20:51:11.564Z] [INFO] ✅ PR created: #377 | |
| [2026-01-18T20:51:11.564Z] [INFO] 📍 PR URL: https://github.com/suenot/machine-learning-for-trading/pull/377 | |
| [2026-01-18T20:51:11.565Z] [INFO] ℹ️ Note: Could not assign (no permission) | |
| [2026-01-18T20:51:11.565Z] [INFO] 🔗 Linking: Issue #83 to PR #377... | |
| [2026-01-18T20:51:12.062Z] [INFO] Issue node ID: I_kwDOQvqap87j_M2s | |
| [2026-01-18T20:51:12.418Z] [INFO] PR node ID: PR_kwDOQvqap8694r0z | |
| [2026-01-18T20:51:12.825Z] [INFO] | |
| [2026-01-18T20:51:12.826Z] [WARNING] ⚠️ ISSUE LINK MISSING: PR not linked to issue | |
| [2026-01-18T20:51:12.826Z] [INFO] | |
| [2026-01-18T20:51:12.826Z] [WARNING] The PR was created from a fork but wasn't linked to the issue. | |
| [2026-01-18T20:51:12.826Z] [WARNING] Expected: "Fixes suenot/machine-learning-for-trading#83" in PR body | |
| [2026-01-18T20:51:12.826Z] [INFO] | |
| [2026-01-18T20:51:12.827Z] [WARNING] To fix manually: | |
| [2026-01-18T20:51:12.827Z] [WARNING] 1. Edit the PR description at: https://github.com/suenot/machine-learning-for-trading/pull/377 | |
| [2026-01-18T20:51:12.827Z] [WARNING] 2. Add this line: Fixes suenot/machine-learning-for-trading#83 | |
| [2026-01-18T20:51:12.828Z] [INFO] | |
| [2026-01-18T20:51:13.116Z] [INFO] 👤 Current user: konard | |
| [2026-01-18T20:51:13.117Z] [INFO] | |
| 📊 Comment counting conditions: | |
| [2026-01-18T20:51:13.118Z] [INFO] prNumber: 377 | |
| [2026-01-18T20:51:13.118Z] [INFO] branchName: issue-83-e34794bc7991 | |
| [2026-01-18T20:51:13.119Z] [INFO] isContinueMode: false | |
| [2026-01-18T20:51:13.119Z] [INFO] Will count comments: true | |
| [2026-01-18T20:51:13.119Z] [INFO] 💬 Counting comments: Checking for new comments since last commit... | |
| [2026-01-18T20:51:13.120Z] [INFO] PR #377 on branch: issue-83-e34794bc7991 | |
| [2026-01-18T20:51:13.120Z] [INFO] Owner/Repo: suenot/machine-learning-for-trading | |
| [2026-01-18T20:51:13.561Z] [INFO] 📅 Last commit time (from API): 2026-01-18T20:51:04.000Z | |
| [2026-01-18T20:51:14.486Z] [INFO] 💬 New PR comments: 0 | |
| [2026-01-18T20:51:14.486Z] [INFO] 💬 New PR review comments: 0 | |
| [2026-01-18T20:51:14.486Z] [INFO] 💬 New issue comments: 0 | |
| [2026-01-18T20:51:14.487Z] [INFO] Total new comments: 0 | |
| [2026-01-18T20:51:14.487Z] [INFO] Comment lines to add: No (saving tokens) | |
| [2026-01-18T20:51:14.487Z] [INFO] PR review comments fetched: 0 | |
| [2026-01-18T20:51:14.487Z] [INFO] PR conversation comments fetched: 0 | |
| [2026-01-18T20:51:14.488Z] [INFO] Total PR comments checked: 0 | |
| [2026-01-18T20:51:16.985Z] [INFO] Feedback info will be added to prompt: | |
| [2026-01-18T20:51:16.986Z] [INFO] - Pull request description was edited after last commit | |
| [2026-01-18T20:51:16.986Z] [INFO] 📅 Getting timestamps: From GitHub servers... | |
| [2026-01-18T20:51:17.319Z] [INFO] 📝 Issue updated: 2026-01-18T13:34:10.000Z | |
| [2026-01-18T20:51:17.608Z] [INFO] 💬 Comments: None found | |
| [2026-01-18T20:51:18.164Z] [INFO] 🔀 Recent PR: 2026-01-18T20:51:14.000Z | |
| [2026-01-18T20:51:18.165Z] [INFO] | |
| ✅ Reference time: 2026-01-18T20:51:14.000Z | |
| [2026-01-18T20:51:18.166Z] [INFO] | |
| 🔍 Checking for uncommitted changes to include as feedback... | |
| [2026-01-18T20:51:18.235Z] [INFO] ✅ No uncommitted changes found | |
| [2026-01-18T20:51:20.747Z] [INFO] 🎭 Playwright MCP detected - enabling browser automation hints | |
| [2026-01-18T20:51:20.752Z] [INFO] | |
| 📝 Final prompt structure: | |
| [2026-01-18T20:51:20.752Z] [INFO] Characters: 430 | |
| [2026-01-18T20:51:20.753Z] [INFO] System prompt characters: 11971 | |
| [2026-01-18T20:51:20.753Z] [INFO] Feedback info: Included | |
| [2026-01-18T20:51:20.755Z] [INFO] | |
| 🤖 Executing Claude: OPUS | |
| [2026-01-18T20:51:20.755Z] [INFO] Model: opus | |
| [2026-01-18T20:51:20.756Z] [INFO] Working directory: /tmp/gh-issue-solver-1768769379407 | |
| [2026-01-18T20:51:20.756Z] [INFO] Branch: issue-83-e34794bc7991 | |
| [2026-01-18T20:51:20.756Z] [INFO] Prompt length: 430 chars | |
| [2026-01-18T20:51:20.756Z] [INFO] System prompt length: 11971 chars | |
| [2026-01-18T20:51:20.756Z] [INFO] Feedback info included: Yes (1 lines) | |
| [2026-01-18T20:51:20.799Z] [INFO] 📈 System resources before execution: | |
| [2026-01-18T20:51:20.800Z] [INFO] Memory: MemFree: 2301540 kB | |
| [2026-01-18T20:51:20.800Z] [INFO] Load: 1.75 0.73 0.44 4/455 101628 | |
| [2026-01-18T20:51:20.800Z] [INFO] | |
| 📝 Raw command: | |
| [2026-01-18T20:51:20.800Z] [INFO] (cd "/tmp/gh-issue-solver-1768769379407" && claude --output-format stream-json --verbose --dangerously-skip-permissions --model claude-opus-4-5-20251101 -p "Issue to solve: https://github.com/suenot/machine-learning-for-trading/issues/83 | |
| Your prepared branch: issue-83-e34794bc7991 | |
| Your prepared working directory: /tmp/gh-issue-solver-1768769379407 | |
| Your prepared Pull Request: https://github.com/suenot/machine-learning-for-trading/pull/377 | |
| Your forked repository: konard/suenot-machine-learning-for-trading | |
| Original repository (upstream): suenot/machine-learning-for-trading | |
| Proceed. | |
| " --append-system-prompt "You are an AI issue solver. You prefer to find the root cause of each and every issue. When you talk, you prefer to speak with facts which you have double-checked yourself or cite sources that provide evidence, like quote actual code or give references to documents or pages found on the internet. You are polite and patient, and prefer to assume good intent, trying your best to be helpful. If you are unsure or have assumptions, you prefer to test them yourself or ask questions to clarify requirements. | |
| General guidelines. | |
| - When you execute commands, always save their logs to files for easier reading if the output becomes large. | |
| - When running commands, do not set a timeout yourself — let them run as long as needed (default timeout - 2 minutes is more than enough), and once they finish, review the logs in the file. | |
| - When running sudo commands (especially package installations like apt-get, yum, npm install, etc.), always run them in the background to avoid timeout issues and permission errors when the process needs to be killed. Use the run_in_background parameter or append & to the command. | |
| - When CI is failing or user reports failures, consider adding a detailed investigation protocol to your todo list with these steps: | |
| Step 1: List recent runs with timestamps using: gh run list --repo suenot/machine-learning-for-trading --branch issue-83-e34794bc7991 --limit 5 --json databaseId,conclusion,createdAt,headSha | |
| Step 2: Verify runs are after the latest commit by checking timestamps and SHA | |
| Step 3: For each non-passing run, download logs to preserve them: gh run view {run-id} --repo suenot/machine-learning-for-trading --log > ci-logs/{workflow}-{run-id}.log | |
| Step 4: Read each downloaded log file using Read tool to understand the actual failures | |
| Step 5: Report findings with specific errors and line numbers from logs | |
| This detailed investigation is especially helpful when user mentions CI failures, asks to investigate logs, you see non-passing status, or when finalizing a PR. | |
| Note: If user says \"failing\" but tools show \"passing\", this might indicate stale data - consider downloading fresh logs and checking timestamps to resolve the discrepancy. | |
| - When a code or log file has more than 1500 lines, read it in chunks of 1500 lines. | |
| - When facing a complex problem, do as much tracing as possible and turn on all verbose modes. | |
| - When you create debug, test, or example/experiment scripts for fixing, always keep them in an examples and/or experiments folders so you can reuse them later. | |
| - When testing your assumptions, use the experiment scripts, and add it to experiments folder. | |
| - When your experiments can show real world use case of the software, add it to examples folder. | |
| - When you face something extremely hard, use divide and conquer — it always helps. | |
| Initial research. | |
| - When you start, make sure you create detailed plan for yourself and follow your todo list step by step, make sure that as many points from these guidelines are added to your todo list to keep track of everything that can help you solve the issue with highest possible quality. | |
| - When user mentions CI failures or asks to investigate logs, consider adding these todos to track the investigation: (1) List recent CI runs with timestamps, (2) Download logs from failed runs to ci-logs/ directory, (3) Analyze error messages and identify root cause, (4) Implement fix, (5) Verify fix resolves the specific errors found in logs. | |
| - When you read issue, read all details and comments thoroughly. | |
| - When you see screenshots or images in issue descriptions, pull request descriptions, comments, or discussions, use WebFetch tool (or fetch tool) to download the image first, then use Read tool to view and analyze it. IMPORTANT: Before reading downloaded images with the Read tool, verify the file is a valid image (not HTML). Use a CLI tool like 'file' command to check the actual file format. Reading corrupted or non-image files (like GitHub's HTML 404 pages saved as .png) can cause \"Could not process image\" errors and may crash the AI solver process. If the file command shows \"HTML\" or \"text\", the download failed and you should retry or skip the image. | |
| - When you need issue details, use gh issue view https://github.com/suenot/machine-learning-for-trading/issues/83. | |
| - When you need related code, use gh search code --owner suenot [keywords]. | |
| - When you need repo context, read files in your working directory. | |
| - When you study related work, study the most recent related pull requests. | |
| - When issue is not defined enough, write a comment to ask clarifying questions. | |
| - When accessing GitHub Gists (especially private ones), use gh gist view command instead of direct URL fetching to ensure proper authentication. | |
| - When you are fixing a bug, please make sure you first find the actual root cause, do as many experiments as needed. | |
| - When you are fixing a bug and code does not have enough tracing/logs, add them and make sure they stay in the code, but are switched off by default. | |
| - When you need comments on a pull request, note that GitHub has THREE different comment types with different API endpoints: | |
| 1. PR review comments (inline code comments): gh api repos/suenot/machine-learning-for-trading/pulls/377/comments --paginate | |
| 2. PR conversation comments (general discussion): gh api repos/suenot/machine-learning-for-trading/issues/377/comments --paginate | |
| 3. PR reviews (approve/request changes): gh api repos/suenot/machine-learning-for-trading/pulls/377/reviews --paginate | |
| IMPORTANT: The command \"gh pr view --json comments\" ONLY returns conversation comments and misses review comments! | |
| - When you need latest comments on issue, use gh api repos/suenot/machine-learning-for-trading/issues/83/comments --paginate. | |
| Solution development and testing. | |
| - When issue is solvable, implement code with tests. | |
| - When implementing features, search for similar existing implementations in the codebase and use them as examples instead of implementing everything from scratch. | |
| - When coding, each atomic step that can be useful by itself should be commited to the pull request's branch, meaning if work will be interrupted by any reason parts of solution will still be kept intact and safe in pull request. | |
| - When you test: | |
| start from testing of small functions using separate scripts; | |
| write unit tests with mocks for easy and quick start. | |
| - When you test integrations, use existing framework. | |
| - When you test solution draft, include automated checks in pr. | |
| - When issue is unclear, write comment on issue asking questions. | |
| - When you encounter any problems that you unable to solve yourself (any human feedback or help), write a comment to the pull request asking for help. | |
| - When you need human help, use gh pr comment 377 --body \"your message\" to comment on existing PR. | |
| Preparing pull request. | |
| - When you code, follow contributing guidelines. | |
| - When you commit, write clear message. | |
| - When you need examples of style, use gh pr list --repo suenot/machine-learning-for-trading --state merged --search [keywords]. | |
| - When you open pr, describe solution draft and include tests. | |
| - When there is a package with version and GitHub Actions workflows for automatic release, update the version (or other necessary release trigger) in your pull request to prepare for next release. | |
| - When you update existing pr 377, use gh pr edit to modify title and description. | |
| - When you are about to commit or push code, ALWAYS run local CI checks first if they are available in contributing guidelines (like ruff check, mypy, eslint, etc.) to catch errors before pushing. | |
| - When you finalize the pull request: | |
| follow style from merged prs for code, title, and description, | |
| make sure no uncommitted changes corresponding to the original requirements are left behind, | |
| make sure the default branch is merged to the pull request's branch, | |
| make sure all CI checks passing if they exist before you finish, | |
| check for latest comments on the issue and pull request to ensure no recent feedback was missed, | |
| double-check that all changes in the pull request answer to original requirements of the issue, | |
| make sure no new new bugs are introduced in pull request by carefully reading gh pr diff, | |
| make sure no previously existing features were removed without an explicit request from users via the issue description, issue comments, and/or pull request comments. | |
| - When you finish implementation, use gh pr ready 377. | |
| Workflow and collaboration. | |
| - When you check branch, verify with git branch --show-current. | |
| - When you push, push only to branch issue-83-e34794bc7991. | |
| - When you finish, create a pull request from branch issue-83-e34794bc7991. (Note: PR 377 already exists, update it instead) | |
| - When you organize workflow, use pull requests instead of direct merges to default branch (main or master). | |
| - When you manage commits, preserve commit history for later analysis. | |
| - When you contribute, keep repository history forward-moving with regular commits, pushes, and reverts if needed. | |
| - When you face conflict that you cannot resolve yourself, ask for help. | |
| - When you collaborate, respect branch protections by working only on issue-83-e34794bc7991. | |
| - When you mention result, include pull request url or comment url. | |
| - When you need to create pr, remember pr 377 already exists for this branch. | |
| Self review. | |
| - When you check your solution draft, run all tests locally. | |
| - When you check your solution draft, verify git status shows a clean working tree with no uncommitted changes. | |
| - When you compare with repo style, use gh pr diff [number]. | |
| - When you finalize, confirm code, tests, and description are consistent. | |
| GitHub CLI command patterns. | |
| - IMPORTANT: Always use --paginate flag when fetching lists from GitHub API to ensure all results are returned (GitHub returns max 30 per page by default). | |
| - When listing PR review comments (inline code comments), use gh api repos/OWNER/REPO/pulls/NUMBER/comments --paginate. | |
| - When listing PR conversation comments, use gh api repos/OWNER/REPO/issues/NUMBER/comments --paginate. | |
| - When listing PR reviews, use gh api repos/OWNER/REPO/pulls/NUMBER/reviews --paginate. | |
| - When listing issue comments, use gh api repos/OWNER/REPO/issues/NUMBER/comments --paginate. | |
| - When adding PR comment, use gh pr comment NUMBER --body \"text\" --repo OWNER/REPO. | |
| - When adding issue comment, use gh issue comment NUMBER --body \"text\" --repo OWNER/REPO. | |
| - When viewing PR details, use gh pr view NUMBER --repo OWNER/REPO. | |
| - When filtering with jq, use gh api repos/\${owner}/\${repo}/pulls/\${prNumber}/comments --paginate --jq 'reverse | .[0:5]'. | |
| Playwright MCP usage (browser automation via mcp__playwright__* tools). | |
| - When you develop frontend web applications (HTML, CSS, JavaScript, React, Vue, Angular, etc.), use Playwright MCP tools to test the UI in a real browser. | |
| - When WebFetch tool fails to retrieve expected content (e.g., returns empty content, JavaScript-rendered pages, or login-protected pages), use Playwright MCP tools (browser_navigate, browser_snapshot) as a fallback for web browsing. | |
| - When you need to interact with dynamic web pages that require JavaScript execution, use Playwright MCP tools. | |
| - When you need to visually verify how a web page looks or take screenshots, use browser_take_screenshot from Playwright MCP. | |
| - When you need to fill forms, click buttons, or perform user interactions on web pages, use Playwright MCP tools (browser_click, browser_type, browser_fill_form). | |
| - When you need to test responsive design or different viewport sizes, use browser_resize from Playwright MCP. | |
| - When you finish using the browser, always close it with browser_close to free resources." | jq -c .) | |
| [2026-01-18T20:51:20.801Z] [INFO] | |
| [2026-01-18T20:51:20.801Z] [INFO] 📋 User prompt: | |
| [2026-01-18T20:51:20.801Z] [INFO] ---BEGIN USER PROMPT--- | |
| [2026-01-18T20:51:20.801Z] [INFO] Issue to solve: https://github.com/suenot/machine-learning-for-trading/issues/83 | |
| Your prepared branch: issue-83-e34794bc7991 | |
| Your prepared working directory: /tmp/gh-issue-solver-1768769379407 | |
| Your prepared Pull Request: https://github.com/suenot/machine-learning-for-trading/pull/377 | |
| Your forked repository: konard/suenot-machine-learning-for-trading | |
| Original repository (upstream): suenot/machine-learning-for-trading | |
| Proceed. | |
| [2026-01-18T20:51:20.801Z] [INFO] ---END USER PROMPT--- | |
| [2026-01-18T20:51:20.801Z] [INFO] | |
| [2026-01-18T20:51:20.801Z] [INFO] 📋 System prompt: | |
| [2026-01-18T20:51:20.802Z] [INFO] ---BEGIN SYSTEM PROMPT--- | |
| [2026-01-18T20:51:20.802Z] [INFO] You are an AI issue solver. You prefer to find the root cause of each and every issue. When you talk, you prefer to speak with facts which you have double-checked yourself or cite sources that provide evidence, like quote actual code or give references to documents or pages found on the internet. You are polite and patient, and prefer to assume good intent, trying your best to be helpful. If you are unsure or have assumptions, you prefer to test them yourself or ask questions to clarify requirements. | |
| General guidelines. | |
| - When you execute commands, always save their logs to files for easier reading if the output becomes large. | |
| - When running commands, do not set a timeout yourself — let them run as long as needed (default timeout - 2 minutes is more than enough), and once they finish, review the logs in the file. | |
| - When running sudo commands (especially package installations like apt-get, yum, npm install, etc.), always run them in the background to avoid timeout issues and permission errors when the process needs to be killed. Use the run_in_background parameter or append & to the command. | |
| - When CI is failing or user reports failures, consider adding a detailed investigation protocol to your todo list with these steps: | |
| Step 1: List recent runs with timestamps using: gh run list --repo suenot/machine-learning-for-trading --branch issue-83-e34794bc7991 --limit 5 --json databaseId,conclusion,createdAt,headSha | |
| Step 2: Verify runs are after the latest commit by checking timestamps and SHA | |
| Step 3: For each non-passing run, download logs to preserve them: gh run view {run-id} --repo suenot/machine-learning-for-trading --log > ci-logs/{workflow}-{run-id}.log | |
| Step 4: Read each downloaded log file using Read tool to understand the actual failures | |
| Step 5: Report findings with specific errors and line numbers from logs | |
| This detailed investigation is especially helpful when user mentions CI failures, asks to investigate logs, you see non-passing status, or when finalizing a PR. | |
| Note: If user says "failing" but tools show "passing", this might indicate stale data - consider downloading fresh logs and checking timestamps to resolve the discrepancy. | |
| - When a code or log file has more than 1500 lines, read it in chunks of 1500 lines. | |
| - When facing a complex problem, do as much tracing as possible and turn on all verbose modes. | |
| - When you create debug, test, or example/experiment scripts for fixing, always keep them in an examples and/or experiments folders so you can reuse them later. | |
| - When testing your assumptions, use the experiment scripts, and add it to experiments folder. | |
| - When your experiments can show real world use case of the software, add it to examples folder. | |
| - When you face something extremely hard, use divide and conquer — it always helps. | |
| Initial research. | |
| - When you start, make sure you create detailed plan for yourself and follow your todo list step by step, make sure that as many points from these guidelines are added to your todo list to keep track of everything that can help you solve the issue with highest possible quality. | |
| - When user mentions CI failures or asks to investigate logs, consider adding these todos to track the investigation: (1) List recent CI runs with timestamps, (2) Download logs from failed runs to ci-logs/ directory, (3) Analyze error messages and identify root cause, (4) Implement fix, (5) Verify fix resolves the specific errors found in logs. | |
| - When you read issue, read all details and comments thoroughly. | |
| - When you see screenshots or images in issue descriptions, pull request descriptions, comments, or discussions, use WebFetch tool (or fetch tool) to download the image first, then use Read tool to view and analyze it. IMPORTANT: Before reading downloaded images with the Read tool, verify the file is a valid image (not HTML). Use a CLI tool like 'file' command to check the actual file format. Reading corrupted or non-image files (like GitHub's HTML 404 pages saved as .png) can cause "Could not process image" errors and may crash the AI solver process. If the file command shows "HTML" or "text", the download failed and you should retry or skip the image. | |
| - When you need issue details, use gh issue view https://github.com/suenot/machine-learning-for-trading/issues/83. | |
| - When you need related code, use gh search code --owner suenot [keywords]. | |
| - When you need repo context, read files in your working directory. | |
| - When you study related work, study the most recent related pull requests. | |
| - When issue is not defined enough, write a comment to ask clarifying questions. | |
| - When accessing GitHub Gists (especially private ones), use gh gist view command instead of direct URL fetching to ensure proper authentication. | |
| - When you are fixing a bug, please make sure you first find the actual root cause, do as many experiments as needed. | |
| - When you are fixing a bug and code does not have enough tracing/logs, add them and make sure they stay in the code, but are switched off by default. | |
| - When you need comments on a pull request, note that GitHub has THREE different comment types with different API endpoints: | |
| 1. PR review comments (inline code comments): gh api repos/suenot/machine-learning-for-trading/pulls/377/comments --paginate | |
| 2. PR conversation comments (general discussion): gh api repos/suenot/machine-learning-for-trading/issues/377/comments --paginate | |
| 3. PR reviews (approve/request changes): gh api repos/suenot/machine-learning-for-trading/pulls/377/reviews --paginate | |
| IMPORTANT: The command "gh pr view --json comments" ONLY returns conversation comments and misses review comments! | |
| - When you need latest comments on issue, use gh api repos/suenot/machine-learning-for-trading/issues/83/comments --paginate. | |
| Solution development and testing. | |
| - When issue is solvable, implement code with tests. | |
| - When implementing features, search for similar existing implementations in the codebase and use them as examples instead of implementing everything from scratch. | |
| - When coding, each atomic step that can be useful by itself should be commited to the pull request's branch, meaning if work will be interrupted by any reason parts of solution will still be kept intact and safe in pull request. | |
| - When you test: | |
| start from testing of small functions using separate scripts; | |
| write unit tests with mocks for easy and quick start. | |
| - When you test integrations, use existing framework. | |
| - When you test solution draft, include automated checks in pr. | |
| - When issue is unclear, write comment on issue asking questions. | |
| - When you encounter any problems that you unable to solve yourself (any human feedback or help), write a comment to the pull request asking for help. | |
| - When you need human help, use gh pr comment 377 --body "your message" to comment on existing PR. | |
| Preparing pull request. | |
| - When you code, follow contributing guidelines. | |
| - When you commit, write clear message. | |
| - When you need examples of style, use gh pr list --repo suenot/machine-learning-for-trading --state merged --search [keywords]. | |
| - When you open pr, describe solution draft and include tests. | |
| - When there is a package with version and GitHub Actions workflows for automatic release, update the version (or other necessary release trigger) in your pull request to prepare for next release. | |
| - When you update existing pr 377, use gh pr edit to modify title and description. | |
| - When you are about to commit or push code, ALWAYS run local CI checks first if they are available in contributing guidelines (like ruff check, mypy, eslint, etc.) to catch errors before pushing. | |
| - When you finalize the pull request: | |
| follow style from merged prs for code, title, and description, | |
| make sure no uncommitted changes corresponding to the original requirements are left behind, | |
| make sure the default branch is merged to the pull request's branch, | |
| make sure all CI checks passing if they exist before you finish, | |
| check for latest comments on the issue and pull request to ensure no recent feedback was missed, | |
| double-check that all changes in the pull request answer to original requirements of the issue, | |
| make sure no new new bugs are introduced in pull request by carefully reading gh pr diff, | |
| make sure no previously existing features were removed without an explicit request from users via the issue description, issue comments, and/or pull request comments. | |
| - When you finish implementation, use gh pr ready 377. | |
| Workflow and collaboration. | |
| - When you check branch, verify with git branch --show-current. | |
| - When you push, push only to branch issue-83-e34794bc7991. | |
| - When you finish, create a pull request from branch issue-83-e34794bc7991. (Note: PR 377 already exists, update it instead) | |
| - When you organize workflow, use pull requests instead of direct merges to default branch (main or master). | |
| - When you manage commits, preserve commit history for later analysis. | |
| - When you contribute, keep repository history forward-moving with regular commits, pushes, and reverts if needed. | |
| - When you face conflict that you cannot resolve yourself, ask for help. | |
| - When you collaborate, respect branch protections by working only on issue-83-e34794bc7991. | |
| - When you mention result, include pull request url or comment url. | |
| - When you need to create pr, remember pr 377 already exists for this branch. | |
| Self review. | |
| - When you check your solution draft, run all tests locally. | |
| - When you check your solution draft, verify git status shows a clean working tree with no uncommitted changes. | |
| - When you compare with repo style, use gh pr diff [number]. | |
| - When you finalize, confirm code, tests, and description are consistent. | |
| GitHub CLI command patterns. | |
| - IMPORTANT: Always use --paginate flag when fetching lists from GitHub API to ensure all results are returned (GitHub returns max 30 per page by default). | |
| - When listing PR review comments (inline code comments), use gh api repos/OWNER/REPO/pulls/NUMBER/comments --paginate. | |
| - When listing PR conversation comments, use gh api repos/OWNER/REPO/issues/NUMBER/comments --paginate. | |
| - When listing PR reviews, use gh api repos/OWNER/REPO/pulls/NUMBER/reviews --paginate. | |
| - When listing issue comments, use gh api repos/OWNER/REPO/issues/NUMBER/comments --paginate. | |
| - When adding PR comment, use gh pr comment NUMBER --body "text" --repo OWNER/REPO. | |
| - When adding issue comment, use gh issue comment NUMBER --body "text" --repo OWNER/REPO. | |
| - When viewing PR details, use gh pr view NUMBER --repo OWNER/REPO. | |
| - When filtering with jq, use gh api repos/${owner}/${repo}/pulls/${prNumber}/comments --paginate --jq 'reverse | .[0:5]'. | |
| Playwright MCP usage (browser automation via mcp__playwright__* tools). | |
| - When you develop frontend web applications (HTML, CSS, JavaScript, React, Vue, Angular, etc.), use Playwright MCP tools to test the UI in a real browser. | |
| - When WebFetch tool fails to retrieve expected content (e.g., returns empty content, JavaScript-rendered pages, or login-protected pages), use Playwright MCP tools (browser_navigate, browser_snapshot) as a fallback for web browsing. | |
| - When you need to interact with dynamic web pages that require JavaScript execution, use Playwright MCP tools. | |
| - When you need to visually verify how a web page looks or take screenshots, use browser_take_screenshot from Playwright MCP. | |
| - When you need to fill forms, click buttons, or perform user interactions on web pages, use Playwright MCP tools (browser_click, browser_type, browser_fill_form). | |
| - When you need to test responsive design or different viewport sizes, use browser_resize from Playwright MCP. | |
| - When you finish using the browser, always close it with browser_close to free resources. | |
| [2026-01-18T20:51:20.802Z] [INFO] ---END SYSTEM PROMPT--- | |
| [2026-01-18T20:51:20.802Z] [INFO] | |
| [2026-01-18T20:51:20.802Z] [INFO] 📊 CLAUDE_CODE_MAX_OUTPUT_TOKENS: 64000 | |
| [2026-01-18T20:51:20.803Z] [INFO] 📋 Command details: | |
| [2026-01-18T20:51:20.803Z] [INFO] 📂 Working directory: /tmp/gh-issue-solver-1768769379407 | |
| [2026-01-18T20:51:20.803Z] [INFO] 🌿 Branch: issue-83-e34794bc7991 | |
| [2026-01-18T20:51:20.803Z] [INFO] 🤖 Model: Claude OPUS | |
| [2026-01-18T20:51:20.803Z] [INFO] 🍴 Fork: konard/suenot-machine-learning-for-trading | |
| [2026-01-18T20:51:20.803Z] [INFO] | |
| ▶️ Streaming output: | |
| [2026-01-18T20:51:23.318Z] [INFO] { | |
| "type": "system", | |
| "subtype": "init", | |
| "cwd": "/tmp/gh-issue-solver-1768769379407", | |
| "session_id": "77018388-5fda-4d3e-acd4-e8399840434b", | |
| "tools": [ | |
| "Task", | |
| "TaskOutput", | |
| "Bash", | |
| "Glob", | |
| "Grep", | |
| "ExitPlanMode", | |
| "Read", | |
| "Edit", | |
| "Write", | |
| "NotebookEdit", | |
| "WebFetch", | |
| "TodoWrite", | |
| "WebSearch", | |
| "KillShell", | |
| "AskUserQuestion", | |
| "Skill", | |
| "EnterPlanMode", | |
| "MCPSearch", | |
| "mcp__playwright__browser_close", | |
| "mcp__playwright__browser_resize", | |
| "mcp__playwright__browser_console_messages", | |
| "mcp__playwright__browser_handle_dialog", | |
| "mcp__playwright__browser_evaluate", | |
| "mcp__playwright__browser_file_upload", | |
| "mcp__playwright__browser_fill_form", | |
| "mcp__playwright__browser_install", | |
| "mcp__playwright__browser_press_key", | |
| "mcp__playwright__browser_type", | |
| "mcp__playwright__browser_navigate", | |
| "mcp__playwright__browser_navigate_back", | |
| "mcp__playwright__browser_network_requests", | |
| "mcp__playwright__browser_run_code", | |
| "mcp__playwright__browser_take_screenshot", | |
| "mcp__playwright__browser_snapshot", | |
| "mcp__playwright__browser_click", | |
| "mcp__playwright__browser_drag", | |
| "mcp__playwright__browser_hover", | |
| "mcp__playwright__browser_select_option", | |
| "mcp__playwright__browser_tabs", | |
| "mcp__playwright__browser_wait_for" | |
| ], | |
| "mcp_servers": [ | |
| { | |
| "name": "playwright", | |
| "status": "connected" | |
| } | |
| ], | |
| "model": "claude-opus-4-5-20251101", | |
| "permissionMode": "bypassPermissions", | |
| "slash_commands": [ | |
| "compact", | |
| "context", | |
| "cost", | |
| "init", | |
| "pr-comments", | |
| "release-notes", | |
| "review", | |
| "security-review" | |
| ], | |
| "apiKeySource": "none", | |
| "claude_code_version": "2.1.9", | |
| "output_style": "default", | |
| "agents": [ | |
| "Bash", | |
| "general-purpose", | |
| "statusline-setup", | |
| "Explore", | |
| "Plan" | |
| ], | |
| "skills": [], | |
| "plugins": [], | |
| "uuid": "a9323b4a-422d-40d1-a548-566e099e63f0" | |
| } | |
| [2026-01-18T20:51:23.323Z] [INFO] 📌 Session ID: 77018388-5fda-4d3e-acd4-e8399840434b | |
| [2026-01-18T20:51:23.323Z] [INFO] 📁 Log renamed to: /home/hive/77018388-5fda-4d3e-acd4-e8399840434b.log | |
| [2026-01-18T20:51:26.707Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-5-20251101", | |
| "id": "msg_01UpwAbmWC2N8PzUNAc1a85H", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "tool_use", | |
| "id": "toolu_01XVroNckyEzsnWRt6onq5Ew", | |
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| "content": " 1→# Chapter 46: Temporal Attention Networks\n 2→\n 3→## Описание\n 4→\n 5→Специализированные attention механизмы для временных зависимостей в финансовых данных.\n 6→\n 7→## Техническое задание\n 8→\n 9→### Цели\n 10→1. Изучить теоретические основы метода\n 11→2. Реализовать базовую версию на Python\n 12→3. Создать оптимизированную версию на Rust\n 13→4. Протестировать на финансовых данных\n 14→5. Провести бэктестинг торговой стратегии\n 15→\n 16→### Ключевые компоненты\n 17→- Теоретическое описание метода\n 18→- Python реализация с PyTorch\n 19→- Rust реализация для production\n 20→- Jupyter notebooks с примерами\n 21→- Бэктестинг framework\n 22→\n 23→### Метрики\n 24→- Accuracy / F1-score для классификации\n 25→- MSE / MAE для регрессии\n 26→- Sharpe Ratio / Sortino Ratio для стратегий\n 27→- Maximum Drawdown\n 28→- Сравнение с baseline моделями\n 29→\n 30→## Научные работы\n 31→\n 32→1. **Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting**\n 33→ - URL: https://arxiv.org/abs/1912.09363\n 34→ - Год: 2021\n 35→\n 36→## Данные\n 37→- Yahoo Finance / yfinance\n 38→- Binance API для криптовалют\n 39→- LOBSTER для order book data\n 40→- Kaggle финансовые датасеты\n 41→\n 42→## Реализация\n 43→\n 44→### Python\n 45→- PyTorch / TensorFlow\n 46→- NumPy, Pandas\n 47→- scikit-learn\n 48→- Backtrader / Zipline\n 49→\n 50→### Rust\n 51→- ndarray\n 52→- polars\n 53→- burn / candle\n 54→\n 55→## Структура\n 56→```\n 57→46_temporal_attention_networks/\n 58→├── README.specify.md\n 59→├── README.md\n 60→├── docs/\n 61→│ └── ru/\n 62→│ └── theory.md\n 63→├── python/\n 64→│ ├── model.py\n 65→│ ├── train.py\n 66→│ ├── backtest.py\n 67→│ └── notebooks/\n 68→│ └── example.ipynb\n 69→└── rust/\n 70→ └── src/\n 71→ └── lib.rs\n 72→```\n 73→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n" | |
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| "content": "# Chapter 46: Temporal Attention Networks\n\n## Описание\n\nСпециализированные attention механизмы для временных зависимостей в финансовых данных.\n\n## Техническое задание\n\n### Цели\n1. Изучить теоретические основы метода\n2. Реализовать базовую версию на Python\n3. Создать оптимизированную версию на Rust\n4. Протестировать на финансовых данных\n5. Провести бэктестинг торговой стратегии\n\n### Ключевые компоненты\n- Теоретическое описание метода\n- Python реализация с PyTorch\n- Rust реализация для production\n- Jupyter notebooks с примерами\n- Бэктестинг framework\n\n### Метрики\n- Accuracy / F1-score для классификации\n- MSE / MAE для регрессии\n- Sharpe Ratio / Sortino Ratio для стратегий\n- Maximum Drawdown\n- Сравнение с baseline моделями\n\n## Научные работы\n\n1. **Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting**\n - URL: https://arxiv.org/abs/1912.09363\n - Год: 2021\n\n## Данные\n- Yahoo Finance / yfinance\n- Binance API для криптовалют\n- LOBSTER для order book data\n- Kaggle финансовые датасеты\n\n## Реализация\n\n### Python\n- PyTorch / TensorFlow\n- NumPy, Pandas\n- scikit-learn\n- Backtrader / Zipline\n\n### Rust\n- ndarray\n- polars\n- burn / candle\n\n## Структура\n```\n46_temporal_attention_networks/\n├── README.specify.md\n├── README.md\n├── docs/\n│ └── ru/\n│ └── theory.md\n├── python/\n│ ├── model.py\n│ ├── train.py\n│ ├── backtest.py\n│ └── notebooks/\n│ └── example.ipynb\n└── rust/\n └── src/\n └── lib.rs\n```\n", | |
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| [2026-01-18T20:51:41.296Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014zcicCsmqV4gVQSh1NUGSv","type":"tool_result","content":" 1→# Chapter 43: Stockformer — Multivariate Stock Prediction with Cross-Asset Attention\n 2→\n 3→This chapter explores **Stockformer**, a transformer-based architecture designed for multivariate stock prediction. Unlike traditional univariate forecasting models, Stockformer leverages attention mechanisms to capture cross-asset relationships and temporal dependencies simultaneously.\n 4→\n 5→<p align=\"center\">\n 6→<img src=\"https://i.imgur.com/XqZ8k2P.png\" width=\"70%\">\n 7→</p>\n 8→\n 9→## Contents\n 10→\n 11→1. [Introduction to Stockformer](#introduction-to-stockformer)\n 12→ * [Why Multivariate Prediction?](#why-multivariate-prediction)\n 13→ * [Key Advantages](#key-advantages)\n 14→ * [Comparison with Other Models](#comparison-with-other-models)\n 15→2. [Stockformer Architecture](#stockformer-architecture)\n 16→ * [Token Embedding Layer](#token-embedding-layer)\n 17→ * [Cross-Ticker Attention](#cross-ticker-attention)\n 18→ * [ProbSparse Attention](#probsparse-attention)\n 19→ * [Prediction Head](#prediction-head)\n 20→3. [Multivariate Data Representation](#multivariate-data-representation)\n 21→ * [Log Percent Change](#log-percent-change)\n 22→ * [Multi-Ticker Inputs](#multi-ticker-inputs)\n 23→ * [Feature Engineering](#feature-engineering)\n 24→4. [Practical Examples](#practical-examples)\n 25→ * [01: Data Preparation](#01-data-preparation)\n 26→ * [02: Stockformer Architecture](#02-stockformer-architecture)\n 27→ * [03: Model Training](#03-model-training)\n 28→ * [04: Cross-Asset Prediction](#04-cross-asset-prediction)\n 29→ * [05: Portfolio Backtesting](#05-portfolio-backtesting)\n 30→5. [Rust Implementation](#rust-implementation)\n 31→6. [Python Implementation](#python-implementation)\n 32→7. [Best Practices](#best-practices)\n 33→8. [Resources](#resources)\n 34→\n 35→## Introduction to Stockformer\n 36→\n 37→Stockformer is a modified Transformer architecture specifically designed for financial time-series forecasting. Instead of treating stock prediction as a simple univariate autoregression problem, Stockformer models the task as a **multivariate forecasting problem**, using attention mechanisms to discover relationships between multiple financial instruments.\n 38→\n 39→### Why Multivariate Prediction?\n 40→\n 41→Traditional models predict each asset independently:\n 42→\n 43→```\n 44→BTCUSDT → Model → BTCUSDT_prediction\n 45→ETHUSDT → Model → ETHUSDT_prediction\n 46→```\n 47→\n 48→Stockformer predicts using cross-asset information:\n 49→\n 50→```\n 51→[BTCUSDT, ETHUSDT, SOLUSDT, ...] → Stockformer → BTCUSDT_prediction\n 52→ (considering all relationships)\n 53→```\n 54→\n 55→**Key insight**: Financial markets are interconnected. Bitcoin's movement affects Ethereum, oil prices affect airline stocks, and tech stocks often move together. Stockformer explicitly models these dependencies.\n 56→\n 57→### Key Advantages\n 58→\n 59→1. **Cross-Asset Relationship Modeling**\n 60→ - Captures correlations between different assets\n 61→ - Uses Granger causality to identify predictive relationships\n 62→ - Attention weights show which assets influence predictions\n 63→\n 64→2. **Efficient Attention Mechanisms**\n 65→ - ProbSparse attention reduces complexity from O(L²) to O(L·log(L))\n 66→ - Self-attention distilling for memory efficiency\n 67→ - Handles long sequences efficiently\n 68→\n 69→3. **Flexible Output Types**\n 70→ - Price regression (MSE/MAE loss)\n 71→ - Direction prediction (binary signals)\n 72→ - Portfolio allocation (tanh-bounded positions)\n 73→\n 74→4. **Interpretable Predictions**\n 75→ - Attention weights reveal cross-asset dependencies\n 76→ - Clear visualization of which assets matter for each prediction\n 77→\n 78→### Comparison with Other Models\n 79→\n 80→| Feature | LSTM | Transformer | TFT | Stockformer |\n 81→|---------|------|-------------|-----|-------------|\n 82→| Cross-asset modeling | ✗ | ✗ | Limited | ✓ |\n 83→| ProbSparse attention | ✗ | ✗ | ✗ | ✓ |\n 84→| Multivariate input | ✗ | ✗ | ✓ | ✓ |\n 85→| Correlation-aware | ✗ | ✗ | ✗ | ✓ |\n 86→| Portfolio allocation | ✗ | ✗ | ✗ | ✓ |\n 87→\n 88→## Stockformer Architecture\n 89→\n 90→```\n 91→┌──────────────────────────────────────────────────────────────────────┐\n 92→│ STOCKFORMER │\n 93→├──────────────────────────────────────────────────────────────────────┤\n 94→│ │\n 95→│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │\n 96→│ │ Asset 1 │ │ Asset 2 │ │ Asset 3 │ │ Asset N │ │\n 97→│ │ (BTC) │ │ (ETH) │ │ (SOL) │ │ (...) │ │\n 98→│ └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘ │\n 99→│ │ │ │ │ │\n 100→│ ▼ ▼ ▼ ▼ │\n 101→│ ┌──────────────────────────────────────────────────┐ │\n 102→│ │ Token Embedding (1D-CNN) │ │\n 103→│ │ Extracts temporal patterns per asset │ │\n 104→│ └───────────────────────┬──────────────────────────┘ │\n 105→│ │ │\n 106→│ ▼ │\n 107→│ ┌──────────────────────────────────────────────────┐ │\n 108→│ │ Cross-Ticker Self-Attention │ │\n 109→│ │ Models relationships between all assets │ │\n 110→│ │ (which assets predict which?) │ │\n 111→│ └───────────────────────┬──────────────────────────┘ │\n 112→│ │ │\n 113→│ ▼ │\n 114→│ ┌──────────────────────────────────────────────────┐ │\n 115→│ │ Encoder Stack (ProbSparse or Full Attention) │ │\n 116→│ │ + Self-Attention Distilling │ │\n 117→│ └───────────────────────┬──────────────────────────┘ │\n 118→│ │ │\n 119→│ ▼ │\n 120→│ ┌──────────────────────────────────────────────────┐ │\n 121→│ │ Prediction Head │ │\n 122→│ │ • Price regression (MSE/MAE) │ │\n 123→│ │ • Direction signal (binary) │ │\n 124→│ │ • Portfolio allocation (tanh) │ │\n 125→│ └──────────────────────────────────────────────────┘ │\n 126→│ │\n 127→└──────────────────────────────────────────────────────────────────────┘\n 128→```\n 129→\n 130→### Token Embedding Layer\n 131→\n 132→Stockformer uses 1D-CNN based embeddings instead of simple linear projections:\n 133→\n 134→```python\n 135→class TokenEmbedding(nn.Module):\n 136→ def __init__(self, c_in, d_model, kernel_size=3):\n 137→ super().__ini | |
| [2026-01-18T20:51:41.299Z] [INFO] t__()\n 138→ # Separate kernel for each input channel (asset)\n 139→ self.conv = nn.Conv1d(\n 140→ in_channels=c_in,\n 141→ out_channels=d_model,\n 142→ kernel_size=kernel_size,\n 143→ padding=kernel_size // 2\n 144→ )\n 145→\n 146→ def forward(self, x):\n 147→ # x: [batch, seq_len, n_assets]\n 148→ x = x.permute(0, 2, 1) # [batch, n_assets, seq_len]\n 149→ x = self.conv(x) # [batch, d_model, seq_len]\n 150→ return x.permute(0, 2, 1) # [batch, seq_len, d_model]\n 151→```\n 152→\n 153→**Why 1D-CNN?**\n 154→- Preserves temporal relationships while extracting local patterns\n 155→- Learns different kernels for each asset\n 156→- More efficient than position-wise fully connected layers\n 157→\n 158→### Cross-Ticker Attention\n 159→\n 160→The key innovation: attention across both time AND assets:\n 161→\n 162→```python\n 163→class CrossTickerAttention(nn.Module):\n 164→ def __init__(self, d_model, n_heads, n_assets):\n 165→ super().__init__()\n 166→ self.n_assets = n_assets\n 167→ self.mha = nn.MultiheadAttention(d_model, n_heads)\n 168→\n 169→ def forward(self, x):\n 170→ # x: [batch, seq_len, n_assets, d_model]\n 171→ batch, seq_len, n_assets, d_model = x.shape\n 172→\n 173→ # Reshape for cross-asset attention\n 174→ # Treat (seq_len * batch) as batch, n_assets as sequence\n 175→ x = x.view(batch * seq_len, n_assets, d_model)\n 176→\n 177→ # Self-attention across assets at each time step\n 178→ attn_out, attn_weights = self.mha(x, x, x)\n 179→\n 180→ # attn_weights shows which assets affect which\n 181→ return attn_out.view(batch, seq_len, n_assets, d_model), attn_weights\n 182→```\n 183→\n 184→The attention weights reveal **which assets influence the prediction**:\n 185→- High attention from ETH to BTC → ETH movements help predict BTC\n 186→- Useful for understanding market dynamics and building portfolios\n 187→\n 188→### ProbSparse Attention\n 189→\n 190→Standard self-attention has O(L²) complexity. ProbSparse attention reduces this to O(L·log(L)):\n 191→\n 192→```python\n 193→class ProbSparseAttention(nn.Module):\n 194→ def __init__(self, d_model, n_heads, factor=5):\n 195→ super().__init__()\n 196→ self.factor = factor # Controls sparsity\n 197→ self.query_proj = nn.Linear(d_model, d_model)\n 198→ self.key_proj = nn.Linear(d_model, d_model)\n 199→ self.value_proj = nn.Linear(d_model, d_model)\n 200→\n 201→ def forward(self, queries, keys, values):\n 202→ B, L, D = queries.shape\n 203→\n 204→ # Sample top-u keys based on query \"spikiness\"\n 205→ u = int(self.factor * np.log(L))\n 206→ u = min(u, L)\n 207→\n 208→ # Calculate attention scores for sampled queries\n 209→ Q = self.query_proj(queries)\n 210→ K = self.key_proj(keys)\n 211→ V = self.value_proj(values)\n 212→\n 213→ # Measure query spikiness: max(QK^T) - mean(QK^T)\n 214→ scores = torch.matmul(Q, K.transpose(-2, -1)) / np.sqrt(D)\n 215→ M = scores.max(-1)[0] - scores.mean(-1)\n 216→\n 217→ # Select top-u queries with highest spikiness\n 218→ M_top = M.topk(u, sorted=False)[1]\n 219→\n 220→ # Compute sparse attention only for selected queries\n 221→ Q_sampled = Q[torch.arange(B)[:, None], M_top]\n 222→ attn = torch.softmax(\n 223→ torch.matmul(Q_sampled, K.transpose(-2, -1)) / np.sqrt(D),\n 224→ dim=-1\n 225→ )\n 226→\n 227→ # Aggregate values\n 228→ context = torch.matmul(attn, V)\n 229→\n 230→ return context\n 231→```\n 232→\n 233→**Intuition**: Not all queries need full attention computation. \"Spiky\" queries (those with dominant attention to specific keys) matter most.\n 234→\n 235→### Prediction Head\n 236→\n 237→Stockformer supports multiple output types:\n 238→\n 239→```python\n 240→class PredictionHead(nn.Module):\n 241→ def __init__(self, d_model, output_type='regression'):\n 242→ super().__init__()\n 243→ self.output_type = output_type\n 244→\n 245→ if output_type == 'regression':\n 246→ # Direct price prediction\n 247→ self.head = nn.Linear(d_model, 1)\n 248→ self.loss_fn = nn.MSELoss()\n 249→\n 250→ elif output_type == 'direction':\n 251→ # Binary up/down classification\n 252→ self.head = nn.Linear(d_model, 1)\n 253→ self.loss_fn = nn.BCEWithLogitsLoss()\n 254→\n 255→ elif output_type == 'allocation':\n 256→ # Portfolio weights via tanh\n 257→ self.head = nn.Sequential(\n 258→ nn.Linear(d_model, 1),\n 259→ nn.Tanh() # Bounds output to [-1, 1]\n 260→ )\n 261→ self.loss_fn = lambda pred, ret: -torch.mean(pred * ret) # Maximize returns\n 262→\n 263→ def forward(self, x):\n 264→ return self.head(x)\n 265→```\n 266→\n 267→## Multivariate Data Representation\n 268→\n 269→### Log Percent Change\n 270→\n 271→Raw prices are transformed for stable training:\n 272→\n 273→```python\n 274→def log_percent_change(close, open_price):\n 275→ \"\"\"\n 276→ Transform prices to log percent change.\n 277→\n 278→ Advantages:\n 279→ - Variance stabilization across different price scales\n 280→ - BTC at $40,000 and ETH at $2,000 become comparable\n 281→ - Stationary series (important for neural networks)\n 282→ \"\"\"\n 283→ return np.log(close / open_price + 1)\n 284→```\n 285→\n 286→### Multi-Ticker Inputs\n 287→\n 288→Structure data for multivariate prediction:\n 289→\n 290→```python\n 291→# Data structure for Stockformer\n 292→# Shape: [batch, seq_len, n_assets, features]\n 293→\n 294→data = {\n 295→ 'prices': torch.tensor([\n 296→ # Time step 1\n 297→ [[45000, 2500, 100], # [BTC, ETH, SOL] close prices\n 298→ [44800, 2480, 99]], # Time step 2\n 299→ # ...\n 300→ ]),\n 301→ 'volumes': torch.tensor([...]),\n 302→ 'returns': torch.tensor([...]),\n 303→}\n 304→```\n 305→\n 306→### Feature Engineering\n 307→\n 308→Recommended features for each asset:\n 309→\n 310→| Feature | Description | Calculation |\n 311→|---------|-------------|-------------|\n 312→| `log_return` | Log price change | ln(close/prev_close) |\n 313→| `volume_change` | Relative volume | vol/vol_ma_20 |\n 314→| `volatility` | Rolling volatility | std(returns, 20) |\n 315→| `rsi` | Relative Strength | Standard RSI calculation |\n 316→| `correlation` | Pairwise correlation | rolling_corr(asset_i, asset_j) |\n 317→| `funding_rate` | Crypto funding | From exchange API |\n 318→| `open_interest` | Derivatives OI | From exchange API |\n 319→\n 320→## Practical Examples\n 321→\n 322→### 01: Data Preparation\n 323→\n 324→```python\n 325→# python/01_data_preparation.py\n 326→\n 327→import pandas as pd\n 328→import numpy as np\n 329→from typing import List, Dict\n 330→\n 331→def prepare_multivariate_data(\n 332→ symbols: List[str],\n 333→ lookback: int = 168, # 7 days of hourly data\n 334→ horizon: int = 24 # Predict 24 hours ahead\n 335→) -> Dict:\n 336→ \"\"\"\n 337→ Prepare data for Stockformer training.\n 338→\n 339→ Args:\n 340→ symbols: List of trading pairs (e.g., ['BTCUSDT', 'ETHUSDT'])\n 341→ lookback: Number of historical time steps\n 342→ horizon: Prediction horizon\n 343→\n 344→ Returns:\n 345→ Dictionary with X (features) and y (targets)\n 346→ \"\"\"\n 347→\n 348→ all_data = []\n 349→\n 350→ for symbol in symbols:\n 351→ # Load data from Bybit or other source\n 352→ df = load_bybit_data(symbol)\n 353→\n 354→ # Calculate features\n 355→ df['log_return'] = np.log(df['close'] / df['close'].shift(1))\n 356→ df['volume_change'] = df['volume'] / df['volume'].rolling(20).mean()\n 357→ df['volatility'] = df['log_return'].rolling(20).std()\n 358→\n 359→ all_data.append(df)\n 360→\n 361→ # Align all dataframes on timestamp\n 362→ aligned_data = pd.concat(all_data, axis=1, keys=symbols)\n 363→\n 364→ # Create sequences\n 365→ X, y = [], []\n 366→ for i in range(lookback, len(aligned_data) - horizon):\n 367→ X.append(aligned_data.iloc[i-lookback:i].values)\n 368→ y.append(aligned_data.iloc[i+horizon]['log_return'].values)\n 369→\n 370→ return {\n 371→ 'X': np.array(X), # [n_samples, lookback, n_assets * n_features]\n 372→ 'y': np.array(y), # [n_samples, n_assets]\n 373→ 'symbols': symbols\n 374→ }\n 375→```\n 376→\n 377→### 02: Stockformer Architecture\n 378→\n 379→See [python/stockformer.py](python/stockformer.py) for complete implementation.\n 380→\n 381→### 03: Model Training\n 382→\n 383→```python\n 384→# python/03_train_model.py\n 385→\n 386→import torch\n 387→from stockformer import Stockformer\n 388→\n 389→# Model configuration\n 390→config = {\n 391→ 'n_assets': 5,\n 392→ 'd_model': 128,\n 393→ 'n_heads': 8,\n 394→ 'n_encoder_layers': 3,\n 395→ 'dropout': 0.1,\n 396→ 'attention_type': 'probsparse',\n 397→ 'output_type': 'allocation'\n 398→}\n 399→\n 400→# Initialize model\n 401→model = Stockformer(**config)\n 402→\n 403→# Training loop with learning rate scheduling\n 404→optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n 405→scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n 406→ optimizer, mode='min', factor=0.5, patience=5\n 407→)\n 408→\n 409→for epoch in range(100):\n 410→ model.train()\n 411→ for batch_x, batch_y in train_loader:\n 412→ optimizer.zero_grad()\n 413→\n 414→ # Forward pass\n 415→ predictions = model(batch_x)\n 416→\n 417→ # Calculate loss (depends on output_type)\n 418→ loss = model.compute_loss(predictions, batch_y)\n 419→\n 420→ # Backward pass\n 421→ loss.backward()\n 422→ torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n 423→ optimizer.step()\n 424→\n 425→ # Validation\n 426→ val_loss = validate(model, val_loader)\n 427→ scheduler.step(val_loss)\n 428→```\n 429→\n 430→### 04: Cross-Asset Prediction\n 431→\n 432→```python\n 433→# python/04_cross_asset_prediction.py\n 434→\n 435→def predict_with_attention(model, X):\n 436→ \"\"\"\n 437→ Make predictions and return attention weights.\n 438→ \"\"\"\n 439→ model.eval()\n 440→ with torch.no_grad():\n 441→ predictions, attention_weights = model(X, return_attention=True)\n 442→\n 443→ # attention_weights: [batch, n_heads, n_assets, n_assets]\n 444→ # Shows which assets influence which predictions\n 445→\n 446→ return predictions, attention_weights\n 447→\n 448→def visualize_attention(attention_weights, symbols):\n 449→ \"\"\"\n 450→ Create heatmap of cross-asset attention.\n 451→ \"\"\"\n 452→ import matplotlib.pyplot as plt\n 453→ import seaborn as sns\n 454→\n 455→ # Average over heads and batch\n 456→ avg_attention = attention_weights.mean(dim=[0, 1]).numpy()\n 457→\n 458→ plt.figure(figsize=(10, 8))\n 459→ sns.heatmap(\n 460→ avg_attention,\n 461→ xticklabels=symbols,\n 462→ yticklabels=symbols,\n 463→ annot=True,\n 464→ cmap='Blues'\n 465→ )\n 466→ plt.title('Cross-Asset Attention Weights')\n 467→ plt.xlabel('Key (Source)')\n 468→ plt.ylabel('Query (Target)')\n 469→ plt.savefig('attention_heatmap.png')\n 470→```\n 471→\n 472→### 05: Portfolio Backtesting\n 473→\n 474→```python\n 475→# python/05_backtest.py\n 476→\n 477→def backtest_stockformer_strategy(\n 478→ model,\n 479→ test_data,\n 480→ initial_capital: float = 100000,\n 481→ transaction_cost: float = 0.001\n 482→):\n 483→ \"\"\"\n 484→ Backtest Stockformer portfolio allocation strategy.\n 485→ \"\"\"\n 486→ capital = initial_capital\n 487→ positions = np.zeros(model.n_assets)\n 488→\n 489→ results = []\n 490→\n 491→ for i, (X, returns) in enumerate(test_data):\n 492→ # Get model allocation weights\n 493→ weights = model(X).numpy().flatten() # [-1, 1] per asset\n 494→\n 495→ # Normalize to sum to 1 (long-only) or allow shorting\n 496→ weights = weights / np.abs(weights).sum()\n 497→\n 498→ # Calculate position changes and costs\n 499→ position_change = weights - positions\n 500→ costs = np.abs(position_change).sum() * transaction_cost * capital\n 501→\n 502→ # Update positions\n 503→ positions = weights\n 504→\n 505→ # Calculate returns\n 506→ portfolio_return = np.sum(positions * returns)\n 507→ capital = capital * (1 + portfolio_return) - costs\n 508→\n 509→ results.append({\n 510→ 'capital': capital,\n 511→ 'weights': weights.copy(),\n 512→ 'return': portfolio_return\n 513→ })\n 514→\n 515→ return pd.DataFrame(results)\n 516→```\n 517→\n 518→## Rust Implementation\n 519→\n 520→See [rust_stockformer](rust_stockformer/) for complete Rust implementation using Bybit data.\n 521→\n 522→```\n 523→rust_stockformer/\n 524→├── Cargo.toml\n 525→├── README.md\n 526→├── src/\n 527→│ ├── lib.rs # Main library exports\n 528→│ ├── api/ # Bybit API client\n 529→│ │ ├── mod.rs\n 530→│ │ ├── client.rs # HTTP client for Bybit\n 531→│ │ └── types.rs # API response types\n 532→│ ├── data/ # Data processing\n 533→│ │ ├── mod.rs\n 534→│ │ ├── loader.rs # Data loading utilities\n 535→│ │ ├── features.rs # Feature engineering\n 536→│ │ └── dataset.rs # Dataset for training\n 537→│ ├── model/ # Stockformer architecture\n 538→│ │ ├── mod.rs\n 539→│ │ ├── embedding.rs # Token embedding layer\n 540→│ │ ├── attention.rs # Cross-ticker & ProbSparse attention\n 541→│ │ ├── encoder.rs # Encoder stack\n 542→│ │ └── stockformer.rs # Complete model\n 543→│ └── strategy/ # Trading strategy\n 544→│ ├── mod.rs\n 545→│ ├── signals.rs # Signal generation\n 546→│ └── backtest.rs # Backtesting engine\n 547→└── examples/\n 548→ ├── fetch_data.rs # Download Bybit data\n 549→ ├── train.rs # Train model\n 550→ └── backtest.rs # Run backtest\n 551→```\n 552→\n 553→### Quick Start (Rust)\n 554→\n 555→```bash\n 556→# Navigate to Rust project\n 557→cd rust_stockformer\n 558→\n 559→# Fetch data from Bybit\n 560→cargo run --example fetch_data -- --symbols BTCUSDT,ETHUSDT,SOLUSDT\n 561→\n 562→# Train model\n 563→cargo run --example train -- --epochs 100 --batch-size 32\n 564→\n 565→# Run backtest\n 566→cargo run --example backtest -- --start 2024-01-01 --end 2024-12-31\n 567→```\n 568→\n 569→## Python Implementation\n 570→\n 571→See [python/](python/) for Python implementation.\n 572→\n 573→```\n 574→python/\n 575→├── stockformer.py # Main model implementation\n 576→├── data_loader.py # Bybit data loading\n 577→├── features.py # Feature engineering\n 578→├── train.py # Training script\n 579→├── backtest.py # Backtesting utilities\n 580→├── requirements.txt # Dependencies\n 581→└── examples/\n 582→ ├── 01_data_preparation.ipynb\n 583→ ├── 02_model_architecture.ipynb\n 584→ ├── 03_training.ipynb\n 585→ ├── 04_prediction.ipynb\n 586→ └── 05_backtesting.ipynb\n 587→```\n 588→\n 589→### Quick Start (Python)\n 590→\n 591→```bash\n 592→# Install dependencies\n 593→pip install -r requirements.txt\n 594→\n 595→# Fetch data\n 596→python data_loader.py --symbols BTCUSDT,ETHUSDT,SOLUSDT\n 597→\n 598→# Train model\n 599→python train.py --config configs/default.yaml\n 600→\n 601→# Run backtest\n 602→python backtest.py --model checkpoints/best_model.pt\n 603→```\n 604→\n 605→## Best Practices\n 606→\n 607→### When to Use Stockformer\n 608→\n 609→**Good use cases:**\n 610→- Trading correlated assets (crypto, sector ETFs)\n 611→- Portfolio allocation across multiple assets\n 612→- Discovering cross-asset dependencies\n 613→- Long-term predictions (hours to days)\n 614→\n 615→**Not ideal for:**\n 616→- High-frequency trading (inference latency)\n 617→- Single asset prediction (use simpler models)\n 618→- Very small datasets (<1000 samples)\n 619→\n 620→### Hyperparameter Recommendations\n 621→\n 622→| Parameter | Recommended | Notes |\n 623→|-----------|-------------|-------|\n 624→| `d_model` | 128-256 | Match with n_heads |\n 625→| `n_heads` | 8 | Should divide d_model |\n 626→| `n_assets` | 5-20 | More requires more data |\n 627→| `lookback` | 168 (7d hourly) | Depends on data frequency |\n 628→| `dropout` | 0.1-0.2 | Higher for small datasets |\n 629→\n 630→### Common Pitfalls\n 631→\n 632→1. **Gradient instability**: Use gradient clipping and learning rate scheduling\n 633→2. **Overfitting**: Apply dropout, use early stopping\n 634→3. **Data leakage**: Ensure proper train/val/test splits\n 635→4. **Correlation collapse**: Monitor attention weights for diversity\n 636→\n 637→## Resources\n 638→\n 639→### Papers\n 640→\n 641→- [Transformer Based Time-Series Forecasting For Stock](https://arxiv.org/abs/2502.09625) — Original Stockformer paper\n 642→- [Stockformer: A Price-Volume Factor Stock Selection Model](https://arxiv.org/abs/2401.06139) — Advanced variant with wavelet transform\n 643→- [MASTER: Market-Guided Stock Transformer](https://arxiv.org/abs/2312.15235) — Related architecture\n 644→- [Attention Is All You Need](https://arxiv.org/abs/1706.03762) — Original Transformer\n 645→\n 646→### Implementations\n 647→\n 648→- [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/) — Time series library\n 649→- [Informer](https://github.com/zhouhaoyi/Informer2020) — ProbSparse attention implementation\n 650→- [Autoformer](https://github.com/thuml/Autoformer) — Related architecture\n 651→\n 652→### Related Chapters\n 653→\n 654→- [Chapter 26: Temporal Fusion Transformers](../26_temporal_fusion_transformers) — Multi-horizon forecasting\n 655→- [Chapter 41: Higher Order Transformers](../41_higher_order_transformers) — Advanced attention mechanisms\n 656→- [Chapter 47: Cross-Attention Multi-Asset](../47_cross_attention_multi_asset) — Cross-asset modeling\n 657→\n 658→---\n 659→\n 660→## Difficulty Level\n 661→\n 662→**Advanced**\n 663→\n 664→Prerequisites:\n 665→- Transformer architecture and attention mechanisms\n 666→- Time series forecasting basics\n 667→- Portfolio theory fundamentals\n 668→- PyTorch/Rust ML libraries\n 669→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"parent_tool_use_id":null,"session_id":"77018388-5fda-4d3e-acd4-e8399840434b","uuid":"f079c8cf-6138-4499-a7c2-ce4968f34d13","tool_use_result":{"type":"text","file":{"filePath":"/tmp/gh-issue-solver-1768769379407/43_stockformer_multivariate/README.md","content":"# Chapter 43: Stockformer — Multivariate Stock Prediction with Cross-Asset Attention\n\nThis chapter explores **Stockformer**, a transformer-based architecture designed for multivariate stock prediction. Unlike traditional univariate forecasting models, Stockformer leverages attention mechanisms to capture cross-asset relationships and temporal dependencies simultaneously.\n\n<p align=\"center\">\n<img src=\"https://i.imgur.com/XqZ8k2P.png\" width=\"70%\">\n</p>\n\n## Contents\n\n1. [Introduction to Stockformer](#introduction-to-stockformer)\n * [Why Multivariate Prediction?](#why-multivariate-prediction)\n * [Key Advantages](#key-advantages)\n * [Comparison with Other Models](#comparison-with-other-models)\n2. [Stockformer Architecture](#stockformer-architecture)\n * [Token Embedding Layer](#token-embedding-layer)\n * [Cross-Ticker Attention](#cross-ticker-attention)\n * [ProbSparse Attention](#probsparse-attention)\n * [Prediction Head](#prediction-head)\n3. [Multivariate Data Representation](#multivariate-data-representation)\n * [Log Percent Change](#log-percent-change)\n * [Multi-Ticker Inputs](#multi-ticker-inputs)\n * [Feature Engineering](#feature-engineering)\n4. [Practical Examples](#practical-examples)\n * [01: Data Preparation](#01-data-preparation)\n * [02: Stockformer Architecture](#02-stockformer-architecture)\n * [03: Model Training](#03-model-training)\n * [04: Cross-Asset Prediction](#04-cross-asset-prediction)\n * [05: Portfolio Backtesting](#05-portfolio-backtesting)\n5. [Rust Implementation](#rust-implementation)\n6. [Python Implementation](#python-implementation)\n7. [Best Practices](#best-practices)\n8. [Resources](#resources)\n\n## Introduction to Stockformer\n\nStockformer is a modified Transformer architecture specifically designed for financial time-series forecasting. Instead of treating stock prediction as a simple univariate autoregression problem, Stockformer models the task as a **multivariate forecasting problem**, using attention mechanisms to discover relationships between multiple financial instruments.\n\n### Why Multivariate Prediction?\n\nTraditional models predict each asset independently:\n\n```\nBTCUSDT → Model → BTCUSDT_prediction\nETHUSDT → Model → ETHUSDT_prediction\n```\n\nStockformer predicts using cross-asset information:\n\n```\n[BTCUSDT, ETHUSDT, SOLUSDT, ...] → Stockformer → BTCUSDT_prediction\n (considering all relationships)\n```\n\n**Key insight**: Financial markets are interconnected. Bitcoin's movement affects Ethereum, oil prices affect airline stocks, and tech stocks often move together. Stockformer explicitly models these dependencies.\n\n### Key Advantages\n\n1. **Cross-Asset Relationship Modeling**\n - Captures correlations between different assets\n - Uses Granger causality to identify predictive relationships\n - Attention weights show which assets influence predictions\n\n2. **Efficient Attention Mechanisms**\n - ProbSparse attention reduces complexity from O(L²) to O(L·log(L))\n - Self-attention distilling for memory efficiency\n - Handles long sequences efficiently\n\n3. **Flexible Output Types**\n - Price regression (MSE/MAE loss)\n - Direction prediction (binary signals)\n - Portfolio allocation (tanh-bounded positions)\n\n4. **Interpretable Predictions**\n - Attention weights reveal cross-asset dependencies\n - Clear visualization of which assets matter for each prediction\n\n### Comparison with Other Models\n\n| Feature | LSTM | Transformer | TFT | Stockformer |\n|---------|------|-------------|-----|-------------|\n| Cross-asset modeling | ✗ | ✗ | Limited | ✓ |\n| ProbSparse attention | ✗ | ✗ | ✗ | ✓ |\n| Multivariate input | ✗ | ✗ | ✓ | ✓ |\n| Correlation-aware | ✗ | ✗ | ✗ | ✓ |\n| Portfolio allocation | ✗ | ✗ | ✗ | ✓ |\n\n## Stockformer Architecture\n\n```\n┌──────────────────────────────────────────────────────────────────────┐\n│ STOCKFORMER │\n├──────────────────────────────────────────────────────────────────────┤\n│ │\n│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │\n│ │ Asset 1 │ │ Asset 2 │ │ Asset 3 │ │ Asset N │ │\n│ │ (BTC) │ │ (ETH) │ │ (SOL) │ │ (...) │ │\n│ └────┬────┘ └────┬────┘ └────┬────┘ └────┬────┘ │\n│ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ │\n│ ┌──────────────────────────────────────────────────┐ │\n│ │ Token Embedding (1D-CNN) │ │\n│ │ Extracts temporal patterns per asset │ │\n│ └───────────────────────┬──────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ┌──────────────────────────────────────────────────┐ │\n│ │ Cross-Ticker Self-Attention │ │\n│ │ Models relationships between all assets │ │\n│ │ (which assets predict which?) │ │\n│ └───────────────────────┬──────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ┌──────────────────────────────────────────────────┐ │\n│ │ Encoder Stack (ProbSparse or Full Attention) │ │\n│ │ + Self-Attention Distilling │ │\n│ └───────────────────────┬──────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ┌──────────────────────────────────────────────────┐ │\n│ │ Prediction Head │ │\n│ │ • Price regression (MSE/MAE) │ │\n│ │ • Direction signal (binary) │ │\n│ │ • Portfolio allocation (tanh) │ │\n│ └──────────────────────────────────────────────────┘ │\n│ │\n└──────────────────────────────────────────────────────────────────────┘\n```\n\n### Token Embedding Layer\n\nStockformer uses 1D-CNN based embeddings instead of simple linear projections:\n\n```python\nclass TokenEmbedding(nn.Module):\n def __init__(self, c_in, d_model, kernel_size=3):\n super().__init__()\n # Separate kernel for each input channel (asset)\n self.conv = nn.Conv1d(\n in_channels=c_in,\n out_channels=d_model,\n kernel_size=kernel_size,\n padding=kernel_size // 2\n )\n\n def forward(self, x):\n # x: [batch, seq_len, n_assets]\n x = x.permute(0, 2, 1) # [batch, n_assets, seq_len]\n x = self.conv(x) # [batch, d_model, seq_len]\n return x.permute(0, 2, 1) # [batch, seq_len, d_model]\n```\n\n**Why 1D-CNN?**\n- Preserves temporal relationships while extracting local patterns\n- Learns different kernels for each asset\n- More efficient than position-wise fully connected layers\n\n### Cross-Ticker Attention\n\nThe key innovation: attention across both time AND assets:\n\n```python\nclass CrossTickerAttention(nn.Module):\n def __init__(self, d_model, n_heads, n_assets):\n super().__init__()\n self.n_assets = n_assets\n self.mha = nn.MultiheadAttention(d_model, n_heads)\n\n def forward(self, x):\n # x: [batch, seq_len, n_assets, d_model]\n batch, seq_len, n_assets, d_model = x.shape\n\n # Reshape for cross-asset attention\n # Treat (seq_len * batch) as batch, n_assets as sequence\n x = x.view(batch * seq_len, n_assets, d_model)\n\n # Self-attention across assets at each time step\n attn_out, attn_weights = self.mha(x, x, x)\n\n # attn_weights shows which assets affect which\n return attn_out.view(batch, seq_len, n_assets, d_model), attn_weights\n```\n\nThe attention weights reveal **which assets influence the prediction**:\n- High attention from ETH to BTC → ETH movements help predict BTC\n- Useful for understanding market dynamics and building portfolios\n\n### ProbSparse Attention\n\nStandard self-attention has O(L²) complexity. ProbSparse attention reduces this to O(L·log(L)):\n\n```python\nclass ProbSparseAttention(nn.Module):\n def __init__(self, d_model, n_heads, factor=5):\n super().__init__()\n self.factor = factor # Controls sparsity\n self.query_proj = nn.Linear(d_model, d_model)\n self.key_proj = nn.Linear(d_model, d_model)\n self.value_proj = nn.Linear(d_model, d_model)\n\n def forward(self, queries, keys, values):\n B, L, D = queries.shape\n\n # Sample top-u keys based on query \"spikiness\"\n u = int(self.factor * np.log(L))\n u = min(u, L)\n\n # Calculate attention scores for sampled queries\n Q = self.query_proj(queries)\n K = self.key_proj(keys)\n V = self.value_proj(values)\n\n # Measure query spikiness: max(QK^T) - mean(QK^T)\n scores = torch.matmul(Q, K.transpose(-2, -1)) / np.sqrt(D)\n M = scores.max(-1)[0] - scores.mean(-1)\n\n # Select top-u queries with highest spikiness\n M_top = M.topk(u, sorted=False)[1]\n\n # Compute sparse attention only for selected queries\n Q_sampled = Q[torch.arange(B)[:, None], M_top]\n attn = torch.softmax(\n torch.matmul(Q_sampled, K.transpose(-2, -1)) / np.sqrt(D),\n dim=-1\n )\n\n # Aggregate values\n context = torch.matmul(attn, V)\n\n return context\n```\n\n**Intuition**: Not all queries need full attention computation. \"Spiky\" queries (those with dominant attention to specific keys) matter most.\n\n### Prediction Head\n\nStockformer supports multiple output types:\n\n```python\nclass PredictionHead(nn.Module):\n def __init__(self, d_model, output_type='regression'):\n super().__init__()\n self.output_type = output_type\n\n if output_type == 'regression':\n # Direct price prediction\n self.head = nn.Linear(d_model, 1)\n self.loss_fn = nn.MSELoss()\n\n elif output_type == 'direction':\n # Binary up/down classification\n self.head = nn.Linear(d_model, 1)\n self.loss_fn = nn.BCEWithLogitsLoss()\n\n elif output_type == 'allocation':\n # Portfolio weights via tanh\n self.head = nn.Sequential(\n nn.Linear(d_model, 1),\n nn.Tanh() # Bounds output to [-1, 1]\n )\n self.loss_fn = lambda pred, ret: -torch.mean(pred * ret) # Maximize returns\n\n def forward(self, x):\n return self.head(x)\n```\n\n## Multivariate Data Representation\n\n### Log Percent Change\n\nRaw prices are transformed for stable training:\n\n```python\ndef log_percent_change(close, open_price):\n \"\"\"\n Transform prices to log percent change.\n\n Advantages:\n - Variance stabilization across different price scales\n - BTC at $40,000 and ETH at $2,000 become comparable\n - Stationary series (important for neural networks)\n \"\"\"\n return np.log(close / open_price + 1)\n```\n\n### Multi-Ticker Inputs\n\nStructure data for multivariate prediction:\n\n```python\n# Data structure for Stockformer\n# Shape: [batch, seq_len, n_assets, features]\n\ndata = {\n 'prices': torch.tensor([\n # Time step 1\n [[45000, 2500, 100], # [BTC, ETH, SOL] close prices\n [44800, 2480, 99]], # Time step 2\n # ...\n ]),\n 'volumes': torch.tensor([...]),\n 'returns': torch.tensor([...]),\n}\n```\n\n### Feature Engineering\n\nRecommended features for each asset:\n\n| Feature | Description | Calculation |\n|---------|-------------|-------------|\n| `log_return` | Log price change | ln(close/prev_close) |\n| `volume_change` | Relative volume | vol/vol_ma_20 |\n| `volatility` | Rolling volatility | std(returns, 20) |\n| `rsi` | Relative Strength | Standard RSI calculation |\n| `correlation` | Pairwise correlation | rolling_corr(asset_i, asset_j) |\n| `funding_rate` | Crypto funding | From exchange API |\n| `open_interest` | Derivatives OI | From exchange API |\n\n## Practical Examples\n\n### 01: Data Preparation\n\n```python\n# python/01_data_preparation.py\n\nimport pandas as pd\nimport numpy as np\nfrom typing import List, Dict\n\ndef prepare_multivariate_data(\n symbols: List[str],\n lookback: int = 168, # 7 days of hourly data\n horizon: int = 24 # Predict 24 hours ahead\n) -> Dict:\n \"\"\"\n Prepare data for Stockformer training.\n\n Args:\n symbols: List of trading pairs (e.g., ['BTCUSDT', 'ETHUSDT'])\n lookback: Number of historical time steps\n horizon: Prediction horizon\n\n Returns:\n Dictionary with X (features) and y (targets)\n \"\"\"\n\n all_data = []\n\n for symbol in symbols:\n # Load data from Bybit or other source\n df = load_bybit_data(symbol)\n\n # Calculate features\n df['log_return'] = np.log(df['close'] / df['close'].shift(1))\n df['volume_change'] = df['volume'] / df['volume'].rolling(20).mean()\n df['volatility'] = df['log_return'].rolling(20).std()\n\n all_data.append(df)\n\n # Align all dataframes on timestamp\n aligned_data = pd.concat(all_data, axis=1, keys=symbols)\n\n # Create sequences\n X, y = [], []\n for i in range(lookback, len(aligned_data) - horizon):\n X.append(aligned_data.iloc[i-lookback:i].values)\n y.append(aligned_data.iloc[i+horizon]['log_return'].values)\n\n return {\n 'X': np.array(X), # [n_samples, lookback, n_assets * n_features]\n 'y': np.array(y), # [n_samples, n_assets]\n 'symbols': symbols\n }\n```\n\n### 02: Stockformer Architecture\n\nSee [python/stockformer.py](python/stockformer.py) for complete implementation.\n\n### 03: Model Training\n\n```python\n# python/03_train_model.py\n\nimport torch\nfrom stockformer import Stockformer\n\n# Model configuration\nconfig = {\n 'n_assets': 5,\n 'd_model': 128,\n 'n_heads': 8,\n 'n_encoder_layers': 3,\n 'dropout': 0.1,\n 'attention_type': 'probsparse',\n 'output_type': 'allocation'\n}\n\n# Initialize model\nmodel = Stockformer(**config)\n\n# Training loop with learning rate scheduling\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)\nscheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n optimizer, mode='min', factor=0.5, patience=5\n)\n\nfor epoch in range(100):\n model.train()\n for batch_x, batch_y in train_loader:\n optimizer.zero_grad()\n\n # Forward pass\n predictions = model(batch_x)\n\n # Calculate loss (depends on output_type)\n loss = model.compute_loss(predictions, batch_y)\n\n # Backward pass\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n optimizer.step()\n\n # Validation\n val_loss = validate(model, val_loader)\n scheduler.step(val_loss)\n```\n\n### 04: Cross-Asset Prediction\n\n```python\n# python/04_cross_asset_prediction.py\n\ndef predict_with_attention(model, X):\n \"\"\"\n Make predictions and return attention weights.\n \"\"\"\n model.eval()\n with torch.no_grad():\n predictions, attention_weights = model(X, return_attention=True)\n\n # attention_weights: [batch, n_heads, n_assets, n_assets]\n # Shows which assets influence which predictions\n\n return predictions, attention_weights\n\ndef visualize_attention(attention_weights, symbols):\n \"\"\"\n Create heatmap of cross-asset attention.\n \"\"\"\n import matplotlib.pyplot as plt\n import seaborn as sns\n\n # Average over heads and batch\n avg_attention = attention_weights.mean(dim=[0, 1]).numpy()\n\n plt.figure(figsize=(10, 8))\n sns.heatmap(\n avg_attention,\n xticklabels=symbols,\n yticklabels=symbols,\n annot=True,\n cmap='Blues'\n )\n plt.title('Cross-Asset Attention Weights')\n plt.xlabel('Key (Source)')\n plt.ylabel('Query (Target)')\n plt.savefig('attention_heatmap.png')\n```\n\n### 05: Portfolio Backtesting\n\n```python\n# python/05_backtest.py\n\ndef backtest_stockformer_strategy(\n model,\n test_data,\n initial_capital: float = 100000,\n transaction_cost: float = 0.001\n):\n \"\"\"\n Backtest Stockformer portfolio allocation strategy.\n \"\"\"\n capital = initial_capital\n positions = np.zeros(model.n_assets)\n\n results = []\n\n for i, (X, returns) in enumerate(test_data):\n # Get model allocation weights\n weights = model(X).numpy().flatten() # [-1, 1] per asset\n\n # Normalize to sum to 1 (long-only) or allow shorting\n weights = weights / np.abs(weights).sum()\n\n # Calculate position changes and costs\n position_change = weights - positions\n costs = np.abs(position_change).sum() * transaction_cost * capital\n\n # Update positions\n positions = weights\n\n # Calculate returns\n portfolio_return = np.sum(positions * returns)\n capital = capital * (1 + portfolio_return) - costs\n\n results.append({\n 'capital': capital,\n 'weights': weights.copy(),\n 'return': portfolio_return\n })\n\n return pd.DataFrame(results)\n```\n\n## Rust Implementation\n\nSee [rust_stockformer](rust_stockformer/) for complete Rust implementation using Bybit data.\n\n```\nrust_stockformer/\n├── Cargo.toml\n├── README.md\n├── src/\n│ ├── lib.rs # Main library exports\n│ ├── api/ # Bybit API client\n│ │ ├── mod.rs\n│ │ ├── client.rs # HTTP client for Bybit\n│ │ └── types.rs # API response types\n│ ├── data/ # Data processing\n│ │ ├── mod.rs\n│ │ ├── loader.rs # Data loading utilities\n│ │ ├── features.rs # Feature engineering\n│ │ └── dataset.rs # Dataset for training\n│ ├── model/ # Stockformer architecture\n│ │ ├── mod.rs\n│ │ ├── embedding.rs # Token embedding layer\n│ │ ├── attention.rs # Cross-ticker & ProbSparse attention\n│ │ ├── encoder.rs # Encoder stack\n│ │ └── stockformer.rs # Complete model\n│ └── strategy/ # Trading strategy\n│ ├── mod.rs\n│ ├── signals.rs # Signal generation\n│ └── backtest.rs # Backtesting engine\n└── examples/\n ├── fetch_data.rs # Download Bybit data\n ├── train.rs # Train model\n └── backtest.rs # Run backtest\n```\n\n### Quick Start (Rust)\n\n```bash\n# Navigate to Rust project\ncd rust_stockformer\n\n# Fetch data from Bybit\ncargo run --example fetch_data -- --symbols BTCUSDT,ETHUSDT,SOLUSDT\n\n# Train model\ncargo run --example train -- --epochs 100 --batch-size 32\n\n# Run backtest\ncargo run --example backtest -- --start 2024-01-01 --end 2024-12-31\n```\n\n## Python Implementation\n\nSee [python/](python/) for Python implementation.\n\n```\npython/\n├── stockformer.py # Main model implementation\n├── data_loader.py # Bybit data loading\n├── features.py # Feature engineering\n├── train.py # Training script\n├── backtest.py # Backtesting utilities\n├── requirements.txt # Dependencies\n└── examples/\n ├── 01_data_preparation.ipynb\n ├── 02_model_architecture.ipynb\n ├── 03_training.ipynb\n ├── 04_prediction.ipynb\n └── 05_backtesting.ipynb\n```\n\n### Quick Start (Python)\n\n```bash\n# Install dependencies\npip install -r requirements.txt\n\n# Fetch data\npython data_loader.py --symbols BTCUSDT,ETHUSDT,SOLUSDT\n\n# Train model\npython train.py --config configs/default.yaml\n\n# Run backtest\npython backtest.py --model checkpoints/best_model.pt\n```\n\n## Best Practices\n\n### When to Use Stockformer\n\n**Good use cases:**\n- Trading correlated assets (crypto, sector ETFs)\n- Portfolio allocation across multiple assets\n- Discovering cross-asset dependencies\n- Long-term predictions (hours to days)\n\n**Not ideal for:**\n- High-frequency trading (inference latency)\n- Single asset prediction (use simpler models)\n- Very small datasets (<1000 samples)\n\n### Hyperparameter Recommendations\n\n| Parameter | Recommended | Notes |\n|-----------|-------------|-------|\n| `d_model` | 128-256 | Match with n_heads |\n| `n_heads` | 8 | Should divide d_model |\n| `n_assets` | 5-20 | More requires more data |\n| `lookback` | 168 (7d hourly) | Depends on data frequency |\n| `dropout` | 0.1-0.2 | Higher for small datasets |\n\n### Common Pitfalls\n\n1. **Gradient instability**: Use gradient clipping and learning rate scheduling\n2. **Overfitting**: Apply dropout, use early stopping\n3. **Data leakage**: Ensure proper train/val/test splits\n4. **Correlation collapse**: Monitor attention weights for diversity\n\n## Resources\n\n### Papers\n\n- [Transformer Based Time-Series Forecasting For Stock](https://arxiv.org/abs/2502.09625) — Original Stockformer paper\n- [Stockformer: A Price-Volume Factor Stock Selection Model](https://arxiv.org/abs/2401.06139) — Advanced variant with wavelet transform\n- [MASTER: Market-Guided Stock Transformer](https://arxiv.org/abs/2312.15235) — Related architecture\n- [Attention Is All You Need](https://arxiv.org/abs/1706.03762) — Original Transformer\n\n### Implementations\n\n- [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/) — Time series library\n- [Informer](https://github.com/zhouhaoyi/Informer2020) — ProbSparse attention implementation\n- [Autoformer](https://github.com/thuml/Autoformer) — Related architecture\n\n### Related Chapters\n\n- [Chapter 26: Temporal Fusion Transformers](../26_temporal_fusion_transformers) — Multi-horizon forecasting\n- [Chapter 41: Higher Order Transformers](../41_higher_order_transformers) — Advanced attention mechanisms\n- [Chapter 47: Cross-Attention Multi-Asset](../47_cross_attention_multi_asset) — Cross-asset modeling\n\n---\n\n## Difficulty Level\n\n**Advanced**\n\nPrerequisites:\n- Transformer architecture and attention mechanisms\n- Time series forecasting basics\n- Portfolio theory fundamentals\n- PyTorch/Rust ML libraries\n","numLines":669,"startLine":1,"totalLines":669}}} | |
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| "content": " 1→# Stockformer: The Team Sport of Stock Prediction\n 2→\n 3→## What is Stockformer?\n 4→\n 5→Imagine you're trying to predict who will win the next basketball game. Would you only look at one player's stats? Of course not! You'd look at the whole team, how they play together, who passes to whom, and how they react to each other.\n 6→\n 7→**Stockformer** does exactly this for the stock market! Instead of predicting one stock alone, it looks at many stocks together and figures out how they influence each other.\n 8→\n 9→---\n 10→\n 11→## The Simple Analogy: Predicting Weather in Your Neighborhood\n 12→\n 13→### Old Way (Single Stock Prediction):\n 14→\n 15→```\n 16→Your House's Temperature → Prediction → Tomorrow's Temperature at Your House\n 17→\n 18→That's it! You only look at your own thermometer.\n 19→```\n 20→\n 21→### Stockformer Way (Multi-Stock Prediction):\n 22→\n 23→```\n 24→Your House's Temperature ]\n 25→Neighbor's House Temperature ] → Stockformer → Tomorrow's Temperature\n 26→School's Temperature ] (understanding ALL the connections!)\n 27→Park's Temperature ]\n 28→\n 29→Stockformer notices:\n 30→- When the park gets cold, your house follows an hour later\n 31→- When the school heats up, the whole neighborhood changes\n 32→- Your neighbor's house is always 2 degrees warmer\n 33→```\n 34→\n 35→**This is MUCH better** because temperatures are connected!\n 36→\n 37→---\n 38→\n 39→## Why Does This Matter for Stocks?\n 40→\n 41→### Example: The Crypto Family\n 42→\n 43→Think of cryptocurrencies like a family:\n 44→\n 45→```\n 46→BITCOIN (BTC) = The Parent\n 47→├── When Bitcoin moves, everyone notices\n 48→├── If Bitcoin is happy (up), kids are usually happy\n 49→└── If Bitcoin is sad (down), kids get worried\n 50→\n 51→ETHEREUM (ETH) = The Older Sibling\n 52→├── Sometimes leads the way\n 53→├── Has its own friends (DeFi tokens)\n 54→└── Influences younger siblings\n 55→\n 56→SOLANA (SOL) = The Younger Sibling\n 57→├── Often follows what ETH does\n 58→├── But sometimes goes its own way\n 59→└── Reacts quickly to family mood\n 60→```\n 61→\n 62→**Stockformer learns all these relationships!** When Bitcoin sneezes, it knows Ethereum might catch a cold, and Solana might need a tissue too!\n 63→\n 64→---\n 65→\n 66→## How Does Stockformer Work? (The Simple Version)\n 67→\n 68→### Step 1: Watch Everyone Together\n 69→\n 70→```\n 71→Instead of this: │ Stockformer does this:\n 72→ │\n 73→BTC → Model → BTC? │ ┌─────┐\n 74→ETH → Model → ETH? │ │ BTC │──┐\n 75→SOL → Model → SOL? │ │ ETH │──┼──→ Stockformer → All predictions!\n 76→ │ │ SOL │──┘\n 77→(Each alone) │ └─────┘\n 78→ │ (Together as a team)\n 79→```\n 80→\n 81→### Step 2: The Attention Mechanism (Who's Looking at Whom?)\n 82→\n 83→Imagine a classroom where everyone can see what everyone else is doing:\n 84→\n 85→```\n 86→STOCKFORMER CLASSROOM:\n 87→\n 88→ │ Looking at │ Looking at │ Looking at\n 89→ │ BTC │ ETH │ SOL\n 90→─────────┼────────────┼────────────┼────────────\n 91→BTC │ - │ \"Oh!\" │ \"Hmm...\"\n 92→─────────┼────────────┼────────────┼────────────\n 93→ETH │ \"!!!\" │ - │ \"Yep\"\n 94→─────────┼────────────┼────────────┼────────────\n 95→SOL │ \"!!!\" │ \"!!!\" │ -\n 96→─────────┴────────────┴────────────┴────────────\n 97→\n 98→Translation:\n 99→- ETH pays A LOT of attention to BTC (\"!!!\")\n 100→- SOL watches both BTC and ETH closely\n 101→- BTC only slightly watches others\n 102→\n 103→Stockformer learns: \"To predict ETH, watch BTC closely!\"\n 104→```\n 105→\n 106→### Step 3: Make Smart Predictions\n 107→\n 108→```\n 109→Old Model:\n 110→\"BTC will go up 2%\" (no reasoning)\n 111→\n 112→Stockformer:\n 113→\"BTC will go up 2% BECAUSE:\n 114→ - ETH went up yesterday (important!)\n 115→ - SOL is showing strength (relevant)\n 116→ - Attention says ETH matters most for this prediction\"\n 117→```\n 118→\n 119→---\n 120→\n 121→## Real-Life Examples Kids Can Understand\n 122→\n 123→### Example 1: The School Lunch Line\n 124→\n 125→```\n 126→PREDICTING IF YOU'LL GET PIZZA TODAY:\n 127→\n 128→Single Prediction (Old Way):\n 129→You → \"Will I get pizza?\" → Maybe?\n 130→\n 131→Multi-Person Prediction (Stockformer Way):\n 132→You ]\n 133→Friend ahead of you ] → Stockformer → Yes! And here's why:\n 134→Line length ]\n 135→Time until class ]\n 136→\n 137→Stockformer notices:\n 138→- Your friend always takes pizza, leaving fewer slices\n 139→- If line is short AND time is long = likely pizza!\n 140→- Your choices depend on others ahead of you\n 141→```\n 142→\n 143→### Example 2: Video Game Item Prices\n 144→\n 145→```\n 146→PREDICTING IF A RARE SWORD'S PRICE WILL GO UP:\n 147→\n 148→Old Way:\n 149→Just look at sword price history → Prediction\n 150→\n 151→Stockformer Way:\n 152→Rare Sword price ]\n 153→Gold price in game ]\n 154→Player count online ] → Stockformer → Better prediction!\n 155→Other rare items ]\n 156→\n 157→Stockformer discovers:\n 158→- When gold is cheap, players buy more swords\n 159→- More players online = more demand = higher price\n 160→- If Shield prices drop, Sword might drop too!\n 161→```\n 162→\n 163→### Example 3: Your Friends' Moods\n 164→\n 165→```\n 166→PREDICTING IF YOUR BEST FRIEND WILL BE HAPPY:\n 167→\n 168→Single Observation:\n 169→Friend's mood yesterday → Today's mood?\n 170→\n 171→Stockformer Observation:\n 172→Friend's mood ]\n 173→Your mood ]\n 174→Class schedule ] → Stockformer → Better guess!\n 175→Weather today ]\n 176→\n 177→Stockformer learns:\n 178→- Your mood affects your friend's mood!\n 179→- Math tests make everyone grumpy\n 180→- Sunny days help everyone\n 181→```\n 182→\n 183→---\n 184→\n 185→## The Magic Components (Simplified!)\n 186→\n 187→### 1. Token Embedding: Making Everyone Speak the Same Language\n 188→\n 189→```\n 190→Problem: How do you compare BTC at $40,000 with SOL at $100?\n 191→\n 192→Solution: Convert everything to \"percentage changes\"\n 193→\n 194→BTC: $40,000 → $40,400 = +1%\n 195→SOL: $100 → $101 = +1%\n 196→\n 197→Now they speak the same language!\n 198→Like converting miles to kilometers so everyone understands.\n 199→```\n 200→\n 201→### 2. Cross-Ticker Attention: Who Influences Whom?\n 202→\n 203→```\n 204→Think of it like a popularity contest:\n 205→\n 206→Most Influential (Gets the most attention):\n 207→🥇 BITCOIN - Everyone watches Bitcoin\n 208→🥈 ETHEREUM - Many coins watch ETH\n 209→🥉 Major coins - Smaller coins watch them\n 210→\n 211→Followers (Pay the most attention to others):\n 212→- Small altcoins watch everything\n 213→- New tokens follow the leaders\n 214→- Meme coins react to sentiment\n 215→\n 216→Stockformer maps all these relationships!\n 217→```\n 218→\n 219→### 3. ProbSparse Attention: Being Smart About What to Watch\n 220→\n 221→```\n 222→Normal Attention: Watch EVERYTHING at ALL times\n 223→Result: Slow and uses lots of memory\n 224→\n 225→ProbSparse Attention: Watch what MATTERS MOST\n 226→\n 227→Like studying for a test:\n 228→❌ Wrong: Read every word in every textbook equally\n 229→✅ Right: Focus on topics likely to be on the test\n 230→\n 231→ProbSparse finds the \"spiky\" important moments:\n 232→\"This moment in BTC really matters!\"\n 233→\"This moment? Not so much, skip it.\"\n 234→```\n 235→\n 236→---\n 237→\n 238→## Fun Quiz Time!\n 239→\n 240→**Question 1**: Why is predicting stocks together better than alone?\n 241→- A) It's more colorful\n 242→- B) Stocks influence each other, so knowing one helps predict another\n 243→- C) Computers like more data\n 244→- D) It's just random\n 245→\n 246→**Answer**: B - Just like how knowing your friend is sick helps predict if you'll catch it!\n 247→\n 248→**Question 2**: What does \"Attention\" mean in Stockformer?\n 249→- A) Paying attention in class\n 250→- B) How much one stock \"watches\" another to make predictions\n 251→- C) Getting people's attention\n 252→- D) None of the above\n 253→\n 254→**Answer**: B - It's like knowing which friends influence your decisions!\n 255→\n 256→**Question 3**: What's ProbSparse Attention good for?\n 257→- A) Making random guesses\n 258→- B) Being fast and efficient by focusing on what matters\n 259→- C) Adding more complexity\n 260→- D) Looking at everything equally\n 261→\n 262→**Answer**: B - Like studying smart, not studying hard!\n 263→\n 264→---\n 265→\n 266→## The Strategy: How Traders Use This\n 267→\n 268→### 1. Portfolio Allocation (Deciding How Much to Invest)\n 269→\n 270→```\n 271→Traditional Way:\n 272→\"I'll put 33% in BTC, 33% in ETH, 33% in SOL\"\n 273→(Equal split, no thought)\n 274→\n 275→Stockformer Way:\n 276→\"Based on attention patterns:\n 277→- BTC looks strong, ETH is following → 50% BTC\n 278→- SOL is uncertain (wide prediction range) → 15% SOL\n 279→- ETH is moderate confidence → 35% ETH\"\n 280→```\n 281→\n 282→### 2. Risk Management (Being Careful)\n 283→\n 284→```\n 285→Confidence = How tight the prediction range is\n 286→\n 287→HIGH Confidence: LOW Confidence:\n 288→\"Price: $100-$102\" \"Price: $80-$120\"\n 289→→ BET MORE! → BET LESS!\n 290→\n 291→Like weather:\n 292→\"90% chance of sun\" = Leave umbrella\n 293→\"50% chance of rain\" = Better bring it!\n 294→```\n 295→\n 296→### 3. Understanding WHY\n 297→\n 298→```\n 299→Traditional Model: \"Buy ETH\"\n 300→You: \"Why?\"\n 301→Model: \"...\"\n 302→\n 303→Stockformer: \"Buy ETH because:\n 304→- BTC attention to ETH is HIGH (they're moving together)\n 305→- ETH leads SOL, and SOL is strong\n 306→- Historical pattern suggests this correlation continues\"\n 307→You: \"That makes sense!\"\n 308→```\n 309→\n 310→---\n 311→\n 312→## Try It Yourself! (No Coding Required)\n 313→\n 314→### Exercise 1: Find Your Own Attention Patterns\n 315→\n 316→Pick 3 things that might be related:\n 317→1. Your energy level\n 318→2. Hours of sleep\n 319→3. Amount of homework\n 320→\n 321→Track for a week:\n 322→```\n 323→Day 1: Sleep: 8h, Homework: 1h, Energy: High\n 324→Day 2: Sleep: 5h, Homework: 3h, Energy: Low\n 325→Day 3: Sleep: 7h, Homework: 2h, Energy: Medium\n 326→...\n 327→```\n 328→\n 329→Now find the patterns:\n 330→- Does more sleep = more energy?\n 331→- Does lots of homework = less sleep = less energy?\n 332→- Which has the MOST influence?\n 333→\n 334→**You just did Attention Analysis!**\n 335→\n 336→### Exercise 2: Predict Together vs. Alone\n 337→\n 338→Try predicting your friend's mood:\n 339→- Just from yesterday's mood (Alone)\n 340→- From their mood + your mood + the weather + day of week (Together)\n 341→\n 342→Which prediction is better? That's Stockformer's advantage!\n 343→\n 344→---\n 345→\n 346→## Key Takeaways (Remember These!)\n 347→\n 348→1. **TOGETHER is BETTER**: Stocks are connected, predict them together!\n 349→\n 350→2. **ATTENTION MATTERS**: Not all stocks affect each other equally - learn who influences whom\n 351→\n 352→3. **BE EFFICIENT**: Focus on what matters (ProbSparse), not everything\n 353→\n 354→4. **UNDERSTAND WHY**: Stockformer explains its predictions through attention weights\n 355→\n 356→5. **CONFIDENCE COUNTS**: Wide prediction range = uncertain, narrow = confident\n 357→\n 358→6. **RELATIONSHIPS CHANGE**: Today's leader might follow tomorrow - keep learning!\n 359→\n 360→---\n 361→\n 362→## The Big Picture\n 363→\n 364→**Traditional Models**: One stock → One prediction\n 365→\n 366→**Stockformer**: Team of stocks → Team of predictions → Understanding how they all connect\n 367→\n 368→It's like the difference between:\n 369→- Watching one player and guessing who wins\n 370→- Watching the whole game and understanding team dynamics\n 371→\n 372→Financial markets are a TEAM SPORT. Stockformer treats them that way!\n 373→\n 374→---\n 375→\n 376→## Fun Fact!\n 377→\n 378→Companies like Google and hedge funds use similar ideas! They realized that predicting one thing at a time is like trying to understand a conversation by only listening to one person. You need the WHOLE context!\n 379→\n 380→**You're learning the same concepts that professionals use to manage billions of dollars!**\n 381→\n 382→Pretty cool, right?\n 383→\n 384→---\n 385→\n 386→*Next time you see Bitcoin move, watch what happens to other cryptos. You're already thinking like Stockformer!*\n 387→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n" | |
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| "content": "# Stockformer: The Team Sport of Stock Prediction\n\n## What is Stockformer?\n\nImagine you're trying to predict who will win the next basketball game. Would you only look at one player's stats? Of course not! You'd look at the whole team, how they play together, who passes to whom, and how they react to each other.\n\n**Stockformer** does exactly this for the stock market! Instead of predicting one stock alone, it looks at many stocks together and figures out how they influence each other.\n\n---\n\n## The Simple Analogy: Predicting Weather in Your Neighborhood\n\n### Old Way (Single Stock Prediction):\n\n```\nYour House's Temperature → Prediction → Tomorrow's Temperature at Your House\n\nThat's it! You only look at your own thermometer.\n```\n\n### Stockformer Way (Multi-Stock Prediction):\n\n```\nYour House's Temperature ]\nNeighbor's House Temperature ] → Stockformer → Tomorrow's Temperature\nSchool's Temperature ] (understanding ALL the connections!)\nPark's Temperature ]\n\nStockformer notices:\n- When the park gets cold, your house follows an hour later\n- When the school heats up, the whole neighborhood changes\n- Your neighbor's house is always 2 degrees warmer\n```\n\n**This is MUCH better** because temperatures are connected!\n\n---\n\n## Why Does This Matter for Stocks?\n\n### Example: The Crypto Family\n\nThink of cryptocurrencies like a family:\n\n```\nBITCOIN (BTC) = The Parent\n├── When Bitcoin moves, everyone notices\n├── If Bitcoin is happy (up), kids are usually happy\n└── If Bitcoin is sad (down), kids get worried\n\nETHEREUM (ETH) = The Older Sibling\n├── Sometimes leads the way\n├── Has its own friends (DeFi tokens)\n└── Influences younger siblings\n\nSOLANA (SOL) = The Younger Sibling\n├── Often follows what ETH does\n├── But sometimes goes its own way\n└── Reacts quickly to family mood\n```\n\n**Stockformer learns all these relationships!** When Bitcoin sneezes, it knows Ethereum might catch a cold, and Solana might need a tissue too!\n\n---\n\n## How Does Stockformer Work? (The Simple Version)\n\n### Step 1: Watch Everyone Together\n\n```\nInstead of this: │ Stockformer does this:\n │\nBTC → Model → BTC? │ ┌─────┐\nETH → Model → ETH? │ │ BTC │──┐\nSOL → Model → SOL? │ │ ETH │──┼──→ Stockformer → All predictions!\n │ │ SOL │──┘\n(Each alone) │ └─────┘\n │ (Together as a team)\n```\n\n### Step 2: The Attention Mechanism (Who's Looking at Whom?)\n\nImagine a classroom where everyone can see what everyone else is doing:\n\n```\nSTOCKFORMER CLASSROOM:\n\n │ Looking at │ Looking at │ Looking at\n │ BTC │ ETH │ SOL\n─────────┼────────────┼────────────┼────────────\nBTC │ - │ \"Oh!\" │ \"Hmm...\"\n─────────┼────────────┼────────────┼────────────\nETH │ \"!!!\" │ - │ \"Yep\"\n─────────┼────────────┼────────────┼────────────\nSOL │ \"!!!\" │ \"!!!\" │ -\n─────────┴────────────┴────────────┴────────────\n\nTranslation:\n- ETH pays A LOT of attention to BTC (\"!!!\")\n- SOL watches both BTC and ETH closely\n- BTC only slightly watches others\n\nStockformer learns: \"To predict ETH, watch BTC closely!\"\n```\n\n### Step 3: Make Smart Predictions\n\n```\nOld Model:\n\"BTC will go up 2%\" (no reasoning)\n\nStockformer:\n\"BTC will go up 2% BECAUSE:\n - ETH went up yesterday (important!)\n - SOL is showing strength (relevant)\n - Attention says ETH matters most for this prediction\"\n```\n\n---\n\n## Real-Life Examples Kids Can Understand\n\n### Example 1: The School Lunch Line\n\n```\nPREDICTING IF YOU'LL GET PIZZA TODAY:\n\nSingle Prediction (Old Way):\nYou → \"Will I get pizza?\" → Maybe?\n\nMulti-Person Prediction (Stockformer Way):\nYou ]\nFriend ahead of you ] → Stockformer → Yes! And here's why:\nLine length ]\nTime until class ]\n\nStockformer notices:\n- Your friend always takes pizza, leaving fewer slices\n- If line is short AND time is long = likely pizza!\n- Your choices depend on others ahead of you\n```\n\n### Example 2: Video Game Item Prices\n\n```\nPREDICTING IF A RARE SWORD'S PRICE WILL GO UP:\n\nOld Way:\nJust look at sword price history → Prediction\n\nStockformer Way:\nRare Sword price ]\nGold price in game ]\nPlayer count online ] → Stockformer → Better prediction!\nOther rare items ]\n\nStockformer discovers:\n- When gold is cheap, players buy more swords\n- More players online = more demand = higher price\n- If Shield prices drop, Sword might drop too!\n```\n\n### Example 3: Your Friends' Moods\n\n```\nPREDICTING IF YOUR BEST FRIEND WILL BE HAPPY:\n\nSingle Observation:\nFriend's mood yesterday → Today's mood?\n\nStockformer Observation:\nFriend's mood ]\nYour mood ]\nClass schedule ] → Stockformer → Better guess!\nWeather today ]\n\nStockformer learns:\n- Your mood affects your friend's mood!\n- Math tests make everyone grumpy\n- Sunny days help everyone\n```\n\n---\n\n## The Magic Components (Simplified!)\n\n### 1. Token Embedding: Making Everyone Speak the Same Language\n\n```\nProblem: How do you compare BTC at $40,000 with SOL at $100?\n\nSolution: Convert everything to \"percentage changes\"\n\nBTC: $40,000 → $40,400 = +1%\nSOL: $100 → $101 = +1%\n\nNow they speak the same language!\nLike converting miles to kilometers so everyone understands.\n```\n\n### 2. Cross-Ticker Attention: Who Influences Whom?\n\n```\nThink of it like a popularity contest:\n\nMost Influential (Gets the most attention):\n🥇 BITCOIN - Everyone watches Bitcoin\n🥈 ETHEREUM - Many coins watch ETH\n🥉 Major coins - Smaller coins watch them\n\nFollowers (Pay the most attention to others):\n- Small altcoins watch everything\n- New tokens follow the leaders\n- Meme coins react to sentiment\n\nStockformer maps all these relationships!\n```\n\n### 3. ProbSparse Attention: Being Smart About What to Watch\n\n```\nNormal Attention: Watch EVERYTHING at ALL times\nResult: Slow and uses lots of memory\n\nProbSparse Attention: Watch what MATTERS MOST\n\nLike studying for a test:\n❌ Wrong: Read every word in every textbook equally\n✅ Right: Focus on topics likely to be on the test\n\nProbSparse finds the \"spiky\" important moments:\n\"This moment in BTC really matters!\"\n\"This moment? Not so much, skip it.\"\n```\n\n---\n\n## Fun Quiz Time!\n\n**Question 1**: Why is predicting stocks together better than alone?\n- A) It's more colorful\n- B) Stocks influence each other, so knowing one helps predict another\n- C) Computers like more data\n- D) It's just random\n\n**Answer**: B - Just like how knowing your friend is sick helps predict if you'll catch it!\n\n**Question 2**: What does \"Attention\" mean in Stockformer?\n- A) Paying attention in class\n- B) How much one stock \"watches\" another to make predictions\n- C) Getting people's attention\n- D) None of the above\n\n**Answer**: B - It's like knowing which friends influence your decisions!\n\n**Question 3**: What's ProbSparse Attention good for?\n- A) Making random guesses\n- B) Being fast and efficient by focusing on what matters\n- C) Adding more complexity\n- D) Looking at everything equally\n\n**Answer**: B - Like studying smart, not studying hard!\n\n---\n\n## The Strategy: How Traders Use This\n\n### 1. Portfolio Allocation (Deciding How Much to Invest)\n\n```\nTraditional Way:\n\"I'll put 33% in BTC, 33% in ETH, 33% in SOL\"\n(Equal split, no thought)\n\nStockformer Way:\n\"Based on attention patterns:\n- BTC looks strong, ETH is following → 50% BTC\n- SOL is uncertain (wide prediction range) → 15% SOL\n- ETH is moderate confidence → 35% ETH\"\n```\n\n### 2. Risk Management (Being Careful)\n\n```\nConfidence = How tight the prediction range is\n\nHIGH Confidence: LOW Confidence:\n\"Price: $100-$102\" \"Price: $80-$120\"\n→ BET MORE! → BET LESS!\n\nLike weather:\n\"90% chance of sun\" = Leave umbrella\n\"50% chance of rain\" = Better bring it!\n```\n\n### 3. Understanding WHY\n\n```\nTraditional Model: \"Buy ETH\"\nYou: \"Why?\"\nModel: \"...\"\n\nStockformer: \"Buy ETH because:\n- BTC attention to ETH is HIGH (they're moving together)\n- ETH leads SOL, and SOL is strong\n- Historical pattern suggests this correlation continues\"\nYou: \"That makes sense!\"\n```\n\n---\n\n## Try It Yourself! (No Coding Required)\n\n### Exercise 1: Find Your Own Attention Patterns\n\nPick 3 things that might be related:\n1. Your energy level\n2. Hours of sleep\n3. Amount of homework\n\nTrack for a week:\n```\nDay 1: Sleep: 8h, Homework: 1h, Energy: High\nDay 2: Sleep: 5h, Homework: 3h, Energy: Low\nDay 3: Sleep: 7h, Homework: 2h, Energy: Medium\n...\n```\n\nNow find the patterns:\n- Does more sleep = more energy?\n- Does lots of homework = less sleep = less energy?\n- Which has the MOST influence?\n\n**You just did Attention Analysis!**\n\n### Exercise 2: Predict Together vs. Alone\n\nTry predicting your friend's mood:\n- Just from yesterday's mood (Alone)\n- From their mood + your mood + the weather + day of week (Together)\n\nWhich prediction is better? That's Stockformer's advantage!\n\n---\n\n## Key Takeaways (Remember These!)\n\n1. **TOGETHER is BETTER**: Stocks are connected, predict them together!\n\n2. **ATTENTION MATTERS**: Not all stocks affect each other equally - learn who influences whom\n\n3. **BE EFFICIENT**: Focus on what matters (ProbSparse), not everything\n\n4. **UNDERSTAND WHY**: Stockformer explains its predictions through attention weights\n\n5. **CONFIDENCE COUNTS**: Wide prediction range = uncertain, narrow = confident\n\n6. **RELATIONSHIPS CHANGE**: Today's leader might follow tomorrow - keep learning!\n\n---\n\n## The Big Picture\n\n**Traditional Models**: One stock → One prediction\n\n**Stockformer**: Team of stocks → Team of predictions → Understanding how they all connect\n\nIt's like the difference between:\n- Watching one player and guessing who wins\n- Watching the whole game and understanding team dynamics\n\nFinancial markets are a TEAM SPORT. Stockformer treats them that way!\n\n---\n\n## Fun Fact!\n\nCompanies like Google and hedge funds use similar ideas! They realized that predicting one thing at a time is like trying to understand a conversation by only listening to one person. You need the WHOLE context!\n\n**You're learning the same concepts that professionals use to manage billions of dollars!**\n\nPretty cool, right?\n\n---\n\n*Next time you see Bitcoin move, watch what happens to other cryptos. You're already thinking like Stockformer!*\n", | |
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| [2026-01-18T20:51:57.147Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016JqwsFoz3BdzA2bQU8Zody","type":"tool_result","content":" 1→\"\"\"\n 2→Stockformer Model Implementation in PyTorch\n 3→\n 4→Provides:\n 5→- StockformerConfig: Model configuration\n 6→- StockformerModel: Main transformer model\n 7→- CrossTickerAttention: Attention between assets\n 8→- ProbSparseAttention: Efficient sparse attention\n 9→\"\"\"\n 10→\n 11→import math\n 12→import torch\n 13→import torch.nn as nn\n 14→import torch.nn.functional as F\n 15→from typing import Optional, Tuple, List\n 16→from dataclasses import dataclass, field\n 17→from enum import Enum\n 18→\n 19→\n 20→class AttentionType(Enum):\n 21→ \"\"\"Type of attention mechanism\"\"\"\n 22→ FULL = \"full\"\n 23→ PROBSPARSE = \"probsparse\"\n 24→ LOCAL = \"local\"\n 25→\n 26→\n 27→class OutputType(Enum):\n 28→ \"\"\"Type of model output\"\"\"\n 29→ REGRESSION = \"regression\"\n 30→ DIRECTION = \"direction\"\n 31→ PORTFOLIO = \"portfolio\"\n 32→ QUANTILE = \"quantile\"\n 33→\n 34→\n 35→@dataclass\n 36→class StockformerConfig:\n 37→ \"\"\"\n 38→ Configuration for Stockformer model\n 39→\n 40→ Example:\n 41→ config = StockformerConfig(\n 42→ num_tickers=5,\n 43→ seq_len=96,\n 44→ d_model=64\n 45→ )\n 46→ \"\"\"\n 47→ # Architecture\n 48→ num_tickers: int = 5\n 49→ seq_len: int = 96\n 50→ input_features: int = 6\n 51→ d_model: int = 64\n 52→ num_heads: int = 4\n 53→ d_ff: int = 256\n 54→ num_encoder_layers: int = 2\n 55→ dropout: float = 0.1\n 56→\n 57→ # Attention\n 58→ attention_type: AttentionType = AttentionType.PROBSPARSE\n 59→ sparsity_factor: float = 5.0\n 60→\n 61→ # Output\n 62→ output_type: OutputType = OutputType.REGRESSION\n 63→ prediction_horizon: int = 1\n 64→ quantiles: List[float] = field(default_factory=lambda: [0.1, 0.5, 0.9])\n 65→\n 66→ # Embedding\n 67→ kernel_size: int = 3\n 68→ use_cross_ticker_attention: bool = True\n 69→ use_positional_encoding: bool = True\n 70→\n 71→ def validate(self):\n 72→ \"\"\"Validate configuration\"\"\"\n 73→ assert self.d_model % self.num_heads == 0, \"d_model must be divisible by num_heads\"\n 74→ assert self.kernel_size % 2 == 1, \"kernel_size must be odd\"\n 75→ assert 0 <= self.dropout <= 1, \"dropout must be in [0, 1]\"\n 76→\n 77→ @property\n 78→ def head_dim(self) -> int:\n 79→ return self.d_model // self.num_heads\n 80→\n 81→ @property\n 82→ def output_dim(self) -> int:\n 83→ if self.output_type == OutputType.REGRESSION:\n 84→ return self.num_tickers\n 85→ elif self.output_type == OutputType.DIRECTION:\n 86→ return self.num_tickers * 3\n 87→ elif self.output_type == OutputType.PORTFOLIO:\n 88→ return self.num_tickers\n 89→ elif self.output_type == OutputType.QUANTILE:\n 90→ return self.num_tickers * len(self.quantiles)\n 91→ return self.num_tickers\n 92→\n 93→\n 94→class TokenEmbedding(nn.Module):\n 95→ \"\"\"\n 96→ Token embedding using 1D convolution\n 97→\n 98→ Converts [batch, seq_len, features] to [batch, seq_len, d_model]\n 99→ \"\"\"\n 100→\n 101→ def __init__(self, config: StockformerConfig):\n 102→ super().__init__()\n 103→ self.conv = nn.Conv1d(\n 104→ in_channels=config.input_features,\n 105→ out_channels=config.d_model,\n 106→ kernel_size=config.kernel_size,\n 107→ padding=config.kernel_size // 2\n 108→ )\n 109→ self.activation = nn.GELU()\n 110→\n 111→ def forward(self, x: torch.Tensor) -> torch.Tensor:\n 112→ # x: [batch, seq_len, features]\n 113→ x = x.transpose(1, 2) # [batch, features, seq_len]\n 114→ x = self.conv(x) # [batch, d_model, seq_len]\n 115→ x = x.transpose(1, 2) # [batch, seq_len, d_model]\n 116→ return self.activation(x)\n 117→\n 118→\n 119→class PositionalEncoding(nn.Module):\n 120→ \"\"\"Sinusoidal positional encoding\"\"\"\n 121→\n 122→ def __init__(self, d_model: int, max_len: int = 5000, dropout: float = 0.1):\n 123→ super().__init__()\n 124→ self.dropout = nn.Dropout(p=dropout)\n 125→\n 126→ pe = torch.zeros(max_len, d_model)\n 127→ position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n 128→ div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n 129→\n 130→ pe[:, 0::2] = torch.sin(position * div_term)\n 131→ pe[:, 1::2] = torch.cos(position * div_term)\n 132→ pe = pe.unsqueeze(0)\n 133→\n 134→ self.register_buffer('pe', pe)\n 135→\n 136→ def forward(self, x: torch.Tensor) -> torch.Tensor:\n 137→ # x: [batch, seq_len, d_model]\n 138→ x = x + self.pe[:, :x.size(1)]\n 139→ return self.dropout(x)\n 140→\n 141→\n 142→class TickerEncoding(nn.Module):\n 143→ \"\"\"Learnable ticker-specific encoding\"\"\"\n 144→\n 145→ def __init__(self, num_tickers: int, d_model: int):\n 146→ super().__init__()\n 147→ self.embedding = nn.Embedding(num_tickers, d_model)\n 148→\n 149→ def forward(self, x: torch.Tensor, ticker_ids: torch.Tensor) -> torch.Tensor:\n 150→ # x: [batch, num_tickers, seq_len, d_model]\n 151→ # ticker_ids: [num_tickers]\n 152→ ticker_emb = self.embedding(ticker_ids) # [num_tickers, d_model]\n 153→ return x + ticker_emb.unsqueeze(0).unsqueeze(2)\n 154→\n 155→\n 156→class CrossTickerAttention(nn.Module):\n 157→ \"\"\"\n 158→ Cross-ticker attention mechanism\n 159→\n 160→ Computes attention between different assets at each timestep\n 161→ \"\"\"\n 162→\n 163→ def __init__(self, config: StockformerConfig):\n 164→ super().__init__()\n 165→ self.num_heads = config.num_heads\n 166→ self.head_dim = config.head_dim\n 167→ self.scale = math.sqrt(self.head_dim)\n 168→\n 169→ self.q_proj = nn.Linear(config.d_model, config.d_model)\n 170→ self.k_proj = nn.Linear(config.d_model, config.d_model)\n 171→ self.v_proj = nn.Linear(config.d_model, config.d_model)\n 172→ self.out_proj = nn.Linear(config.d_model, config.d_model)\n 173→\n 174→ self.dropout = nn.Dropout(config.dropout)\n 175→\n 176→ def forward(\n 177→ self,\n 178→ x: torch.Tensor,\n 179→ return_attention: bool = False\n 180→ ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n 181→ \"\"\"\n 182→ Args:\n 183→ x: [batch, num_tickers, seq_len, d_model]\n 184→ return_attention: Whether to return attention weights\n 185→\n 186→ Returns:\n 187→ output: [batch, num_tickers, seq_len, d_model]\n 188→ attention: [batch, num_heads, num_tickers, num_tickers] (optional)\n 189→ \"\"\"\n 190→ batch, num_tickers, seq_len, d_model = x.shape\n 191→\n 192→ # Reshape for cross-ticker attention at each timestep\n 193→ # [batch, seq_len, num_tickers, d_model]\n 194→ x = x.transpose(1, 2)\n 195→\n 196→ # Project to Q, K, V\n 197→ q = self.q_proj(x)\n 198→ k = self.k_proj(x)\n 199→ v = self.v_proj(x)\n 200→\n 201→ # Reshape for multi-head attention\n 202→ # [batch, seq_len, num_heads, num_tickers, head_dim]\n 203→ q = q.view(batch, seq_len, num_tickers, self.num_heads, self.head_dim).transpose(2, 3)\n 204→ k = k.view(batch, seq_len, num_tickers, self.num_heads, self.head_dim).transpose(2, 3)\n 205→ v = v.view(batch, seq_len, num_tickers, self.num_heads, self.head_dim).transpose(2, 3)\n 206→\n 207→ # Attention: [batch, seq_len, num_heads, num_tickers, num_tickers]\n 208→ attn = torch.matmul(q, k.transpose(-2, -1)) / self.scale\n 209→ attn = F.softmax(attn, dim=-1)\n 210→ attn = self.dropout(attn)\n 211→\n 212→ # Output: [batch, seq_len, num_heads, num_tickers, head_dim]\n 213→ out = torch.matmul(attn, v)\n 214→\n 215→ # Reshape back\n 216→ out = out.transpose(2, 3).contiguous()\n 217→ out = out.view(batch, seq_len, num_tickers, d_model)\n 218→ out = self.out_proj(out)\n 219→\n 220→ # [batch, num_tickers, seq_len, d_model]\n 221→ out = out.transpose(1, 2)\n 222→\n 223→ # Average attention over time for interpretability\n 224→ avg_attn = attn.mean(dim=1) if return_attention else None\n 225→\n 226→ return out, avg_attn\n 227→\n 228→\n 229→class ProbSparseAttention(nn.Module):\n 230→ \"\"\"\n 231→ ProbSparse self-attention mechanism\n 232→\n 233→ Reduces complexity from O(L²) to O(L·log(L))\n 234→ \"\"\"\n 235→\n 236→ def __init__(self, config: StockformerConfig):\n 237→ super().__init__()\n 238→ self.num_heads = config.num_heads\n 239→ self.head_dim = config.head_dim\n 240→ self.scale = math.sqrt(self.head_dim)\n 241→ self.sparsity_factor = config.sparsity_factor\n 242→\n 243→ self.q_proj = nn.Linear(config.d_model, config.d_model)\n 244→ self.k_proj = nn.Linear(config.d_model, config.d_model)\n 245→ self.v_proj = nn.Linear(config.d_model, config.d_model)\n 246→ self.out_proj = nn.Linear(config.d_model, config.d_model)\n 247→\n 248→ self.dropout = nn.Dropout(config.dropout)\n 249→\n 250→ def forward(\n 251→ self,\n 252→ x: torch.Tensor,\n 253→ return_attention: bool = False\n 254→ ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n 255→ \"\"\"\n 256→ Args:\n 257→ x: [batch, seq_len, d_model]\n 258→ return_attention: Whether to return attention weights\n 259→\n 260→ Returns:\n 261→ output: [batch, seq_len, d_model]\n 262→ attention: [batch, num_heads, seq_len, seq_len] (optional)\n 263→ \"\"\"\n 264→ batch, seq_len, d_model = x.shape\n 265→\n 266→ # Calculate number of top queries\n 267→ u = max(1, min(seq_len, int(self.sparsity_factor * math.log(seq_len + 1))))\n 268→\n 269→ # Project to Q, K, V\n 270→ q = self.q_proj(x).view(batch, seq_len, self.num_heads, self.head_dim).transpose(1, 2)\n 271→ k = self.k_proj(x).view(batch, seq_len, self.num_heads, self.head_dim).transpose(1, 2)\n 272→ v = self.v_proj(x).view(batch, seq_len, self.num_heads, self.head_dim).transpose(1, 2)\n 273→\n 274→ # Compute sparsity measurement M(q) = max(Q·K^T) - mean(Q·K^T)\n 275→ scores = torch.matmul(q, k.transpose(-2, -1)) / self.scale\n 276→\n 277→ M = scores.max(dim=-1)[0] - scores.mean(dim=-1) # [batch, num_heads, seq_len]\n 278→\n 279→ # Select top-u queries\n 280→ _, top_indices = M.topk(u, dim=-1) # [batch, num_heads, u]\n 281→\n 282→ # Full attention only for selected queries\n 283→ attn = F.softmax(scores, dim=-1)\n 284→ attn = self.dropout(attn)\n 285→\n 286→ # Output\n 287→ out = torch.matmul(attn, v)\n 288→ out = out.transpose(1, 2).contiguous().view(batch, seq_len, d_model)\n 289→ out = self.out_proj(out)\n 290→\n 291→ return out, attn if return_attention else None\n 292→\n 293→\n 294→class EncoderLayer(nn.Module):\n 295→ \"\"\"Transformer encoder layer with optional cross-ticker attention\"\"\"\n 296→\n 297→ def __init__(self, config: StockformerConfig):\n 298→ super().__init__()\n 299→\n 300→ self.self_attention = ProbSparseAttention(config)\n 301→ self.cross_ticker_attention = CrossTickerAttention(config) if config.use_cross_ticker_attention else None\n 302→\n 303→ self.norm1 = nn.LayerNorm(config.d_model)\n 304→ self.norm2 = nn.LayerNorm(config.d_model)\n 305→ self.norm3 = nn.LayerNorm(config.d_model) if config.use_cross_ticker_attention else None\n 306→\n 307→ self.ff = nn.Sequential(\n 308→ nn.Linear(config.d_model, config.d_ff),\n 309→ nn.GELU(),\n 310→ nn.Dropout(config.dropout),\n 311→ nn.Linear(config.d_ff, config.d_model),\n 312→ nn.Dropout(config.dropout)\n 313→ )\n 314→\n 315→ self.dropout = nn.Dropout(config.dropout)\n 316→\n 317→ def forward(\n 318→ self,\n 319→ x: torch.Tensor,\n 320→ return_attention: bool = False\n 321→ ) -> Tuple[torch.Tensor, dict]:\n 322→ \"\"\"\n 323→ Args:\n 324→ x: [batch, num_tickers, seq_len, d_model]\n 325→\n 326→ Returns:\n 327→ output: [batch, num_tickers, seq_len, d_model]\n 328→ attention_dict: Dictionary of attention weights\n 329→ \"\"\"\n 330→ batch, num_tickers, seq_len, d_model = x.shape\n 331→ attention_dict = {}\n 332→\n 333→ # Self-attention for each ticker\n 334→ x_flat = x.view(batch * num_tickers, seq_len, d_model)\n 335→ attn_out, temporal_attn = self.self_attention(x_flat, return_attention)\n 336→ attn_out = attn_out.view(batch, num_tickers, seq_len, d_model)\n 337→\n 338→ x = self.norm1(x + self.dropout(attn_out))\n 339→\n 340→ if temporal_attn is not None:\n 341→ attention_dict['temporal'] = temporal_attn.view(batch, num_tickers, self.self_attention.num_heads, seq_len, seq_len)\n 342→\n 343→ # Cross-ticker attention\n 344→ if self.cross_ticker_attention is not None:\n 345→ cross_out, cross_attn = self.cross_ticker_attention(x, return_attention)\n 346→ x = self.norm3(x + self.dropout(cross_out))\n 347→\n 348→ if cross_attn is not None:\n 349→ attention_dict['cross_ticker'] = cross_attn\n 350→\n 351→ # Feed-forward\n 352→ ff_out = self.ff(x)\n 353→ x = self.norm2(x + ff_out)\n 354→\n 355→ return x, attention_dict\n 356→\n 357→\n 358→class StockformerModel(nn.Module):\n 359→ \"\"\"\n 360→ Stockformer: Multivariate Stock Prediction Model\n 361→\n 362→ Example:\n 363→ config = StockformerConfig(num_tickers=5, seq_len=96)\n 364→ model = StockformerModel(config)\n 365→\n 366→ x = torch.randn(2, 5, 96, 6) # [batch, tickers, seq_len, features]\n 367→ output = model(x)\n 368→ print(output['predictions'].shape) # [2, 5]\n 369→ \"\"\"\n 370→\n 371→ def __init__(self, config: StockformerConfig):\n 372→ super().__init__()\n 373→ config.validate()\n 374→ self.config = config\n 375→\n 376→ # Embedding layers\n 377→ self.token_embedding = TokenEmbedding(config)\n 378→ self.positional_encoding = PositionalEncoding(\n 379→ config.d_model, config.seq_len * 2, config.dropout\n 380→ ) if config.use_positional_encoding else None\n 381→ self.ticker_encoding = TickerEncoding(config.num_tickers, config.d_model)\n 382→\n 383→ # Encoder layers\n 384→ self.encoder_layers = nn.ModuleList([\n 385→ EncoderLayer(config) for _ in range(config.num_encoder_layers)\n 386→ ])\n 387→\n 388→ # Output head\n 389→ self.output_head = self._build_output_head(config)\n 390→\n 391→ # Register ticker IDs\n 392→ self.register_buffer('ticker_ids', torch.arange(config.num_tickers))\n 393→\n 394→ def _build_output_head(self, config: StockformerConfig) -> nn.Module:\n 395→ \"\"\"Build output projection layer based on output type\"\"\"\n 396→ if config.output_type == OutputType.QUANTILE:\n 397→ return nn.Linear(config.d_model, len(config.quantiles))\n 398→ elif config.output_type == OutputType.DIRECTION:\n 399→ return nn.Linear(config.d_model, 3)\n 400→ else:\n 401→ return nn.Linear(config.d_model, 1)\n 402→\n 403→ def forward(\n 404→ self,\n 405→ x: torch.Tensor,\n 406→ return_attention: bool = False\n 407→ ) -> dict:\n 408→ \"\"\"\n 409→ Forward pass\n 410→\n 411→ Args:\n 412→ x: Input tensor [batch, num_tickers, seq_len, features]\n 413→ return_attention: Whether to return attention weights\n 414→\n 415→ Returns:\n 416→ Dictionary with:\n 417→ - predictions: [batch, output_dim]\n 418→ - attention_weights: Optional attention weights\n 419→ - confidence: Optional confidence scores\n 420→ \"\"\"\n 421→ batch, num_tickers, seq_len, features = x.shape\n 422→\n 423→ # Token embedding for each ticker\n 424→ x_embedded = []\n 425→ for t in range(num_tickers):\n 426→ emb = self.token_embedding(x[:, t]) # [batch, seq_len, d_model]\n 427→ x_embedded.append(emb)\n 428→ x = torch.stack(x_embedded, dim=1) # [batch, num_tickers, seq_len, d_model]\n 429→\n 430→ # Add positional encoding\n 431→ if self.positional_encoding is not None:\n 432→ for t in range(num_tickers):\n 433→ x[:, t] = self.positional_encoding(x[:, t])\n 434→\n 435→ # Add ticker encoding\n 436→ x = self.ticker_encoding(x, self.ticker_ids)\n 437→\n 438→ # Encoder layers\n 439→ all_attention = {}\n 440→ for i, layer in enumerate(self.encoder_layers):\n 441→ x, attn_dict = layer(x, return_attention)\n 442→ if attn_dict:\n 443→ all_attention[f'layer_{i}'] = attn_dict\n 444→\n 445→ # Pool: take last timestep\n 446→ x = x[:, :, -1, :] # [batch, num_tickers, d_model]\n 447→\n 448→ # Output projection\n 449→ predictions = self._compute_output(x)\n 450→\n 451→ result = {\n 452→ 'predictions': predictions,\n 453→ 'attention_weights': all_attention if return_attention else None\n 454→ }\n 455→\n 456→ # Add confidence for quantile regression\n 457→ if self.config.output_type == OutputType.QUANTILE:\n 458→ result['confidence'] = self._compute_confidence(predictions)\n 459→\n 460→ return result\n 461→\n 462→ def _compute_output(self, x: torch.Tensor) -> torch.Tensor:\n 463→ \"\"\"Compute output predictions\"\"\"\n 464→ batch, num_tickers, d_model = x.shape\n 465→\n 466→ if self.config.output_type == OutputType.PORTFOLIO:\n 467→ # Portfolio weights sum to 1\n 468→ logits = self.output_head(x).squeeze(-1) # [batch, num_tickers]\n 469→ return F.softmax(logits, dim=-1)\n 470→\n 471→ elif self.config.output_type == OutputType.DIRECTION:\n 472→ # Per-ticker classification\n 473→ logits = self.output_head(x) # [batch, num_tickers, 3]\n 474→ probs = F.softmax(logits, dim=-1)\n 475→ return probs.view(batch, -1) # [batch, num_tickers * 3]\n 476→\n 477→ elif self.config.output_type == OutputType.QUANTILE:\n 478→ # Per-ticker quantile prediction\n 479→ quantiles = self.output_head(x) # [batch, num_tickers, num_quantiles]\n 480→ return quantiles.view(batch, -1)\n 481→\n 482→ else: # REGRESSION\n 483→ return self.output_head(x).squeeze(-1) # [batch, num_tickers]\n 484→\n 485→ def _compute_confidence(self, predictions: torch.Tensor) -> torch.Tensor:\n 486→ \"\"\"Compute confidence from quantile predictions\"\"\"\n 487→ batch = predictions.size(0)\n 488→ num_quantiles = len(self.config.quantiles)\n 489→ num_tickers = self.config.num_tickers\n 490→\n 491→ predictions = predictions.view(batch, num_tickers, num_quantiles)\n 492→ interval_width = (predictions[:, :, -1] - predictions[:, :, 0]).abs()\n 493→ confidence = 1.0 / (1.0 + interval_width)\n 494→\n 495→ return confidence\n 496→\n 497→\n 498→if __name__ == \"__main__\":\n 499→ # Test the model\n 500→ print(\"Testing Stockformer model...\")\n 501→\n 502→ config = StockformerConfig(\n 503→ num_tickers=5,\n 504→ seq_len=48,\n 505→ input_features=6,\n 506→ d_model=32,\n 507→ num_heads=4,\n 508→ num_encoder_layers=2\n 509→ )\n 510→\n 511→ model = StockformerModel(config)\n 512→ print(f\"Model parameters: {sum(p.numel() for p in model.parameters()):,}\")\n 513→\n 514→ # Test forward pass\n 515→ x = torch.randn(2, 5, 48, 6)\n 516→ output = model(x, return_attention=True)\n 517→\n 518→ print(f\"Predictions shape: {output['predictions'].shape}\")\n 519→ print(f\"Attention weights available: {output['attention_weights'] is not None}\")\n 520→\n 521→ # Test different output types\n 522→ for output_type in OutputType:\n 523→ config.output_type = output_type\n 524→ model = StockformerModel(config)\n 525→ output = model(x)\n 526→ print(f\"{output_type.value}: predictions shape = {output['predictions'].shape}\")\n 527→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"parent_tool_use_id":null,"session_id":"77018388-5fda-4d3e-acd4-e8399840434b","uuid":"16c1d93c-aeee-49c5-8072-a85e83a4d967","tool_use_result":{"type":"text","file":{"filePath":"/tmp/gh-issue-solver-1768769379407/43_stockformer_multivariate/python/model.py","content":"\"\"\"\nStockformer Model Implementation in PyTorch\n\nProvides:\n- StockformerConfig: Model configuration\n- StockformerModel: Main transformer model\n- CrossTickerAttention: Attention between assets\n- ProbSparseAttention: Efficient sparse attention\n\"\"\"\n\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom typing import Optional, Tuple, List\nfrom dataclasses import dataclass, field\nfrom enum import Enum\n\n\nclass AttentionType(Enum):\n \"\"\"Type of attention mechanism\"\"\"\n FULL = \"full\"\n PROBSPARSE = \"probsparse\"\n LOCAL = \"local\"\n\n\nclass OutputType(Enum):\n \"\"\"Type of model output\"\"\"\n REGRESSION = \"regression\"\n DIRECTION = \"direction\"\n PORTFOLIO = \"portfolio\"\n QUANTILE = \"quantile\"\n\n\n@dataclass\nclass StockformerConfig:\n \"\"\"\n Configuration for Stockformer model\n\n Example:\n config = StockformerConfig(\n num_tickers=5,\n seq_len=96,\n d_model=64\n )\n \"\"\"\n # Architecture\n num_tickers: int = 5\n seq_len: int = 96\n input_features: int = 6\n d_model: int = 64\n num_heads: int = 4\n d_ff: int = 256\n num_encoder_layers: int = 2\n dropout: float = 0.1\n\n # Attention\n attention_type: AttentionType = AttentionType.PROBSPARSE\n sparsity_factor: float = 5.0\n\n # Output\n output_type: OutputType = OutputType.REGRESSION\n prediction_horizon: int = 1\n quantiles: List[float] = field(default_factory=lambda: [0.1, 0.5, 0.9])\n\n # Embedding\n kernel_size: int = 3\n use_cross_ticker_attention: bool = True\n use_positional_encoding: bool = True\n\n def validate(self):\n \"\"\"Validate configuration\"\"\"\n assert self.d_model % self.num_heads == 0, \"d_model must be divisible by num_heads\"\n assert self.kernel_size % 2 == 1, \"kernel_size must be odd\"\n assert 0 <= self.dropout <= 1, \"dropout must be in [0, 1]\"\n\n @property\n def head_dim(self) -> int:\n return self.d_model // self.num_heads\n\n @property\n def output_dim(self) -> int:\n if self.output_type == OutputType.REGRESSION:\n return self.num_tickers\n elif self.output_type == OutputType.DIRECTION:\n return self.num_tickers * 3\n elif self.output_type == OutputType.PORTFOLIO:\n return self.num_tickers\n elif self.output_type == OutputType.QUANTILE:\n return self.num_tickers * len(self.quantiles)\n return self.num_tickers\n\n\nclass TokenEmbedding(nn.Module):\n \"\"\"\n Token embedding using 1D convolution\n\n Converts [batch, seq_len, features] to [batch, seq_len, d_model]\n \"\"\"\n\n def __init__(self, config: StockformerConfig):\n super().__init__()\n self.conv = nn.Conv1d(\n in_channels=config.input_features,\n out_channels=config.d_model,\n kernel_size=config.kernel_size,\n padding=config.kernel_size // 2\n )\n self.activation = nn.GELU()\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # x: [batch, seq_len, features]\n x = x.transpose(1, 2) # [batch, features, seq_len]\n x = self.conv(x | |
| [2026-01-18T20:51:57.150Z] [INFO] ) # [batch, d_model, seq_len]\n x = x.transpose(1, 2) # [batch, seq_len, d_model]\n return self.activation(x)\n\n\nclass PositionalEncoding(nn.Module):\n \"\"\"Sinusoidal positional encoding\"\"\"\n\n def __init__(self, d_model: int, max_len: int = 5000, dropout: float = 0.1):\n super().__init__()\n self.dropout = nn.Dropout(p=dropout)\n\n pe = torch.zeros(max_len, d_model)\n position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n\n pe[:, 0::2] = torch.sin(position * div_term)\n pe[:, 1::2] = torch.cos(position * div_term)\n pe = pe.unsqueeze(0)\n\n self.register_buffer('pe', pe)\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n # x: [batch, seq_len, d_model]\n x = x + self.pe[:, :x.size(1)]\n return self.dropout(x)\n\n\nclass TickerEncoding(nn.Module):\n \"\"\"Learnable ticker-specific encoding\"\"\"\n\n def __init__(self, num_tickers: int, d_model: int):\n super().__init__()\n self.embedding = nn.Embedding(num_tickers, d_model)\n\n def forward(self, x: torch.Tensor, ticker_ids: torch.Tensor) -> torch.Tensor:\n # x: [batch, num_tickers, seq_len, d_model]\n # ticker_ids: [num_tickers]\n ticker_emb = self.embedding(ticker_ids) # [num_tickers, d_model]\n return x + ticker_emb.unsqueeze(0).unsqueeze(2)\n\n\nclass CrossTickerAttention(nn.Module):\n \"\"\"\n Cross-ticker attention mechanism\n\n Computes attention between different assets at each timestep\n \"\"\"\n\n def __init__(self, config: StockformerConfig):\n super().__init__()\n self.num_heads = config.num_heads\n self.head_dim = config.head_dim\n self.scale = math.sqrt(self.head_dim)\n\n self.q_proj = nn.Linear(config.d_model, config.d_model)\n self.k_proj = nn.Linear(config.d_model, config.d_model)\n self.v_proj = nn.Linear(config.d_model, config.d_model)\n self.out_proj = nn.Linear(config.d_model, config.d_model)\n\n self.dropout = nn.Dropout(config.dropout)\n\n def forward(\n self,\n x: torch.Tensor,\n return_attention: bool = False\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n \"\"\"\n Args:\n x: [batch, num_tickers, seq_len, d_model]\n return_attention: Whether to return attention weights\n\n Returns:\n output: [batch, num_tickers, seq_len, d_model]\n attention: [batch, num_heads, num_tickers, num_tickers] (optional)\n \"\"\"\n batch, num_tickers, seq_len, d_model = x.shape\n\n # Reshape for cross-ticker attention at each timestep\n # [batch, seq_len, num_tickers, d_model]\n x = x.transpose(1, 2)\n\n # Project to Q, K, V\n q = self.q_proj(x)\n k = self.k_proj(x)\n v = self.v_proj(x)\n\n # Reshape for multi-head attention\n # [batch, seq_len, num_heads, num_tickers, head_dim]\n q = q.view(batch, seq_len, num_tickers, self.num_heads, self.head_dim).transpose(2, 3)\n k = k.view(batch, seq_len, num_tickers, self.num_heads, self.head_dim).transpose(2, 3)\n v = v.view(batch, seq_len, num_tickers, self.num_heads, self.head_dim).transpose(2, 3)\n\n # Attention: [batch, seq_len, num_heads, num_tickers, num_tickers]\n attn = torch.matmul(q, k.transpose(-2, -1)) / self.scale\n attn = F.softmax(attn, dim=-1)\n attn = self.dropout(attn)\n\n # Output: [batch, seq_len, num_heads, num_tickers, head_dim]\n out = torch.matmul(attn, v)\n\n # Reshape back\n out = out.transpose(2, 3).contiguous()\n out = out.view(batch, seq_len, num_tickers, d_model)\n out = self.out_proj(out)\n\n # [batch, num_tickers, seq_len, d_model]\n out = out.transpose(1, 2)\n\n # Average attention over time for interpretability\n avg_attn = attn.mean(dim=1) if return_attention else None\n\n return out, avg_attn\n\n\nclass ProbSparseAttention(nn.Module):\n \"\"\"\n ProbSparse self-attention mechanism\n\n Reduces complexity from O(L²) to O(L·log(L))\n \"\"\"\n\n def __init__(self, config: StockformerConfig):\n super().__init__()\n self.num_heads = config.num_heads\n self.head_dim = config.head_dim\n self.scale = math.sqrt(self.head_dim)\n self.sparsity_factor = config.sparsity_factor\n\n self.q_proj = nn.Linear(config.d_model, config.d_model)\n self.k_proj = nn.Linear(config.d_model, config.d_model)\n self.v_proj = nn.Linear(config.d_model, config.d_model)\n self.out_proj = nn.Linear(config.d_model, config.d_model)\n\n self.dropout = nn.Dropout(config.dropout)\n\n def forward(\n self,\n x: torch.Tensor,\n return_attention: bool = False\n ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:\n \"\"\"\n Args:\n x: [batch, seq_len, d_model]\n return_attention: Whether to return attention weights\n\n Returns:\n output: [batch, seq_len, d_model]\n attention: [batch, num_heads, seq_len, seq_len] (optional)\n \"\"\"\n batch, seq_len, d_model = x.shape\n\n # Calculate number of top queries\n u = max(1, min(seq_len, int(self.sparsity_factor * math.log(seq_len + 1))))\n\n # Project to Q, K, V\n q = self.q_proj(x).view(batch, seq_len, self.num_heads, self.head_dim).transpose(1, 2)\n k = self.k_proj(x).view(batch, seq_len, self.num_heads, self.head_dim).transpose(1, 2)\n v = self.v_proj(x).view(batch, seq_len, self.num_heads, self.head_dim).transpose(1, 2)\n\n # Compute sparsity measurement M(q) = max(Q·K^T) - mean(Q·K^T)\n scores = torch.matmul(q, k.transpose(-2, -1)) / self.scale\n\n M = scores.max(dim=-1)[0] - scores.mean(dim=-1) # [batch, num_heads, seq_len]\n\n # Select top-u queries\n _, top_indices = M.topk(u, dim=-1) # [batch, num_heads, u]\n\n # Full attention only for selected queries\n attn = F.softmax(scores, dim=-1)\n attn = self.dropout(attn)\n\n # Output\n out = torch.matmul(attn, v)\n out = out.transpose(1, 2).contiguous().view(batch, seq_len, d_model)\n out = self.out_proj(out)\n\n return out, attn if return_attention else None\n\n\nclass EncoderLayer(nn.Module):\n \"\"\"Transformer encoder layer with optional cross-ticker attention\"\"\"\n\n def __init__(self, config: StockformerConfig):\n super().__init__()\n\n self.self_attention = ProbSparseAttention(config)\n self.cross_ticker_attention = CrossTickerAttention(config) if config.use_cross_ticker_attention else None\n\n self.norm1 = nn.LayerNorm(config.d_model)\n self.norm2 = nn.LayerNorm(config.d_model)\n self.norm3 = nn.LayerNorm(config.d_model) if config.use_cross_ticker_attention else None\n\n self.ff = nn.Sequential(\n nn.Linear(config.d_model, config.d_ff),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.d_ff, config.d_model),\n nn.Dropout(config.dropout)\n )\n\n self.dropout = nn.Dropout(config.dropout)\n\n def forward(\n self,\n x: torch.Tensor,\n return_attention: bool = False\n ) -> Tuple[torch.Tensor, dict]:\n \"\"\"\n Args:\n x: [batch, num_tickers, seq_len, d_model]\n\n Returns:\n output: [batch, num_tickers, seq_len, d_model]\n attention_dict: Dictionary of attention weights\n \"\"\"\n batch, num_tickers, seq_len, d_model = x.shape\n attention_dict = {}\n\n # Self-attention for each ticker\n x_flat = x.view(batch * num_tickers, seq_len, d_model)\n attn_out, temporal_attn = self.self_attention(x_flat, return_attention)\n attn_out = attn_out.view(batch, num_tickers, seq_len, d_model)\n\n x = self.norm1(x + self.dropout(attn_out))\n\n if temporal_attn is not None:\n attention_dict['temporal'] = temporal_attn.view(batch, num_tickers, self.self_attention.num_heads, seq_len, seq_len)\n\n # Cross-ticker attention\n if self.cross_ticker_attention is not None:\n cross_out, cross_attn = self.cross_ticker_attention(x, return_attention)\n x = self.norm3(x + self.dropout(cross_out))\n\n if cross_attn is not None:\n attention_dict['cross_ticker'] = cross_attn\n\n # Feed-forward\n ff_out = self.ff(x)\n x = self.norm2(x + ff_out)\n\n return x, attention_dict\n\n\nclass StockformerModel(nn.Module):\n \"\"\"\n Stockformer: Multivariate Stock Prediction Model\n\n Example:\n config = StockformerConfig(num_tickers=5, seq_len=96)\n model = StockformerModel(config)\n\n x = torch.randn(2, 5, 96, 6) # [batch, tickers, seq_len, features]\n output = model(x)\n print(output['predictions'].shape) # [2, 5]\n \"\"\"\n\n def __init__(self, config: StockformerConfig):\n super().__init__()\n config.validate()\n self.config = config\n\n # Embedding layers\n self.token_embedding = TokenEmbedding(config)\n self.positional_encoding = PositionalEncoding(\n config.d_model, config.seq_len * 2, config.dropout\n ) if config.use_positional_encoding else None\n self.ticker_encoding = TickerEncoding(config.num_tickers, config.d_model)\n\n # Encoder layers\n self.encoder_layers = nn.ModuleList([\n EncoderLayer(config) for _ in range(config.num_encoder_layers)\n ])\n\n # Output head\n self.output_head = self._build_output_head(config)\n\n # Register ticker IDs\n self.register_buffer('ticker_ids', torch.arange(config.num_tickers))\n\n def _build_output_head(self, config: StockformerConfig) -> nn.Module:\n \"\"\"Build output projection layer based on output type\"\"\"\n if config.output_type == OutputType.QUANTILE:\n return nn.Linear(config.d_model, len(config.quantiles))\n elif config.output_type == OutputType.DIRECTION:\n return nn.Linear(config.d_model, 3)\n else:\n return nn.Linear(config.d_model, 1)\n\n def forward(\n self,\n x: torch.Tensor,\n return_attention: bool = False\n ) -> dict:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor [batch, num_tickers, seq_len, features]\n return_attention: Whether to return attention weights\n\n Returns:\n Dictionary with:\n - predictions: [batch, output_dim]\n - attention_weights: Optional attention weights\n - confidence: Optional confidence scores\n \"\"\"\n batch, num_tickers, seq_len, features = x.shape\n\n # Token embedding for each ticker\n x_embedded = []\n for t in range(num_tickers):\n emb = self.token_embedding(x[:, t]) # [batch, seq_len, d_model]\n x_embedded.append(emb)\n x = torch.stack(x_embedded, dim=1) # [batch, num_tickers, seq_len, d_model]\n\n # Add positional encoding\n if self.positional_encoding is not None:\n for t in range(num_tickers):\n x[:, t] = self.positional_encoding(x[:, t])\n\n # Add ticker encoding\n x = self.ticker_encoding(x, self.ticker_ids)\n\n # Encoder layers\n all_attention = {}\n for i, layer in enumerate(self.encoder_layers):\n x, attn_dict = layer(x, return_attention)\n if attn_dict:\n all_attention[f'layer_{i}'] = attn_dict\n\n # Pool: take last timestep\n x = x[:, :, -1, :] # [batch, num_tickers, d_model]\n\n # Output projection\n predictions = self._compute_output(x)\n\n result = {\n 'predictions': predictions,\n 'attention_weights': all_attention if return_attention else None\n }\n\n # Add confidence for quantile regression\n if self.config.output_type == OutputType.QUANTILE:\n result['confidence'] = self._compute_confidence(predictions)\n\n return result\n\n def _compute_output(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute output predictions\"\"\"\n batch, num_tickers, d_model = x.shape\n\n if self.config.output_type == OutputType.PORTFOLIO:\n # Portfolio weights sum to 1\n logits = self.output_head(x).squeeze(-1) # [batch, num_tickers]\n return F.softmax(logits, dim=-1)\n\n elif self.config.output_type == OutputType.DIRECTION:\n # Per-ticker classification\n logits = self.output_head(x) # [batch, num_tickers, 3]\n probs = F.softmax(logits, dim=-1)\n return probs.view(batch, -1) # [batch, num_tickers * 3]\n\n elif self.config.output_type == OutputType.QUANTILE:\n # Per-ticker quantile prediction\n quantiles = self.output_head(x) # [batch, num_tickers, num_quantiles]\n return quantiles.view(batch, -1)\n\n else: # REGRESSION\n return self.output_head(x).squeeze(-1) # [batch, num_tickers]\n\n def _compute_confidence(self, predictions: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute confidence from quantile predictions\"\"\"\n batch = predictions.size(0)\n num_quantiles = len(self.config.quantiles)\n num_tickers = self.config.num_tickers\n\n predictions = predictions.view(batch, num_tickers, num_quantiles)\n interval_width = (predictions[:, :, -1] - predictions[:, :, 0]).abs()\n confidence = 1.0 / (1.0 + interval_width)\n\n return confidence\n\n\nif __name__ == \"__main__\":\n # Test the model\n print(\"Testing Stockformer model...\")\n\n config = StockformerConfig(\n num_tickers=5,\n seq_len=48,\n input_features=6,\n d_model=32,\n num_heads=4,\n num_encoder_layers=2\n )\n\n model = StockformerModel(config)\n print(f\"Model parameters: {sum(p.numel() for p in model.parameters()):,}\")\n\n # Test forward pass\n x = torch.randn(2, 5, 48, 6)\n output = model(x, return_attention=True)\n\n print(f\"Predictions shape: {output['predictions'].shape}\")\n print(f\"Attention weights available: {output['attention_weights'] is not None}\")\n\n # Test different output types\n for output_type in OutputType:\n config.output_type = output_type\n model = StockformerModel(config)\n output = model(x)\n print(f\"{output_type.value}: predictions shape = {output['predictions'].shape}\")\n","numLines":527,"startLine":1,"totalLines":527}}} | |
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| "content": " 1→[package]\n 2→name = \"stockformer\"\n 3→version = \"0.1.0\"\n 4→edition = \"2021\"\n 5→authors = [\"ML4Trading\"]\n 6→description = \"Stockformer: Multivariate stock prediction with cross-asset attention using Bybit data\"\n 7→license = \"MIT\"\n 8→readme = \"README.md\"\n 9→\n 10→[dependencies]\n 11→# HTTP client for API requests\n 12→reqwest = { version = \"0.11\", features = [\"json\", \"rustls-tls\"] }\n 13→\n 14→# Async runtime\n 15→tokio = { version = \"1.35\", features = [\"full\"] }\n 16→\n 17→# JSON serialization\n 18→serde = { version = \"1.0\", features = [\"derive\"] }\n 19→serde_json = \"1.0\"\n 20→\n 21→# Numeric computing\n 22→ndarray = { version = \"0.15\", features = [\"serde\"] }\n 23→ndarray-rand = \"0.14\"\n 24→\n 25→# Random number generation\n 26→rand = \"0.8\"\n 27→rand_distr = \"0.4\"\n 28→\n 29→# Linear algebra\n 30→nalgebra = \"0.32\"\n 31→\n 32→# Statistics\n 33→statrs = \"0.16\"\n 34→\n 35→# Time handling\n 36→chrono = { version = \"0.4\", features = [\"serde\"] }\n 37→\n 38→# Error handling\n 39→thiserror = \"1.0\"\n 40→anyhow = \"1.0\"\n 41→\n 42→# Logging\n 43→log = \"0.4\"\n 44→env_logger = \"0.11\"\n 45→tracing = \"0.1\"\n 46→\n 47→# CSV for data export\n 48→csv = \"1.3\"\n 49→\n 50→# Progress bars\n 51→indicatif = \"0.17\"\n 52→\n 53→# Command line arguments\n 54→clap = { version = \"4.4\", features = [\"derive\"] }\n 55→\n 56→# Async utilities\n 57→futures = \"0.3\"\n 58→\n 59→# HMAC for API authentication\n 60→hmac = \"0.12\"\n 61→sha2 = \"0.10\"\n 62→hex = \"0.4\"\n 63→\n 64→[dev-dependencies]\n 65→tokio-test = \"0.4\"\n 66→approx = \"0.5\"\n 67→\n 68→[[example]]\n 69→name = \"fetch_data\"\n 70→path = \"examples/fetch_data.rs\"\n 71→\n 72→[[example]]\n 73→name = \"train\"\n 74→path = \"examples/train.rs\"\n 75→\n 76→[[example]]\n 77→name = \"predict\"\n 78→path = \"examples/predict.rs\"\n 79→\n 80→[[example]]\n 81→name = \"backtest\"\n 82→path = \"examples/backtest.rs\"\n 83→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n" | |
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| "content": "[package]\nname = \"stockformer\"\nversion = \"0.1.0\"\nedition = \"2021\"\nauthors = [\"ML4Trading\"]\ndescription = \"Stockformer: Multivariate stock prediction with cross-asset attention using Bybit data\"\nlicense = \"MIT\"\nreadme = \"README.md\"\n\n[dependencies]\n# HTTP client for API requests\nreqwest = { version = \"0.11\", features = [\"json\", \"rustls-tls\"] }\n\n# Async runtime\ntokio = { version = \"1.35\", features = [\"full\"] }\n\n# JSON serialization\nserde = { version = \"1.0\", features = [\"derive\"] }\nserde_json = \"1.0\"\n\n# Numeric computing\nndarray = { version = \"0.15\", features = [\"serde\"] }\nndarray-rand = \"0.14\"\n\n# Random number generation\nrand = \"0.8\"\nrand_distr = \"0.4\"\n\n# Linear algebra\nnalgebra = \"0.32\"\n\n# Statistics\nstatrs = \"0.16\"\n\n# Time handling\nchrono = { version = \"0.4\", features = [\"serde\"] }\n\n# Error handling\nthiserror = \"1.0\"\nanyhow = \"1.0\"\n\n# Logging\nlog = \"0.4\"\nenv_logger = \"0.11\"\ntracing = \"0.1\"\n\n# CSV for data export\ncsv = \"1.3\"\n\n# Progress bars\nindicatif = \"0.17\"\n\n# Command line arguments\nclap = { version = \"4.4\", features = [\"derive\"] }\n\n# Async utilities\nfutures = \"0.3\"\n\n# HMAC for API authentication\nhmac = \"0.12\"\nsha2 = \"0.10\"\nhex = \"0.4\"\n\n[dev-dependencies]\ntokio-test = \"0.4\"\napprox = \"0.5\"\n\n[[example]]\nname = \"fetch_data\"\npath = \"examples/fetch_data.rs\"\n\n[[example]]\nname = \"train\"\npath = \"examples/train.rs\"\n\n[[example]]\nname = \"predict\"\npath = \"examples/predict.rs\"\n\n[[example]]\nname = \"backtest\"\npath = \"examples/backtest.rs\"\n", | |
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| "content": " 1→//! # Stockformer\n 2→//!\n 3→//! Библиотека для мультивариантного прогнозирования криптовалют с использованием\n 4→//! Stockformer архитектуры и данных с биржи Bybit.\n 5→//!\n 6→//! ## Особенности\n 7→//!\n 8→//! - Кросс-активное внимание для моделирования связей между активами\n 9→//! - ProbSparse attention для эффективной обработки длинных последовательностей\n 10→//! - Поддержка нескольких типов выхода: регрессия, направление, аллокация портфеля\n 11→//!\n 12→//! ## Модули\n 13→//!\n 14→//! - `api` - Клиент для работы с Bybit API\n 15→//! - `data` - Загрузка и предобработка мультивариантных данных\n 16→//! - `model` - Реализация архитектуры Stockformer\n 17→//! - `strategy` - Торговая стратегия и бэктестинг\n 18→//!\n 19→//! ## Пример использования\n 20→//!\n 21→//! ```no_run\n 22→//! use stockformer::{BybitClient, MultiAssetLoader, StockformerModel, StockformerConfig};\n 23→//!\n 24→//! #[tokio::main]\n 25→//! async fn main() {\n 26→//! // Получаем данные для нескольких активов\n 27→//! let client = BybitClient::new();\n 28→//! let symbols = vec![\"BTCUSDT\", \"ETHUSDT\", \"SOLUSDT\"];\n 29→//!\n 30→//! let mut all_klines = Vec::new();\n 31→//! for symbol in &symbols {\n 32→//! let klines = client.get_klines(symbol, \"1h\", 1000).await.unwrap();\n 33→//! all_klines.push(klines);\n 34→//! }\n 35→//!\n 36→//! // Подготавливаем мультивариантные данные\n 37→//! let loader = MultiAssetLoader::new(symbols.clone());\n 38→//! let dataset = loader.prepare_dataset(&all_klines, 168, 24).unwrap();\n 39→//!\n 40→//! // Создаём модель Stockformer\n 41→//! let config = StockformerConfig {\n 42→//! n_assets: symbols.len(),\n 43→//! d_model: 128,\n 44→//! n_heads: 8,\n 45→//! n_encoder_layers: 3,\n 46→//! attention_type: AttentionType::ProbSparse,\n 47→//! output_type: OutputType::Allocation,\n 48→//! ..Default::default()\n 49→//! };\n 50→//! let model = StockformerModel::new(config);\n 51→//!\n 52→//! // Делаем прогноз с весами внимания\n 53→//! let (predictions, attention_weights) = model.predict_with_attention(&dataset);\n 54→//! }\n 55→//! ```\n 56→\n 57→pub mod api;\n 58→pub mod data;\n 59→pub mod model;\n 60→pub mod strategy;\n 61→\n 62→// Re-exports для удобства\n 63→pub use api::{BybitClient, BybitError, Kline, OrderBook, Ticker};\n 64→pub use data::{MultiAssetLoader, MultiAssetDataset, Features, CorrelationMatrix};\n 65→pub use model::{\n 66→ CrossTickerAttention, ProbSparseAttention, TokenEmbedding,\n 67→ StockformerConfig, StockformerModel, AttentionType, OutputType,\n 68→};\n 69→pub use strategy::{BacktestResult, PortfolioSignal, SignalGenerator, PortfolioStrategy};\n 70→\n 71→/// Версия библиотеки\n 72→pub const VERSION: &str = env!(\"CARGO_PKG_VERSION\");\n 73→\n 74→/// Настройки по умолчанию\n 75→pub mod defaults {\n 76→ /// Размер скрытого слоя\n 77→ pub const D_MODEL: usize = 128;\n 78→\n 79→ /// Количество голов внимания\n 80→ pub const N_HEADS: usize = 8;\n 81→\n 82→ /// Количество активов по умолчанию\n 83→ pub const N_ASSETS: usize = 5;\n 84→\n 85→ /// Количество encoder layers\n 86→ pub const N_ENCODER_LAYERS: usize = 3;\n 87→\n 88→ /// Dropout rate\n 89→ pub const DROPOUT: f64 = 0.1;\n 90→\n 91→ /// Длина encoder context\n 92→ pub const ENCODER_LENGTH: usize = 168; // 7 дней часовых данных\n 93→\n 94→ /// Длина prediction horizon\n 95→ pub const PREDICTION_LENGTH: usize = 24; // 24 часа\n 96→\n 97→ /// Скорость обучения\n 98→ pub const LEARNING_RATE: f64 = 0.001;\n 99→\n 100→ /// Размер батча\n 101→ pub const BATCH_SIZE: usize = 32;\n 102→\n 103→ /// Количество эпох\n 104→ pub const EPOCHS: usize = 100;\n 105→\n 106→ /// Фактор разреженности для ProbSparse attention\n 107→ pub const SPARSITY_FACTOR: usize = 5;\n 108→}\n 109→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n" | |
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| "content": "//! # Stockformer\n//!\n//! Библиотека для мультивариантного прогнозирования криптовалют с использованием\n//! Stockformer архитектуры и данных с биржи Bybit.\n//!\n//! ## Особенности\n//!\n//! - Кросс-активное внимание для моделирования связей между активами\n//! - ProbSparse attention для эффективной обработки длинных последовательностей\n//! - Поддержка нескольких типов выхода: регрессия, направление, аллокация портфеля\n//!\n//! ## Модули\n//!\n//! - `api` - Клиент для работы с Bybit API\n//! - `data` - Загрузка и предобработка мультивариантных данных\n//! - `model` - Реализация архитектуры Stockformer\n//! - `strategy` - Торговая стратегия и бэктестинг\n//!\n//! ## Пример использования\n//!\n//! ```no_run\n//! use stockformer::{BybitClient, MultiAssetLoader, StockformerModel, StockformerConfig};\n//!\n//! #[tokio::main]\n//! async fn main() {\n//! // Получаем данные для нескольких активов\n//! let client = BybitClient::new();\n//! let symbols = vec![\"BTCUSDT\", \"ETHUSDT\", \"SOLUSDT\"];\n//!\n//! let mut all_klines = Vec::new();\n//! for symbol in &symbols {\n//! let klines = client.get_klines(symbol, \"1h\", 1000).await.unwrap();\n//! all_klines.push(klines);\n//! }\n//!\n//! // Подготавливаем мультивариантные данные\n//! let loader = MultiAssetLoader::new(symbols.clone());\n//! let dataset = loader.prepare_dataset(&all_klines, 168, 24).unwrap();\n//!\n//! // Создаём модель Stockformer\n//! let config = StockformerConfig {\n//! n_assets: symbols.len(),\n//! d_model: 128,\n//! n_heads: 8,\n//! n_encoder_layers: 3,\n//! attention_type: AttentionType::ProbSparse,\n//! output_type: OutputType::Allocation,\n//! ..Default::default()\n//! };\n//! let model = StockformerModel::new(config);\n//!\n//! // Делаем прогноз с весами внимания\n//! let (predictions, attention_weights) = model.predict_with_attention(&dataset);\n//! }\n//! ```\n\npub mod api;\npub mod data;\npub mod model;\npub mod strategy;\n\n// Re-exports для удобства\npub use api::{BybitClient, BybitError, Kline, OrderBook, Ticker};\npub use data::{MultiAssetLoader, MultiAssetDataset, Features, CorrelationMatrix};\npub use model::{\n CrossTickerAttention, ProbSparseAttention, TokenEmbedding,\n StockformerConfig, StockformerModel, AttentionType, OutputType,\n};\npub use strategy::{BacktestResult, PortfolioSignal, SignalGenerator, PortfolioStrategy};\n\n/// Версия библиотеки\npub const VERSION: &str = env!(\"CARGO_PKG_VERSION\");\n\n/// Настройки по умолчанию\npub mod defaults {\n /// Размер скрытого слоя\n pub const D_MODEL: usize = 128;\n\n /// Количество голов внимания\n pub const N_HEADS: usize = 8;\n\n /// Количество активов по умолчанию\n pub const N_ASSETS: usize = 5;\n\n /// Количество encoder layers\n pub const N_ENCODER_LAYERS: usize = 3;\n\n /// Dropout rate\n pub const DROPOUT: f64 = 0.1;\n\n /// Длина encoder context\n pub const ENCODER_LENGTH: usize = 168; // 7 дней часовых данных\n\n /// Длина prediction horizon\n pub const PREDICTION_LENGTH: usize = 24; // 24 часа\n\n /// Скорость обучения\n pub const LEARNING_RATE: f64 = 0.001;\n\n /// Размер батча\n pub const BATCH_SIZE: usize = 32;\n\n /// Количество эпох\n pub const EPOCHS: usize = 100;\n\n /// Фактор разреженности для ProbSparse attention\n pub const SPARSITY_FACTOR: usize = 5;\n}\n", | |
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| "text": "Now I have a good understanding of the expected structure. Let me start creating the chapter content. First, let me research \"Temporal Attention Networks\" to understand the topic better." | |
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| "content": "Web search results for query: \"Temporal Attention Networks financial time series forecasting trading 2024 2025\"\n\nLinks: [{\"title\":\"Time Series Forecasting with Attention-Augmented Recurrent Networks: A Financial Market Application | Proceedings of the 2025 2nd International Conference on Computer and Multimedia Technology\",\"url\":\"https://dl.acm.org/doi/10.1145/3757749.3757774\"},{\"title\":\"Time Series Forecasting with Attention-Augmented Recurrent Networks: A Financial Market Application\",\"url\":\"https://www.researchgate.net/publication/392338228_Time_Series_Forecasting_with_Attention-Augmented_Recurrent_Networks_A_Financial_Market_Application\"},{\"title\":\"Revolutionizing Time Series Prediction with LSTM with the Attention Mechanism | by Dr. Ernesto Lee | Medium\",\"url\":\"https://drlee.io/revolutionizing-time-series-prediction-with-lstm-with-the-attention-mechanism-090833a19af9\"},{\"title\":\"MSTAN: A multi-scale temporal attention network for stock prediction | Request PDF\",\"url\":\"https://www.researchgate.net/publication/398598476_MSTAN_A_multi-scale_temporal_attention_network_for_stock_prediction\"},{\"title\":\"RCSAN residual enhanced channel spatial attention network for stock price forecasting | Scientific Reports\",\"url\":\"https://www.nature.com/articles/s41598-025-06885-y\"},{\"title\":\"Interpretable multi-horizon time series forecasting of cryptocurrencies by leverage temporal fusion transformer - PMC\",\"url\":\"https://pmc.ncbi.nlm.nih.gov/articles/PMC11605417/\"},{\"title\":\"(PDF) Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis\",\"url\":\"https://www.researchgate.net/publication/321510957_Temporal_Attention-Augmented_Bilinear_Network_for_Financial_Time-Series_Data_Analysis\"},{\"title\":\"Stock market price forecasting using evolving graph neural network⋆\",\"url\":\"https://ceur-ws.org/Vol-4035/Paper6.pdf\"},{\"title\":\"Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction | IEEE Conference Publication | IEEE Xplore\",\"url\":\"https://ieeexplore.ieee.org/document/9909957/\"},{\"title\":\"Research on deep learning model for stock prediction by integrating frequency domain and time series features | Scientific Reports\",\"url\":\"https://www.nature.com/articles/s41598-025-14872-6\"}]\n\nHere are the search results for \"Temporal Attention Networks financial time series forecasting trading 2024 2025\":\n\n## Key Research Findings\n\n### Attention-Augmented RNN Models (2024-2025)\nResearch proposes stock price prediction models based on a combination of recurrent neural networks (RNN) and attention mechanisms, with the goal of improving forecasting accuracy of financial time series data.(no content)These models have been empirically evaluated on real-world stock price data (e.g., Apple Inc.) for 2024-2025, where the attention mechanism significantly enhances the ability to learn significant features from previous time series and improves modeling long-term dependencies.\n\n### Multi-Scale Temporal Attention Network (MSTAN)\nForecasting stock movements is a crucial research endeavor in finance, and previous methodologies encounter challenges including limited comprehension of interrelations among stock data elements, diminished accuracy in extensive series, and struggles with anomaly points.(no content)The PMANet model, founded on Multi-scale Timing Feature Attention, amalgamates Multi-scale Timing Feature Convolution and Ant Particle Swarm Optimization, elevating the understanding of dependencies through Probabilistic Positional Attention.\n\n### R-CSAN Model (2025)\nThe Residual-enhanced Channel-Spatial Attention Network (R-CSAN) integrates channel-spatial adaptive attention mechanisms with residual connections to capture multidimensional complex patterns in financial time series, using an encoder-decoder architecture.(no content)The model reduces RMSE by 17.3–49.3% compared to traditional methods and 6.2–11.6% compared to Transformer variants, with R² reaching 93.17% and return on investment up to 482.64%.(no content)In practical applications, R-CSAN's outputs can be integrated into quantitative trading strategies including breakout trading, moving average crossover signals, and portfolio allocation optimization.\n\n### Temporal Fusion Transformer (TFT)\nTFT is a deep-learning algorithm that leverages the benefits of both transformers and temporal convolutional networks (TCNs) to predict time-series data, built on an attention-based deep neural network architecture.(no content)TFT integrates multi-horizon forecasting with a clear understanding of temporal dynamics, where the attention mechanism adds new levels of interpretability to the model.\n\n### TCN with Attention\nNovel deep learning models integrate Temporal Convolutional Networks (TCN) with Attention mechanisms to predict stock prices and assess risk, designed with dual outputs to forecast future prices while simultaneously predicting risk metrics including volatility and Sharpe Ratio.(no content)By utilizing dilated convolutions, TCN captures both short-term and long-term dependencies, with attention layers focusing on critical time steps. On datasets covering 2008-2024, the TCN with attention model achieved MAE of 1.23 for MasterCard and 1.45 for Visa.\n\n### Temporal Graph Attention Networks\nResearch underscores the advantage of incorporating dynamic clustering and time-aware graph attention for more accurate and robust stock price forecasting, where time series represented as relationship graphs significantly increase forecasting accuracy.\n\n### Multi-Head Temporal Attention\nThe temporal attention mechanism is a neural layer design that has gained popularity due to its ability to focus on important temporal events. Multi-head attention extends the capability of neural networks to focus simultaneously on multiple temporal instances.\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks." | |
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| "title": "Time Series Forecasting with Attention-Augmented Recurrent Networks: A Financial Market Application | Proceedings of the 2025 2nd International Conference on Computer and Multimedia Technology", | |
| "url": "https://dl.acm.org/doi/10.1145/3757749.3757774" | |
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| "title": "Time Series Forecasting with Attention-Augmented Recurrent Networks: A Financial Market Application", | |
| "url": "https://www.researchgate.net/publication/392338228_Time_Series_Forecasting_with_Attention-Augmented_Recurrent_Networks_A_Financial_Market_Application" | |
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| "title": "Revolutionizing Time Series Prediction with LSTM with the Attention Mechanism | by Dr. Ernesto Lee | Medium", | |
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| "title": "MSTAN: A multi-scale temporal attention network for stock prediction | Request PDF", | |
| "url": "https://www.researchgate.net/publication/398598476_MSTAN_A_multi-scale_temporal_attention_network_for_stock_prediction" | |
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| "title": "RCSAN residual enhanced channel spatial attention network for stock price forecasting | Scientific Reports", | |
| "url": "https://www.nature.com/articles/s41598-025-06885-y" | |
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| "title": "Interpretable multi-horizon time series forecasting of cryptocurrencies by leverage temporal fusion transformer - PMC", | |
| "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11605417/" | |
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| "title": "(PDF) Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis", | |
| "url": "https://www.researchgate.net/publication/321510957_Temporal_Attention-Augmented_Bilinear_Network_for_Financial_Time-Series_Data_Analysis" | |
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| "title": "Stock market price forecasting using evolving graph neural network⋆", | |
| "url": "https://ceur-ws.org/Vol-4035/Paper6.pdf" | |
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| "title": "Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction | IEEE Conference Publication | IEEE Xplore", | |
| "url": "https://ieeexplore.ieee.org/document/9909957/" | |
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| [2026-01-18T20:52:54.395Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CdPRTxsEwA3etNyWGpwtgc","type":"tool_result","content":"Web search results for query: \"TABL Temporal Attention-Augmented Bilinear Network financial time series paper\"\n\nLinks: [{\"title\":\"[1712.00975] Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis\",\"url\":\"https://arxiv.org/abs/1712.00975\"},{\"title\":\"GitHub - viebboy/TABL: Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis\",\"url\":\"https://github.com/viebboy/TABL\"},{\"title\":\"[PDF] Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis | Semantic Scholar\",\"url\":\"https://www.semanticscholar.org/paper/Temporal-Attention-Augmented-Bilinear-Network-for-Tran-Iosifidis/29c9174881a736acd2b2256dec7da1952ac29683\"},{\"title\":\"(PDF) Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis\",\"url\":\"https://www.researchgate.net/publication/321510957_Temporal_Attention-Augmented_Bilinear_Network_for_Financial_Time-Series_Data_Analysis\"},{\"title\":\"GitHub - LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis: Pytorch implementation of TABL from Temporal Attention Augmented Bilinear Network for Financial Time Series Data Analysis\",\"url\":\"https://github.com/LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis\"},{\"title\":\"Augmented bilinear network for incremental multi-stock time-series classification - ScienceDirect\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S0031320323003059\"},{\"title\":\"Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis | IEEE Journals & Magazine | IEEE Xplore\",\"url\":\"http://ieeexplore.ieee.org/iel7/5962385/6104215/08476227.pdf\"},{\"title\":\"[PDF] Multi-head Temporal Attention-Augmented Bilinear Netwo | |
| [2026-01-18T20:52:54.422Z] [INFO] rk for Financial time series prediction | Semantic Scholar\",\"url\":\"https://www.semanticscholar.org/paper/Multi-head-Temporal-Attention-Augmented-Bilinear-Shabani-Tran/0cfe552237f5ae21a7f25cacd68e386d8b66a8d8\"},{\"title\":\"Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis\",\"url\":\"https://ideas.repec.org/p/arx/papers/1712.00975.html\"},{\"title\":\"TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis/TABL_train_test.ipynb at main · LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis\",\"url\":\"https://github.com/LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis/blob/main/TABL_train_test.ipynb\"}]\n\nHere are the search results for TABL (Temporal Attention-Augmented Bilinear Network) for financial time series:\n\n## Overview\n\nThe paper proposes a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information.\n\n## Paper Details\n\nThe paper \"Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis\" was authored by Dat Thanh Tran, Alexandros Iosifidis, Juho Kanniainen, and M. Gabbouj, published in IEEE Transactions on Neural Networks and Learning Systems in 2017 (volume 30, pages 1407-1418).\n\n## Key Contributions\n\nThe researchers proposed a bilinear structure equipped with an attention mechanism that can highlight the temporal importance of each temporal event in multivariate time-series.(no content)The proposed structure was validated in the problem of predicting mid-price movements (increasing, decreasing, stationary) using Limit Order Book information, and empirical analysis showed that the proposed structures outperformed existing models, even LSTM and CNN, while being relatively fast.(no content)The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest.\n\n## Technical Details\n\nThe proposed layer leverages the idea of bilinear projection and is augmented with a temporal attention mechanism.(no content)The authors provide theoretical analysis on the complexity of the proposed layer in comparison with existing attention mechanisms in recurrent structure, indicating that the proposed layer possesses much lower memory and computational complexity.\n\n## Performance\n\nExtensive experiments in a large-scale Limit Order Book dataset show the effectiveness of the proposed architecture: with only 2 hidden-layers, they can surpass existing state-of-the-art models by a large margin.(no content)Even with 1 hidden-layer, TABL performs similarly or largely better than the previous state-of-the-art results obtained from LSTM network. Although being deep with 7 hidden layers, the CNN model is greatly inferior to the proposed ones.\n\n## Resources\n\n- **arXiv**: https://arxiv.org/abs/1712.00975\n- **IEEE Xplore**: http://ieeexplore.ieee.org/iel7/5962385/6104215/08476227.pdf\n- **Original Code (Python 2.7)**: https://github.com/viebboy/TABL\n- **PyTorch Implementation**: https://github.com/LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks."}]},"parent_tool_use_id":null,"session_id":"77018388-5fda-4d3e-acd4-e8399840434b","uuid":"7dc37740-ee69-4d97-b02f-33d5a97e454a","tool_use_result":{"query":"TABL Temporal Attention-Augmented Bilinear Network financial time series paper","results":[{"tool_use_id":"srvtoolu_01KNPK5wk4rEJoicHxMHSdS7","content":[{"title":"[1712.00975] Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis","url":"https://arxiv.org/abs/1712.00975"},{"title":"GitHub - viebboy/TABL: Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis","url":"https://github.com/viebboy/TABL"},{"title":"[PDF] Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis | Semantic Scholar","url":"https://www.semanticscholar.org/paper/Temporal-Attention-Augmented-Bilinear-Network-for-Tran-Iosifidis/29c9174881a736acd2b2256dec7da1952ac29683"},{"title":"(PDF) Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis","url":"https://www.researchgate.net/publication/321510957_Temporal_Attention-Augmented_Bilinear_Network_for_Financial_Time-Series_Data_Analysis"},{"title":"GitHub - LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis: Pytorch implementation of TABL from Temporal Attention Augmented Bilinear Network for Financial Time Series Data Analysis","url":"https://github.com/LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis"},{"title":"Augmented bilinear network for incremental multi-stock time-series classification - ScienceDirect","url":"https://www.sciencedirect.com/science/article/pii/S0031320323003059"},{"title":"Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis | IEEE Journals & Magazine | IEEE Xplore","url":"http://ieeexplore.ieee.org/iel7/5962385/6104215/08476227.pdf"},{"title":"[PDF] Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction | Semantic Scholar","url":"https://www.semanticscholar.org/paper/Multi-head-Temporal-Attention-Augmented-Bilinear-Shabani-Tran/0cfe552237f5ae21a7f25cacd68e386d8b66a8d8"},{"title":"Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis","url":"https://ideas.repec.org/p/arx/papers/1712.00975.html"},{"title":"TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis/TABL_train_test.ipynb at main · LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis","url":"https://github.com/LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis/blob/main/TABL_train_test.ipynb"}]},"Here are the search results for TABL (Temporal Attention-Augmented Bilinear Network) for financial time series:\n\n## Overview\n\nThe paper proposes a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information.\n\n## Paper Details\n\nThe paper \"Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis\" was authored by Dat Thanh Tran, Alexandros Iosifidis, Juho Kanniainen, and M. Gabbouj, published in IEEE Transactions on Neural Networks and Learning Systems in 2017 (volume 30, pages 1407-1418).\n\n## Key Contributions\n\nThe researchers proposed a bilinear structure equipped with an attention mechanism that can highlight the temporal importance of each temporal event in multivariate time-series.(no content)The proposed structure was validated in the problem of predicting mid-price movements (increasing, decreasing, stationary) using Limit Order Book information, and empirical analysis showed that the proposed structures outperformed existing models, even LSTM and CNN, while being relatively fast.(no content)The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest.\n\n## Technical Details\n\nThe proposed layer leverages the idea of bilinear projection and is augmented with a temporal attention mechanism.(no content)The authors provide theoretical analysis on the complexity of the proposed layer in comparison with existing attention mechanisms in recurrent structure, indicating that the proposed layer possesses much lower memory and computational complexity.\n\n## Performance\n\nExtensive experiments in a large-scale Limit Order Book dataset show the effectiveness of the proposed architecture: with only 2 hidden-layers, they can surpass existing state-of-the-art models by a large margin.(no content)Even with 1 hidden-layer, TABL performs similarly or largely better than the previous state-of-the-art results obtained from LSTM network. Although being deep with 7 hidden layers, the CNN model is greatly inferior to the proposed ones.\n\n## Resources\n\n- **arXiv**: https://arxiv.org/abs/1712.00975\n- **IEEE Xplore**: http://ieeexplore.ieee.org/iel7/5962385/6104215/08476227.pdf\n- **Original Code (Python 2.7)**: https://github.com/viebboy/TABL\n- **PyTorch Implementation**: https://github.com/LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis"],"durationSeconds":21.016719285999994}} | |
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| [2026-01-18T20:54:59.890Z] [INFO] {"type":"assistant","message":{"model":"claude-opus-4-5-20251101","id":"msg_01BDKeeZKugafkoH7BMk4js8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0189RieoeyezHdcDrvH6WDct","name":"Write","input":{"file_path":"/tmp/gh-issue-solver-1768769379407/46_temporal_attention_networks/README.md","content":"# Chapter 46: Temporal Attention Networks for Financial Time-Series\n\nThis chapter explores **Temporal Attention Networks** — specialized attention mechanisms designed to capture temporal dependencies in financial data. We focus on the **TABL (Temporal Attention-Augmented Bilinear Layer)** architecture and its variants, which have proven highly effective for predicting market movements from Limit Order Book (LOB) data and other time-series sources.\n\n<p align=\"center\">\n<img src=\"https://i.imgur.com/Zy8R4qF.png\" width=\"70%\">\n</p>\n\n## Contents\n\n1. [Introduction to Temporal Attention](#introduction-to-temporal-attention)\n * [Why Temporal Attention?](#why-temporal-attention)\n * [Key Advantages](#key-advantages)\n * [Comparison with Other Models](#comparison-with-other-models)\n2. [TABL Architecture](#tabl-architecture)\n * [Bilinear Projection](#bilinear-projection)\n * [Temporal Attention Mechanism](#temporal-attention-mechanism)\n * [BL (Bilinear Layer)](#bl-bilinear-layer)\n * [TABL (Temporal Attention Bilinear Layer)](#tabl-temporal-attention-bilinear-layer)\n3. [Multi-Head Temporal Attention](#multi-head-temporal-attention)\n * [Multi-Head TABL](#multi-head-tabl)\n * [Parallel Attention Heads](#parallel-attention-heads)\n4. [Data Processing](#data-processing)\n * [Limit Order Book Features](#limit-order-book-features)\n * [OHLCV Features](#ohlcv-features)\n * [Feature Engineering](#feature-engineering)\n5. [Practical Examples](#practical-examples)\n * [01: Data Preparation](#01-data-preparation)\n * [02: TABL Architecture](#02-tabl-architecture)\n * [03: Model Training](#03-model-training)\n * [04: Attention Visualization](#04-attention-visualization)\n * [05: Trading Strategy](#05-trading-strategy)\n6. [Rust Implementation](#rust-implementation)\n7. [Python Implementation](#python-implementation)\n8. [Best Practices](#best-practices)\n9. [Resources](#resources)\n\n## Introduction to Temporal Attention\n\nTemporal Attention Networks are designed to solve a fundamental challenge in financial forecasting: **which past events matter most for predicting the future?**\n\nUnlike standard recurrent models that process all time steps equally, temporal attention mechanisms learn to **focus on the most informative moments** in the input sequence.\n\n### Why Temporal Attention?\n\nTraditional models treat all time steps equally:\n\n```\nTime: t-5 t-4 t-3 t-2 t-1 t\nWeight: 1 1 1 1 1 1\n (All events equally important)\n```\n\nTemporal Attention learns adaptive weights:\n\n```\nTime: t-5 t-4 t-3 t-2 t-1 t\nWeight: 0.05 0.10 0.40 0.30 0.10 0.05\n (Attention focuses on t-3 and t-2)\n```\n\n**Key insight**: In financial markets, certain moments carry disproportionate importance — large trades, sudden volatility spikes, or specific patterns often precede price movements. Temporal attention automatically learns to identify these critical moments.\n\n### Key Advantages\n\n1. **Automatic Feature Selection in Time**\n - Learns which time steps are relevant for prediction\n - No manual feature engineering of \"important moments\"\n - Adapts to different market conditions\n\n2. **Interpretability**\n - Attention weights reveal which events influenced predictions\n - Useful for understanding model decisions\n - Enables post-hoc analysis of trading signals\n\n3. **Computational Efficiency**\n - TABL has O(T·D) complexity vs O(T²·D) for self-attention\n - Much faster than LSTM for long sequences\n - Lower memory requirements\n\n4. **Strong Performance**\n - Outperforms LSTM and CNN on LOB prediction tasks\n - Achieves state-of-the-art on FI-2010 benchmark\n - Effective with just 1-2 layers\n\n### Comparison with Other Models\n\n| Feature | LSTM | CNN | Transformer | TABL |\n|---------|------|-----|-------------|------|\n| Temporal attention | ✗ | ✗ | ✓ (self) | ✓ (learned) |\n| Complexity | O(T·D²) | O(T·K·D) | O(T²·D) | O(T·D) |\n| Interpretability | Low | Low | Medium | High |\n| LOB prediction | Good | Good | Good | Best |\n| Memory efficient | ✗ | ✓ | ✗ | ✓ |\n| Few layers needed | ✗ | ✗ | ✗ | ✓ |\n\n## TABL Architecture\n\nThe TABL architecture combines **bilinear projections** with **temporal attention** to create an efficient and interpretable model.\n\n```\n┌──────────────────────────────────────────────────────────────────────┐\n│ TEMPORAL ATTENTION NETWORK │\n├──────────────────────────────────────────────────────────────────────┤\n│ │\n│ Input: X ∈ ℝ^(T×D) │\n│ (T timesteps, D features) │\n│ │\n│ ┌──────────────────────────────────────────────────────────┐ │\n│ │ Bilinear Projection (BL) │ │\n│ │ │ │\n│ │ H = σ(W₁ · X · W₂ + b) │ │\n│ │ │ │\n│ │ W₁ ∈ ℝ^(T'×T) - Temporal projection │ │\n│ │ W₂ ∈ ℝ^(D×D') - Feature projection │ │\n│ │ H ∈ ℝ^(T'×D') - Compressed representation │ │\n│ │ │ │\n│ └──────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ┌──────────────────────────────────────────────────────────┐ │\n│ │ Temporal Attention (TA) │ │\n│ │ │ │\n│ │ α = softmax(w · tanh(U · X^T)) │ │\n│ │ c = X^T · α │ │\n│ │ │ │\n│ │ α ∈ ℝ^T - Attention weights │ │\n│ │ c ∈ ℝ^D - Context vector │ │\n│ │ │ │\n│ └──────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ┌──────────────────────────────────────────────────────────┐ │\n│ │ Output Layer │ │\n│ │ │ │\n│ │ y = softmax(W_out · flatten(H, c) + b_out) │ │\n│ │ │ │\n│ │ 3-class: Up / Stationary / Down │ │\n│ │ │ │\n│ └──────────────────────────────────────────────────────────┘ │\n│ │\n└──────────────────────────────────────────────────────────────────────┘\n```\n\n### Bilinear Projection\n\nThe bilinear layer performs two simultaneous linear transformations:\n\n```python\nclass BilinearLayer(nn.Module):\n \"\"\"\n Bilinear projection: H = σ(W₁ · X · W₂ + b)\n\n Transforms (T, D) → (T', D') by projecting both\n temporal and feature dimensions simultaneously.\n \"\"\"\n\n def __init__(self, T_in, T_out, D_in, D_out, dropout=0.1):\n super().__init__()\n # Temporal projection: (T_out, T_in)\n self.W1 = nn.Parameter(torch.randn(T_out, T_in) * 0.01)\n # Feature projection: (D_in, D_out)\n self.W2 = nn.Parameter(torch.randn(D_in, D_out) * 0.01)\n # Bias: (T_out, D_out)\n self.bias = nn.Parameter(torch.zeros(T_out, D_out))\n self.dropout = nn.Dropout(dropout)\n self.activation = nn.ReLU()\n\n def forward(self, x):\n # x: (batch, T_in, D_in)\n # W1·X: (batch, T_out, D_in)\n out = torch.matmul(self.W1, x)\n # W1·X·W2: (batch, T_out, D_out)\n out = torch.matmul(out, self.W2)\n out = out + self.bias\n out = self.activation(out)\n return self.dropout(out)\n```\n\n**Why Bilinear?**\n- Captures interactions between time and features\n- More expressive than simple linear layers\n- Reduces dimensionality in both axes efficiently\n\n### Temporal Attention Mechanism\n\nThe temporal attention computes a weighted sum over time steps:\n\n```python\nclass TemporalAttention(nn.Module):\n \"\"\"\n Temporal attention: α = softmax(w · tanh(U · X^T))\n\n Learns to focus on important time steps.\n \"\"\"\n\n def __init__(self, D, attention_dim=64):\n super().__init__()\n # Project features to attention space\n self.U = nn.Linear(D, attention_dim, bias=False)\n # Attention query vector\n self.w = nn.Linear(attention_dim, 1, bias=False)\n\n def forward(self, x):\n # x: (batch, T, D)\n # Compute attention scores\n h = torch.tanh(self.U(x)) # (batch, T, attention_dim)\n scores = self.w(h).squeeze(-1) # (batch, T)\n\n # Softmax over time dimension\n alpha = F.softmax(scores, dim=-1) # (batch, T)\n\n # Weighted sum: context vector\n context = torch.bmm(\n alpha.unsqueeze(1), # (batch, 1, T)\n x # (batch, T, D)\n ).squeeze(1) # (batch, D)\n\n return context, alpha\n```\n\n**Interpretation:**\n- `alpha[t]` indicates importance of time step t\n- High attention on specific events reveals model's focus\n- Context vector `c` summarizes the sequence\n\n### BL (Bilinear Layer)\n\nThe BL layer is the attention-free version:\n\n```python\nclass BL(nn.Module):\n \"\"\"Bilinear Layer without attention\"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.bilinear = BilinearLayer(\n config.seq_len, config.hidden_T,\n config.input_dim, config.hidden_D\n )\n\n def forward(self, x):\n h = self.bilinear(x)\n return h.flatten(1) # (batch, hidden_T * hidden_D)\n```\n\n### TABL (Temporal Attention Bilinear Layer)\n\nThe full TABL combines both components:\n\n```python\nclass TABL(nn.Module):\n \"\"\"\n Temporal Attention-Augmented Bilinear Layer\n\n Combines bilinear projection with temporal attention.\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.bilinear = BilinearLayer(\n config.seq_len, config.hidden_T,\n config.input_dim, config.hidden_D\n )\n self.attention = TemporalAttention(\n config.input_dim,\n config.attention_dim\n )\n\n def forward(self, x, return_attention=False):\n # Bilinear projection\n h = self.bilinear(x) # (batch, hidden_T, hidden_D)\n h_flat = h.flatten(1) # (batch, hidden_T * hidden_D)\n\n # Temporal attention\n context, alpha = self.attention(x) # (batch, D), (batch, T)\n\n # Concatenate\n out = torch.cat([h_flat, context], dim=-1)\n\n if return_attention:\n return out, alpha\n return out\n```\n\n## Multi-Head Temporal Attention\n\nExtending TABL with multiple attention heads allows the model to focus on different aspects simultaneously:\n\n### Multi-Head TABL\n\n```python\nclass MultiHeadTABL(nn.Module):\n \"\"\"\n Multi-Head Temporal Attention Bilinear Layer\n\n Uses multiple attention heads to capture different\n temporal patterns in the data.\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.n_heads = config.n_heads\n\n # Shared bilinear projection\n self.bilinear = BilinearLayer(\n config.seq_len, config.hidden_T,\n config.input_dim, config.hidden_D\n )\n\n # Multiple attention heads\n self.attention_heads = nn.ModuleList([\n TemporalAttention(config.input_dim, config.attention_dim)\n for _ in range(config.n_heads)\n ])\n\n # Head combination\n self.head_combine = nn.Linear(\n config.n_heads * config.input_dim,\n config.input_dim\n )\n\n def forward(self, x, return_attention=False):\n # Bilinear projection\n h = self.bilinear(x)\n h_flat = h.flatten(1)\n\n # Multi-head attention\n contexts = []\n alphas = []\n for head in self.attention_heads:\n ctx, alpha = head(x)\n contexts.append(ctx)\n alphas.append(alpha)\n\n # Combine heads\n multi_context = torch.cat(contexts, dim=-1)\n combined = self.head_combine(multi_context)\n\n # Final output\n out = torch.cat([h_flat, combined], dim=-1)\n\n if return_attention:\n return out, torch.stack(alphas, dim=1) # (batch, n_heads, T)\n return out\n```\n\n### Parallel Attention Heads\n\nEach head can focus on different patterns:\n- **Head 1**: Short-term price movements\n- **Head 2**: Volume spikes\n- **Head 3**: Order book imbalances\n- **Head 4**: Trend patterns\n\n```\nMulti-Head Attention Visualization:\n\nTime: t-10 t-9 t-8 t-7 t-6 t-5 t-4 t-3 t-2 t-1\nHead 1: ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ██▓▓ ████ ██▓▓ ░░░░ ░░░░\n (Focuses on mid-term events)\n\nHead 2: ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ████ ████\n (Focuses on recent events)\n\nHead 3: ████ ░░░░ ░░░░ ████ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░\n (Focuses on periodic patterns)\n\nCombined: ▓▓▓▓ ░░░░ ░░░░ ▓▓▓▓ ░░░░ ▓▓▓▓ ████ ▓▓▓▓ ████ ████\n (Aggregated attention)\n```\n\n## Data Processing\n\n### Limit Order Book Features\n\nLOB data provides rich information about market microstructure:\n\n```python\ndef extract_lob_features(lob_snapshot):\n \"\"\"\n Extract features from Limit Order Book snapshot.\n\n Returns features for TABL input.\n \"\"\"\n features = {}\n\n # Price levels (typically 10 levels each side)\n features['ask_prices'] = lob_snapshot['asks'][:, 0] # (10,)\n features['bid_prices'] = lob_snapshot['bids'][:, 0] # (10,)\n features['ask_volumes'] = lob_snapshot['asks'][:, 1] # (10,)\n features['bid_volumes'] = lob_snapshot['bids'][:, 1] # (10,)\n\n # Derived features\n mid_price = (features['ask_prices'][0] + features['bid_prices'][0]) / 2\n spread = features['ask_prices'][0] - features['bid_prices'][0]\n\n # Order imbalance\n total_ask_vol = features['ask_volumes'].sum()\n total_bid_vol = features['bid_volumes'].sum()\n imbalance = (total_bid_vol - total_ask_vol) / (total_bid_vol + total_ask_vol)\n\n features['mid_price'] = mid_price\n features['spread'] = spread\n features['imbalance'] = imbalance\n\n return features\n```\n\n### OHLCV Features\n\nFor crypto/stock data using OHLCV:\n\n```python\ndef extract_ohlcv_features(df):\n \"\"\"\n Extract features from OHLCV data.\n \"\"\"\n features = pd.DataFrame()\n\n # Price features\n features['log_return'] = np.log(df['close'] / df['close'].shift(1))\n features['high_low_range'] = (df['high'] - df['low']) / df['close']\n features['close_open_diff'] = (df['close'] - df['open']) / df['open']\n\n # Volume features\n features['volume_ma_ratio'] = df['volume'] / df['volume'].rolling(20).mean()\n features['volume_std'] = df['volume'].rolling(20).std() / df['volume'].rolling(20).mean()\n\n # Technical indicators\n features['rsi'] = compute_rsi(df['close'], 14)\n features['macd'] = compute_macd(df['close'])\n\n # Volatility\n features['volatility'] = features['log_return'].rolling(20).std()\n\n return features.dropna()\n```\n\n### Feature Engineering\n\nRecommended features for temporal attention models:\n\n| Feature | Description | Importance |\n|---------|-------------|------------|\n| `log_return` | Log price change | High |\n| `spread` | Bid-ask spread | High |\n| `imbalance` | Order book imbalance | High |\n| `volume_ratio` | Volume vs MA | Medium |\n| `volatility` | Rolling volatility | Medium |\n| `price_levels` | LOB price levels | Medium |\n| `volume_levels` | LOB volume levels | Medium |\n\n## Practical Examples\n\n### 01: Data Preparation\n\n```python\n# python/01_data_preparation.py\n\nimport numpy as np\nimport pandas as pd\nfrom typing import Tuple, List\n\ndef prepare_tabl_data(\n df: pd.DataFrame,\n lookback: int = 100,\n horizon: int = 10,\n threshold: float = 0.0002\n) -> Tuple[np.ndarray, np.ndarray]:\n \"\"\"\n Prepare data for TABL model training.\n\n Args:\n df: DataFrame with OHLCV data\n lookback: Number of time steps to look back\n horizon: Prediction horizon\n threshold: Threshold for direction classification\n\n Returns:\n X: Features (n_samples, lookback, n_features)\n y: Labels (n_samples,) - 0: down, 1: stationary, 2: up\n \"\"\"\n # Extract features\n features = extract_features(df)\n\n # Normalize features\n features_norm = (features - features.mean()) / features.std()\n\n # Create sequences\n X, y = [], []\n\n for i in range(lookback, len(features_norm) - horizon):\n # Input sequence\n x_seq = features_norm.iloc[i-lookback:i].values\n\n # Target: future return\n future_return = np.log(\n df['close'].iloc[i + horizon] / df['close'].iloc[i]\n )\n\n # Classify direction\n if future_return > threshold:\n label = 2 # Up\n elif future_return < -threshold:\n label = 0 # Down\n else:\n label = 1 # Stationary\n\n X.append(x_seq)\n y.append(label)\n\n return np.array(X), np.array(y)\n\n\ndef extract_features(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Extract features from OHLCV data.\"\"\"\n features = pd.DataFrame(index=df.index)\n\n # Returns\n features['return'] = np.log(df['close'] / df['close'].shift(1))\n\n # Price position\n features['hl_position'] = (df['close'] - df['low']) / (df['high'] - df['low'] + 1e-8)\n\n # Volume\n vol_ma = df['volume'].rolling(20).mean()\n features['vol_ratio'] = df['volume'] / vol_ma\n\n # Volatility\n features['volatility'] = features['return'].rolling(20).std()\n\n # Momentum\n features['momentum_5'] = df['close'] / df['close'].shift(5) - 1\n features['momentum_10'] = df['close'] / df['close'].shift(10) - 1\n\n return features.dropna()\n```\n\n### 02: TABL Architecture\n\nSee [python/model.py](python/model.py) for complete implementation.\n\n### 03: Model Training\n\n```python\n# python/03_train_model.py\n\nimport torch\nfrom model import TABLModel, TABLConfig\n\n# Configuration\nconfig = TABLConfig(\n seq_len=100,\n input_dim=6,\n hidden_T=20,\n hidden_D=32,\n attention_dim=64,\n n_heads=4,\n n_classes=3,\n dropout=0.2\n)\n\n# Initialize model\nmodel = TABLModel(config)\nprint(f\"Parameters: {sum(p.numel() for p in model.parameters()):,}\")\n\n# Loss and optimizer\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)\nscheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n optimizer, mode='max', factor=0.5, patience=5\n)\n\n# Training loop\nbest_acc = 0\nfor epoch in range(100):\n model.train()\n train_loss = 0\n\n for batch_x, batch_y in train_loader:\n optimizer.zero_grad()\n\n # Forward pass\n logits = model(batch_x)\n loss = criterion(logits, batch_y)\n\n # Backward pass\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n optimizer.step()\n\n train_loss += loss.item()\n\n # Validation\n model.eval()\n correct = 0\n total = 0\n\n with torch.no_grad():\n for batch_x, batch_y in val_loader:\n logits = model(batch_x)\n preds = logits.argmax(dim=1)\n correct += (preds == batch_y).sum().item()\n total += batch_y.size(0)\n\n val_acc = correct / total\n scheduler.step(val_acc)\n\n if val_acc > best_acc:\n best_acc = val_acc\n torch.save(model.state_dict(), 'best_model.pt')\n\n print(f\"Epoch {epoch+1}: Loss={train_loss/len(train_loader):.4f}, Val Acc={val_acc:.4f}\")\n```\n\n### 04: Attention Visualization\n\n```python\n# python/04_attention_visualization.py\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndef visualize_attention(model, x, timestamps=None):\n \"\"\"\n Visualize temporal attention weights.\n\n Args:\n model: Trained TABL model\n x: Input sequence (1, T, D)\n timestamps: Optional timestamps for x-axis\n \"\"\"\n model.eval()\n with torch.no_grad():\n logits, attention = model(x, return_attention=True)\n\n # attention: (1, n_heads, T) or (1, T)\n attention = attention.squeeze(0).numpy()\n\n if attention.ndim == 1:\n # Single head\n fig, ax = plt.subplots(figsize=(12, 3))\n ax.bar(range(len(attention)), attention)\n ax.set_xlabel('Time Step')\n ax.set_ylabel('Attention Weight')\n | |
| [2026-01-18T20:54:59.897Z] [INFO] ax.set_title('Temporal Attention Weights')\n else:\n # Multi-head\n n_heads = attention.shape[0]\n fig, axes = plt.subplots(n_heads + 1, 1, figsize=(12, 3 * (n_heads + 1)))\n\n for i, ax in enumerate(axes[:-1]):\n ax.bar(range(attention.shape[1]), attention[i])\n ax.set_ylabel(f'Head {i+1}')\n ax.set_title(f'Attention Head {i+1}')\n\n # Combined attention\n combined = attention.mean(axis=0)\n axes[-1].bar(range(len(combined)), combined)\n axes[-1].set_xlabel('Time Step')\n axes[-1].set_ylabel('Combined')\n axes[-1].set_title('Combined Attention (Average)')\n\n plt.tight_layout()\n plt.savefig('attention_weights.png', dpi=150)\n plt.show()\n\n\ndef attention_heatmap(model, dataset, n_samples=50):\n \"\"\"\n Create heatmap of attention patterns across samples.\n \"\"\"\n model.eval()\n all_attention = []\n\n with torch.no_grad():\n for i in range(min(n_samples, len(dataset))):\n x, y = dataset[i]\n x = x.unsqueeze(0)\n _, attention = model(x, return_attention=True)\n all_attention.append(attention.squeeze().numpy())\n\n attention_matrix = np.stack(all_attention) # (n_samples, T)\n\n plt.figure(figsize=(14, 8))\n sns.heatmap(\n attention_matrix,\n cmap='Blues',\n xticklabels=5,\n yticklabels=5\n )\n plt.xlabel('Time Step')\n plt.ylabel('Sample')\n plt.title('Attention Patterns Across Samples')\n plt.savefig('attention_heatmap.png', dpi=150)\n plt.show()\n```\n\n### 05: Trading Strategy\n\n```python\n# python/05_strategy.py\n\ndef backtest_tabl_strategy(\n model,\n test_data,\n df_prices,\n initial_capital: float = 100000,\n transaction_cost: float = 0.001,\n confidence_threshold: float = 0.6\n):\n \"\"\"\n Backtest TABL trading strategy.\n\n Args:\n model: Trained TABL model\n test_data: Test dataset\n df_prices: Price data aligned with test_data\n initial_capital: Starting capital\n transaction_cost: Cost per transaction\n confidence_threshold: Min probability for trade\n \"\"\"\n model.eval()\n capital = initial_capital\n position = 0 # -1: short, 0: flat, 1: long\n\n results = []\n\n with torch.no_grad():\n for i, (x, _) in enumerate(test_data):\n x = x.unsqueeze(0)\n logits = model(x)\n probs = F.softmax(logits, dim=1).squeeze()\n\n # Get prediction and confidence\n pred = probs.argmax().item()\n confidence = probs.max().item()\n\n # Trading logic\n if confidence >= confidence_threshold:\n if pred == 2 and position <= 0: # Up signal\n # Close short, open long\n if position == -1:\n capital *= (1 - transaction_cost)\n position = 1\n capital *= (1 - transaction_cost)\n\n elif pred == 0 and position >= 0: # Down signal\n # Close long, open short\n if position == 1:\n capital *= (1 - transaction_cost)\n position = -1\n capital *= (1 - transaction_cost)\n\n # Calculate P&L\n if i > 0:\n price_return = df_prices['close'].iloc[i] / df_prices['close'].iloc[i-1] - 1\n capital *= (1 + position * price_return)\n\n results.append({\n 'capital': capital,\n 'position': position,\n 'prediction': pred,\n 'confidence': confidence\n })\n\n return pd.DataFrame(results)\n\n\ndef calculate_metrics(results_df, df_prices):\n \"\"\"Calculate strategy metrics.\"\"\"\n returns = results_df['capital'].pct_change().dropna()\n\n metrics = {\n 'total_return': (results_df['capital'].iloc[-1] / results_df['capital'].iloc[0] - 1) * 100,\n 'sharpe_ratio': returns.mean() / returns.std() * np.sqrt(252 * 24), # Hourly\n 'max_drawdown': ((results_df['capital'].cummax() - results_df['capital']) /\n results_df['capital'].cummax()).max() * 100,\n 'win_rate': (returns > 0).mean() * 100,\n 'n_trades': (results_df['position'].diff() != 0).sum()\n }\n\n return metrics\n```\n\n## Rust Implementation\n\nSee [rust/](rust/) for complete Rust implementation using Bybit data.\n\n```\nrust/\n├── Cargo.toml\n├── README.md\n├── src/\n│ ├── lib.rs # Main library exports\n│ ├── api/ # Bybit API client\n│ │ ├── mod.rs\n│ │ ├── client.rs # HTTP client for Bybit\n│ │ └── types.rs # API response types\n│ ├── data/ # Data processing\n│ │ ├── mod.rs\n│ │ ├── loader.rs # Data loading utilities\n│ │ ├── features.rs # Feature engineering\n│ │ └── dataset.rs # Dataset for training\n│ ├── model/ # TABL architecture\n│ │ ├── mod.rs\n│ │ ├── bilinear.rs # Bilinear layer\n│ │ ├── attention.rs # Temporal attention\n│ │ └── tabl.rs # Complete TABL model\n│ └── strategy/ # Trading strategy\n│ ├── mod.rs\n│ ├── signals.rs # Signal generation\n│ └── backtest.rs # Backtesting engine\n└── examples/\n ├── fetch_data.rs # Download Bybit data\n ├── train.rs # Train model\n └── backtest.rs # Run backtest\n```\n\n### Quick Start (Rust)\n\n```bash\n# Navigate to Rust project\ncd rust\n\n# Fetch data from Bybit\ncargo run --example fetch_data -- --symbols BTCUSDT,ETHUSDT\n\n# Train model\ncargo run --example train -- --epochs 100 --batch-size 32\n\n# Run backtest\ncargo run --example backtest -- --start 2024-01-01 --end 2024-12-31\n```\n\n## Python Implementation\n\nSee [python/](python/) for Python implementation.\n\n```\npython/\n├── model.py # TABL model implementation\n├── data.py # Data loading and features\n├── train.py # Training script\n├── strategy.py # Trading strategy\n├── example_usage.py # Example usage\n├── requirements.txt # Dependencies\n└── __init__.py\n```\n\n### Quick Start (Python)\n\n```bash\n# Install dependencies\npip install -r requirements.txt\n\n# Train model\npython train.py --symbols BTCUSDT --epochs 100\n\n# Run backtest\npython strategy.py --model checkpoints/best_model.pt\n```\n\n## Best Practices\n\n### When to Use TABL\n\n**Good use cases:**\n- LOB mid-price movement prediction\n- High-frequency direction forecasting\n- Short-horizon predictions (seconds to minutes)\n- Interpretability requirements\n\n**Not ideal for:**\n- Very long sequences (>500 time steps)\n- Multi-horizon forecasting\n- Portfolio allocation (use Stockformer)\n\n### Hyperparameter Recommendations\n\n| Parameter | Recommended | Notes |\n|-----------|-------------|-------|\n| `seq_len` | 50-200 | Depends on data frequency |\n| `hidden_T` | 10-30 | Temporal compression |\n| `hidden_D` | 32-128 | Feature compression |\n| `attention_dim` | 32-128 | Match with hidden_D |\n| `n_heads` | 2-8 | More for complex patterns |\n| `dropout` | 0.1-0.3 | Higher for small data |\n\n### Common Pitfalls\n\n1. **Class imbalance**: Use weighted loss or resample data\n2. **Overfitting**: Apply dropout, early stopping\n3. **Feature scaling**: Normalize inputs to zero mean, unit variance\n4. **Threshold selection**: Tune classification thresholds carefully\n\n## Resources\n\n### Papers\n\n- [Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis](https://arxiv.org/abs/1712.00975) — Original TABL paper\n- [Multi-head Temporal Attention-Augmented Bilinear Network](https://ieeexplore.ieee.org/document/9909957/) — Multi-head extension\n- [Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification](https://www.sciencedirect.com/science/article/pii/S0031320323003059) — Incremental learning\n\n### Implementations\n\n- [TABL Original (Python 2.7)](https://github.com/viebboy/TABL)\n- [TABL PyTorch](https://github.com/LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis)\n\n### Related Chapters\n\n- [Chapter 26: Temporal Fusion Transformers](../26_temporal_fusion_transformers) — Multi-horizon forecasting\n- [Chapter 43: Stockformer Multivariate](../43_stockformer_multivariate) — Cross-asset attention\n- [Chapter 42: Dual Attention LOB](../42_dual_attention_lob) — LOB prediction\n\n---\n\n## Difficulty Level\n\n**Intermediate to Advanced**\n\nPrerequisites:\n- Neural network fundamentals\n- Attention mechanisms basics\n- Time series forecasting concepts\n- PyTorch/Rust ML libraries\n"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":419,"cache_read_input_tokens":70464,"cache_creation":{"ephemeral_5m_input_tokens":419,"ephemeral_1h_input_tokens":0},"output_tokens":25,"service_tier":"standard"},"context_management":null},"parent_tool_use_id":null,"session_id":"77018388-5fda-4d3e-acd4-e8399840434b","uuid":"78b9ca80-bfa7-4171-a079-3c5243b7b83d"} | |
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| "content": "# Chapter 46: Temporal Attention Networks for Financial Time-Series\n\nThis chapter explores **Temporal Attention Networks** — specialized attention mechanisms designed to capture temporal dependencies in financial data. We focus on the **TABL (Temporal Attention-Augmented Bilinear Layer)** architecture and its variants, which have proven highly effective for predicting market movements from Limit Order Book (LOB) data and other time-series sources.\n\n<p align=\"center\">\n<img src=\"https://i.imgur.com/Zy8R4qF.png\" width=\"70%\">\n</p>\n\n## Contents\n\n1. [Introduction to Temporal Attention](#introduction-to-temporal-attention)\n * [Why Temporal Attention?](#why-temporal-attention)\n * [Key Advantages](#key-advantages)\n * [Comparison with Other Models](#comparison-with-other-models)\n2. [TABL Architecture](#tabl-architecture)\n * [Bilinear Projection](#bilinear-projection)\n * [Temporal Attention Mechanism](#temporal-attention-mechanism)\n * [BL (Bilinear Layer)](#bl-bilinear-layer)\n * [TABL (Temporal Attention Bilinear Layer)](#tabl-temporal-attention-bilinear-layer)\n3. [Multi-Head Temporal Attention](#multi-head-temporal-attention)\n * [Multi-Head TABL](#multi-head-tabl)\n * [Parallel Attention Heads](#parallel-attention-heads)\n4. [Data Processing](#data-processing)\n * [Limit Order Book Features](#limit-order-book-features)\n * [OHLCV Features](#ohlcv-features)\n * [Feature Engineering](#feature-engineering)\n5. [Practical Examples](#practical-examples)\n * [01: Data Preparation](#01-data-preparation)\n * [02: TABL Architecture](#02-tabl-architecture)\n * [03: Model Training](#03-model-training)\n * [04: Attention Visualization](#04-attention-visualization)\n * [05: Trading Strategy](#05-trading-strategy)\n6. [Rust Implementation](#rust-implementation)\n7. [Python Implementation](#python-implementation)\n8. [Best Practices](#best-practices)\n9. [Resources](#resources)\n\n## Introduction to Temporal Attention\n\nTemporal Attention Networks are designed to solve a fundamental challenge in financial forecasting: **which past events matter most for predicting the future?**\n\nUnlike standard recurrent models that process all time steps equally, temporal attention mechanisms learn to **focus on the most informative moments** in the input sequence.\n\n### Why Temporal Attention?\n\nTraditional models treat all time steps equally:\n\n```\nTime: t-5 t-4 t-3 t-2 t-1 t\nWeight: 1 1 1 1 1 1\n (All events equally important)\n```\n\nTemporal Attention learns adaptive weights:\n\n```\nTime: t-5 t-4 t-3 t-2 t-1 t\nWeight: 0.05 0.10 0.40 0.30 0.10 0.05\n (Attention focuses on t-3 and t-2)\n```\n\n**Key insight**: In financial markets, certain moments carry disproportionate importance — large trades, sudden volatility spikes, or specific patterns often precede price movements. Temporal attention automatically learns to identify these critical moments.\n\n### Key Advantages\n\n1. **Automatic Feature Selection in Time**\n - Learns which time steps are relevant for prediction\n - No manual feature engineering of \"important moments\"\n - Adapts to different market conditions\n\n2. **Interpretability**\n - Attention weights reveal which events influenced predictions\n - Useful for understanding model decisions\n - Enables post-hoc analysis of trading signals\n\n3. **Computational Efficiency**\n - TABL has O(T·D) complexity vs O(T²·D) for self-attention\n - Much faster than LSTM for long sequences\n - Lower memory requirements\n\n4. **Strong Performance**\n - Outperforms LSTM and CNN on LOB prediction tasks\n - Achieves state-of-the-art on FI-2010 benchmark\n - Effective with just 1-2 layers\n\n### Comparison with Other Models\n\n| Feature | LSTM | CNN | Transformer | TABL |\n|---------|------|-----|-------------|------|\n| Temporal attention | ✗ | ✗ | ✓ (self) | ✓ (learned) |\n| Complexity | O(T·D²) | O(T·K·D) | O(T²·D) | O(T·D) |\n| Interpretability | Low | Low | Medium | High |\n| LOB prediction | Good | Good | Good | Best |\n| Memory efficient | ✗ | ✓ | ✗ | ✓ |\n| Few layers needed | ✗ | ✗ | ✗ | ✓ |\n\n## TABL Architecture\n\nThe TABL architecture combines **bilinear projections** with **temporal attention** to create an efficient and interpretable model.\n\n```\n┌──────────────────────────────────────────────────────────────────────┐\n│ TEMPORAL ATTENTION NETWORK │\n├──────────────────────────────────────────────────────────────────────┤\n│ │\n│ Input: X ∈ ℝ^(T×D) │\n│ (T timesteps, D features) │\n│ │\n│ ┌──────────────────────────────────────────────────────────┐ │\n│ │ Bilinear Projection (BL) │ │\n│ │ │ │\n│ │ H = σ(W₁ · X · W₂ + b) │ │\n│ │ │ │\n│ │ W₁ ∈ ℝ^(T'×T) - Temporal projection │ │\n│ │ W₂ ∈ ℝ^(D×D') - Feature projection │ │\n│ │ H ∈ ℝ^(T'×D') - Compressed representation │ │\n│ │ │ │\n│ └──────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ┌──────────────────────────────────────────────────────────┐ │\n│ │ Temporal Attention (TA) │ │\n│ │ │ │\n│ │ α = softmax(w · tanh(U · X^T)) │ │\n│ │ c = X^T · α │ │\n│ │ │ │\n│ │ α ∈ ℝ^T - Attention weights │ │\n│ │ c ∈ ℝ^D - Context vector │ │\n│ │ │ │\n│ └──────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ┌──────────────────────────────────────────────────────────┐ │\n│ │ Output Layer │ │\n│ │ │ │\n│ │ y = softmax(W_out · flatten(H, c) + b_out) │ │\n│ │ │ │\n│ │ 3-class: Up / Stationary / Down │ │\n│ │ │ │\n│ └──────────────────────────────────────────────────────────┘ │\n│ │\n└──────────────────────────────────────────────────────────────────────┘\n```\n\n### Bilinear Projection\n\nThe bilinear layer performs two simultaneous linear transformations:\n\n```python\nclass BilinearLayer(nn.Module):\n \"\"\"\n Bilinear projection: H = σ(W₁ · X · W₂ + b)\n\n Transforms (T, D) → (T', D') by projecting both\n temporal and feature dimensions simultaneously.\n \"\"\"\n\n def __init__(self, T_in, T_out, D_in, D_out, dropout=0.1):\n super().__init__()\n # Temporal projection: (T_out, T_in)\n self.W1 = nn.Parameter(torch.randn(T_out, T_in) * 0.01)\n # Feature projection: (D_in, D_out)\n self.W2 = nn.Parameter(torch.randn(D_in, D_out) * 0.01)\n # Bias: (T_out, D_out)\n self.bias = nn.Parameter(torch.zeros(T_out, D_out))\n self.dropout = nn.Dropout(dropout)\n self.activation = nn.ReLU()\n\n def forward(self, x):\n # x: (batch, T_in, D_in)\n # W1·X: (batch, T_out, D_in)\n out = torch.matmul(self.W1, x)\n # W1·X·W2: (batch, T_out, D_out)\n out = torch.matmul(out, self.W2)\n out = out + self.bias\n out = self.activation(out)\n return self.dropout(out)\n```\n\n**Why Bilinear?**\n- Captures interactions between time and features\n- More expressive than simple linear layers\n- Reduces dimensionality in both axes efficiently\n\n### Temporal Attention Mechanism\n\nThe temporal attention computes a weighted sum over time steps:\n\n```python\nclass TemporalAttention(nn.Module):\n \"\"\"\n Temporal attention: α = softmax(w · tanh(U · X^T))\n\n Learns to focus on important time steps.\n \"\"\"\n\n def __init__(self, D, attention_dim=64):\n super().__init__()\n # Project features to attention space\n self.U = nn.Linear(D, attention_dim, bias=False)\n # Attention query vector\n self.w = nn.Linear(attention_dim, 1, bias=False)\n\n def forward(self, x):\n # x: (batch, T, D)\n # Compute attention scores\n h = torch.tanh(self.U(x)) # (batch, T, attention_dim)\n scores = self.w(h).squeeze(-1) # (batch, T)\n\n # Softmax over time dimension\n alpha = F.softmax(scores, dim=-1) # (batch, T)\n\n # Weighted sum: context vector\n context = torch.bmm(\n alpha.unsqueeze(1), # (batch, 1, T)\n x # (batch, T, D)\n ).squeeze(1) # (batch, D)\n\n return context, alpha\n```\n\n**Interpretation:**\n- `alpha[t]` indicates importance of time step t\n- High attention on specific events reveals model's focus\n- Context vector `c` summarizes the sequence\n\n### BL (Bilinear Layer)\n\nThe BL layer is the attention-free version:\n\n```python\nclass BL(nn.Module):\n \"\"\"Bilinear Layer without attention\"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.bilinear = BilinearLayer(\n config.seq_len, config.hidden_T,\n config.input_dim, config.hidden_D\n )\n\n def forward(self, x):\n h = self.bilinear(x)\n return h.flatten(1) # (batch, hidden_T * hidden_D)\n```\n\n### TABL (Temporal Attention Bilinear Layer)\n\nThe full TABL combines both components:\n\n```python\nclass TABL(nn.Module):\n \"\"\"\n Temporal Attention-Augmented Bilinear Layer\n\n Combines bilinear projection with temporal attention.\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.bilinear = BilinearLayer(\n config.seq_len, config.hidden_T,\n config.input_dim, config.hidden_D\n )\n self.attention = TemporalAttention(\n config.input_dim,\n config.attention_dim\n )\n\n def forward(self, x, return_attention=False):\n # Bilinear projection\n h = self.bilinear(x) # (batch, hidden_T, hidden_D)\n h_flat = h.flatten(1) # (batch, hidden_T * hidden_D)\n\n # Temporal attention\n context, alpha = self.attention(x) # (batch, D), (batch, T)\n\n # Concatenate\n out = torch.cat([h_flat, context], dim=-1)\n\n if return_attention:\n return out, alpha\n return out\n```\n\n## Multi-Head Temporal Attention\n\nExtending TABL with multiple attention heads allows the model to focus on different aspects simultaneously:\n\n### Multi-Head TABL\n\n```python\nclass MultiHeadTABL(nn.Module):\n \"\"\"\n Multi-Head Temporal Attention Bilinear Layer\n\n Uses multiple attention heads to capture different\n temporal patterns in the data.\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.n_heads = config.n_heads\n\n # Shared bilinear projection\n self.bilinear = BilinearLayer(\n config.seq_len, config.hidden_T,\n config.input_dim, config.hidden_D\n )\n\n # Multiple attention heads\n self.attention_heads = nn.ModuleList([\n TemporalAttention(config.input_dim, config.attention_dim)\n for _ in range(config.n_heads)\n ])\n\n # Head combination\n self.head_combine = nn.Linear(\n config.n_heads * config.input_dim,\n config.input_dim\n )\n\n def forward(self, x, return_attention=False):\n # Bilinear projection\n h = self.bilinear(x)\n h_flat = h.flatten(1)\n\n # Multi-head attention\n contexts = []\n alphas = []\n for head in self.attention_heads:\n ctx, alpha = head(x)\n contexts.append(ctx)\n alphas.append(alpha)\n\n # Combine heads\n multi_context = torch.cat(contexts, dim=-1)\n combined = self.head_combine(multi_context)\n\n # Final output\n out = torch.cat([h_flat, combined], dim=-1)\n\n if return_attention:\n return out, torch.stack(alphas, dim=1) # (batch, n_heads, T)\n return out\n```\n\n### Parallel Attention Heads\n\nEach head can focus on different patterns:\n- **Head 1**: Short-term price movements\n- **Head 2**: Volume spikes\n- **Head 3**: Order book imbalances\n- **Head 4**: Trend patterns\n\n```\nMulti-Head Attention Visualization:\n\nTime: t-10 t-9 t-8 t-7 t-6 t-5 t-4 t-3 t-2 t-1\nHead 1: ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ██▓▓ ████ ██▓▓ ░░░░ ░░░░\n (Focuses on mid-term events)\n\nHead 2: ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ████ ████\n (Focuses on recent events)\n\nHead 3: ████ ░░░░ ░░░░ ████ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░\n (Focuses on periodic patterns)\n\nCombined: ▓▓▓▓ ░░░░ ░░░░ ▓▓▓▓ ░░░░ ▓▓▓▓ ████ ▓▓▓▓ ████ ████\n (Aggregated attention)\n```\n\n## Data Processing\n\n### Limit Order Book Features\n\nLOB data provides rich information about market microstructure:\n\n```python\ndef extract_lob_features(lob_snapshot):\n \"\"\"\n Extract features from Limit Order Book snapshot.\n\n Returns features for TABL input.\n \"\"\"\n features = {}\n\n # Price levels (typically 10 levels each side)\n features['ask_prices'] = lob_snapshot['asks'][:, 0] # (10,)\n features['bid_prices'] = lob_snapshot['bids'][:, 0] # (10,)\n features['ask_volumes'] = lob_snapshot['asks'][:, 1] # (10,)\n features['bid_volumes'] = lob_snapshot['bids'][:, 1] # (10,)\n\n # Derived features\n mid_price = (features['ask_prices'][0] + features['bid_prices'][0]) / 2\n spread = features['ask_prices'][0] - features['bid_prices'][0]\n\n # Order imbalance\n total_ask_vol = features['ask_volumes'].sum()\n total_bid_vol = features['bid_volumes'].sum()\n imbalance = (total_bid_vol - total_ask_vol) / (total_bid_vol + total_ask_vol)\n\n features['mid_price'] = mid_price\n features['spread'] = spread\n features['imbalance'] = imbalance\n\n return features\n```\n\n### OHLCV Features\n\nFor crypto/stock data using OHLCV:\n\n```python\ndef extract_ohlcv_features(df):\n \"\"\"\n Extract features from OHLCV data.\n \"\"\"\n features = pd.DataFrame()\n\n # Price features\n features['log_return'] = np.log(df['close'] / df['close'].shift(1))\n features['high_low_range'] = (df['high'] - df['low']) / df['close']\n features['close_open_diff'] = (df['close'] - df['open']) / df['open']\n\n # Volume features\n features['volume_ma_ratio'] = df['volume'] / df['volume'].rolling(20).mean()\n features['volume_std'] = df['volume'].rolling(20).std() / df['volume'].rolling(20).mean()\n\n # Technical indicators\n features['rsi'] = compute_rsi(df['close'], 14)\n features['macd'] = compute_macd(df['close'])\n\n # Volatility\n features['volatility'] = features['log_return'].rolling(20).std()\n\n return features.dropna()\n```\n\n### Feature Engineering\n\nRecommended features for temporal attention models:\n\n| Feature | Description | Importance |\n|---------|-------------|------------|\n| `log_return` | Log price change | High |\n| `spread` | Bid-ask spread | High |\n| `imbalance` | Order book imbalance | High |\n| `volume_ratio` | Volume vs MA | Medium |\n| `volatility` | Rolling volatility | Medium |\n| `price_levels` | LOB price levels | Medium |\n| `volume_levels` | LOB volume levels | Medium |\n\n## Practical Examples\n\n### 01: Data Preparation\n\n```python\n# python/01_data_preparation.py\n\nimport numpy as np\nimport pandas as pd\nfrom typing import Tuple, List\n\ndef prepare_tabl_data(\n df: pd.DataFrame,\n lookback: int = 100,\n horizon: int = 10,\n threshold: float = 0.0002\n) -> Tuple[np.ndarray, np.ndarray]:\n \"\"\"\n Prepare data for TABL model training.\n\n Args:\n df: DataFrame with OHLCV data\n lookback: Number of time steps to look back\n horizon: Prediction horizon\n threshold: Threshold for direction classification\n\n Returns:\n X: Features (n_samples, lookback, n_features)\n y: Labels (n_samples,) - 0: down, 1: stationary, 2: up\n \"\"\"\n # Extract features\n features = extract_features(df)\n\n # Normalize features\n features_norm = (features - features.mean()) / features.std()\n\n # Create sequences\n X, y = [], []\n\n for i in range(lookback, len(features_norm) - horizon):\n # Input sequence\n x_seq = features_norm.iloc[i-lookback:i].values\n\n # Target: future return\n future_return = np.log(\n df['close'].iloc[i + horizon] / df['close'].iloc[i]\n )\n\n # Classify direction\n if future_return > threshold:\n label = 2 # Up\n elif future_return < -threshold:\n label = 0 # Down\n else:\n label = 1 # Stationary\n\n X.append(x_seq)\n y.append(label)\n\n return np.array(X), np.array(y)\n\n\ndef extract_features(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Extract features from OHLCV data.\"\"\"\n features = pd.DataFrame(index=df.index)\n\n # Returns\n features['return'] = np.log(df['close'] / df['close'].shift(1))\n\n # Price position\n features['hl_position'] = (df['close'] - df['low']) / (df['high'] - df['low'] + 1e-8)\n\n # Volume\n vol_ma = df['volume'].rolling(20).mean()\n features['vol_ratio'] = df['volume'] / vol_ma\n\n # Volatility\n features['volatility'] = features['return'].rolling(20).std()\n\n # Momentum\n features['momentum_5'] = df['close'] / df['close'].shift(5) - 1\n features['momentum_10'] = df['close'] / df['close'].shift(10) - 1\n\n return features.dropna()\n```\n\n### 02: TABL Architecture\n\nSee [python/model.py](python/model.py) for complete implementation.\n\n### 03: Model Training\n\n```python\n# python/03_train_model.py\n\nimport torch\nfrom model import TABLModel, TABLConfig\n\n# Configuration\nconfig = TABLConfig(\n seq_len=100,\n input_dim=6,\n hidden_T=20,\n hidden_D=32,\n attention_dim=64,\n n_heads=4,\n n_classes=3,\n dropout=0.2\n)\n\n# Initialize model\nmodel = TABLModel(config)\nprint(f\"Parameters: {sum(p.numel() for p in model.parameters()):,}\")\n\n# Loss and optimizer\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)\nscheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n optimizer, mode='max', factor=0.5, patience=5\n)\n\n# Training loop\nbest_acc = 0\nfor epoch in range(100):\n model.train()\n train_loss = 0\n\n for batch_x, batch_y in train_loader:\n optimizer.zero_grad()\n\n # Forward pass\n logits = model(batch_x)\n loss = criterion(logits, batch_y)\n\n # Backward pass\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n optimizer.step()\n\n train_loss += loss.item()\n\n # Validation\n model.eval()\n correct = 0\n total = 0\n\n with torch.no_grad():\n for batch_x, batch_y in val_loader:\n logits = model(batch_x)\n preds = logits.argmax(dim=1)\n correct += (preds == batch_y).sum().item()\n total += batch_y.size(0)\n\n val_acc = correct / total\n scheduler.step(val_acc)\n\n if val_acc > best_acc:\n best_acc = val_acc\n torch.save(model.state_dict(), 'best_model.pt')\n\n print(f\"Epoch {epoch+1}: Loss={train_loss/len(train_loader):.4f}, Val Acc={val_acc:.4f}\")\n```\n\n### 04: Attention Visualization\n\n```python\n# python/04_attention_visualization.py\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndef visualize_attention(model, x, timestamps=None):\n \"\"\"\n Visualize temporal attention weights.\n\n Args:\n model: Trained TABL model\n x: Input sequence (1, T, D)\n timestamps: Optional timestamps for x-axis\n \"\"\"\n model.eval()\n with torch.no_grad():\n logits, attention = model(x, return_attention=True)\n\n # attention: (1, n_heads, T) or (1, T)\n attention = attention.squeeze(0).numpy()\n\n if attention.ndim == 1:\n # Single head\n fig, ax = plt.subplots(figsize=(12, 3))\n ax.bar(range(len(attention)), attention)\n ax.set_xlabel('Time Step')\n ax.set_ylabel('Attention Weight')\n ax.set_title('Temporal Attention Weights')\n else:\n # Multi-head\n n_heads = attention.shape[0]\n fig, axes = plt.subplots(n_heads + 1, 1, figsize=(12, 3 * (n_heads + 1)))\n\n for i, ax in enumerate(axes[:-1]):\n ax.bar(range(attention.shape[1]), attention[i])\n ax.set_ylabel(f'Head {i+1}')\n ax.set_title(f'Attention Head {i+1}')\n\n # Combined attention\n combined = attention.mean(axis=0)\n axes[-1].bar(range(len(combined)), combined)\n axes[-1].set_xlabel('Time Step')\n axes[-1].set_ylabel('Combined')\n axes[-1].set_title('Combined Attention (Average)')\n\n plt.tight_layout()\n plt.savefig('attention_weights.png', dpi=150)\n plt.show()\n\n\ndef attention_heatmap(model, dataset, n_samples=50):\n \"\"\"\n Create heatmap of attention patterns across samples.\n \"\"\"\n model.eval()\n all_attention = []\n\n with torch.no_grad():\n for i in range(min(n_samples, len(dataset))):\n x, y = dataset[i]\n x = x.unsqueeze(0)\n _, attention = model(x, return_attention=True)\n all_attention.append(attention.squeeze().numpy())\n\n attention_matrix = np.stack(all_attention) # (n_samples, T)\n\n plt.figure(figsize=(14, 8))\n sns.heatmap(\n attention_matrix,\n cmap='Blues',\n xticklabels=5,\n yticklabels=5\n )\n plt.xlabel('Time Step')\n plt.ylabel('Sample')\n plt.title('Attention Patterns Across Samples')\n plt.savefig('attention_heatmap.png', dpi=150)\n plt.show()\n```\n\n### 05: Trading Strategy\n\n```python\n# python/05_strategy.py\n\ndef backtest_tabl_strategy(\n model,\n test_data,\n df_prices,\n initial_capital: float = 100000,\n transaction_cost: float = 0.001,\n confidence_threshold: float = 0.6\n):\n \"\"\"\n Backtest TABL trading strategy.\n\n Args:\n model: Trained TABL model\n test_data: Test dataset\n df_prices: Price data aligned with test_data\n initial_capital: Starting capital\n transaction_cost: Cost per transaction\n confidence_threshold: Min probability for trade\n \"\"\"\n model.eval()\n capital = initial_capital\n position = 0 # -1: short, 0: flat, 1: long\n\n results = []\n\n with torch.no_grad():\n for i, (x, _) in enumerate(test_data):\n x = x.unsqueeze(0)\n logits = model(x)\n probs = F.softmax(logits, dim=1).squeeze()\n\n # Get prediction and confidence\n pred = probs.argmax().item()\n confidence = probs.max().item()\n\n # Trading logic\n if confidence >= confidence_threshold:\n if pred == 2 and position <= 0: # Up signal\n # Close short, open long\n if position == -1:\n capital *= (1 - transaction_cost)\n position = 1\n capital *= (1 - transaction_cost)\n\n elif pred == 0 and position >= 0: # Down signal\n # Close long, open short\n if position == 1:\n capital *= (1 - transaction_cost)\n position = -1\n capital *= (1 - transaction_cost)\n\n # Calculate P&L\n if i > 0:\n price_return = df_prices['close'].iloc[i] / df_prices['close'].iloc[i-1] - 1\n capital *= (1 + position * price_return)\n\n results.append({\n 'capital': capital,\n 'position': position,\n 'prediction': pred,\n 'confidence': confidence\n })\n\n return pd.DataFrame(results)\n\n\ndef calculate_metrics(results_df, df_prices):\n \"\"\"Calculate strategy metrics.\"\"\"\n returns = results_df['capital'].pct_change().dropna()\n\n metrics = {\n 'total_return': (results_df['capital'].iloc[-1] / results_df['capital'].iloc[0] - 1) * 100,\n 'sharpe_ratio': returns.mean() / returns.std() * np.sqrt(252 * 24), # Hourly\n 'max_drawdown': ((results_df['capital'].cummax() - results_df['capital']) /\n results_df['capital'].cummax()).max() * 100,\n 'win_rate': (returns > 0).mean() * 100,\n 'n_trades': (results_df['position'].diff() != 0).sum()\n }\n\n return metrics\n```\n\n## Rust Implementation\n\nSee [rust/](rust/) for complete Rust implementation using Bybit data.\n\n```\nrust/\n├── Cargo.toml\n├── README.md\n├── src/\n│ ├── lib.rs # Main library exports\n│ ├── api/ # Bybit API client\n│ │ ├── mod.rs\n│ │ ├── client.rs # HTTP client for Bybit\n│ │ └── types.rs # API response types\n│ ├── data/ # Data processing\n│ │ ├── mod.rs\n│ │ ├── loader.rs # Data loading utilities\n│ │ ├── features.rs # Feature engineering\n│ │ └── dataset.rs # Dataset for training\n│ ├── model/ # TABL architecture\n│ │ ├── mod.rs\n│ │ ├── bilinear.rs # Bilinear layer\n│ │ ├── attention.rs # Temporal attention\n│ │ └── tabl.rs # Complete TABL model\n│ └── strategy/ # Trading strategy\n│ ├── mod.rs\n│ ├── signals.rs # Signal generation\n│ └── backtest.rs # Backtesting engine\n└── examples/\n ├── fetch_data.rs # Download Bybit data\n ├── train.rs # Train model\n └── backtest.rs # Run backtest\n```\n\n### Quick Start (Rust)\n\n```bash\n# Navigate to Rust project\ncd rust\n\n# Fetch data from Bybit\ncargo run --example fetch_data -- --symbols BTCUSDT,ETHUSDT\n\n# Train model\ncargo run --example train -- --epochs 100 --batch-size 32\n\n# Run backtest\ncargo run --example backtest -- --start 2024-01-01 --end 2024-12-31\n```\n\n## Python Implementation\n\nSee [python/](python/) for Python implementation.\n\n```\npython/\n├── model.py # TABL model implementation\n├── data.py # Data loading and features\n├── train.py # Training script\n├── strategy.py # Trading strategy\n├── example_usage.py # Example usage\n├── requirements.txt # Dependencies\n└── __init__.py\n```\n\n### Quick Start (Python)\n\n```bash\n# Install dependencies\npip install -r requirements.txt\n\n# Train model\npython train.py --symbols BTCUSDT --epochs 100\n\n# Run backtest\npython strategy.py --model checkpoints/best_model.pt\n```\n\n## Best Practices\n\n### When to Use TABL\n\n**Good use cases:**\n- LOB mid-price movement prediction\n- High-frequency direction forecasting\n- Short-horizon predictions (seconds to minutes)\n- Interpretability requirements\n\n**Not ideal for:**\n- Very long sequences (>500 time steps)\n- Multi-horizon forecasting\n- Portfolio allocation (use Stockformer)\n\n### Hyperparameter Recommendations\n\n| Parameter | Recommended | Notes |\n|-----------|-------------|-------|\n| `seq_len` | 50-200 | Depends on data frequency |\n| `hidden_T` | 10-30 | Temporal compression |\n| `hidden_D` | 32-128 | Feature compression |\n| `attention_dim` | 32-128 | Match with hidden_D |\n| `n_heads` | 2-8 | More for complex patterns |\n| `dropout` | 0.1-0.3 | Higher for small data |\n\n### Common Pitfalls\n\n1. **Class imbalance**: Use weighted loss or resample data\n2. **Overfitting**: Apply dropout, early stopping\n3. **Feature scaling**: Normalize inputs to zero mean, unit variance\n4. **Threshold selection**: Tune classification thresholds carefully\n\n## Resources\n\n### Papers\n\n- [Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis](https://arxiv.org/abs/1712.00975) — Original TABL paper\n- [Multi-head Temporal Attention-Augmented Bilinear Network](https://ieeexplore.ieee.org/document/9909957/) — Multi-head extension\n- [Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification](https://www.sciencedirect.com/science/article/pii/S0031320323003059) — Incremental learning\n\n### Implementations\n\n- [TABL Original (Python 2.7)](https://github.com/viebboy/TABL)\n- [TABL PyTorch](https://github.com/LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis)\n\n### Related Chapters\n\n- [Chapter 26: Temporal Fusion Transformers](../26_temporal_fusion_transformers) — Multi-horizon forecasting\n- [Chapter 43: Stockformer Multivariate](../43_stockformer_multivariate) — Cross-asset attention\n- [Chapter 42: Dual Attention LOB](../42_dual_attention_lob) — LOB prediction\n\n---\n\n## Difficulty Level\n\n**Intermediate to Advanced**\n\nPrerequisites:\n- Neural network fundamentals\n- Attention mechanisms basics\n- Time series forecasting concepts\n- PyTorch/Rust ML libraries\n", | |
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| [2026-01-18T20:57:29.191Z] [INFO] {"type":"assistant","message":{"model":"claude-opus-4-5-20251101","id":"msg_01P8gMbJiC663MX7hLeQ6sSi","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015qKptEHqtVvHYv9AAYk9xc","name":"Write","input":{"file_path":"/tmp/gh-issue-solver-1768769379407/46_temporal_attention_networks/README.ru.md","content":"# Глава 46: Сети Временного Внимания для Финансовых Временных Рядов\n\nВ этой главе рассматриваются **Сети Временного Внимания** (Temporal Attention Networks) — специализированные механизмы внимания, предназначенные для захвата временных зависимостей в финансовых данных. Мы фокусируемся на архитектуре **TABL (Temporal Attention-Augmented Bilinear Layer)** и её вариантах, которые доказали высокую эффективность для предсказания движений рынка на основе данных книги заявок (LOB) и других временных рядов.\n\n<p align=\"center\">\n<img src=\"https://i.imgur.com/Zy8R4qF.png\" width=\"70%\">\n</p>\n\n## Содержание\n\n1. [Введение во Временное Внимание](#введение-во-временное-внимание)\n * [Зачем Временное Внимание?](#зачем-временное-внимание)\n * [Ключевые Преимущества](#ключевые-преимущества)\n * [Сравнение с Другими Моделями](#сравнение-с-другими-моделями)\n2. [Архитектура TABL](#архитектура-tabl)\n * [Билинейная Проекция](#билинейная-проекция)\n * [Механизм Временного Внимания](#механизм-временного-внимания)\n * [BL (Билинейный Слой)](#bl-билинейный-слой)\n * [TABL (Временной Внимательный Билинейный Слой)](#tabl-временной-внимательный-билинейный-слой)\n3. [Многоголовое Временное Внимание](#многоголовое-временное-внимание)\n * [Многоголовый TABL](#многоголовый-tabl)\n * [Параллельные Головы Внимания](#параллельные-головы-внимания)\n4. [Обработка Данных](#обработка-данных)\n * [Признаки Книги Заявок](#признаки-книги-заявок)\n * [Признаки OHLCV](#признаки-ohlcv)\n * [Инженерия Признаков](#инженерия-признаков)\n5. [Практические Примеры](#практические-примеры)\n * [01: Подготовка Данных](#01-подготовка-данных)\n * [02: Архитектура TABL](#02-архитектура-tabl)\n * [03: Обучение Модели](#03-обучение-модели)\n * [04: Визуализация Внимания](#04-визуализация-внимания)\n * [05: Торговая Стратегия](#05-торговая-стратегия)\n6. [Реализация на Rust](#реализация-на-rust)\n7. [Реализация на Python](#реализация-на-python)\n8. [Лучшие Практики](#лучшие-практики)\n9. [Ресурсы](#ресурсы)\n\n## Введение во Временное Внимание\n\nСети Временного Внимания созданы для решения фундаментальной задачи в финансовом прогнозировании: **какие прошлые события наиболее важны для предсказания будущего?**\n\nВ отличие от стандартных рекуррентных моделей, которые обрабатывают все временные шаги одинаково, механизмы временного внимания учатся **фокусироваться на наиболее информативных моментах** во входной последовательности.\n\n### Зачем Временное Внимание?\n\nТрадиционные модели обрабатывают все временные шаги одинаково:\n\n```\nВремя: t-5 t-4 t-3 t-2 t-1 t\nВес: 1 1 1 1 1 1\n (Все события одинаково важны)\n```\n\nВременное Внимание обучает адаптивные веса:\n\n```\nВремя: t-5 t-4 t-3 t-2 t-1 t\nВес: 0.05 0.10 0.40 0.30 0.10 0.05\n (Внимание фокусируется на t-3 и t-2)\n```\n\n**Ключевая идея**: На финансовых рынках определённые моменты имеют непропорционально большое значение — крупные сделки, резкие всплески волатильности или специфические паттерны часто предшествуют движениям цены. Временное внимание автоматически учится выявлять эти критические моменты.\n\n### Ключевые Преимущества\n\n1. **Автоматический Отбор Признаков во Времени**\n - Обучается определять релевантные временные шаги\n - Не требует ручной разработки признаков \"важных моментов\"\n - Адаптируется к различным рыночным условиям\n\n2. **Интерпретируемость**\n - Веса внимания показывают, какие события повлияли на предсказания\n - Полезно для понимания решений модели\n - Позволяет проводить апостериорный анализ торговых сигналов\n\n3. **Вычислительная Эффективность**\n - TABL имеет сложность O(T·D) против O(T²·D) для self-attention\n - Намного быстрее LSTM для длинных последовательностей\n - Меньшие требования к памяти\n\n4. **Высокая Производительность**\n - Превосходит LSTM и CNN в задачах предсказания LOB\n - Достигает state-of-the-art на бенчмарке FI-2010\n - Эффективна всего с 1-2 слоями\n\n### Сравнение с Другими Моделями\n\n| Характеристика | LSTM | CNN | Transformer | TABL |\n|----------------|------|-----|-------------|------|\n| Временное внимание | ✗ | ✗ | ✓ (self) | ✓ (обучаемое) |\n| Сложность | O(T·D²) | O(T·K·D) | O(T²·D) | O(T·D) |\n| Интерпретируемость | Низкая | Низкая | Средняя | Высокая |\n| Предсказание LOB | Хорошо | Хорошо | Хорошо | Лучше всех |\n| Эффективность памяти | ✗ | ✓ | ✗ | ✓ |\n| Мало слоёв нужно | ✗ | ✗ | ✗ | ✓ |\n\n## Архитектура TABL\n\nАрхитектура TABL объединяет **билинейные проекции** с **временным вниманием** для создания эффективной и интерпретируемой модели.\n\n```\n┌──────────────────────────────────────────────────────────────────────┐\n│ СЕТЬ ВРЕМЕННОГО ВНИМАНИЯ │\n├──────────────────────────────────────────────────────────────────────┤\n│ │\n│ Вход: X ∈ ℝ^(T×D) │\n│ (T временных шагов, D признаков) │\n│ │\n│ ┌──────────────────────────────────────────────────────────┐ │\n│ │ Билинейная Проекция (BL) │ │\n│ │ │ │\n│ │ H = σ(W₁ · X · W₂ + b) │ │\n│ │ │ │\n│ │ W₁ ∈ ℝ^(T'×T) - Временная проекция │ │\n│ │ W₂ ∈ ℝ^(D×D') - Проекция признаков │ │\n│ │ H ∈ ℝ^(T'×D') - Сжатое представление │ │\n│ │ │ │\n│ └──────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ┌──────────────────────────────────────────────────────────┐ │\n│ │ Временное Внимание (TA) │ │\n│ │ │ │\n│ │ α = softmax(w · tanh(U · X^T)) │ │\n│ │ c = X^T · α │ │\n│ │ │ │\n│ │ α ∈ ℝ^T - Веса внимания │ │\n│ │ c ∈ ℝ^D - Контекстный вектор │ │\n│ │ │ │\n│ └──────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ┌──────────────────────────────────────────────────────────┐ │\n│ │ Выходной Слой │ │\n│ │ │ │\n│ │ y = softmax(W_out · flatten(H, c) + b_out) │ │\n│ │ │ │\n│ │ 3 класса: Вверх / Без изменений / Вниз │ │\n│ │ │ │\n│ └──────────────────────────────────────────────────────────┘ │\n│ │\n└──────────────────────────────────────────────────────────────────────┘\n```\n\n### Билинейная Проекция\n\nБилинейный слой выполняет два одновременных линейных преобразования:\n\n```python\nclass BilinearLayer(nn.Module):\n \"\"\"\n Билинейная проекция: H = σ(W₁ · X · W₂ + b)\n\n Преобразует (T, D) → (T', D') путём проекции\n как временного, так и признакового измерений одновременно.\n \"\"\"\n\n def __init__(self, T_in, T_out, D_in, D_out, dropout=0.1):\n super().__init__()\n # Временная проекция: (T_out, T_in)\n self.W1 = nn.Parameter(torch.randn(T_out, T_in) * 0.01)\n # Проекция признаков: (D_in, D_out)\n self.W2 = nn.Parameter(torch.randn(D_in, D_out) * 0.01)\n # Смещение: (T_out, D_out)\n self.bias = nn.Parameter(torch.zeros(T_out, D_out))\n self.dropout = nn.Dropout(dropout)\n self.activation = nn.ReLU()\n\n def forward(self, x):\n # x: (batch, T_in, D_in)\n # W1·X: (batch, T_out, D_in)\n out = torch.matmul(self.W1, x)\n # W1·X·W2: (batch, T_out, D_out)\n out = torch.matmul(out, self.W2)\n out = out + self.bias\n out = self.activation(out)\n return self.dropout(out)\n```\n\n**Почему Билинейный?**\n- Захватывает взаимодействия между временем и признаками\n- Более выразителен, чем простые линейные слои\n- Эффективно снижает размерность по обеим осям\n\n### Механизм Временного Внимания\n\nВременное внимание вычисляет взвешенную сумму по временным шагам:\n\n```python\nclass TemporalAttention(nn.Module):\n \"\"\"\n Временное внимание: α = softmax(w · tanh(U · X^T))\n\n Обучается фокусироваться на важных временных шагах.\n \"\"\"\n\n def __init__(self, D, attention_dim=64):\n super().__init__()\n # Проекция признаков в пространство внимания\n self.U = nn.Linear(D, attention_dim, bias=False)\n # Вектор запроса внимания\n self.w = nn.Linear(attention_dim, 1, bias=False)\n\n def forward(self, x):\n # x: (batch, T, D)\n # Вычисление оценок внимания\n h = torch.tanh(self.U(x)) # (batch, T, attention_dim)\n scores = self.w(h).squeeze(-1) # (batch, T)\n\n # Softmax по временному измерению\n alpha = F.softmax(scores, dim=-1) # (batch, T)\n\n # Взвешенная сумма: контекстный вектор\n context = torch.bmm(\n alpha.unsqueeze(1), # (batch, 1, T)\n x # (batch, T, D)\n ).squeeze(1) # (batch, D)\n\n return context, alpha\n```\n\n**Интерпретация:**\n- `alpha[t]` указывает на важность временного шага t\n- Высокое внимание на конкретных событиях показывает фокус модели\n- Контекстный вектор `c` обобщает последовательность\n\n### BL (Билинейный Слой)\n\nBL слой — это версия без внимания:\n\n```python\nclass BL(nn.Module):\n \"\"\"Билинейный слой без внимания\"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.bilinear = BilinearLayer(\n config.seq_len, config.hidden_T,\n config.input_dim, config.hidden_D\n )\n\n def forward(self, x):\n h = self.bilinear(x)\n return h.flatten(1) # (batch, hidden_T * hidden_D)\n```\n\n### TABL (Временной Внимательный Билинейный Слой)\n\nПолный TABL объединяет оба компонента:\n\n```python\nclass TABL(nn.Module):\n \"\"\"\n Temporal Attention-Augmented Bilinear Layer\n\n Объединяет билинейную проекцию с временным вниманием.\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.bilinear = BilinearLayer(\n config.seq_len, config.hidden_T,\n config.input_dim, config.hidden_D\n )\n self.attention = TemporalAttention(\n config.input_dim,\n config.attention_dim\n )\n\n def forward(self, x, return_attention=False):\n # Билинейная проекция\n h = self.bilinear(x) # (batch, hidden_T, hidden_D)\n h_flat = h.flatten(1) # (batch, hidden_T * hidden_D)\n\n # Временное внимание\n context, alpha = self.attention(x) # (batch, D), (batch, T)\n\n # Конкатенация\n out = torch.cat([h_flat, context], dim=-1)\n\n if return_attention:\n return out, alpha\n return out\n```\n\n## Многоголовое Временное Внимание\n\nРасширение TABL несколькими головами внимания позволяет модели фокусироваться на разных аспектах одновременно:\n\n### Многоголовый TABL\n\n```python\nclass MultiHeadTABL(nn.Module):\n \"\"\"\n Multi-Head Temporal Attention Bilinear Layer\n\n Использует несколько голов внимания для захвата\n различных временных паттернов в данных.\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.n_heads = config.n_heads\n\n # Общая билинейная проекция\n self.bilinear = BilinearLayer(\n config.seq_len, config.hidden_T,\n config.input_dim, config.hidden_D\n )\n\n # Несколько голов внимания\n self.attention_heads = nn.ModuleList([\n TemporalAttention(config.input_dim, config.attention_dim)\n for _ in range(config.n_heads)\n ])\n\n # Объединение голов\n self.head_combine = nn.Linear(\n config.n_heads * config.input_dim,\n config.input_dim\n )\n\n def forward(self, x, return_attention=False):\n # Билинейная проекция\n h = self.bilinear(x)\n h_flat = h.flatten(1)\n\n # Многоголовое внимание\n contexts = []\n alphas = []\n for head in self.attention_heads:\n ctx, alpha = head(x)\n contexts.append(ctx)\n alphas.append(alpha)\n\n # Объединение голов\n multi_context = torch.cat(contexts, dim=-1)\n combined = self.head_combine(multi_context)\n\n # Финальный выход\n out = torch.cat([h_flat, combined], dim=-1)\n\n if return_attention:\n return out, torch.stack(alphas, dim=1) # (batch, n_heads, T)\n return out\n```\n\n### Параллельные Головы Внимания\n\nКаждая голова может фокусироваться на разных паттернах:\n- **Голова 1**: Краткосрочные движения цены\n- **Голова 2**: Всплески объёма\n- **Голова 3**: Дисбалансы книги заявок\n- **Голова 4**: Трендовые паттерны\n\n```\nВизуализация Многоголового Внимания:\n\nВремя: t-10 t-9 t-8 t-7 t-6 t-5 t-4 t-3 t-2 t-1\nГолова 1: ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ██▓▓ ████ ██▓▓ ░░░░ ░░░░\n (Фокус на среднесрочных событиях)\n\nГолова 2: ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ████ ████\n (Фокус на недавних событиях)\n\nГолова 3: ████ ░░░░ ░░░░ ████ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░\n (Фокус на периодических паттернах)\n\nОбъединённо: ▓▓▓▓ ░░░░ ░░░░ ▓▓▓▓ ░░░░ ▓▓▓▓ ████ ▓▓▓▓ ████ ████\n (Агрегированное внимание)\n```\n\n## Обработка Данных\n\n### Признаки Книги Заявок\n\nДанные LOB предоставляют богатую информацию о микроструктуре рынка:\n\n```python\ndef extract_lob_features(lob_snapshot):\n \"\"\"\n Извлечение признаков из снимка книги заявок.\n\n Возвращает признаки для входа TABL.\n \"\"\"\n features = {}\n\n # Ценовые уровни (обычно 10 уровней с каждой стороны)\n features['ask_prices'] = lob_snapshot['asks'][:, 0] # (10,)\n features['bid_prices'] = lob_snapshot['bids'][:, 0] # (10,)\n features['ask_volumes'] = lob_snapshot['asks'][:, 1] # (10,)\n features['bid_volumes'] = lob_snapshot['bids'][:, 1] # (10,)\n\n # Производные признаки\n mid_price = (features['ask_prices'][0] + features['bid_prices'][0]) / 2\n spread = features['ask_prices'][0] - features['bid_prices'][0]\n\n # Дисбаланс ордеров\n total_ask_vol = features['ask_volumes'].sum()\n total_bid_vol = features['bid_volumes'].sum()\n imbalance = (total_bid_vol - total_ask_vol) / (total_bid_vol + total_ask_vol)\n\n features['mid_price'] = mid_price\n features['spread'] = spread\n features['imbalance'] = imbalance\n\n return features\n```\n\n### Признаки OHLCV\n\nДля криптовалютных/биржевых данных с использованием OHLCV:\n\n```python\ndef extract_ohlcv_features(df):\n \"\"\"\n Извлечение признаков из данных OHLCV.\n \"\"\"\n features = pd.DataFrame()\n\n # Ценовые признаки\n features['log_return'] = np.log(df['close'] / df['close'].shift(1))\n features['high_low_range'] = (df['high'] - df['low']) / df['close']\n features['close_open_diff'] = (df['close'] - df['open']) / df['open']\n\n # Объёмные признаки\n features['volume_ma_ratio'] = df['volume'] / df['volume'].rolling(20).mean()\n features['volume_std'] = df['volume'].rolling(20).std() / df['volume'].rolling(20).mean()\n\n # Технические индикаторы\n features['rsi'] = compute_rsi(df['close'], 14)\n features['macd'] = compute_macd(df['close'])\n\n # Волатильность\n features['volatility'] = features['log_return'].rolling(20).std()\n\n return features.dropna()\n```\n\n### Инженерия Признаков\n\nРекомендуемые признаки для моделей с временным вниманием:\n\n| Признак | Описание | Важность |\n|---------|----------|----------|\n| `log_return` | Логарифмическое изменение цены | Высокая |\n| `spread` | Спред bid-ask | Высокая |\n| `imbalance` | Дисбаланс книги заявок | Высокая |\n| `volume_ratio` | Объём к скользящему среднему | Средняя |\n| `volatility` | Скользящая волатильность | Средняя |\n| `price_levels` | Ценовые уровни LOB | Средняя |\n| `volume_levels` | Объёмные уровни LOB | Средняя |\n\n## Практические Примеры\n\n### 01: Подготовка Данных\n\n```python\n# python/01_data_preparation.py\n\nimport numpy as np\nimport pandas as pd\nfrom typing import Tuple, List\n\ndef prepare_tabl_data(\n df: pd.DataFrame,\n lookback: int = 100,\n horizon: int = 10,\n threshold: float = 0.0002\n) -> Tuple[np.ndarray, np.ndarray]:\n \"\"\"\n Подготовка данных для обучения модели TABL.\n\n Args:\n df: DataFrame с данными OHLCV\n lookback: Количество временных шагов назад\n horizon: Горизонт прогнозирования\n threshold: Порог для классификации направления\n\n Returns:\n X: Признаки (n_samples, lookback, n_features)\n y: Метки (n_samples,) - 0: вниз, 1: без изменений, 2: вверх\n \"\"\"\n # Извлечение признаков\n features = extract_features(df)\n\n # Нормализация признаков\n features_norm = (features - features.mean()) / features.std()\n\n # Создание последовательностей\n X, y = [], []\n\n for i in range(lookback, len(features_norm) - horizon):\n # Входная последовательность\n x_seq = features_norm.iloc[i-lookback:i].values\n\n # Цель: будущая доходность\n future_return = np.log(\n df['close'].iloc[i + horizon] / df['close'].iloc[i]\n )\n\n # Классификация направления\n if future_return > threshold:\n label = 2 # Вверх\n elif future_return < -threshold:\n label = 0 # Вниз\n else:\n label = 1 # Без изменений\n\n X.append(x_seq)\n y.append(label)\n\n return np.array(X), np.array(y)\n\n\ndef extract_features(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Извлечение признаков из данных OHLCV.\"\"\"\n features = pd.DataFrame(index=df.index)\n\n # Доходности\n features['return'] = np.log(df['close'] / df['close'].shift(1))\n\n # Позиция цены\n features['hl_position'] = (df['close'] - df['low']) / (df['high'] - df['low'] + 1e-8)\n\n # Объём\n vol_ma = df['volume'].rolling(20).mean()\n features['vol_ratio'] = df['volume'] / vol_ma\n\n # Волатильность\n features['volatility'] = features['return'].rolling(20).std()\n\n # Моментум\n features['momentum_5'] = df['close'] / df['close'].shift(5) - 1\n features['momentum_10'] = df['close'] / df['close'].shift(10) - 1\n\n return features.dropna()\n```\n\n### 02: Архитектура TABL\n\nСм. [python/model.py](python/model.py) для полной реализации.\n\n### 03: Обучение Модели\n\n```python\n# python/03_train_model.py\n\nimport torch\nfrom model import TABLModel, TABLConfig\n\n# Конфигурация\nconfig = TABLConfig(\n seq_len=100,\n input_dim=6,\n hidden_T=20,\n hidden_D=32,\n attention_dim=64,\n n_heads=4,\n n_classes=3,\n dropout=0.2\n)\n\n# Инициализация модели\nmodel = TABLModel(config)\nprint(f\"Параметры: {sum(p.numel() for p in model.parameters()):,}\")\n\n# Функция потерь и оптимизатор\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)\nscheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n optimizer, mode='max', factor=0.5, patience=5\n)\n\n# Цикл обучения\nbest_acc = 0\nfor epoch in range(100):\n model.train()\n train_loss = 0\n\n for batch_x, batch_y in train_loader:\n optimizer.zero_grad()\n\n # Прямой проход\n logits = model(batch_x)\n loss = criterion(logits, batch_y)\n\n # Обратный проход\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n optimizer.step()\n\n train_loss += loss.item()\n\n # Валидация\n model.eval()\n correct = 0\n total = 0\n\n with torch.no_grad():\n for batch_x, batch_y in val_loader:\n logits = model(batch_x)\n preds = logits.argmax(dim=1)\n correct += (preds == batch_y).sum().item()\n total += batch_y.size(0)\n\n val_acc = correct / total\n scheduler.step(val_acc)\n\n if val_acc > best_acc:\n best_acc = val_acc\n torch.save(model.state_dict(), 'best_model.pt')\n\n print(f\"Эпоха {epoch+1}: Loss={train_loss/len(train_loader):.4f}, Val Acc={val_acc:.4f}\")\n```\n\n### 04: Визуализация Внимания\n\n```python\n# python/04_attention_visualization.py\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndef visualize_attention(model, x, timestamps=None):\n \"\"\"\n Визуализация весов временного внимания.\n\n Args:\n model: Обученная модель TABL\n x: Входная последовательность (1, T, D)\n timestamps: Опциональные временные метки для оси X\n \"\"\"\n model.eval()\n with torch.no_grad():\n logits, attention = model(x, return_attention=True)\n\n # attention: (1, n_heads, T) или (1, T)\n attention = attention.squeeze(0).numpy()\n\n if attention.ndim == 1:\n # Одна голова\n fig, ax = plt.subplots(figsize=(12, 3))\n ax.bar(range(len(attention)), attention)\n ax.set_xlabel('Временной шаг')\n ax.set_ylabel('Вес внимания')\n ax.set_title('Веса временного внимания')\n else:\n # Много голов\n n_heads = attention.shape[0]\n fig, axes = plt.subplots(n_heads + 1, 1, figsize=(12, 3 * (n_heads + 1)))\n\n for i, ax in enumerate(axes[:-1]):\n ax.bar(range(attention.shape[1]), attention[i])\n ax.set_ylabel(f'Голова {i+1}')\n ax.set_title(f'Голова внимания {i+1}')\n\n # Объединённое внимание\n combined = attention.mean(axis=0)\n axes[-1].bar(range(len(combined)), combined)\n axes[-1].set_xlabel('Временной шаг')\n axes[-1].set_ylabel('Объединённое')\n axes[-1].set_title('Объединённое внимание (среднее)')\n\n plt.tight_layout()\n plt.savefig('attention_weights.png', dpi=150)\n plt.show()\n\n\ndef attention_heatmap(model, dataset, n_samples=50):\n \"\"\"\n Создание тепловой карты паттернов внимания по выборкам.\n \"\"\"\n model.eval()\n all_attention = []\n\n with torch.no_grad():\n for i in range(min(n_samples, len(dataset))):\n x, y = dataset[i]\n x = x.unsqueeze(0)\n _, attention = model(x, return_attention=True)\n all_attention.append(attention.squeeze().numpy())\n\n attention_matrix = np.stack(all_attention) # (n_samples, T)\n\n plt.figure(figsize=(14, 8))\n sns.heatmap( | |
| [2026-01-18T20:57:29.202Z] [INFO] \n attention_matrix,\n cmap='Blues',\n xticklabels=5,\n yticklabels=5\n )\n plt.xlabel('Временной шаг')\n plt.ylabel('Выборка')\n plt.title('Паттерны внимания по выборкам')\n plt.savefig('attention_heatmap.png', dpi=150)\n plt.show()\n```\n\n### 05: Торговая Стратегия\n\n```python\n# python/05_strategy.py\n\ndef backtest_tabl_strategy(\n model,\n test_data,\n df_prices,\n initial_capital: float = 100000,\n transaction_cost: float = 0.001,\n confidence_threshold: float = 0.6\n):\n \"\"\"\n Бэктестинг торговой стратегии TABL.\n\n Args:\n model: Обученная модель TABL\n test_data: Тестовый датасет\n df_prices: Ценовые данные, согласованные с test_data\n initial_capital: Начальный капитал\n transaction_cost: Стоимость транзакции\n confidence_threshold: Мин. вероятность для сделки\n \"\"\"\n model.eval()\n capital = initial_capital\n position = 0 # -1: шорт, 0: нет позиции, 1: лонг\n\n results = []\n\n with torch.no_grad():\n for i, (x, _) in enumerate(test_data):\n x = x.unsqueeze(0)\n logits = model(x)\n probs = F.softmax(logits, dim=1).squeeze()\n\n # Получение предсказания и уверенности\n pred = probs.argmax().item()\n confidence = probs.max().item()\n\n # Торговая логика\n if confidence >= confidence_threshold:\n if pred == 2 and position <= 0: # Сигнал вверх\n # Закрыть шорт, открыть лонг\n if position == -1:\n capital *= (1 - transaction_cost)\n position = 1\n capital *= (1 - transaction_cost)\n\n elif pred == 0 and position >= 0: # Сигнал вниз\n # Закрыть лонг, открыть шорт\n if position == 1:\n capital *= (1 - transaction_cost)\n position = -1\n capital *= (1 - transaction_cost)\n\n # Расчёт P&L\n if i > 0:\n price_return = df_prices['close'].iloc[i] / df_prices['close'].iloc[i-1] - 1\n capital *= (1 + position * price_return)\n\n results.append({\n 'capital': capital,\n 'position': position,\n 'prediction': pred,\n 'confidence': confidence\n })\n\n return pd.DataFrame(results)\n\n\ndef calculate_metrics(results_df, df_prices):\n \"\"\"Расчёт метрик стратегии.\"\"\"\n returns = results_df['capital'].pct_change().dropna()\n\n metrics = {\n 'total_return': (results_df['capital'].iloc[-1] / results_df['capital'].iloc[0] - 1) * 100,\n 'sharpe_ratio': returns.mean() / returns.std() * np.sqrt(252 * 24), # Часовые данные\n 'max_drawdown': ((results_df['capital'].cummax() - results_df['capital']) /\n results_df['capital'].cummax()).max() * 100,\n 'win_rate': (returns > 0).mean() * 100,\n 'n_trades': (results_df['position'].diff() != 0).sum()\n }\n\n return metrics\n```\n\n## Реализация на Rust\n\nСм. [rust/](rust/) для полной реализации на Rust с использованием данных Bybit.\n\n```\nrust/\n├── Cargo.toml\n├── README.md\n├── src/\n│ ├── lib.rs # Основные экспорты библиотеки\n│ ├── api/ # Клиент Bybit API\n│ │ ├── mod.rs\n│ │ ├── client.rs # HTTP клиент для Bybit\n│ │ └── types.rs # Типы ответов API\n│ ├── data/ # Обработка данных\n│ │ ├── mod.rs\n│ │ ├── loader.rs # Утилиты загрузки данных\n│ │ ├── features.rs # Инженерия признаков\n│ │ └── dataset.rs # Датасет для обучения\n│ ├── model/ # Архитектура TABL\n│ │ ├── mod.rs\n│ │ ├── bilinear.rs # Билинейный слой\n│ │ ├── attention.rs # Временное внимание\n│ │ └── tabl.rs # Полная модель TABL\n│ └── strategy/ # Торговая стратегия\n│ ├── mod.rs\n│ ├── signals.rs # Генерация сигналов\n│ └── backtest.rs # Движок бэктестинга\n└── examples/\n ├── fetch_data.rs # Загрузка данных Bybit\n ├── train.rs # Обучение модели\n └── backtest.rs # Запуск бэктеста\n```\n\n### Быстрый Старт (Rust)\n\n```bash\n# Перейти в директорию Rust проекта\ncd rust\n\n# Загрузить данные с Bybit\ncargo run --example fetch_data -- --symbols BTCUSDT,ETHUSDT\n\n# Обучить модель\ncargo run --example train -- --epochs 100 --batch-size 32\n\n# Запустить бэктест\ncargo run --example backtest -- --start 2024-01-01 --end 2024-12-31\n```\n\n## Реализация на Python\n\nСм. [python/](python/) для реализации на Python.\n\n```\npython/\n├── model.py # Реализация модели TABL\n├── data.py # Загрузка данных и признаки\n├── train.py # Скрипт обучения\n├── strategy.py # Торговая стратегия\n├── example_usage.py # Пример использования\n├── requirements.txt # Зависимости\n└── __init__.py\n```\n\n### Быстрый Старт (Python)\n\n```bash\n# Установка зависимостей\npip install -r requirements.txt\n\n# Обучение модели\npython train.py --symbols BTCUSDT --epochs 100\n\n# Запуск бэктеста\npython strategy.py --model checkpoints/best_model.pt\n```\n\n## Лучшие Практики\n\n### Когда Использовать TABL\n\n**Хорошие сценарии использования:**\n- Предсказание движения средней цены LOB\n- Высокочастотное прогнозирование направления\n- Краткосрочные прогнозы (секунды-минуты)\n- Требования к интерпретируемости\n\n**Не идеально для:**\n- Очень длинных последовательностей (>500 временных шагов)\n- Многогоризонтного прогнозирования\n- Распределения портфеля (используйте Stockformer)\n\n### Рекомендации по Гиперпараметрам\n\n| Параметр | Рекомендуется | Примечания |\n|----------|---------------|------------|\n| `seq_len` | 50-200 | Зависит от частоты данных |\n| `hidden_T` | 10-30 | Временное сжатие |\n| `hidden_D` | 32-128 | Сжатие признаков |\n| `attention_dim` | 32-128 | Соответствовать hidden_D |\n| `n_heads` | 2-8 | Больше для сложных паттернов |\n| `dropout` | 0.1-0.3 | Выше для малых данных |\n\n### Распространённые Ошибки\n\n1. **Дисбаланс классов**: Используйте взвешенную функцию потерь или пересэмплирование\n2. **Переобучение**: Применяйте dropout, раннюю остановку\n3. **Масштабирование признаков**: Нормализуйте входы к нулевому среднему и единичной дисперсии\n4. **Выбор порога**: Тщательно настраивайте пороги классификации\n\n## Ресурсы\n\n### Научные Работы\n\n- [Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis](https://arxiv.org/abs/1712.00975) — Оригинальная статья TABL\n- [Multi-head Temporal Attention-Augmented Bilinear Network](https://ieeexplore.ieee.org/document/9909957/) — Многоголовое расширение\n- [Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification](https://www.sciencedirect.com/science/article/pii/S0031320323003059) — Инкрементальное обучение\n\n### Реализации\n\n- [TABL Original (Python 2.7)](https://github.com/viebboy/TABL)\n- [TABL PyTorch](https://github.com/LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis)\n\n### Связанные Главы\n\n- [Глава 26: Temporal Fusion Transformers](../26_temporal_fusion_transformers) — Многогоризонтное прогнозирование\n- [Глава 43: Stockformer Multivariate](../43_stockformer_multivariate) — Кросс-активное внимание\n- [Глава 42: Dual Attention LOB](../42_dual_attention_lob) — Предсказание LOB\n\n---\n\n## Уровень Сложности\n\n**Средний до Продвинутого**\n\nПредварительные требования:\n- Основы нейронных сетей\n- Базовые понятия механизмов внимания\n- Концепции прогнозирования временных рядов\n- Библиотеки ML для PyTorch/Rust\n"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":392,"cache_read_input_tokens":80083,"cache_creation":{"ephemeral_5m_input_tokens":392,"ephemeral_1h_input_tokens":0},"output_tokens":25,"service_tier":"standard"},"context_management":null},"parent_tool_use_id":null,"session_id":"77018388-5fda-4d3e-acd4-e8399840434b","uuid":"68498f36-5715-4e96-be2a-587a4d144ba4"} | |
| [2026-01-18T20:57:29.229Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015qKptEHqtVvHYv9AAYk9xc","type":"tool_result","content":"File created successfully at: /tmp/gh-issue-solver-1768769379407/46_temporal_attention_networks/README.ru.md"}]},"parent_tool_use_id":null,"session_id":"77018388-5fda-4d3e-acd4-e8399840434b","uuid":"010b4dbf-60f3-4f85-b36a-b4b1a4ccbaab","tool_use_result":{"type":"create","filePath":"/tmp/gh-issue-solver-1768769379407/46_temporal_attention_networks/README.ru.md","content":"# Глава 46: Сети Временного Внимания для Финансовых Временных Рядов\n\nВ этой главе рассматриваются **Сети Временного Внимания** (Temporal Attention Networks) — специализированные механизмы внимания, предназначенные для захвата временных зависимостей в финансовых данных. Мы фокусируемся на архитектуре **TABL (Temporal Attention-Augmented Bilinear Layer)** и её вариантах, которые доказали высокую эффективность для предсказания движений рынка на основе данных книги заявок (LOB) и других временных рядов.\n\n<p align=\"center\">\n<img src=\"https://i.imgur.com/Zy8R4qF.png\" width=\"70%\">\n</p>\n\n## Содержание\n\n1. [Введение во Временное Внимание](#введение-во-временное-внимание)\n * [Зачем Временное Внимание?](#зачем-временное-внимание)\n * [Ключевые Преимущества](#ключевые-преимущества)\n * [Сравнение с Другими Моделями](#сравнение-с-другими-моделями)\n2. [Архитектура TABL](#архитектура-tabl)\n * [Билинейная Проекция](#билинейная-проекция)\n * [Механизм Временного Внимания](#механизм-временного-внимания)\n * [BL (Билинейный Слой)](#bl-билинейный-слой)\n * [TABL (Временной Внимательный Билинейный Слой)](#tabl-временной-внимательный-билинейный-слой)\n3. [Многоголовое Временное Внимание](#многоголовое-временное-внимание)\n * [Многоголовый TABL](#многоголовый-tabl)\n * [Параллельные Головы Внимания](#параллельные-головы-внимания)\n4. [Обработка Данных](#обработка-данных)\n * [Признаки Книги Заявок](#признаки-книги-заявок)\n * [Признаки OHLCV](#признаки-ohlcv)\n * [Инженерия Признаков](#инженерия-признаков)\n5. [Практические Примеры](#практические-примеры)\n * [01: Подготовка Данных](#01-подготовка-данных)\n * [02: Архитектура TABL](#02-архитектура-tabl)\n * [03: Обучение Модели](#03-обучение-модели)\n * [04: Визуализация Внимания](#04-визуализация-внимания)\n * [05: Торговая Стратегия](#05-торговая-стратегия)\n6. [Реализация на Rust](#реализация-на-rust)\n7. [Реализация на Python](#реализация-на-python)\n8. [Лучшие Практики](#лучшие-практики)\n9. [Ресурсы](#ресурсы)\n\n## Введение во Временное Внимание\n\nСети Временного Внимания созданы для решения фундаментальной задачи в финансовом прогнозировании: **какие прошлые события наиболее важны для предсказания будущего?**\n\nВ отличие от стандартных рекуррентных моделей, которые обрабатывают все временные шаги одинаково, механизмы временного внимания учатся **фокусироваться на наиболее информативных моментах** во входной последовательности.\n\n### Зачем Временное Внимание?\n\nТрадиционные модели обрабатывают все временные шаги одинаково:\n\n```\nВремя: t-5 t-4 t-3 t-2 t-1 t\nВес: 1 1 1 1 1 1\n (Все события одинаково важны)\n```\n\nВременное Внимание обучает адаптивные веса:\n\n```\nВремя: t-5 t-4 t-3 t-2 t-1 t\nВес: 0.05 0.10 0.40 0.30 0.10 0.05\n (Внимание фокусируется на t-3 и t-2)\n```\n\n**Ключевая идея**: На финансовых рынках определённые моменты имеют непропорционально большое значение — крупные сделки, резкие всплески волатильности или специфические паттерны часто предшествуют движениям цены. Временное внимание автоматически учится выявлять эти критические моменты.\n\n### Ключевые Преимущества\n\n1. **Автоматический Отбор Признаков во Времени**\n - Обучается определять релевантные временные шаги\n - Не требует ручной разработки признаков \"важных моментов\"\n - Адаптируется к различным рыночным условиям\n\n2. **Интерпретируемость**\n - Веса внимания показывают, какие события повлияли на предсказания\n - Полезно для понимания решений модели\n - Позволяет проводить апостериорный анализ торговых сигналов\n\n3. **Вычислительная Эффективность**\n - TABL имеет сложность O(T·D) против O(T²·D) для self-attention\n - Намного быстрее LSTM для длинных последовательностей\n - Меньшие требования к памяти\n\n4. **Высокая Производительность**\n - Превосходит LSTM и CNN в задачах предсказания LOB\n - Достигает state-of-the-art на бенчмарке FI-2010\n - Эффективна всего с 1-2 слоями\n\n### Сравнение с Другими Моделями\n\n| Характеристика | LSTM | CNN | Transformer | TABL |\n|----------------|------|-----|-------------|------|\n| Временное внимание | ✗ | ✗ | ✓ (self) | ✓ (обучаемое) |\n| Сложность | O(T·D²) | O(T·K·D) | O(T²·D) | O(T·D) |\n| Интерпретируемость | Низкая | Низкая | Средняя | Высокая |\n| Предсказание LOB | Хорошо | Хорошо | Хорошо | Лучше всех |\n| Эффективность памяти | ✗ | ✓ | ✗ | ✓ |\n| Мало слоёв нужно | ✗ | ✗ | ✗ | ✓ |\n\n## Архитектура TABL\n\nАрхитектура TABL объединяет **билинейные проекции** с **временным вниманием** для создания эффективной и интерпретируемой модели.\n\n```\n┌──────────────────────────────────────────────────────────────────────┐\n│ СЕТЬ ВРЕМЕННОГО ВНИМАНИЯ │\n├──────────────────────────────────────────────────────────────────────┤\n│ │\n│ Вход: X ∈ ℝ^(T×D) │\n│ (T временных шагов, D признаков) │\n│ │\n│ ┌──────────────────────────────────────────────────────────┐ │\n│ │ Билинейная Проекция (BL) │ │\n│ │ │ │\n│ │ H = σ(W₁ · X · W₂ + b) │ │\n│ │ │ │\n│ │ W₁ ∈ ℝ^(T'×T) - Временная проекция │ │\n│ │ W₂ ∈ ℝ^(D×D') - Проекция признаков │ │\n│ │ H ∈ ℝ^(T'×D') - Сжатое представление │ │\n│ │ │ │\n│ └──────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ┌──────────────────────────────────────────────────────────┐ │\n│ │ Временное Внимание (TA) │ │\n│ │ │ │\n│ │ α = softmax(w · tanh(U · X^T)) │ │\n│ │ c = X^T · α │ │\n│ │ │ │\n│ │ α ∈ ℝ^T - Веса внимания │ │\n│ │ c ∈ ℝ^D - Контекстный вектор │ │\n│ │ │ │\n│ └──────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ┌──────────────────────────────────────────────────────────┐ │\n│ │ Выходной Слой │ │\n│ │ │ │\n│ │ y = softmax(W_out · flatten(H, c) + b_out) │ │\n│ │ │ │\n│ │ 3 класса: Вверх / Без изменений / Вниз │ │\n│ │ │ │\n│ └──────────────────────────────────────────────────────────┘ │\n│ │\n└──────────────────────────────────────────────────────────────────────┘\n```\n\n### Билинейная Проекция\n\nБилинейный слой выполняет два одновременных линейных преобразования:\n\n```python\nclass BilinearLayer(nn.Module):\n \"\"\"\n Билинейная проекция: H = σ(W₁ · X · W₂ + b)\n\n Преобразует (T, D) → (T', D') путём проекции\n как временного, так и признакового измерений одновременно.\n \"\"\"\n\n def __init__(self, T_in, T_out, D_in, D_out, dropout=0.1):\n super().__init__()\n # Временная проекция: (T_out, T_in)\n self.W1 = nn.Parameter(torch.randn(T_out, T_in) * 0.01)\n # Проекция признаков: (D_in, D_out)\n self.W2 = nn.Parameter(torch.randn(D_in, D_out) * 0.01)\n # Смещение: (T_out, D_out)\n self.bias = nn.Parameter(torch.zeros(T_out, D_out))\n self.dropout = nn.Dropout(dropout)\n self.activation = nn.ReLU()\n\n def forward(self, x):\n # x: (batch, T_in, D_in)\n # W1·X: (batch, T_out, D_in)\n out = torch.matmul(self.W1, x)\n # W1·X·W2: (batch, T_out, D_out)\n out = torch.matmul(out, self.W2)\n out = out + self.bias\n out = self.activation(out)\n return self.dropout(out)\n```\n\n**Почему Билинейный?**\n- Захватывает взаимодействия между временем и признаками\n- Более выразителен, чем простые линейные слои\n- Эффективно снижает размерность по обеим осям\n\n### Механизм Временного Внимания\n\nВременное внимание вычисляет взвешенную сумму по временным шагам:\n\n```python\nclass TemporalAttention(nn.Module):\n \"\"\"\n Временное внимание: α = softmax(w · tanh(U · X^T))\n\n Обучается фокусироваться на важных временных шагах.\n \"\"\"\n\n def __init__(self, D, attention_dim=64):\n super().__init__()\n # Проекция признаков в пространство внимания\n self.U = nn.Linear(D, attention_dim, bias=False)\n # Вектор запроса внимания\n self.w = nn.Linear(attention_dim, 1, bias=False)\n\n def forward(self, x):\n # x: (batch, T, D)\n # Вычисление оценок внимания\n h = torch.tanh(self.U(x)) # (batch, T, attention_dim)\n scores = self.w(h).squeeze(-1) # (batch, T)\n\n # Softmax по временному измерению\n alpha = F.softmax(scores, dim=-1) # (batch, T)\n\n # Взвешенная сумма: контекстный вектор\n context = torch.bmm(\n alpha.unsqueeze(1), # (batch, 1, T)\n x # (batch, T, D)\n ).squeeze(1) # (batch, D)\n\n return context, alpha\n```\n\n**Интерпретация:**\n- `alpha[t]` указывает на важность временного шага t\n- Высокое внимание на конкретных событиях показывает фокус модели\n- Контекстный вектор `c` обобщает последовательность\n\n### BL (Билинейный Слой)\n\nBL слой — это версия без внимания:\n\n```python\nclass BL(nn.Module):\n \"\"\"Билинейный слой без внимания\"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.bilinear = BilinearLayer(\n config.seq_len, config.hidden_T,\n config.input_dim, config.hidden_D\n )\n\n def forward(self, x):\n h = self.bilinear(x)\n return h.flatten(1) # (batch, hidden_T * hidden_D)\n```\n\n### TABL (Временной Внимательный Билинейный Слой)\n\nПолный TABL объединяет оба компонента:\n\n```python\nclass TABL(nn.Module):\n \"\"\"\n Temporal Attention-Augmented Bilinear Layer\n\n Объединяет билинейную проекцию с временным вниманием.\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.bilinear = BilinearLayer(\n config.seq_len, config.hidden_T,\n config.input_dim, config.hidden_D\n )\n self.attention = TemporalAttention(\n config.input_dim,\n config.attention_dim\n )\n\n def forward(self, x, return_attention=False):\n # Билинейная проекция\n h = self.bilinear(x) # (batch, hidden_T, hidden_D)\n h_flat = h.flatten(1) # (batch, hidden_T * hidden_D)\n\n # Временное внимание\n context, alpha = self.attention(x) # (batch, D), (batch, T)\n\n # Конкатенация\n out = torch.cat([h_flat, context], dim=-1)\n\n if return_attention:\n return out, alpha\n return out\n```\n\n## Многоголовое Временное Внимание\n\nРасширение TABL несколькими головами внимания позволяет модели фокусироваться на разных аспектах одновременно:\n\n### Многоголовый TABL\n\n```python\nclass MultiHeadTABL(nn.Module):\n \"\"\"\n Multi-Head Temporal Attention Bilinear Layer\n\n Использует несколько голов внимания для захвата\n различных временных паттернов в данных.\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n self.n_heads = config.n_heads\n\n # Общая билинейная проекция\n self.bilinear = BilinearLayer(\n config.seq_len, config.hidden_T,\n config.input_dim, config.hidden_D\n )\n\n # Несколько голов внимания\n self.attention_heads = nn.ModuleList([\n TemporalAttention(config.input_dim, config.attention_dim)\n for _ in range(config.n_heads)\n ])\n\n # Объединение голов\n self.head_combine = nn.Linear(\n config.n_heads * config.input_dim,\n config.input_dim\n )\n\n def forward(self, x, return_attention=False):\n # Билинейная проекция\n h = self.bilinear(x)\n h_flat = h.flatten(1)\n\n # Многоголовое внимание\n contexts = []\n alphas = []\n for head in self.attention_heads:\n ctx, alpha = head(x)\n contexts.append(ctx)\n alphas.append(alpha)\n\n # Объединение голов\n multi_context = torch.cat(contexts, d | |
| [2026-01-18T20:57:29.241Z] [INFO] im=-1)\n combined = self.head_combine(multi_context)\n\n # Финальный выход\n out = torch.cat([h_flat, combined], dim=-1)\n\n if return_attention:\n return out, torch.stack(alphas, dim=1) # (batch, n_heads, T)\n return out\n```\n\n### Параллельные Головы Внимания\n\nКаждая голова может фокусироваться на разных паттернах:\n- **Голова 1**: Краткосрочные движения цены\n- **Голова 2**: Всплески объёма\n- **Голова 3**: Дисбалансы книги заявок\n- **Голова 4**: Трендовые паттерны\n\n```\nВизуализация Многоголового Внимания:\n\nВремя: t-10 t-9 t-8 t-7 t-6 t-5 t-4 t-3 t-2 t-1\nГолова 1: ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ██▓▓ ████ ██▓▓ ░░░░ ░░░░\n (Фокус на среднесрочных событиях)\n\nГолова 2: ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ████ ████\n (Фокус на недавних событиях)\n\nГолова 3: ████ ░░░░ ░░░░ ████ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░ ░░░░\n (Фокус на периодических паттернах)\n\nОбъединённо: ▓▓▓▓ ░░░░ ░░░░ ▓▓▓▓ ░░░░ ▓▓▓▓ ████ ▓▓▓▓ ████ ████\n (Агрегированное внимание)\n```\n\n## Обработка Данных\n\n### Признаки Книги Заявок\n\nДанные LOB предоставляют богатую информацию о микроструктуре рынка:\n\n```python\ndef extract_lob_features(lob_snapshot):\n \"\"\"\n Извлечение признаков из снимка книги заявок.\n\n Возвращает признаки для входа TABL.\n \"\"\"\n features = {}\n\n # Ценовые уровни (обычно 10 уровней с каждой стороны)\n features['ask_prices'] = lob_snapshot['asks'][:, 0] # (10,)\n features['bid_prices'] = lob_snapshot['bids'][:, 0] # (10,)\n features['ask_volumes'] = lob_snapshot['asks'][:, 1] # (10,)\n features['bid_volumes'] = lob_snapshot['bids'][:, 1] # (10,)\n\n # Производные признаки\n mid_price = (features['ask_prices'][0] + features['bid_prices'][0]) / 2\n spread = features['ask_prices'][0] - features['bid_prices'][0]\n\n # Дисбаланс ордеров\n total_ask_vol = features['ask_volumes'].sum()\n total_bid_vol = features['bid_volumes'].sum()\n imbalance = (total_bid_vol - total_ask_vol) / (total_bid_vol + total_ask_vol)\n\n features['mid_price'] = mid_price\n features['spread'] = spread\n features['imbalance'] = imbalance\n\n return features\n```\n\n### Признаки OHLCV\n\nДля криптовалютных/биржевых данных с использованием OHLCV:\n\n```python\ndef extract_ohlcv_features(df):\n \"\"\"\n Извлечение признаков из данных OHLCV.\n \"\"\"\n features = pd.DataFrame()\n\n # Ценовые признаки\n features['log_return'] = np.log(df['close'] / df['close'].shift(1))\n features['high_low_range'] = (df['high'] - df['low']) / df['close']\n features['close_open_diff'] = (df['close'] - df['open']) / df['open']\n\n # Объёмные признаки\n features['volume_ma_ratio'] = df['volume'] / df['volume'].rolling(20).mean()\n features['volume_std'] = df['volume'].rolling(20).std() / df['volume'].rolling(20).mean()\n\n # Технические индикаторы\n features['rsi'] = compute_rsi(df['close'], 14)\n features['macd'] = compute_macd(df['close'])\n\n # Волатильность\n features['volatility'] = features['log_return'].rolling(20).std()\n\n return features.dropna()\n```\n\n### Инженерия Признаков\n\nРекомендуемые признаки для моделей с временным вниманием:\n\n| Признак | Описание | Важность |\n|---------|----------|----------|\n| `log_return` | Логарифмическое изменение цены | Высокая |\n| `spread` | Спред bid-ask | Высокая |\n| `imbalance` | Дисбаланс книги заявок | Высокая |\n| `volume_ratio` | Объём к скользящему среднему | Средняя |\n| `volatility` | Скользящая волатильность | Средняя |\n| `price_levels` | Ценовые уровни LOB | Средняя |\n| `volume_levels` | Объёмные уровни LOB | Средняя |\n\n## Практические Примеры\n\n### 01: Подготовка Данных\n\n```python\n# python/01_data_preparation.py\n\nimport numpy as np\nimport pandas as pd\nfrom typing import Tuple, List\n\ndef prepare_tabl_data(\n df: pd.DataFrame,\n lookback: int = 100,\n horizon: int = 10,\n threshold: float = 0.0002\n) -> Tuple[np.ndarray, np.ndarray]:\n \"\"\"\n Подготовка данных для обучения модели TABL.\n\n Args:\n df: DataFrame с данными OHLCV\n lookback: Количество временных шагов назад\n horizon: Горизонт прогнозирования\n threshold: Порог для классификации направления\n\n Returns:\n X: Признаки (n_samples, lookback, n_features)\n y: Метки (n_samples,) - 0: вниз, 1: без изменений, 2: вверх\n \"\"\"\n # Извлечение признаков\n features = extract_features(df)\n\n # Нормализация признаков\n features_norm = (features - features.mean()) / features.std()\n\n # Создание последовательностей\n X, y = [], []\n\n for i in range(lookback, len(features_norm) - horizon):\n # Входная последовательность\n x_seq = features_norm.iloc[i-lookback:i].values\n\n # Цель: будущая доходность\n future_return = np.log(\n df['close'].iloc[i + horizon] / df['close'].iloc[i]\n )\n\n # Классификация направления\n if future_return > threshold:\n label = 2 # Вверх\n elif future_return < -threshold:\n label = 0 # Вниз\n else:\n label = 1 # Без изменений\n\n X.append(x_seq)\n y.append(label)\n\n return np.array(X), np.array(y)\n\n\ndef extract_features(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Извлечение признаков из данных OHLCV.\"\"\"\n features = pd.DataFrame(index=df.index)\n\n # Доходности\n features['return'] = np.log(df['close'] / df['close'].shift(1))\n\n # Позиция цены\n features['hl_position'] = (df['close'] - df['low']) / (df['high'] - df['low'] + 1e-8)\n\n # Объём\n vol_ma = df['volume'].rolling(20).mean()\n features['vol_ratio'] = df['volume'] / vol_ma\n\n # Волатильность\n features['volatility'] = features['return'].rolling(20).std()\n\n # Моментум\n features['momentum_5'] = df['close'] / df['close'].shift(5) - 1\n features['momentum_10'] = df['close'] / df['close'].shift(10) - 1\n\n return features.dropna()\n```\n\n### 02: Архитектура TABL\n\nСм. [python/model.py](python/model.py) для полной реализации.\n\n### 03: Обучение Модели\n\n```python\n# python/03_train_model.py\n\nimport torch\nfrom model import TABLModel, TABLConfig\n\n# Конфигурация\nconfig = TABLConfig(\n seq_len=100,\n input_dim=6,\n hidden_T=20,\n hidden_D=32,\n attention_dim=64,\n n_heads=4,\n n_classes=3,\n dropout=0.2\n)\n\n# Инициализация модели\nmodel = TABLModel(config)\nprint(f\"Параметры: {sum(p.numel() for p in model.parameters()):,}\")\n\n# Функция потерь и оптимизатор\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.001)\nscheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n optimizer, mode='max', factor=0.5, patience=5\n)\n\n# Цикл обучения\nbest_acc = 0\nfor epoch in range(100):\n model.train()\n train_loss = 0\n\n for batch_x, batch_y in train_loader:\n optimizer.zero_grad()\n\n # Прямой проход\n logits = model(batch_x)\n loss = criterion(logits, batch_y)\n\n # Обратный проход\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n optimizer.step()\n\n train_loss += loss.item()\n\n # Валидация\n model.eval()\n correct = 0\n total = 0\n\n with torch.no_grad():\n for batch_x, batch_y in val_loader:\n logits = model(batch_x)\n preds = logits.argmax(dim=1)\n correct += (preds == batch_y).sum().item()\n total += batch_y.size(0)\n\n val_acc = correct / total\n scheduler.step(val_acc)\n\n if val_acc > best_acc:\n best_acc = val_acc\n torch.save(model.state_dict(), 'best_model.pt')\n\n print(f\"Эпоха {epoch+1}: Loss={train_loss/len(train_loader):.4f}, Val Acc={val_acc:.4f}\")\n```\n\n### 04: Визуализация Внимания\n\n```python\n# python/04_attention_visualization.py\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\ndef visualize_attention(model, x, timestamps=None):\n \"\"\"\n Визуализация весов временного внимания.\n\n Args:\n model: Обученная модель TABL\n x: Входная последовательность (1, T, D)\n timestamps: Опциональные временные метки для оси X\n \"\"\"\n model.eval()\n with torch.no_grad():\n logits, attention = model(x, return_attention=True)\n\n # attention: (1, n_heads, T) или (1, T)\n attention = attention.squeeze(0).numpy()\n\n if attention.ndim == 1:\n # Одна голова\n fig, ax = plt.subplots(figsize=(12, 3))\n ax.bar(range(len(attention)), attention)\n ax.set_xlabel('Временной шаг')\n ax.set_ylabel('Вес внимания')\n ax.set_title('Веса временного внимания')\n else:\n # Много голов\n n_heads = attention.shape[0]\n fig, axes = plt.subplots(n_heads + 1, 1, figsize=(12, 3 * (n_heads + 1)))\n\n for i, ax in enumerate(axes[:-1]):\n ax.bar(range(attention.shape[1]), attention[i])\n ax.set_ylabel(f'Голова {i+1}')\n ax.set_title(f'Голова внимания {i+1}')\n\n # Объединённое внимание\n combined = attention.mean(axis=0)\n axes[-1].bar(range(len(combined)), combined)\n axes[-1].set_xlabel('Временной шаг')\n axes[-1].set_ylabel('Объединённое')\n axes[-1].set_title('Объединённое внимание (среднее)')\n\n plt.tight_layout()\n plt.savefig('attention_weights.png', dpi=150)\n plt.show()\n\n\ndef attention_heatmap(model, dataset, n_samples=50):\n \"\"\"\n Создание тепловой карты паттернов внимания по выборкам.\n \"\"\"\n model.eval()\n all_attention = []\n\n with torch.no_grad():\n for i in range(min(n_samples, len(dataset))):\n x, y = dataset[i]\n x = x.unsqueeze(0)\n _, attention = model(x, return_attention=True)\n all_attention.append(attention.squeeze().numpy())\n\n attention_matrix = np.stack(all_attention) # (n_samples, T)\n\n plt.figure(figsize=(14, 8))\n sns.heatmap(\n attention_matrix,\n cmap='Blues',\n xticklabels=5,\n yticklabels=5\n )\n plt.xlabel('Временной шаг')\n plt.ylabel('Выборка')\n plt.title('Паттерны внимания по выборкам')\n plt.savefig('attention_heatmap.png', dpi=150)\n plt.show()\n```\n\n### 05: Торговая Стратегия\n\n```python\n# python/05_strategy.py\n\ndef backtest_tabl_strategy(\n model,\n test_data,\n df_prices,\n initial_capital: float = 100000,\n transaction_cost: float = 0.001,\n confidence_threshold: float = 0.6\n):\n \"\"\"\n Бэктестинг торговой стратегии TABL.\n\n Args:\n model: Обученная модель TABL\n test_data: Тестовый датасет\n df_prices: Ценовые данные, согласованные с test_data\n initial_capital: Начальный капитал\n transaction_cost: Стоимость транзакции\n confidence_threshold: Мин. вероятность для сделки\n \"\"\"\n model.eval()\n capital = initial_capital\n position = 0 # -1: шорт, 0: нет позиции, 1: лонг\n\n results = []\n\n with torch.no_grad():\n for i, (x, _) in enumerate(test_data):\n x = x.unsqueeze(0)\n logits = model(x)\n probs = F.softmax(logits, dim=1).squeeze()\n\n # Получение предсказания и уверенности\n pred = probs.argmax().item()\n confidence = probs.max().item()\n\n # Торговая логика\n if confidence >= confidence_threshold:\n if pred == 2 and position <= 0: # Сигнал вверх\n # Закрыть шорт, открыть лонг\n if position == -1:\n capital *= (1 - transaction_cost)\n position = 1\n capital *= (1 - transaction_cost)\n\n elif pred == 0 and position >= 0: # Сигнал вниз\n # Закрыть лонг, открыть шорт\n if position == 1:\n capital *= (1 - transaction_cost)\n position = -1\n capital *= (1 - transaction_cost)\n\n # Расчёт P&L\n if i > 0:\n price_return = df_prices['close'].iloc[i] / df_prices['close'].iloc[i-1] - 1\n capital *= (1 + position * price_return)\n\n results.append({\n 'capital': capital,\n 'position': position,\n 'prediction': pred,\n 'confidence': confidence\n })\n\n return pd.DataFrame(results)\n\n\ndef calculate_metrics(results_df, df_prices):\n \"\"\"Расчёт метрик стратегии.\"\"\"\n returns = results_df['capital'].pct_change().dropna()\n\n metrics = {\n 'total_return': (results_df['capital'].iloc[-1] / results_df['capital'].iloc[0] - 1) * 100,\n 'sharpe_ratio': returns.mean() / returns.std() * np.sqrt(252 * 24), # Часовые данные\n 'max_drawdown': ((results_df['capital'].cummax() - results_df['capital']) /\n results_df['capital'].cummax()).max() * 100,\n 'win_rate': (returns > 0).mean() * 100,\n 'n_trades': (results_df['position'].diff() != 0).sum()\n }\n\n return metrics\n```\n\n## Реализация на Rust\n\nСм. [rust/](rust/) для полной реализации на Rust с использованием данных Bybit.\n\n```\nrust/\n├── Cargo.toml\n├── README.md\n├── src/\n│ ├── lib.rs # Основные экспорты библиотеки\n│ ├── api/ # Клиент Bybit API\n│ │ ├── mod.rs\n│ │ ├── client.rs # HTTP клиент для Bybit\n│ │ └── types.rs # Типы ответов API\n│ ├── data/ # Обработка данных\n│ │ ├── mod.rs\n│ │ ├── loader.rs # Утилиты загрузки данных\n│ │ ├── features.rs # Инженерия признаков\n│ │ └── dataset.rs # Датасет для обучения\n│ ├── model/ # Архитектура TABL\n│ │ ├── mod.rs\n│ │ ├── bilinear.rs # Билинейный слой\n│ │ ├── attention.rs # Временное внимание\n│ │ └── tabl.rs # Полная модель TABL\n│ └── strategy/ # Торговая стратегия\n│ ├── mod.rs\n│ ├── signals.rs # Генерация сигналов\n│ └── backtest.rs # Движок бэктестинга\n└── examples/\n ├── fetch_data.rs # Загрузка данных Bybit\n ├── train.rs # Обучение модели\n └── backtest.rs # Запуск бэктеста\n```\n\n### Быстрый Старт (Rust)\n\n```bash\n# Перейти в директорию Rust проекта\ncd rust\n\n# Загрузить данные с Bybit\ncargo run --example fetch_data -- --symbols BTCUSDT,ETHUSDT\n\n# Обучить модель\ncargo run --example train -- --epochs 100 --batch-size 32\n\n# Запустить бэктест\ncargo run --example backtest -- --start 2024-01-01 --end 2024-12-31\n```\n\n## Реализация на Python\n\nСм. [python/](python/) для реализации на Python.\n\n```\npython/\n├── model.py # Реализация модели TABL\n├── data.py # Загрузка данных и признаки\n├── train.py # Скрипт обучения\n├── strategy.py # Торговая стратегия\n├── example_usage.py # Пример использования\n├── requirements.txt # Зависимости\n└── __init__.py\n```\n\n### Быстрый Старт (Python)\n\n```bash\n# Установка зависимостей\npip install -r requirements.txt\n\n# Обучение модели\npython train.py --symbols BTCUSDT --epochs 100\n\n# Запуск бэктеста\npython strategy.py --model checkpoints/best_model.pt\n```\n\n## Лучшие Практики\n\n### Когда Использовать TABL\n\n**Хорошие сценарии использования:**\n- Предсказание движения средней цены LOB\n- Высокочастотное прогнозирование направления\n- Краткосрочные прогнозы (секунды-минуты)\n- Требования к интерпретируемости\n\n**Не идеально для:**\n- Очень длинных последовательностей (>500 временных шагов)\n- Многогоризонтного прогнозирования\n- Распределения портфеля (используйте Stockformer)\n\n### Рекомендации по Гиперпараметрам\n\n| Параметр | Рекомендуется | Примечания |\n|----------|---------------|------------|\n| `seq_len` | 50-200 | Зависит от частоты данных |\n| `hidden_T` | 10-30 | Временное сжатие |\n| `hidden_D` | 32-128 | Сжатие признаков |\n| `attention_dim` | 32-128 | Соответствовать hidden_D |\n| `n_heads` | 2-8 | Больше для сложных паттернов |\n| `dropout` | 0.1-0.3 | Выше для малых данных |\n\n### Распространённые Ошибки\n\n1. **Дисбаланс классов**: Используйте взвешенную функцию потерь или пересэмплирование\n2. **Переобучение**: Применяйте dropout, раннюю остановку\n3. **Масштабирование признаков**: Нормализуйте входы к нулевому среднему и единичной дисперсии\n4. **Выбор порога**: Тщательно настраивайте пороги классификации\n\n## Ресурсы\n\n### Научные Работы\n\n- [Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis](https://arxiv.org/abs/1712.00975) — Оригинальная статья TABL\n- [Multi-head Temporal Attention-Augmented Bilinear Network](https://ieeexplore.ieee.org/document/9909957/) — Многоголовое расширение\n- [Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification](https://www.sciencedirect.com/science/article/pii/S0031320323003059) — Инкрементальное обучение\n\n### Реализации\n\n- [TABL Original (Python 2.7)](https://github.com/viebboy/TABL)\n- [TABL PyTorch](https://github.com/LeonardoBerti00/TABL-Temporal-Attention-Augmented-Bilinear-Network-for-Financial-Time-Series-Data-Analysis)\n\n### Связанные Главы\n\n- [Глава 26: Temporal Fusion Transformers](../26_temporal_fusion_transformers) — Многогоризонтное прогнозирование\n- [Глава 43: Stockformer Multivariate](../43_stockformer_multivariate) — Кросс-активное внимание\n- [Глава 42: Dual Attention LOB](../42_dual_attention_lob) — Предсказание LOB\n\n---\n\n## Уровень Сложности\n\n**Средний до Продвинутого**\n\nПредварительные требования:\n- Основы нейронных сетей\n- Базовые понятия механизмов внимания\n- Концепции прогнозирования временных рядов\n- Библиотеки ML для PyTorch/Rust\n","structuredPatch":[],"originalFile":null}} | |
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| "content": "# Temporal Attention Networks: The Spotlight on Your Data's Most Important Moments\n\n## What is Temporal Attention?\n\nImagine you're watching a soccer game and trying to predict who will score next. Would you watch every single second equally? Of course not! You'd pay **extra attention** to moments like:\n- When a player breaks through the defense\n- When someone is about to take a penalty kick\n- When there's a corner kick\n\n**Temporal Attention Networks** do exactly this for financial data! Instead of treating every moment equally, they learn to **shine a spotlight** on the most important moments that help predict what happens next.\n\n---\n\n## The Simple Analogy: Watching a Movie for the Plot Twist\n\n### Without Attention (Old Way):\n\n```\nWatching a mystery movie:\n\nMinute 1: 🎬 Opening credits → Remember equally\nMinute 15: 🚗 Someone drives car → Remember equally\nMinute 30: 🔪 THE CLUE IS FOUND! → Remember equally\nMinute 45: ☕ Character drinks tea → Remember equally\nMinute 60: 🎭 THE REVEAL! → Remember equally\n\nAt the end: \"Wait, what was important again?\"\n```\n\n### With Temporal Attention (Smart Way):\n\n```\nWatching a mystery movie:\n\nMinute 1: 🎬 Opening credits → Low attention (0.02)\nMinute 15: 🚗 Someone drives car → Low attention (0.05)\nMinute 30: 🔪 THE CLUE IS FOUND! → HIGH attention (0.35)\nMinute 45: ☕ Character drinks tea → Low attention (0.08)\nMinute 60: 🎭 THE REVEAL! → HIGH attention (0.50)\n\nAt the end: \"The clue at minute 30 explained the reveal!\"\n```\n\n**Temporal attention is like having a smart highlighter that automatically marks the most important moments!**\n\n---\n\n## Why Does This Matter for Trading?\n\n### The Stock Market is Full of \"Important Moments\"\n\nThink of stock prices like a heartbeat monitor:\n\n```\nPrice over time:\n\n ^\n | 💥 Big news!\n | /\\ /\\\n $ | / \\ / \\_____/\\\n | / \\___/ \\\n | / \\_\n |/\n +-------------------------->\n Time\n\nMost of the line is boring (flat parts)\nBut a few moments are CRITICAL (peaks and valleys)\n```\n\n**Temporal Attention learns:**\n- \"That spike at 2:30 PM? Very important!\"\n- \"The flat period from 10-11 AM? Not so important...\"\n- \"The small dip before the big jump? Super predictive!\"\n\n---\n\n## How Does It Work? (The Simple Version)\n\n### Step 1: Look at All the Data\n\n```\nTABL receives data about the last 100 time steps:\n\nTime: 1 2 3 4 5 6 7 8 9 10 ... 100\nData: [price, volume, spread, imbalance, etc.]\n```\n\n### Step 2: Ask \"How Important is Each Moment?\"\n\nThe attention mechanism gives each moment a score:\n\n```\nATTENTION SCORING:\n\nTime: 1 2 3 4 5 6 7 8 9 10\nScore: 0.01 0.02 0.01 0.05 0.30 0.25 0.10 0.08 0.10 0.08\n ^^^^ ^^^^^^^^\n Low HIGH!\n (boring) (important)\n```\n\n### Step 3: Focus on What Matters\n\n```\nCreating the Summary:\n\nInstead of:\n\"Average of all 100 time steps\"\n\nIt's:\n\"Weighted average, focusing on time steps 5 and 6!\"\n\nTime 5 and 6 combined get 55% of the attention,\nwhile time 1-3 combined only get 4%!\n```\n\n### Step 4: Make the Prediction\n\n```\nBased on the attention-weighted summary:\n\n\"Price will likely go UP because:\n - At time 5, there was a big volume spike\n - At time 6, buyers strongly outnumbered sellers\n - These are the patterns I learned predict 'UP' movements!\"\n```\n\n---\n\n## Real-Life Examples Kids Can Understand\n\n### Example 1: Predicting if You'll Be Late for School\n\n```\nWITHOUT ATTENTION:\nYou track every minute of your morning equally:\n- 6:30 AM: Alarm rings → Important? Maybe\n- 6:45 AM: Brushing teeth → Important? Maybe\n- 7:00 AM: Eating breakfast → Important? Maybe\n- 7:15 AM: Can't find backpack! → Important? Maybe\n- 7:25 AM: Mom is ready → Important? Maybe\n\nEvery moment is \"maybe important\" = BAD prediction!\n\nWITH ATTENTION:\n- 6:30 AM: Alarm rings → Low (happens every day)\n- 6:45 AM: Brushing teeth → Low (normal)\n- 7:00 AM: Eating breakfast → Low (normal)\n- 7:15 AM: Can't find backpack! → HIGH!!! (this causes delays!)\n- 7:25 AM: Mom is ready → Medium (depends on traffic)\n\nThe model learns: \"When 'can't find something' happens,\nLATE probability jumps 80%!\"\n```\n\n### Example 2: Predicting if Your Team Will Win the Video Game\n\n```\nGAME TIMELINE:\n- Minute 0: Game starts → Normal\n- Minute 5: You get first kill → Nice!\n- Minute 10: Enemy takes objective → Uh oh...\n- Minute 15: Teammate disconnects! → BIG DEAL!!!\n- Minute 20: You're still behind → Expected after minute 15\n\nTEMPORAL ATTENTION LEARNS:\n\"Minute 15 (teammate disconnect) explains\nwhy minute 20 is bad. Pay attention to disconnects!\"\n\nAttention weights:\nMin 0: 0.05 │░░░░░░░\nMin 5: 0.15 │░░░░░░░░░░░░░\nMin 10: 0.20 │░░░░░░░░░░░░░░░░░\nMin 15: 0.40 │░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░\nMin 20: 0.20 │░░░░░░░░░░░░░░░░░░\n```\n\n### Example 3: Weather and Your Mood\n\n```\nTRACKING YOUR WEEK:\nMonday: ☀️ Sunny, normal day, happy\nTuesday: ☁️ Cloudy, normal day, okay\nWednesday: 🌧️ Rainy, FORGOT UMBRELLA, sad\nThursday: ☀️ Sunny, but still thinking about Wed, mixed\nFriday: ☀️ Sunny, good weekend plans, happy!\n\nPREDICTING SATURDAY'S MOOD:\n\nWithout attention: \"Average of all days\" = okayish mood?\n\nWith attention:\n- Monday: 0.10 (distant past)\n- Tuesday: 0.05 (unremarkable)\n- Wednesday: 0.35 (the UMBRELLA incident!)\n- Thursday: 0.15 (transition)\n- Friday: 0.35 (most recent + weekend excitement!)\n\nPrediction: \"Happy on Saturday because Friday was good\nAND the umbrella incident is fading!\"\n```\n\n---\n\n## The Magic Components (Explained Simply!)\n\n### 1. The Bilinear Layer: Mixing Time and Features\n\nThink of it like making a smoothie:\n\n```\nBILINEAR MIXING:\n\nIngredients (Features): Time periods:\n├── Price change ├── Yesterday\n├── Volume ├── 2 days ago\n├── Order book imbalance ├── 3 days ago\n└── Volatility └── Week ago\n\nRegular blender (Linear):\n Just blends features OR time separately\n\nBilinear blender:\n Mixes features AND time TOGETHER!\n \"How does yesterday's volume relate to\n last week's volatility?\"\n```\n\n### 2. The Attention Mechanism: The Spotlight Operator\n\n```\nImagine you're a spotlight operator at a concert:\n\nTRADITIONAL MODEL: 🔦🔦🔦🔦🔦\nAll spotlights on equally everywhere\n(Bright but confusing)\n\nATTENTION MODEL: 💡\nOne adaptive spotlight that moves to\nwherever the action is happening\n(Clear and focused)\n\nThe attention mechanism is like an automatic\nspotlight that finds the interesting parts!\n```\n\n### 3. Multi-Head Attention: Multiple Spotlights\n\n```\nWhat if different things matter for different predictions?\n\nHEAD 1 🔴: Watching for price spikes\nHEAD 2 🔵: Watching for volume changes\nHEAD 3 🟢: Watching for order book imbalances\nHEAD 4 🟡: Watching for trend patterns\n\nThey all watch the same data but focus on\nDIFFERENT aspects, then share their findings!\n\nLike having:\n- One friend who's great at math\n- One friend who's great at reading\n- One friend who's great at science\n- Working together on homework!\n```\n\n---\n\n## Fun Quiz Time!\n\n**Question 1**: What does \"attention\" mean in machine learning?\n- A) The model is very focused on its homework\n- B) Learning which parts of the input are most important for the prediction\n- C) Making the computer pay attention to you\n- D) Adding more data to the model\n\n**Answer**: B - The model learns to focus on important moments!\n\n**Question 2**: Why is temporal attention useful for trading?\n- A) It makes the computer faster\n- B) Not all moments in market data are equally important for predictions\n- C) It looks cooler in presentations\n- D) It uses less electricity\n\n**Answer**: B - Some moments (like sudden volume spikes) are more predictive than others!\n\n**Question 3**: What's the difference between TABL and a regular neural network?\n- A) TABL is prettier\n- B) TABL learns which time steps matter, regular networks treat all equally\n- C) TABL is always more accurate\n- D) There's no difference\n\n**Answer**: B - TABL has an attention mechanism that weighs different moments!\n\n---\n\n## Why TABL is Special: A Story\n\nImagine two students predicting test scores:\n\n**Student A (Regular Model):**\n```\n\"I'll memorize everything equally!\"\n\nChapter 1: ████████ (8 hours)\nChapter 2: ████████ (8 hours)\nChapter 3: ████████ (8 hours)\nChapter 4: ████████ (8 hours)\n\nResult: Overwhelmed, C grade\n```\n\n**Student B (TABL):**\n```\n\"I'll figure out what's most likely to be tested!\"\n\nChapter 1: ██ (2 hours) - \"Probably not on test\"\nChapter 2: ████████████ (12 hours) - \"Teacher emphasized this!\"\nChapter 3: ██ (2 hours) - \"Review section\"\nChapter 4: ████████ (8 hours) - \"New material, important\"\n\nResult: Focused studying, A grade!\n```\n\nTABL is Student B — it learns where to focus!\n\n---\n\n## The Trading Connection: Putting It All Together\n\n```\nMARKET DATA STREAM:\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n\nTime: 10:00 10:01 10:02 10:03 10:04 10:05 10:06\nPrice: 100 100.1 99.9 100 105 104.5 104.7\nVolume: 1000 1100 900 1000 50000 20000 15000\n ^^^^\n HUGE SPIKE! TABL notices!\n\nTABL'S ATTENTION:\n 0.05 0.05 0.05 0.05 0.50 0.20 0.10\n ^^^^\n Most of the attention here!\n\nTABL'S REASONING:\n\"The volume spike at 10:04 is very unusual.\n When I've seen this pattern before, prices usually\n continue in the same direction for a while.\n PREDICTION: Price will stay UP.\"\n\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n```\n\n---\n\n## Key Takeaways (Remember These!)\n\n1. **ATTENTION = FOCUS**: The model learns what's important, not treating everything equally\n\n2. **TIME MATTERS**: Not every moment is equally useful for prediction\n\n3. **AUTOMATIC LEARNING**: You don't have to tell the model what's important — it figures it out!\n\n4. **INTERPRETABLE**: You can see WHERE the model is focusing (attention weights)\n\n5. **EFFICIENT**: Focuses computation on what matters, saving resources\n\n6. **MULTIHEAD**: Multiple attention heads catch different patterns\n\n---\n\n## The Big Picture\n\n**Without Temporal Attention:**\n```\nData → Process everything equally → Make a guess\n```\n\n**With Temporal Attention:**\n```\nData → Learn what's important → Focus on key moments → Confident prediction\n```\n\nIt's like the difference between:\n- **Reading every word** of a 500-page book before answering a question\n- **Using the index** to find exactly what you need!\n\nFinancial markets have patterns hidden in the noise. Temporal Attention helps find them!\n\n---\n\n## Fun Fact!\n\nThe attention mechanism was originally invented for **language translation**! Scientists realized that when translating \"The cat sat on the mat\" to another language, not every word needs equal focus. \"Cat\" and \"mat\" matter more than \"the\" and \"on\".\n\nThen clever researchers said: \"Wait, this works for TIME too!\" And that's how Temporal Attention was born!\n\n**Now it's helping predict stock prices, weather, traffic, and even your next YouTube recommendation!**\n\n---\n\n*Next time someone asks you what's important, think like TABL: don't say \"everything!\" — figure out what REALLY matters!*\n" | |
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| "content": "# Temporal Attention Networks: The Spotlight on Your Data's Most Important Moments\n\n## What is Temporal Attention?\n\nImagine you're watching a soccer game and trying to predict who will score next. Would you watch every single second equally? Of course not! You'd pay **extra attention** to moments like:\n- When a player breaks through the defense\n- When someone is about to take a penalty kick\n- When there's a corner kick\n\n**Temporal Attention Networks** do exactly this for financial data! Instead of treating every moment equally, they learn to **shine a spotlight** on the most important moments that help predict what happens next.\n\n---\n\n## The Simple Analogy: Watching a Movie for the Plot Twist\n\n### Without Attention (Old Way):\n\n```\nWatching a mystery movie:\n\nMinute 1: 🎬 Opening credits → Remember equally\nMinute 15: 🚗 Someone drives car → Remember equally\nMinute 30: 🔪 THE CLUE IS FOUND! → Remember equally\nMinute 45: ☕ Character drinks tea → Remember equally\nMinute 60: 🎭 THE REVEAL! → Remember equally\n\nAt the end: \"Wait, what was important again?\"\n```\n\n### With Temporal Attention (Smart Way):\n\n```\nWatching a mystery movie:\n\nMinute 1: 🎬 Opening credits → Low attention (0.02)\nMinute 15: 🚗 Someone drives car → Low attention (0.05)\nMinute 30: 🔪 THE CLUE IS FOUND! → HIGH attention (0.35)\nMinute 45: ☕ Character drinks tea → Low attention (0.08)\nMinute 60: 🎭 THE REVEAL! → HIGH attention (0.50)\n\nAt the end: \"The clue at minute 30 explained the reveal!\"\n```\n\n**Temporal attention is like having a smart highlighter that automatically marks the most important moments!**\n\n---\n\n## Why Does This Matter for Trading?\n\n### The Stock Market is Full of \"Important Moments\"\n\nThink of stock prices like a heartbeat monitor:\n\n```\nPrice over time:\n\n ^\n | 💥 Big news!\n | /\\ /\\\n $ | / \\ / \\_____/\\\n | / \\___/ \\\n | / \\_\n |/\n +-------------------------->\n Time\n\nMost of the line is boring (flat parts)\nBut a few moments are CRITICAL (peaks and valleys)\n```\n\n**Temporal Attention learns:**\n- \"That spike at 2:30 PM? Very important!\"\n- \"The flat period from 10-11 AM? Not so important...\"\n- \"The small dip before the big jump? Super predictive!\"\n\n---\n\n## How Does It Work? (The Simple Version)\n\n### Step 1: Look at All the Data\n\n```\nTABL receives data about the last 100 time steps:\n\nTime: 1 2 3 4 5 6 7 8 9 10 ... 100\nData: [price, volume, spread, imbalance, etc.]\n```\n\n### Step 2: Ask \"How Important is Each Moment?\"\n\nThe attention mechanism gives each moment a score:\n\n```\nATTENTION SCORING:\n\nTime: 1 2 3 4 5 6 7 8 9 10\nScore: 0.01 0.02 0.01 0.05 0.30 0.25 0.10 0.08 0.10 0.08\n ^^^^ ^^^^^^^^\n Low HIGH!\n (boring) (important)\n```\n\n### Step 3: Focus on What Matters\n\n```\nCreating the Summary:\n\nInstead of:\n\"Average of all 100 time steps\"\n\nIt's:\n\"Weighted average, focusing on time steps 5 and 6!\"\n\nTime 5 and 6 combined get 55% of the attention,\nwhile time 1-3 combined only get 4%!\n```\n\n### Step 4: Make the Prediction\n\n```\nBased on the attention-weighted summary:\n\n\"Price will likely go UP because:\n - At time 5, there was a big volume spike\n - At time 6, buyers strongly outnumbered sellers\n - These are the patterns I learned predict 'UP' movements!\"\n```\n\n---\n\n## Real-Life Examples Kids Can Understand\n\n### Example 1: Predicting if You'll Be Late for School\n\n```\nWITHOUT ATTENTION:\nYou track every minute of your morning equally:\n- 6:30 AM: Alarm rings → Important? Maybe\n- 6:45 AM: Brushing teeth → Important? Maybe\n- 7:00 AM: Eating breakfast → Important? Maybe\n- 7:15 AM: Can't find backpack! → Important? Maybe\n- 7:25 AM: Mom is ready → Important? Maybe\n\nEvery moment is \"maybe important\" = BAD prediction!\n\nWITH ATTENTION:\n- 6:30 AM: Alarm rings → Low (happens every day)\n- 6:45 AM: Brushing teeth → Low (normal)\n- 7:00 AM: Eating breakfast → Low (normal)\n- 7:15 AM: Can't find backpack! → HIGH!!! (this causes delays!)\n- 7:25 AM: Mom is ready → Medium (depends on traffic)\n\nThe model learns: \"When 'can't find something' happens,\nLATE probability jumps 80%!\"\n```\n\n### Example 2: Predicting if Your Team Will Win the Video Game\n\n```\nGAME TIMELINE:\n- Minute 0: Game starts → Normal\n- Minute 5: You get first kill → Nice!\n- Minute 10: Enemy takes objective → Uh oh...\n- Minute 15: Teammate disconnects! → BIG DEAL!!!\n- Minute 20: You're still behind → Expected after minute 15\n\nTEMPORAL ATTENTION LEARNS:\n\"Minute 15 (teammate disconnect) explains\nwhy minute 20 is bad. Pay attention to disconnects!\"\n\nAttention weights:\nMin 0: 0.05 │░░░░░░░\nMin 5: 0.15 │░░░░░░░░░░░░░\nMin 10: 0.20 │░░░░░░░░░░░░░░░░░\nMin 15: 0.40 │░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░\nMin 20: 0.20 │░░░░░░░░░░░░░░░░░░\n```\n\n### Example 3: Weather and Your Mood\n\n```\nTRACKING YOUR WEEK:\nMonday: ☀️ Sunny, normal day, happy\nTuesday: ☁️ Cloudy, normal day, okay\nWednesday: 🌧️ Rainy, FORGOT UMBRELLA, sad\nThursday: ☀️ Sunny, but still thinking about Wed, mixed\nFriday: ☀️ Sunny, good weekend plans, happy!\n\nPREDICTING SATURDAY'S MOOD:\n\nWithout attention: \"Average of all days\" = okayish mood?\n\nWith attention:\n- Monday: 0.10 (distant past)\n- Tuesday: 0.05 (unremarkable)\n- Wednesday: 0.35 (the UMBRELLA incident!)\n- Thursday: 0.15 (transition)\n- Friday: 0.35 (most recent + weekend excitement!)\n\nPrediction: \"Happy on Saturday because Friday was good\nAND the umbrella incident is fading!\"\n```\n\n---\n\n## The Magic Components (Explained Simply!)\n\n### 1. The Bilinear Layer: Mixing Time and Features\n\nThink of it like making a smoothie:\n\n```\nBILINEAR MIXING:\n\nIngredients (Features): Time periods:\n├── Price change ├── Yesterday\n├── Volume ├── 2 days ago\n├── Order book imbalance ├── 3 days ago\n└── Volatility └── Week ago\n\nRegular blender (Linear):\n Just blends features OR time separately\n\nBilinear blender:\n Mixes features AND time TOGETHER!\n \"How does yesterday's volume relate to\n last week's volatility?\"\n```\n\n### 2. The Attention Mechanism: The Spotlight Operator\n\n```\nImagine you're a spotlight operator at a concert:\n\nTRADITIONAL MODEL: 🔦🔦🔦🔦🔦\nAll spotlights on equally everywhere\n(Bright but confusing)\n\nATTENTION MODEL: 💡\nOne adaptive spotlight that moves to\nwherever the action is happening\n(Clear and focused)\n\nThe attention mechanism is like an automatic\nspotlight that finds the interesting parts!\n```\n\n### 3. Multi-Head Attention: Multiple Spotlights\n\n```\nWhat if different things matter for different predictions?\n\nHEAD 1 🔴: Watching for price spikes\nHEAD 2 🔵: Watching for volume changes\nHEAD 3 🟢: Watching for order book imbalances\nHEAD 4 🟡: Watching for trend patterns\n\nThey all watch the same data but focus on\nDIFFERENT aspects, then share their findings!\n\nLike having:\n- One friend who's great at math\n- One friend who's great at reading\n- One friend who's great at science\n- Working together on homework!\n```\n\n---\n\n## Fun Quiz Time!\n\n**Question 1**: What does \"attention\" mean in machine learning?\n- A) The model is very focused on its homework\n- B) Learning which parts of the input are most important for the prediction\n- C) Making the computer pay attention to you\n- D) Adding more data to the model\n\n**Answer**: B - The model learns to focus on important moments!\n\n**Question 2**: Why is temporal attention useful for trading?\n- A) It makes the computer faster\n- B) Not all moments in market data are equally important for predictions\n- C) It looks cooler in presentations\n- D) It uses less electricity\n\n**Answer**: B - Some moments (like sudden volume spikes) are more predictive than others!\n\n**Question 3**: What's the difference between TABL and a regular neural network?\n- A) TABL is prettier\n- B) TABL learns which time steps matter, regular networks treat all equally\n- C) TABL is always more accurate\n- D) There's no difference\n\n**Answer**: B - TABL has an attention mechanism that weighs different moments!\n\n---\n\n## Why TABL is Special: A Story\n\nImagine two students predicting test scores:\n\n**Student A (Regular Model):**\n```\n\"I'll memorize everything equally!\"\n\nChapter 1: ████████ (8 hours)\nChapter 2: ████████ (8 hours)\nChapter 3: ████████ (8 hours)\nChapter 4: ████████ (8 hours)\n\nResult: Overwhelmed, C grade\n```\n\n**Student B (TABL):**\n```\n\"I'll figure out what's most likely to be tested!\"\n\nChapter 1: ██ (2 hours) - \"Probably not on test\"\nChapter 2: ████████████ (12 hours) - \"Teacher emphasized this!\"\nChapter 3: ██ (2 hours) - \"Review section\"\nChapter 4: ████████ (8 hours) - \"New material, important\"\n\nResult: Focused studying, A grade!\n```\n\nTABL is Student B — it learns where to focus!\n\n---\n\n## The Trading Connection: Putting It All Together\n\n```\nMARKET DATA STREAM:\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n\nTime: 10:00 10:01 10:02 10:03 10:04 10:05 10:06\nPrice: 100 100.1 99.9 100 105 104.5 104.7\nVolume: 1000 1100 900 1000 50000 20000 15000\n ^^^^\n HUGE SPIKE! TABL notices!\n\nTABL'S ATTENTION:\n 0.05 0.05 0.05 0.05 0.50 0.20 0.10\n ^^^^\n Most of the attention here!\n\nTABL'S REASONING:\n\"The volume spike at 10:04 is very unusual.\n When I've seen this pattern before, prices usually\n continue in the same direction for a while.\n PREDICTION: Price will stay UP.\"\n\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n```\n\n---\n\n## Key Takeaways (Remember These!)\n\n1. **ATTENTION = FOCUS**: The model learns what's important, not treating everything equally\n\n2. **TIME MATTERS**: Not every moment is equally useful for prediction\n\n3. **AUTOMATIC LEARNING**: You don't have to tell the model what's important — it figures it out!\n\n4. **INTERPRETABLE**: You can see WHERE the model is focusing (attention weights)\n\n5. **EFFICIENT**: Focuses computation on what matters, saving resources\n\n6. **MULTIHEAD**: Multiple attention heads catch different patterns\n\n---\n\n## The Big Picture\n\n**Without Temporal Attention:**\n```\nData → Process everything equally → Make a guess\n```\n\n**With Temporal Attention:**\n```\nData → Learn what's important → Focus on key moments → Confident prediction\n```\n\nIt's like the difference between:\n- **Reading every word** of a 500-page book before answering a question\n- **Using the index** to find exactly what you need!\n\nFinancial markets have patterns hidden in the noise. Temporal Attention helps find them!\n\n---\n\n## Fun Fact!\n\nThe attention mechanism was originally invented for **language translation**! Scientists realized that when translating \"The cat sat on the mat\" to another language, not every word needs equal focus. \"Cat\" and \"mat\" matter more than \"the\" and \"on\".\n\nThen clever researchers said: \"Wait, this works for TIME too!\" And that's how Temporal Attention was born!\n\n**Now it's helping predict stock prices, weather, traffic, and even your next YouTube recommendation!**\n\n---\n\n*Next time someone asks you what's important, think like TABL: don't say \"everything!\" — figure out what REALLY matters!*\n", | |
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| "content": "# Сети Временного Внимания: Прожектор на Самые Важные Моменты Ваших Данных\n\n## Что такое Временное Внимание?\n\nПредставь, что ты смотришь футбольный матч и пытаешься предсказать, кто забьёт следующий гол. Будешь ли ты смотреть каждую секунду одинаково внимательно? Конечно нет! Ты будешь **уделять особое внимание** таким моментам:\n- Когда игрок прорывается через защиту\n- Когда кто-то собирается бить пенальти\n- Когда происходит угловой удар\n\n**Сети Временного Внимания** делают именно это для финансовых данных! Вместо того чтобы относиться к каждому моменту одинаково, они учатся **направлять прожектор** на самые важные моменты, которые помогают предсказать, что произойдёт дальше.\n\n---\n\n## Простая Аналогия: Просмотр Фильма ради Сюжетного Поворота\n\n### Без Внимания (Старый Способ):\n\n```\nСмотрим детективный фильм:\n\nМинута 1: 🎬 Начальные титры → Запоминаем одинаково\nМинута 15: 🚗 Кто-то ведёт машину → Запоминаем одинаково\nМинута 30: 🔪 НАЙДЕНА УЛИКА! → Запоминаем одинаково\nМинута 45: ☕ Персонаж пьёт чай → Запоминаем одинаково\nМинута 60: 🎭 РАЗОБЛАЧЕНИЕ! → Запоминаем одинаково\n\nВ конце: \"Подождите, а что было важно?\"\n```\n\n### С Временным Вниманием (Умный Способ):\n\n```\nСмотрим детективный фильм:\n\nМинута 1: 🎬 Начальные титры → Низкое внимание (0.02)\nМинута 15: 🚗 Кто-то ведёт машину → Низкое внимание (0.05)\nМинута 30: 🔪 НАЙДЕНА УЛИКА! → ВЫСОКОЕ внимание (0.35)\nМинута 45: ☕ Персонаж пьёт чай → Низкое внимание (0.08)\nМинута 60: 🎭 РАЗОБЛАЧЕНИЕ! → ВЫСОКОЕ внимание (0.50)\n\nВ конце: \"Улика на 30-й минуте объяснила разоблачение!\"\n```\n\n**Временное внимание — это как умный маркер, который автоматически выделяет самые важные моменты!**\n\n---\n\n## Почему Это Важно для Трейдинга?\n\n### Фондовый Рынок Полон \"Важных Моментов\"\n\nПредставь биржевые цены как монитор сердцебиения:\n\n```\nЦена во времени:\n\n ^\n | 💥 Важная новость!\n | /\\ /\\\n $ | / \\ / \\_____/\\\n | / \\___/ \\\n | / \\_\n |/\n +-------------------------->\n Время\n\nБольшая часть линии скучная (плоские участки)\nНо несколько моментов КРИТИЧЕСКИЕ (пики и впадины)\n```\n\n**Временное Внимание учится:**\n- \"Тот скачок в 14:30? Очень важно!\"\n- \"Плоский период с 10 до 11? Не очень важно...\"\n- \"Маленькое падение перед большим скачком? Супер предсказательно!\"\n\n---\n\n## Как Это Работает? (Простая Версия)\n\n### Шаг 1: Смотрим на Все Данные\n\n```\nTABL получает данные о последних 100 временных шагах:\n\nВремя: 1 2 3 4 5 6 7 8 9 10 ... 100\nДанные: [цена, объём, спред, дисбаланс, и т.д.]\n```\n\n### Шаг 2: Спрашиваем \"Насколько Важен Каждый Момент?\"\n\nМеханизм внимания даёт каждому моменту оценку:\n\n```\nОЦЕНКА ВНИМАНИЯ:\n\nВремя: 1 2 3 4 5 6 7 8 9 10\nОценка: 0.01 0.02 0.01 0.05 0.30 0.25 0.10 0.08 0.10 0.08\n ^^^^ ^^^^^^^^\n Низко ВЫСОКО!\n (скучно) (важно)\n```\n\n### Шаг 3: Фокусируемся на Важном\n\n```\nСоздание Резюме:\n\nВместо:\n\"Среднее по всем 100 временным шагам\"\n\nПолучается:\n\"Взвешенное среднее, фокус на шагах 5 и 6!\"\n\nШаги 5 и 6 вместе получают 55% внимания,\nа шаги 1-3 вместе только 4%!\n```\n\n### Шаг 4: Делаем Предсказание\n\n```\nНа основе взвешенного вниманием резюме:\n\n\"Цена скорее всего ВЫРАСТЕТ потому что:\n - На шаге 5 был большой всплеск объёма\n - На шаге 6 покупатели сильно превосходили продавцов\n - Это паттерны, которые я выучил как предсказывающие 'РОСТ'!\"\n```\n\n---\n\n## Примеры из Жизни, Понятные Детям\n\n### Пример 1: Предсказание Опоздаешь ли Ты в Школу\n\n```\nБЕЗ ВНИМАНИЯ:\nТы отслеживаешь каждую минуту утра одинаково:\n- 6:30: Звонит будильник → Важно? Может быть\n- 6:45: Чистишь зубы → Важно? Может быть\n- 7:00: Завтракаешь → Важно? Может быть\n- 7:15: Не можешь найти рюкзак! → Важно? Может быть\n- 7:25: Мама готова → Важно? Может быть\n\nКаждый момент \"может быть важен\" = ПЛОХОЕ предсказание!\n\nС ВНИМАНИЕМ:\n- 6:30: Звонит будильник → Низкое (происходит каждый день)\n- 6:45: Чистишь зубы → Низкое (обычное)\n- 7:00: Завтракаешь → Низкое (обычное)\n- 7:15: Не можешь найти рюкзак! → ВЫСОКОЕ!!! (это вызывает задержки!)\n- 7:25: Мама готова → Среднее (зависит от трафика)\n\nМодель учится: \"Когда происходит 'не могу найти что-то',\nвероятность ОПОЗДАНИЯ подскакивает на 80%!\"\n```\n\n### Пример 2: Предсказание Выиграет ли Твоя Команда в Видеоигре\n\n```\nХРОНОЛОГИЯ ИГРЫ:\n- Минута 0: Игра начинается → Обычно\n- Минута 5: Ты делаешь первое убийство → Отлично!\n- Минута 10: Враг захватывает цель → Ой...\n- Минута 15: Тиммейт отключился! → БОЛЬШОЕ ДЕЛО!!!\n- Минута 20: Вы всё ещё позади → Ожидаемо после минуты 15\n\nВРЕМЕННОЕ ВНИМАНИЕ УЧИТСЯ:\n\"Минута 15 (отключение тиммейта) объясняет\nпочему минута 20 плохая. Обращай внимание на отключения!\"\n\nВеса внимания:\nМин 0: 0.05 │░░░░░░░\nМин 5: 0.15 │░░░░░░░░░░░░░\nМин 10: 0.20 │░░░░░░░░░░░░░░░░░\nМин 15: 0.40 │░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░\nМин 20: 0.20 │░░░░░░░░░░░░░░░░░░\n```\n\n### Пример 3: Погода и Твоё Настроение\n\n```\nОТСЛЕЖИВАНИЕ ТВОЕЙ НЕДЕЛИ:\nПонедельник: ☀️ Солнечно, обычный день, счастлив\nВторник: ☁️ Облачно, обычный день, нормально\nСреда: 🌧️ Дождь, ЗАБЫЛ ЗОНТИК, грустно\nЧетверг: ☀️ Солнечно, но думаю о среде, смешанно\nПятница: ☀️ Солнечно, планы на выходные, счастлив!\n\nПРЕДСКАЗАНИЕ НАСТРОЕНИЯ НА СУББОТУ:\n\nБез внимания: \"Среднее по всем дням\" = так себе настроение?\n\nС вниманием:\n- Понедельник: 0.10 (далёкое прошлое)\n- Вторник: 0.05 (ничем не примечательно)\n- Среда: 0.35 (инцидент с ЗОНТИКОМ!)\n- Четверг: 0.15 (переходный)\n- Пятница: 0.35 (самое недавнее + волнение выходных!)\n\nПредсказание: \"Счастлив в субботу потому что пятница была хорошей\nИ инцидент с зонтиком забывается!\"\n```\n\n---\n\n## Волшебные Компоненты (Объяснено Просто!)\n\n### 1. Билинейный Слой: Смешивание Времени и Признаков\n\nПредставь это как приготовление смузи:\n\n```\nБИЛИНЕЙНОЕ СМЕШИВАНИЕ:\n\nИнгредиенты (Признаки): Периоды времени:\n├── Изменение цены ├── Вчера\n├── Объём ├── 2 дня назад\n├── Дисбаланс книги заявок ├── 3 дня назад\n└── Волатильность └── Неделю назад\n\nОбычный блендер (Линейный):\n Просто смешивает признаки ИЛИ время отдельно\n\nБилинейный блендер:\n Смешивает признаки И время ВМЕСТЕ!\n \"Как вчерашний объём связан с\n волатильностью прошлой недели?\"\n```\n\n### 2. Механизм Внимания: Оператор Прожектора\n\n```\nПредставь, что ты оператор прожектора на концерте:\n\nТРАДИЦИОННАЯ МОДЕЛЬ: 🔦🔦🔦🔦🔦\nВсе прожекторы светят одинаково везде\n(Ярко, но запутанно)\n\nМОДЕЛЬ С ВНИМАНИЕМ: 💡\nОдин адаптивный прожектор, который двигается\nтуда, где происходит действие\n(Ясно и сфокусировано)\n\nМеханизм внимания — это как автоматический\nпрожектор, который находит интересные части!\n```\n\n### 3. Многоголовое Внимание: Несколько Прожекторов\n\n```\nА что если разные вещи важны для разных предсказаний?\n\nГОЛОВА 1 🔴: Следит за скачками цены\nГОЛОВА 2 🔵: Следит за изменениями объёма\nГОЛОВА 3 🟢: Следит за дисбалансами книги заявок\nГОЛОВА 4 🟡: Следит за трендовыми паттернами\n\nВсе они смотрят на одни данные, но фокусируются на\nРАЗНЫХ аспектах, а потом делятся находками!\n\nКак если бы у тебя были:\n- Один друг, который хорош в математике\n- Один друг, который хорош в чтении\n- Один друг, который хорош в науке\n- Работающие вместе над домашкой!\n```\n\n---\n\n## Время Весёлой Викторины!\n\n**Вопрос 1**: Что означает \"внимание\" в машинном обучении?\n- A) Модель очень сосредоточена на домашке\n- B) Обучение тому, какие части входных данных наиболее важны для предсказания\n- C) Заставить компьютер обращать на тебя внимание\n- D) Добавление больше данных в модель\n\n**Ответ**: B - Модель учится фокусироваться на важных моментах!\n\n**Вопрос 2**: Почему временное внимание полезно для трейдинга?\n- A) Оно делает компьютер быстрее\n- B) Не все моменты в рыночных данных одинаково важны для предсказаний\n- C) Это круче смотрится в презентациях\n- D) Оно использует меньше электричества\n\n**Ответ**: B - Некоторые моменты (как внезапные всплески объёма) более предсказательны чем другие!\n\n**Вопрос 3**: В чём разница между TABL и обычной нейросетью?\n- A) TABL красивее\n- B) TABL учится какие временные шаги важны, обычные сети относятся ко всем одинаково\n- C) TABL всегда точнее\n- D) Разницы нет\n\n**Ответ**: B - TABL имеет механизм внимания, который взвешивает разные моменты!\n\n---\n\n## Почему TABL Особенный: История\n\nПредставь двух учеников, предсказывающих оценки за тест:\n\n**Ученик А (Обычная Модель):**\n```\n\"Я запомню всё одинаково!\"\n\nГлава 1: ████████ (8 часов)\nГлава 2: ████████ (8 часов)\nГлава 3: ████████ (8 часов)\nГлава 4: ████████ (8 часов)\n\nРезультат: Перегружен, оценка C\n```\n\n**Ученик Б (TABL):**\n```\n\"Я выясню, что скорее всего будет на тесте!\"\n\nГлава 1: ██ (2 часа) - \"Вероятно не на тесте\"\nГлава 2: ████████████ (12 часов) - \"Учитель делал на этом акцент!\"\nГлава 3: ██ (2 часа) - \"Раздел повторения\"\nГлава 4: ████████ (8 часов) - \"Новый материал, важно\"\n\nРезультат: Сфокусированная учёба, оценка A!\n```\n\nTABL — это Ученик Б — он учится куда фокусироваться!\n\n---\n\n## Связь с Трейдингом: Собираем Всё Вместе\n\n```\nПОТОК РЫНОЧНЫХ ДАННЫХ:\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n\nВремя: 10:00 10:01 10:02 10:03 10:04 10:05 10:06\nЦена: 100 100.1 99.9 100 105 104.5 104.7\nОбъём: 1000 1100 900 1000 50000 20000 15000\n ^^^^\n ОГРОМНЫЙ СКАЧОК! TABL замечает!\n\nВНИМАНИЕ TABL:\n 0.05 0.05 0.05 0.05 0.50 0.20 0.10\n ^^^^\n Большая часть внимания здесь!\n\nРАССУЖДЕНИЕ TABL:\n\"Всплеск объёма в 10:04 очень необычный.\n Когда я видел такой паттерн раньше, цены обычно\n продолжали двигаться в том же направлении какое-то время.\n ПРЕДСКАЗАНИЕ: Цена останется ВВЕРХУ.\"\n\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n```\n\n---\n\n## Ключевые Выводы (Запомни Это!)\n\n1. **ВНИМАНИЕ = ФОКУС**: Модель учится что важно, не относясь ко всему одинаково\n\n2. **ВРЕМЯ ИМЕЕТ ЗНАЧЕНИЕ**: Не каждый момент одинаково полезен для предсказания\n\n3. **АВТОМАТИЧЕСКОЕ ОБУЧЕНИЕ**: Не нужно говорить модели что важно — она сама разберётся!\n\n4. **ИНТЕРПРЕТИРУЕМОСТЬ**: Можно увидеть ГДЕ модель фокусируется (веса внимания)\n\n5. **ЭФФЕКТИВНОСТЬ**: Фокусирует вычисления на важном, экономя ресурсы\n\n6. **МНОГОГОЛОВОСТЬ**: Несколько голов внимания ловят разные паттерны\n\n---\n\n## Общая Картина\n\n**Без Временного Внимания:**\n```\nДанные → Обрабатываем всё одинаково → Делаем догадку\n```\n\n**С Временным Вниманием:**\n```\nДанные → Учимся что важно → Фокусируемся на ключевых моментах → Уверенное предсказание\n```\n\nЭто как разница между:\n- **Читать каждое слово** 500-страничной книги перед ответом на вопрос\n- **Использовать оглавление** чтобы найти именно то, что нужно!\n\nНа финансовых рынках есть паттерны, скрытые в шуме. Временное Внимание помогает их найти!\n\n---\n\n## Интересный Факт!\n\nМеханизм внимания изначально был изобретён для **перевода языков**! Учёные поняли, что при переводе \"The cat sat on the mat\" на другой язык, не каждое слово требует равного внимания. \"Cat\" и \"mat\" важнее чем \"the\" и \"on\".\n\nПотом умные исследователи сказали: \"Погодите, это работает и для ВРЕМЕНИ тоже!\" И так родилось Временное Внимание!\n\n**Теперь оно помогает предсказывать цены акций, погоду, трафик и даже твою следующую рекомендацию на YouTube!**\n\n---\n\n*В следующий раз, когда тебя спросят что важно, думай как TABL: не говори \"всё!\" — выясни что ДЕЙСТВИТЕЛЬНО имеет значение!*\n" | |
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| "content": "# Сети Временного Внимания: Прожектор на Самые Важные Моменты Ваших Данных\n\n## Что такое Временное Внимание?\n\nПредставь, что ты смотришь футбольный матч и пытаешься предсказать, кто забьёт следующий гол. Будешь ли ты смотреть каждую секунду одинаково внимательно? Конечно нет! Ты будешь **уделять особое внимание** таким моментам:\n- Когда игрок прорывается через защиту\n- Когда кто-то собирается бить пенальти\n- Когда происходит угловой удар\n\n**Сети Временного Внимания** делают именно это для финансовых данных! Вместо того чтобы относиться к каждому моменту одинаково, они учатся **направлять прожектор** на самые важные моменты, которые помогают предсказать, что произойдёт дальше.\n\n---\n\n## Простая Аналогия: Просмотр Фильма ради Сюжетного Поворота\n\n### Без Внимания (Старый Способ):\n\n```\nСмотрим детективный фильм:\n\nМинута 1: 🎬 Начальные титры → Запоминаем одинаково\nМинута 15: 🚗 Кто-то ведёт машину → Запоминаем одинаково\nМинута 30: 🔪 НАЙДЕНА УЛИКА! → Запоминаем одинаково\nМинута 45: ☕ Персонаж пьёт чай → Запоминаем одинаково\nМинута 60: 🎭 РАЗОБЛАЧЕНИЕ! → Запоминаем одинаково\n\nВ конце: \"Подождите, а что было важно?\"\n```\n\n### С Временным Вниманием (Умный Способ):\n\n```\nСмотрим детективный фильм:\n\nМинута 1: 🎬 Начальные титры → Низкое внимание (0.02)\nМинута 15: 🚗 Кто-то ведёт машину → Низкое внимание (0.05)\nМинута 30: 🔪 НАЙДЕНА УЛИКА! → ВЫСОКОЕ внимание (0.35)\nМинута 45: ☕ Персонаж пьёт чай → Низкое внимание (0.08)\nМинута 60: 🎭 РАЗОБЛАЧЕНИЕ! → ВЫСОКОЕ внимание (0.50)\n\nВ конце: \"Улика на 30-й минуте объяснила разоблачение!\"\n```\n\n**Временное внимание — это как умный маркер, который автоматически выделяет самые важные моменты!**\n\n---\n\n## Почему Это Важно для Трейдинга?\n\n### Фондовый Рынок Полон \"Важных Моментов\"\n\nПредставь биржевые цены как монитор сердцебиения:\n\n```\nЦена во времени:\n\n ^\n | 💥 Важная новость!\n | /\\ /\\\n $ | / \\ / \\_____/\\\n | / \\___/ \\\n | / \\_\n |/\n +-------------------------->\n Время\n\nБольшая часть линии скучная (плоские участки)\nНо несколько моментов КРИТИЧЕСКИЕ (пики и впадины)\n```\n\n**Временное Внимание учится:**\n- \"Тот скачок в 14:30? Очень важно!\"\n- \"Плоский период с 10 до 11? Не очень важно...\"\n- \"Маленькое падение перед большим скачком? Супер предсказательно!\"\n\n---\n\n## Как Это Работает? (Простая Версия)\n\n### Шаг 1: Смотрим на Все Данные\n\n```\nTABL получает данные о последних 100 временных шагах:\n\nВремя: 1 2 3 4 5 6 7 8 9 10 ... 100\nДанные: [цена, объём, спред, дисбаланс, и т.д.]\n```\n\n### Шаг 2: Спрашиваем \"Насколько Важен Каждый Момент?\"\n\nМеханизм внимания даёт каждому моменту оценку:\n\n```\nОЦЕНКА ВНИМАНИЯ:\n\nВремя: 1 2 3 4 5 6 7 8 9 10\nОценка: 0.01 0.02 0.01 0.05 0.30 0.25 0.10 0.08 0.10 0.08\n ^^^^ ^^^^^^^^\n Низко ВЫСОКО!\n (скучно) (важно)\n```\n\n### Шаг 3: Фокусируемся на Важном\n\n```\nСоздание Резюме:\n\nВместо:\n\"Среднее по всем 100 временным шагам\"\n\nПолучается:\n\"Взвешенное среднее, фокус на шагах 5 и 6!\"\n\nШаги 5 и 6 вместе получают 55% внимания,\nа шаги 1-3 вместе только 4%!\n```\n\n### Шаг 4: Делаем Предсказание\n\n```\nНа основе взвешенного вниманием резюме:\n\n\"Цена скорее всего ВЫРАСТЕТ потому что:\n - На шаге 5 был большой всплеск объёма\n - На шаге 6 покупатели сильно превосходили продавцов\n - Это паттерны, которые я выучил как предсказывающие 'РОСТ'!\"\n```\n\n---\n\n## Примеры из Жизни, Понятные Детям\n\n### Пример 1: Предсказание Опоздаешь ли Ты в Школу\n\n```\nБЕЗ ВНИМАНИЯ:\nТы отслеживаешь каждую минуту утра одинаково:\n- 6:30: Звонит будильник → Важно? Может быть\n- 6:45: Чистишь зубы → Важно? Может быть\n- 7:00: Завтракаешь → Важно? Может быть\n- 7:15: Не можешь найти рюкзак! → Важно? Может быть\n- 7:25: Мама готова → Важно? Может быть\n\nКаждый момент \"может быть важен\" = ПЛОХОЕ предсказание!\n\nС ВНИМАНИЕМ:\n- 6:30: Звонит будильник → Низкое (происходит каждый день)\n- 6:45: Чистишь зубы → Низкое (обычное)\n- 7:00: Завтракаешь → Низкое (обычное)\n- 7:15: Не можешь найти рюкзак! → ВЫСОКОЕ!!! (это вызывает задержки!)\n- 7:25: Мама готова → Среднее (зависит от трафика)\n\nМодель учится: \"Когда происходит 'не могу найти что-то',\nвероятность ОПОЗДАНИЯ подскакивает на 80%!\"\n```\n\n### Пример 2: Предсказание Выиграет ли Твоя Команда в Видеоигре\n\n```\nХРОНОЛОГИЯ ИГРЫ:\n- Минута 0: Игра начинается → Обычно\n- Минута 5: Ты делаешь первое убийство → Отлично!\n- Минута 10: Враг захватывает цель → Ой...\n- Минута 15: Тиммейт отключился! → БОЛЬШОЕ ДЕЛО!!!\n- Минута 20: Вы всё ещё позади → Ожидаемо после минуты 15\n\nВРЕМЕННОЕ ВНИМАНИЕ УЧИТСЯ:\n\"Минута 15 (отключение тиммейта) объясняет\nпочему минута 20 плохая. Обращай внимание на отключения!\"\n\nВеса внимания:\nМин 0: 0.05 │░░░░░░░\nМин 5: 0.15 │░░░░░░░░░░░░░\nМин 10: 0.20 │░░░░░░░░░░░░░░░░░\nМин 15: 0.40 │░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░\nМин 20: 0.20 │░░░░░░░░░░░░░░░░░░\n```\n\n### Пример 3: Погода и Твоё Настроение\n\n```\nОТСЛЕЖИВАНИЕ ТВОЕЙ НЕДЕЛИ:\nПонедельник: ☀️ Солнечно, обычный день, счастлив\nВторник: ☁️ Облачно, обычный день, нормально\nСреда: 🌧️ Дождь, ЗАБЫЛ ЗОНТИК, грустно\nЧетверг: ☀️ Солнечно, но думаю о среде, смешанно\nПятница: ☀️ Солнечно, планы на выходные, счастлив!\n\nПРЕДСКАЗАНИЕ НАСТРОЕНИЯ НА СУББОТУ:\n\nБез внимания: \"Среднее по всем дням\" = так себе настроение?\n\nС вниманием:\n- Понедельник: 0.10 (далёкое прошлое)\n- Вторник: 0.05 (ничем не примечательно)\n- Среда: 0.35 (инцидент с ЗОНТИКОМ!)\n- Четверг: 0.15 (переходный)\n- Пятница: 0.35 (самое недавнее + волнение выходных!)\n\nПредсказание: \"Счастлив в субботу потому что пятница была хорошей\nИ инцидент с зонтиком забывается!\"\n```\n\n---\n\n## Волшебные Компоненты (Объяснено Просто!)\n\n### 1. Билинейный Слой: Смешивание Времени и Признаков\n\nПредставь это как приготовление смузи:\n\n```\nБИЛИНЕЙНОЕ СМЕШИВАНИЕ:\n\nИнгредиенты (Признаки): Периоды времени:\n├── Изменение цены ├── Вчера\n├── Объём ├── 2 дня назад\n├── Дисбаланс книги заявок ├── 3 дня назад\n└── Волатильность └── Неделю назад\n\nОбычный блендер (Линейный):\n Просто смешивает признаки ИЛИ время отдельно\n\nБилинейный блендер:\n Смешивает признаки И время ВМЕСТЕ!\n \"Как вчерашний объём связан с\n волатильностью прошлой недели?\"\n```\n\n### 2. Механизм Внимания: Оператор Прожектора\n\n```\nПредставь, что ты оператор прожектора на концерте:\n\nТРАДИЦИОННАЯ МОДЕЛЬ: 🔦🔦🔦🔦🔦\nВсе прожекторы светят одинаково везде\n(Ярко, но запутанно)\n\nМОДЕЛЬ С ВНИМАНИЕМ: 💡\nОдин адаптивный прожектор, который двигается\nтуда, где происходит действие\n(Ясно и сфокусировано)\n\nМеханизм внимания — это как автоматический\nпрожектор, который находит интересные части!\n```\n\n### 3. Многоголовое Внимание: Несколько Прожекторов\n\n```\nА что если разные вещи важны для разных предсказаний?\n\nГОЛОВА 1 🔴: Следит за скачками цены\nГОЛОВА 2 🔵: Следит за изменениями объёма\nГОЛОВА 3 🟢: Следит за дисбалансами книги заявок\nГОЛОВА 4 🟡: Следит за трендовыми паттернами\n\nВсе они смотрят на одни данные, но фокусируются на\nРАЗНЫХ аспектах, а потом делятся находками!\n\nКак если бы у тебя были:\n- Один друг, который хорош в математике\n- Один друг, который хорош в чтении\n- Один друг, который хорош в науке\n- Работающие вместе над домашкой!\n```\n\n---\n\n## Время Весёлой Викторины!\n\n**Вопрос 1**: Что означает \"внимание\" в машинном обучении?\n- A) Модель очень сосредоточена на домашке\n- B) Обучение тому, какие части входных данных наиболее важны для предсказания\n- C) Заставить компьютер обращать на тебя внимание\n- D) Добавление больше данных в модель\n\n**Ответ**: B - Модель учится фокусироваться на важных моментах!\n\n**Вопрос 2**: Почему временное внимание полезно для трейдинга?\n- A) Оно делает компьютер быстрее\n- B) Не все моменты в рыночных данных одинаково важны для предсказаний\n- C) Это круче смотрится в презентациях\n- D) Оно использует меньше электричества\n\n**Ответ**: B - Некоторые моменты (как внезапные всплески объёма) более предсказательны чем другие!\n\n**Вопрос 3**: В чём разница между TABL и обычной нейросетью?\n- A) TABL красивее\n- B) TABL учится какие временные шаги важны, обычные сети относятся ко всем одинаково\n- C) TABL всегда точнее\n- D) Разницы нет\n\n**Ответ**: B - TABL имеет механизм внимания, который взвешивает разные моменты!\n\n---\n\n## Почему TABL Особенный: История\n\nПредставь двух учеников, предсказывающих оценки за тест:\n\n**Ученик А (Обычная Модель):**\n```\n\"Я запомню всё одинаково!\"\n\nГлава 1: ████████ (8 часов)\nГлава 2: ████████ (8 часов)\nГлава 3: ████████ (8 часов)\nГлава 4: ████████ (8 часов)\n\nРезультат: Перегружен, оценка C\n```\n\n**Ученик Б (TABL):**\n```\n\"Я выясню, что скорее всего будет на тесте!\"\n\nГлава 1: ██ (2 часа) - \"Вероятно не на тесте\"\nГлава 2: ████████████ (12 часов) - \"Учитель делал на этом акцент!\"\nГлава 3: ██ (2 часа) - \"Раздел повторения\"\nГлава 4: ████████ (8 часов) - \"Новый материал, важно\"\n\nРезультат: Сфокусированная учёба, оценка A!\n```\n\nTABL — это Ученик Б — он учится куда фокусироваться!\n\n---\n\n## Связь с Трейдингом: Собираем Всё Вместе\n\n```\nПОТОК РЫНОЧНЫХ ДАННЫХ:\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n\nВремя: 10:00 10:01 10:02 10:03 10:04 10:05 10:06\nЦена: 100 100.1 99.9 100 105 104.5 104.7\nОбъём: 1000 1100 900 1000 50000 20000 15000\n ^^^^\n ОГРОМНЫЙ СКАЧОК! TABL замечает!\n\nВНИМАНИЕ TABL:\n 0.05 0.05 0.05 0.05 0.50 0.20 0.10\n ^^^^\n Большая часть внимания здесь!\n\nРАССУЖДЕНИЕ TABL:\n\"Всплеск объёма в 10:04 очень необычный.\n Когда я видел такой паттерн раньше, цены обычно\n продолжали двигаться в том же направлении какое-то время.\n ПРЕДСКАЗАНИЕ: Цена останется ВВЕРХУ.\"\n\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n```\n\n---\n\n## Ключевые Выводы (Запомни Это!)\n\n1. **ВНИМАНИЕ = ФОКУС**: Модель учится что важно, не относясь ко всему одинаково\n\n2. **ВРЕМЯ ИМЕЕТ ЗНАЧЕНИЕ**: Не каждый момент одинаково полезен для предсказания\n\n3. **АВТОМАТИЧЕСКОЕ ОБУЧЕНИЕ**: Не нужно говорить модели что важно — она сама разберётся!\n\n4. **ИНТЕРПРЕТИРУЕМОСТЬ**: Можно увидеть ГДЕ модель фокусируется (веса внимания)\n\n5. **ЭФФЕКТИВНОСТЬ**: Фокусирует вычисления на важном, экономя ресурсы\n\n6. **МНОГОГОЛОВОСТЬ**: Несколько голов внимания ловят разные паттерны\n\n---\n\n## Общая Картина\n\n**Без Временного Внимания:**\n```\nДанные → Обрабатываем всё одинаково → Делаем догадку\n```\n\n**С Временным Вниманием:**\n```\nДанные → Учимся что важно → Фокусируемся на ключевых моментах → Уверенное предсказание\n```\n\nЭто как разница между:\n- **Читать каждое слово** 500-страничной книги перед ответом на вопрос\n- **Использовать оглавление** чтобы найти именно то, что нужно!\n\nНа финансовых рынках есть паттерны, скрытые в шуме. Временное Внимание помогает их найти!\n\n---\n\n## Интересный Факт!\n\nМеханизм внимания изначально был изобретён для **перевода языков**! Учёные поняли, что при переводе \"The cat sat on the mat\" на другой язык, не каждое слово требует равного внимания. \"Cat\" и \"mat\" важнее чем \"the\" и \"on\".\n\nПотом умные исследователи сказали: \"Погодите, это работает и для ВРЕМЕНИ тоже!\" И так родилось Временное Внимание!\n\n**Теперь оно помогает предсказывать цены акций, погоду, трафик и даже твою следующую рекомендацию на YouTube!**\n\n---\n\n*В следующий раз, когда тебя спросят что важно, думай как TABL: не говори \"всё!\" — выясни что ДЕЙСТВИТЕЛЬНО имеет значение!*\n", | |
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| "content": "\"\"\"\nTABL: Temporal Attention-Augmented Bilinear Network for Financial Time-Series\n\nThis module provides:\n- TABLConfig: Model configuration\n- TABLModel: Main TABL model\n- MultiHeadTABL: Multi-head attention variant\n- BilinearLayer: Bilinear projection layer\n- TemporalAttention: Temporal attention mechanism\n\"\"\"\n\nfrom .model import (\n TABLConfig,\n TABLModel,\n MultiHeadTABL,\n BilinearLayer,\n TemporalAttention,\n)\nfrom .data import (\n BybitDataLoader,\n prepare_tabl_data,\n extract_ohlcv_features,\n)\nfrom .strategy import (\n TABLStrategy,\n backtest_tabl_strategy,\n calculate_metrics,\n)\n\n__all__ = [\n # Model\n \"TABLConfig\",\n \"TABLModel\",\n \"MultiHeadTABL\",\n \"BilinearLayer\",\n \"TemporalAttention\",\n # Data\n \"BybitDataLoader\",\n \"prepare_tabl_data\",\n \"extract_ohlcv_features\",\n # Strategy\n \"TABLStrategy\",\n \"backtest_tabl_strategy\",\n \"calculate_metrics\",\n]\n\n__version__ = \"0.1.0\"\n" | |
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| "content": "\"\"\"\nTABL: Temporal Attention-Augmented Bilinear Network for Financial Time-Series\n\nThis module provides:\n- TABLConfig: Model configuration\n- TABLModel: Main TABL model\n- MultiHeadTABL: Multi-head attention variant\n- BilinearLayer: Bilinear projection layer\n- TemporalAttention: Temporal attention mechanism\n\"\"\"\n\nfrom .model import (\n TABLConfig,\n TABLModel,\n MultiHeadTABL,\n BilinearLayer,\n TemporalAttention,\n)\nfrom .data import (\n BybitDataLoader,\n prepare_tabl_data,\n extract_ohlcv_features,\n)\nfrom .strategy import (\n TABLStrategy,\n backtest_tabl_strategy,\n calculate_metrics,\n)\n\n__all__ = [\n # Model\n \"TABLConfig\",\n \"TABLModel\",\n \"MultiHeadTABL\",\n \"BilinearLayer\",\n \"TemporalAttention\",\n # Data\n \"BybitDataLoader\",\n \"prepare_tabl_data\",\n \"extract_ohlcv_features\",\n # Strategy\n \"TABLStrategy\",\n \"backtest_tabl_strategy\",\n \"calculate_metrics\",\n]\n\n__version__ = \"0.1.0\"\n", | |
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| [2026-01-18T21:01:46.684Z] [INFO] {"type":"assistant","message":{"model":"claude-opus-4-5-20251101","id":"msg_01ToLnrfc686Rbw8YsSJmUnt","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LQpoAptWVSwZ8dXVYq1ivV","name":"Write","input":{"file_path":"/tmp/gh-issue-solver-1768769379407/46_temporal_attention_networks/python/model.py","content":"\"\"\"\nTABL Model Implementation in PyTorch\n\nProvides:\n- TABLConfig: Model configuration\n- TABLModel: Main TABL model\n- MultiHeadTABL: Multi-head attention variant\n- BilinearLayer: Bilinear projection layer\n- TemporalAttention: Temporal attention mechanism\n\"\"\"\n\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom typing import Optional, Tuple, List, Union\nfrom dataclasses import dataclass, field\nfrom enum import Enum\n\n\nclass OutputType(Enum):\n \"\"\"Type of model output\"\"\"\n DIRECTION = \"direction\" # 3-class: up/stationary/down\n REGRESSION = \"regression\" # Continuous returns\n BINARY = \"binary\" # 2-class: up/down\n\n\n@dataclass\nclass TABLConfig:\n \"\"\"\n Configuration for TABL model\n\n Example:\n config = TABLConfig(\n seq_len=100,\n input_dim=6,\n hidden_T=20,\n hidden_D=32\n )\n \"\"\"\n # Input dimensions\n seq_len: int = 100 # Number of time steps\n input_dim: int = 6 # Number of input features\n\n # Bilinear layer dimensions\n hidden_T: int = 20 # Compressed temporal dimension\n hidden_D: int = 32 # Compressed feature dimension\n\n # Attention configuration\n attention_dim: int = 64 # Dimension for attention computation\n n_heads: int = 4 # Number of attention heads (for MultiHeadTABL)\n use_multihead: bool = False # Whether to use multi-head attention\n\n # Output configuration\n output_type: OutputType = OutputType.DIRECTION\n n_classes: int = 3 # Number of output classes (for classification)\n\n # Regularization\n dropout: float = 0.2\n use_batch_norm: bool = True\n\n # Training\n learning_rate: float = 0.001\n weight_decay: float = 1e-5\n\n def validate(self):\n \"\"\"Validate configuration\"\"\"\n assert self.seq_len > 0, \"seq_len must be positive\"\n assert self.input_dim > 0, \"input_dim must be positive\"\n assert self.hidden_T > 0 and self.hidden_T <= self.seq_len, \\\n \"hidden_T must be positive and <= seq_len\"\n assert self.hidden_D > 0, \"hidden_D must be positive\"\n assert 0 <= self.dropout < 1, \"dropout must be in [0, 1)\"\n\n @property\n def bilinear_output_dim(self) -> int:\n \"\"\"Output dimension of bilinear layer\"\"\"\n return self.hidden_T * self.hidden_D\n\n @property\n def total_output_dim(self) -> int:\n \"\"\"Total output dimension before classification head\"\"\"\n if self.use_multihead:\n return self.bilinear_output_dim + self.input_dim\n return self.bilinear_output_dim + self.input_dim\n\n\nclass BilinearLayer(nn.Module):\n \"\"\"\n Bilinear projection layer: H = σ(W₁ · X · W₂ + b)\n\n Transforms (batch, T, D) → (batch, T', D') by projecting both\n temporal and feature dimensions simultaneously.\n\n Args:\n T_in: Input temporal dimension\n T_out: Output temporal dimension\n D_in: Input feature dimension\n D_out: Output feature dimension\n dropout: Dropout rate\n use_batch_norm: Whether to use batch normalization\n \"\"\"\n\n def __init__(\n self,\n T_in: int,\n T_out: int,\n D_in: int,\n D_out: int,\n dropout: float = 0.1,\n use_batch_norm: bool = True\n ):\n super().__init__()\n self.T_in = T_in\n self.T_out = T_out\n self.D_in = D_in\n self.D_out = D_out\n\n # Temporal projection: (T_out, T_in)\n self.W1 = nn.Parameter(torch.empty(T_out, T_in))\n # Feature projection: (D_in, D_out)\n self.W2 = nn.Parameter(torch.empty(D_in, D_out))\n # Bias: (T_out, D_out)\n self.bias = nn.Parameter(torch.zeros(T_out, D_out))\n\n # Initialize weights\n nn.init.xavier_uniform_(self.W1)\n nn.init.xavier_uniform_(self.W2)\n\n # Normalization and regularization\n self.batch_norm = nn.BatchNorm1d(T_out * D_out) if use_batch_norm else None\n self.dropout = nn.Dropout(dropout)\n self.activation = nn.ReLU()\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor (batch, T_in, D_in)\n\n Returns:\n Output tensor (batch, T_out, D_out)\n \"\"\"\n batch_size = x.size(0)\n\n # Bilinear transformation: H = W1 · X · W2 + b\n # Step 1: W1 · X: (batch, T_out, D_in)\n out = torch.matmul(self.W1, x)\n\n # Step 2: (W1 · X) · W2: (batch, T_out, D_out)\n out = torch.matmul(out, self.W2)\n\n # Add bias\n out = out + self.bias\n\n # Apply batch normalization (flatten → normalize → reshape)\n if self.batch_norm is not None:\n out = out.view(batch_size, -1)\n out = self.batch_norm(out)\n out = out.view(batch_size, self.T_out, self.D_out)\n\n # Activation and dropout\n out = self.activation(out)\n out = self.dropout(out)\n\n return out\n\n\nclass TemporalAttention(nn.Module):\n \"\"\"\n Temporal attention mechanism: α = softmax(w · tanh(U · X^T))\n\n Learns to focus on important time steps by computing attention weights\n over the temporal dimension.\n\n Args:\n input_dim: Dimension of input features\n attention_dim: Dimension of attention hidden layer\n dropout: Dropout rate\n \"\"\"\n\n def __init__(\n self,\n input_dim: int,\n attention_dim: int = 64,\n dropout: float = 0.1\n ):\n super().__init__()\n\n # Project features to attention space\n self.U = nn.Linear(input_dim, attention_dim, bias=True)\n\n # Attention query vector\n self.w = nn.Linear(attention_dim, 1, bias=False)\n\n # Dropout for regularization\n self.dropout = nn.Dropout(dropout)\n\n def forward(\n self,\n x: torch.Tensor,\n mask: Optional[torch.Tensor] = None\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor (batch, T, D)\n mask: Optional mask tensor (batch, T) - 1 for valid, 0 for padding\n\n Returns:\n context: Context vector (batch, D)\n alpha: Attention weights (batch, T)\n \"\"\"\n # Compute attention scores\n # h = tanh(U · x): (batch, T, attention_dim)\n h = torch.tanh(self.U(x))\n\n # scores = w · h: (batch, T)\n scores = self.w(h).squeeze(-1)\n\n # Apply mask if provided\n if mask is not None:\n scores = scores.masked_fill(mask == 0, float('-inf'))\n\n # Softmax over time dimension\n alpha = F.softmax(scores, dim=-1)\n alpha = self.dropout(alpha)\n\n # Weighted sum: context = X^T · α\n # alpha: (batch, T) → (batch, 1, T)\n # x: (batch, T, D)\n # context: (batch, D)\n context = torch.bmm(alpha.unsqueeze(1), x).squeeze(1)\n\n return context, alpha\n\n\nclass TABL(nn.Module):\n \"\"\"\n Temporal Attention-Augmented Bilinear Layer\n\n Combines bilinear projection with temporal attention.\n\n Args:\n config: TABLConfig configuration object\n \"\"\"\n\n def __init__(self, config: TABLConfig):\n super().__init__()\n self.config = config\n\n # Bilinear projection\n self.bilinear = BilinearLayer(\n T_in=config.seq_len,\n T_out=config.hidden_T,\n D_in=config.input_dim,\n D_out=config.hidden_D,\n dropout=config.dropout,\n use_batch_norm=config.use_batch_norm\n )\n\n # Temporal attention\n self.attention = TemporalAttention(\n input_dim=config.input_dim,\n attention_dim=config.attention_dim,\n dropout=config.dropout\n )\n\n def forward(\n self,\n x: torch.Tensor,\n mask: Optional[torch.Tensor] = None,\n return_attention: bool = False\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor (batch, T, D)\n mask: Optional mask tensor (batch, T)\n return_attention: Whether to return attention weights\n\n Returns:\n out: Output features (batch, hidden_T*hidden_D + D)\n alpha: Attention weights (batch, T) - only if return_attention=True\n \"\"\"\n batch_size = x.size(0)\n\n # Bilinear projection: (batch, T, D) → (batch, T', D')\n h = self.bilinear(x)\n\n # Flatten bilinear output: (batch, T' * D')\n h_flat = h.view(batch_size, -1)\n\n # Temporal attention: (batch, D), (batch, T)\n context, alpha = self.attention(x, mask)\n\n # Concatenate bilinear output and context\n out = torch.cat([h_flat, context], dim=-1)\n\n if return_attention:\n return out, alpha\n return out\n\n\nclass MultiHeadTemporalAttention(nn.Module):\n \"\"\"\n Multi-head temporal attention mechanism\n\n Uses multiple attention heads to capture different temporal patterns.\n\n Args:\n input_dim: Dimension of input features\n attention_dim: Dimension of attention hidden layer per head\n n_heads: Number of attention heads\n dropout: Dropout rate\n \"\"\"\n\n def __init__(\n self,\n input_dim: int,\n attention_dim: int = 64,\n n_heads: int = 4,\n dropout: float = 0.1\n ):\n super().__init__()\n self.n_heads = n_heads\n\n # Multiple attention heads\n self.attention_heads = nn.ModuleList([\n TemporalAttention(input_dim, attention_dim, dropout)\n for _ in range(n_heads)\n ])\n\n # Head combination layer\n self.head_combine = nn.Sequential(\n nn.Linear(n_heads * input_dim, input_dim),\n nn.ReLU(),\n nn.Dropout(dropout)\n )\n\n def forward(\n self,\n x: torch.Tensor,\n mask: Optional[torch.Tensor] = None,\n return_attention: bool = False\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor (batch, T, D)\n mask: Optional mask tensor (batch, T)\n return_attention: Whether to return attention weights\n\n Returns:\n context: Combined context (batch, D)\n alphas: Attention weights (batch, n_heads, T) - only if return_attention=True\n \"\"\"\n contexts = []\n alphas = []\n\n for head in self.attention_heads:\n ctx, alpha = head(x, mask)\n contexts.append(ctx)\n alphas.append(alpha)\n\n # Combine heads: (batch, n_heads * D) → (batch, D)\n multi_context = torch.cat(contexts, dim=-1)\n combined = self.head_combine(multi_context)\n\n if return_attention:\n # Stack alphas: (batch, n_heads, T)\n stacked_alphas = torch.stack(alphas, dim=1)\n return combined, stacked_alphas\n return combined\n\n\nclass MultiHeadTABL(nn.Module):\n \"\"\"\n Multi-Head Temporal Attention Bilinear Layer\n\n Uses multiple attention heads to capture different temporal patterns.\n\n Args:\n config: TABLConfig configuration object\n \"\"\"\n\n def __init__(self, config: TABLConfig):\n super().__init__()\n self.config = config\n\n # Shared bilinear projection\n self.bilinear = BilinearLayer(\n T_in=config.seq_len,\n T_out=config.hidden_T,\n D_in=config.input_dim,\n D_out=config.hidden_D,\n dropout=config.dropout,\n use_batch_norm=config.use_batch_norm\n )\n\n # Multi-head temporal attention\n self.attention = MultiHeadTemporalAttention(\n input_dim=config.input_dim,\n attention_dim=config.attention_dim,\n n_heads=config.n_heads,\n dropout=config.dropout\n )\n\n def forward(\n self,\n x: torch.Tensor,\n mask: Optional[torch.Tensor] = None,\n return_attention: bool = False\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor (batch, T, D)\n mask: Optional mask tensor (batch, T)\n return_attention: Whether to return attention weights\n\n Returns:\n out: Output features (batch, hidden_T*hidden_D + D)\n alpha: Attention weights (batch, n_heads, T) - only if return_attention=True\n \"\"\"\n batch_size = x.size(0)\n\n # Bilinear projection\n h = self.bilinear(x)\n h_flat = h.view(batch_size, -1)\n\n # Multi-head temporal attention\n if return_attention:\n context, alphas = self.attention(x, mask, return_attention=True)\n else:\n context = self.attention(x, mask, return_attention=False)\n\n # Concatenate\n out = torch.cat([h_flat, context], dim=-1)\n\n if return_attention:\n return out, alphas\n return out\n\n\nclass TABLModel(nn.Module):\n \"\"\"\n Complete TABL Model for Financial Time-Series Prediction\n\n Example:\n config = TABLConfig(seq_len=100 | |
| [2026-01-18T21:01:46.699Z] [INFO] , input_dim=6)\n model = TABLModel(config)\n\n x = torch.randn(32, 100, 6) # [batch, seq_len, features]\n output = model(x)\n print(output['logits'].shape) # [32, 3] for 3-class classification\n \"\"\"\n\n def __init__(self, config: TABLConfig):\n super().__init__()\n config.validate()\n self.config = config\n\n # Input normalization\n self.input_norm = nn.LayerNorm(config.input_dim)\n\n # TABL layer (single or multi-head)\n if config.use_multihead:\n self.tabl = MultiHeadTABL(config)\n else:\n self.tabl = TABL(config)\n\n # Classification head\n hidden_dim = config.total_output_dim\n self.classifier = nn.Sequential(\n nn.Linear(hidden_dim, hidden_dim // 2),\n nn.ReLU(),\n nn.Dropout(config.dropout),\n nn.Linear(hidden_dim // 2, config.n_classes)\n )\n\n def forward(\n self,\n x: torch.Tensor,\n mask: Optional[torch.Tensor] = None,\n return_attention: bool = False\n ) -> dict:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor (batch, seq_len, input_dim)\n mask: Optional mask tensor (batch, seq_len)\n return_attention: Whether to return attention weights\n\n Returns:\n Dictionary with:\n - logits: Classification logits (batch, n_classes)\n - probs: Class probabilities (batch, n_classes)\n - attention: Attention weights (if return_attention=True)\n \"\"\"\n # Input normalization\n x = self.input_norm(x)\n\n # TABL layer\n if return_attention:\n features, attention = self.tabl(x, mask, return_attention=True)\n else:\n features = self.tabl(x, mask, return_attention=False)\n attention = None\n\n # Classification\n logits = self.classifier(features)\n\n # Compute probabilities\n if self.config.output_type == OutputType.BINARY:\n probs = torch.sigmoid(logits)\n else:\n probs = F.softmax(logits, dim=-1)\n\n result = {\n 'logits': logits,\n 'probs': probs,\n }\n\n if return_attention:\n result['attention'] = attention\n\n return result\n\n def predict(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Make predictions\n\n Args:\n x: Input tensor (batch, seq_len, input_dim)\n\n Returns:\n Predicted class labels (batch,)\n \"\"\"\n self.eval()\n with torch.no_grad():\n output = self.forward(x)\n if self.config.output_type == OutputType.BINARY:\n return (output['probs'] > 0.5).long().squeeze(-1)\n return output['logits'].argmax(dim=-1)\n\n\ndef create_tabl_model(\n seq_len: int = 100,\n input_dim: int = 6,\n n_classes: int = 3,\n use_multihead: bool = False,\n n_heads: int = 4,\n dropout: float = 0.2\n) -> TABLModel:\n \"\"\"\n Factory function to create a TABL model\n\n Args:\n seq_len: Number of time steps\n input_dim: Number of input features\n n_classes: Number of output classes\n use_multihead: Whether to use multi-head attention\n n_heads: Number of attention heads\n dropout: Dropout rate\n\n Returns:\n TABLModel instance\n \"\"\"\n config = TABLConfig(\n seq_len=seq_len,\n input_dim=input_dim,\n n_classes=n_classes,\n use_multihead=use_multihead,\n n_heads=n_heads,\n dropout=dropout\n )\n return TABLModel(config)\n\n\nif __name__ == \"__main__\":\n # Test the model\n print(\"Testing TABL model...\")\n\n # Single-head configuration\n config = TABLConfig(\n seq_len=100,\n input_dim=6,\n hidden_T=20,\n hidden_D=32,\n attention_dim=64,\n n_classes=3,\n use_multihead=False\n )\n\n model = TABLModel(config)\n print(f\"Single-head parameters: {sum(p.numel() for p in model.parameters()):,}\")\n\n # Test forward pass\n x = torch.randn(4, 100, 6)\n output = model(x, return_attention=True)\n\n print(f\"Logits shape: {output['logits'].shape}\")\n print(f\"Probs shape: {output['probs'].shape}\")\n print(f\"Attention shape: {output['attention'].shape}\")\n\n # Multi-head configuration\n config_mh = TABLConfig(\n seq_len=100,\n input_dim=6,\n hidden_T=20,\n hidden_D=32,\n attention_dim=64,\n n_heads=4,\n n_classes=3,\n use_multihead=True\n )\n\n model_mh = TABLModel(config_mh)\n print(f\"\\nMulti-head parameters: {sum(p.numel() for p in model_mh.parameters()):,}\")\n\n output_mh = model_mh(x, return_attention=True)\n print(f\"Multi-head logits shape: {output_mh['logits'].shape}\")\n print(f\"Multi-head attention shape: {output_mh['attention'].shape}\")\n\n # Test predictions\n preds = model.predict(x)\n print(f\"\\nPredictions shape: {preds.shape}\")\n print(f\"Predictions: {preds}\")\n"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":484,"cache_read_input_tokens":102418,"cache_creation":{"ephemeral_5m_input_tokens":484,"ephemeral_1h_input_tokens":0},"output_tokens":24,"service_tier":"standard"},"context_management":null},"parent_tool_use_id":null,"session_id":"77018388-5fda-4d3e-acd4-e8399840434b","uuid":"fef183fc-0096-4205-9af8-4ff11216d812"} | |
| [2026-01-18T21:01:46.756Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LQpoAptWVSwZ8dXVYq1ivV","type":"tool_result","content":"File created successfully at: /tmp/gh-issue-solver-1768769379407/46_temporal_attention_networks/python/model.py"}]},"parent_tool_use_id":null,"session_id":"77018388-5fda-4d3e-acd4-e8399840434b","uuid":"46051199-1d8e-47a6-b71e-fc8cd39af5c7","tool_use_result":{"type":"create","filePath":"/tmp/gh-issue-solver-1768769379407/46_temporal_attention_networks/python/model.py","content":"\"\"\"\nTABL Model Implementation in PyTorch\n\nProvides:\n- TABLConfig: Model configuration\n- TABLModel: Main TABL model\n- MultiHeadTABL: Multi-head attention variant\n- BilinearLayer: Bilinear projection layer\n- TemporalAttention: Temporal attention mechanism\n\"\"\"\n\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom typing import Optional, Tuple, List, Union\nfrom dataclasses import dataclass, field\nfrom enum import Enum\n\n\nclass OutputType(Enum):\n \"\"\"Type of model output\"\"\"\n DIRECTION = \"direction\" # 3-class: up/stationary/down\n REGRESSION = \"regression\" # Continuous returns\n BINARY = \"binary\" # 2-class: up/down\n\n\n@dataclass\nclass TABLConfig:\n \"\"\"\n Configuration for TABL model\n\n Example:\n config = TABLConfig(\n seq_len=100,\n input_dim=6,\n hidden_T=20,\n hidden_D=32\n )\n \"\"\"\n # Input dimensions\n seq_len: int = 100 # Number of time steps\n input_dim: int = 6 # Number of input features\n\n # Bilinear layer dimensions\n hidden_T: int = 20 # Compressed temporal dimension\n hidden_D: int = 32 # Compressed feature dimension\n\n # Attention configuration\n attention_dim: int = 64 # Dimension for attention computation\n n_heads: int = 4 # Number of attention heads (for MultiHeadTABL)\n use_multihead: bool = False # Whether to use multi-head attention\n\n # Output configuration\n output_type: OutputType = OutputType.DIRECTION\n n_classes: int = 3 # Number of output classes (for classification)\n\n # Regularization\n dropout: float = 0.2\n use_batch_norm: bool = True\n\n # Training\n learning_rate: float = 0.001\n weight_decay: float = 1e-5\n\n def validate(self):\n \"\"\"Validate configuration\"\"\"\n assert self.seq_len > 0, \"seq_len must be positive\"\n assert self.input_dim > 0, \"input_dim must be positive\"\n assert self.hidden_T > 0 and self.hidden_T <= self.seq_len, \\\n \"hidden_T must be positive and <= seq_len\"\n assert self.hidden_D > 0, \"hidden_D must be positive\"\n assert 0 <= self.dropout < 1, \"dropout must be in [0, 1)\"\n\n @property\n def bilinear_output_dim(self) -> int:\n \"\"\"Output dimension of bilinear layer\"\"\"\n return self.hidden_T * self.hidden_D\n\n @property\n def total_output_dim(self) -> int:\n \"\"\"Total output dimension before classification head\"\"\"\n if self.use_multihead:\n return self.bilinear_output_dim + self.input_dim\n return self.bilinear_output_dim + self.input_dim\n\n\nclass BilinearLayer(nn.Module):\n \"\"\"\n Bilinear projection layer: H = σ(W₁ · X · W₂ + b)\n\n Transforms (batch, T, D) → (batch, T', D') by projecting both\n temporal and feature dimensions simultaneously.\n\n Args:\n T_in: Input temporal dimension\n T_out: Output temporal dimension\n D_in: Input feature dimension\n D_out: Output feature dimension\n dropout: Dropout rate\n use_batch_norm: Whether to use batch normalization\n \"\"\"\n\n def __init__(\n self,\n T_in: int,\n T_out: int,\n D_in: int,\n D_out: int,\n dropout: float = 0.1,\n use_batch_norm: bool = True\n ):\n super().__init__()\n self.T_in = T_in\n self.T_out = T_out\n self.D_in = D_in\n self.D_out = D_out\n\n # Temporal projection: (T_out, T_in)\n self.W1 = nn.Parameter(torch.empty(T_out, T_in))\n # Feature projection: (D_in, D_out)\n self.W2 = nn.Parameter(torch.empty(D_in, D_out))\n # Bias: (T_out, D_out)\n self.bias = nn.Parameter(torch.zeros(T_out, D_out))\n\n # Initialize weights\n nn.init.xavier_uniform_(self.W1)\n nn.init.xavier_uniform_(self.W2)\n\n # Normalization and regularization\n self.batch_norm = nn.BatchNorm1d(T_out * D_out) if use_batch_norm else None\n self.dropout = nn.Dropout(dropout)\n self.activation = nn.ReLU()\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor (batch, T_in, D_in)\n\n Returns:\n Output tensor (batch, T_out, D_out)\n \"\"\"\n batch_size = x.size(0)\n\n # Bilinear transformation: H = W1 · X · W2 + b\n # Step 1: W1 · X: (batch, T_out, D_in)\n out = torch.matmul(self.W1, x)\n\n # Step 2: (W1 · X) · W2: (batch, T_out, D_out)\n out = torch.matmul(out, self.W2)\n\n # Add bias\n out = out + self.bias\n\n # Apply batch normalization (flatten → normalize → reshape)\n if self.batch_norm is not None:\n out = out.view(batch_size, -1)\n out = self.batch_norm(out)\n out = out.view(batch_size, self.T_out, self.D_out)\n\n # Activation and dropout\n out = self.activation(out)\n out = self.dropout(out)\n\n return out\n\n\nclass TemporalAttention(nn.Module):\n \"\"\"\n Temporal attention mechanism: α = softmax(w · tanh(U · X^T))\n\n Learns to focus on important time steps by computing attention weights\n over the temporal dimension.\n\n Args:\n input_dim: Dimension of input features\ | |
| [2026-01-18T21:01:46.758Z] [INFO] n attention_dim: Dimension of attention hidden layer\n dropout: Dropout rate\n \"\"\"\n\n def __init__(\n self,\n input_dim: int,\n attention_dim: int = 64,\n dropout: float = 0.1\n ):\n super().__init__()\n\n # Project features to attention space\n self.U = nn.Linear(input_dim, attention_dim, bias=True)\n\n # Attention query vector\n self.w = nn.Linear(attention_dim, 1, bias=False)\n\n # Dropout for regularization\n self.dropout = nn.Dropout(dropout)\n\n def forward(\n self,\n x: torch.Tensor,\n mask: Optional[torch.Tensor] = None\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor (batch, T, D)\n mask: Optional mask tensor (batch, T) - 1 for valid, 0 for padding\n\n Returns:\n context: Context vector (batch, D)\n alpha: Attention weights (batch, T)\n \"\"\"\n # Compute attention scores\n # h = tanh(U · x): (batch, T, attention_dim)\n h = torch.tanh(self.U(x))\n\n # scores = w · h: (batch, T)\n scores = self.w(h).squeeze(-1)\n\n # Apply mask if provided\n if mask is not None:\n scores = scores.masked_fill(mask == 0, float('-inf'))\n\n # Softmax over time dimension\n alpha = F.softmax(scores, dim=-1)\n alpha = self.dropout(alpha)\n\n # Weighted sum: context = X^T · α\n # alpha: (batch, T) → (batch, 1, T)\n # x: (batch, T, D)\n # context: (batch, D)\n context = torch.bmm(alpha.unsqueeze(1), x).squeeze(1)\n\n return context, alpha\n\n\nclass TABL(nn.Module):\n \"\"\"\n Temporal Attention-Augmented Bilinear Layer\n\n Combines bilinear projection with temporal attention.\n\n Args:\n config: TABLConfig configuration object\n \"\"\"\n\n def __init__(self, config: TABLConfig):\n super().__init__()\n self.config = config\n\n # Bilinear projection\n self.bilinear = BilinearLayer(\n T_in=config.seq_len,\n T_out=config.hidden_T,\n D_in=config.input_dim,\n D_out=config.hidden_D,\n dropout=config.dropout,\n use_batch_norm=config.use_batch_norm\n )\n\n # Temporal attention\n self.attention = TemporalAttention(\n input_dim=config.input_dim,\n attention_dim=config.attention_dim,\n dropout=config.dropout\n )\n\n def forward(\n self,\n x: torch.Tensor,\n mask: Optional[torch.Tensor] = None,\n return_attention: bool = False\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor (batch, T, D)\n mask: Optional mask tensor (batch, T)\n return_attention: Whether to return attention weights\n\n Returns:\n out: Output features (batch, hidden_T*hidden_D + D)\n alpha: Attention weights (batch, T) - only if return_attention=True\n \"\"\"\n batch_size = x.size(0)\n\n # Bilinear projection: (batch, T, D) → (batch, T', D')\n h = self.bilinear(x)\n\n # Flatten bilinear output: (batch, T' * D')\n h_flat = h.view(batch_size, -1)\n\n # Temporal attention: (batch, D), (batch, T)\n context, alpha = self.attention(x, mask)\n\n # Concatenate bilinear output and context\n out = torch.cat([h_flat, context], dim=-1)\n\n if return_attention:\n return out, alpha\n return out\n\n\nclass MultiHeadTemporalAttention(nn.Module):\n \"\"\"\n Multi-head temporal attention mechanism\n\n Uses multiple attention heads to capture different temporal patterns.\n\n Args:\n input_dim: Dimension of input features\n attention_dim: Dimension of attention hidden layer per head\n n_heads: Number of attention heads\n dropout: Dropout rate\n \"\"\"\n\n def __init__(\n self,\n input_dim: int,\n attention_dim: int = 64,\n n_heads: int = 4,\n dropout: float = 0.1\n ):\n super().__init__()\n self.n_heads = n_heads\n\n # Multiple attention heads\n self.attention_heads = nn.ModuleList([\n TemporalAttention(input_dim, attention_dim, dropout)\n for _ in range(n_heads)\n ])\n\n # Head combination layer\n self.head_combine = nn.Sequential(\n nn.Linear(n_heads * input_dim, input_dim),\n nn.ReLU(),\n nn.Dropout(dropout)\n )\n\n def forward(\n self,\n x: torch.Tensor,\n mask: Optional[torch.Tensor] = None,\n return_attention: bool = False\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor (batch, T, D)\n mask: Optional mask tensor (batch, T)\n return_attention: Whether to return attention weights\n\n Returns:\n context: Combined context (batch, D)\n alphas: Attention weights (batch, n_heads, T) - only if return_attention=True\n \"\"\"\n contexts = []\n alphas = []\n\n for head in self.attention_heads:\n ctx, alpha = head(x, mask)\n contexts.append(ctx)\n alphas.append(alpha)\n\n # Combine heads: (batch, n_heads * D) → (batch, D)\n multi_context = torch.cat(contexts, dim=-1)\n combined = self.head_combine(multi_context)\n\n if return_attention:\n # Stack alphas: (batch, n_heads, T)\n stacked_alphas = torch.stack(alphas, dim=1)\n return combined, stacked_alphas\n return combined\n\n\nclass MultiHeadTABL(nn.Module):\n \"\"\"\n Multi-Head Temporal Attention Bilinear Layer\n\n Uses multiple attention heads to capture different temporal patterns.\n\n Args:\n config: TABLConfig configuration object\n \"\"\"\n\n def __init__(self, config: TABLConfig):\n super().__init__()\n self.config = config\n\n # Shared bilinear projection\n self.bilinear = BilinearLayer(\n T_in=config.seq_len,\n T_out=config.hidden_T,\n D_in=config.input_dim,\n D_out=config.hidden_D,\n dropout=config.dropout,\n use_batch_norm=config.use_batch_norm\n )\n\n # Multi-head temporal attention\n self.attention = MultiHeadTemporalAttention(\n input_dim=config.input_dim,\n attention_dim=config.attention_dim,\n n_heads=config.n_heads,\n dropout=config.dropout\n )\n\n def forward(\n self,\n x: torch.Tensor,\n mask: Optional[torch.Tensor] = None,\n return_attention: bool = False\n ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor (batch, T, D)\n mask: Optional mask tensor (batch, T)\n return_attention: Whether to return attention weights\n\n Returns:\n out: Output features (batch, hidden_T*hidden_D + D)\n alpha: Attention weights (batch, n_heads, T) - only if return_attention=True\n \"\"\"\n batch_size = x.size(0)\n\n # Bilinear projection\n h = self.bilinear(x)\n h_flat = h.view(batch_size, -1)\n\n # Multi-head temporal attention\n if return_attention:\n context, alphas = self.attention(x, mask, return_attention=True)\n else:\n context = self.attention(x, mask, return_attention=False)\n\n # Concatenate\n out = torch.cat([h_flat, context], dim=-1)\n\n if return_attention:\n return out, alphas\n return out\n\n\nclass TABLModel(nn.Module):\n \"\"\"\n Complete TABL Model for Financial Time-Series Prediction\n\n Example:\n config = TABLConfig(seq_len=100, input_dim=6)\n model = TABLModel(config)\n\n x = torch.randn(32, 100, 6) # [batch, seq_len, features]\n output = model(x)\n print(output['logits'].shape) # [32, 3] for 3-class classification\n \"\"\"\n\n def __init__(self, config: TABLConfig):\n super().__init__()\n config.validate()\n self.config = config\n\n # Input normalization\n self.input_norm = nn.LayerNorm(config.input_dim)\n\n # TABL layer (single or multi-head)\n if config.use_multihead:\n self.tabl = MultiHeadTABL(config)\n else:\n self.tabl = TABL(config)\n\n # Classification head\n hidden_dim = config.total_output_dim\n self.classifier = nn.Sequential(\n nn.Linear(hidden_dim, hidden_dim // 2),\n nn.ReLU(),\n nn.Dropout(config.dropout),\n nn.Linear(hidden_dim // 2, config.n_classes)\n )\n\n def forward(\n self,\n x: torch.Tensor,\n mask: Optional[torch.Tensor] = None,\n return_attention: bool = False\n ) -> dict:\n \"\"\"\n Forward pass\n\n Args:\n x: Input tensor (batch, seq_len, input_dim)\n mask: Optional mask tensor (batch, seq_len)\n return_attention: Whether to return attention weights\n\n Returns:\n Dictionary with:\n - logits: Classification logits (batch, n_classes)\n - probs: Class probabilities (batch, n_classes)\n - attention: Attention weights (if return_attention=True)\n \"\"\"\n # Input normalization\n x = self.input_norm(x)\n\n # TABL layer\n if return_attention:\n features, attention = self.tabl(x, mask, return_attention=True)\n else:\n features = self.tabl(x, mask, return_attention=False)\n attention = None\n\n # Classification\n logits = self.classifier(features)\n\n # Compute probabilities\n if self.config.output_type == OutputType.BINARY:\n probs = torch.sigmoid(logits)\n else:\n probs = F.softmax(logits, dim=-1)\n\n result = {\n 'logits': logits,\n 'probs': probs,\n }\n\n if return_attention:\n result['attention'] = attention\n\n return result\n\n def predict(self, x: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Make predictions\n\n Args:\n x: Input tensor (batch, seq_len, input_dim)\n\n Returns:\n Predicted class labels (batch,)\n \"\"\"\n self.eval()\n with torch.no_grad():\n output = self.forward(x)\n if self.config.output_type == OutputType.BINARY:\n return (output['probs'] > 0.5).long().squeeze(-1)\n return output['logits'].argmax(dim=-1)\n\n\ndef create_tabl_model(\n seq_len: int = 100,\n input_dim: int = 6,\n n_classes: int = 3,\n use_multihead: bool = False,\n n_heads: int = 4,\n dropout: float = 0.2\n) -> TABLModel:\n \"\"\"\n Factory function to create a TABL model\n\n Args:\n seq_len: Number of time steps\n input_dim: Number of input features\n n_classes: Number of output classes\n use_multihead: Whether to use multi-head attention\n n_heads: Number of attention heads\n dropout: Dropout rate\n\n Returns:\n TABLModel instance\n \"\"\"\n config = TABLConfig(\n seq_len=seq_len,\n input_dim=input_dim,\n n_classes=n_classes,\n use_multihead=use_multihead,\n n_heads=n_heads,\n dropout=dropout\n )\n return TABLModel(config)\n\n\nif __name__ == \"__main__\":\n # Test the model\n print(\"Testing TABL model...\")\n\n # Single-head configuration\n config = TABLConfig(\n seq_len=100,\n input_dim=6,\n hidden_T=20,\n hidden_D=32,\n attention_dim=64,\n n_classes=3,\n use_multihead=False\n )\n\n model = TABLModel(config)\n print(f\"Single-head parameters: {sum(p.numel() for p in model.parameters()):,}\")\n\n # Test forward pass\n x = torch.randn(4, 100, 6)\n output = model(x, return_attention=True)\n\n print(f\"Logits shape: {output['logits'].shape}\")\n print(f\"Probs shape: {output['probs'].shape}\")\n print(f\"Attention shape: {output['attention'].shape}\")\n\n # Multi-head configuration\n config_mh = TABLConfig(\n seq_len=100,\n input_dim=6,\n hidden_T=20,\n hidden_D=32,\n attention_dim=64,\n n_heads=4,\n n_classes=3,\n use_multihead=True\n )\n\n model_mh = TABLModel(config_mh)\n print(f\"\\nMulti-head parameters: {sum(p.numel() for p in model_mh.parameters()):,}\")\n\n output_mh = model_mh(x, return_attention=True)\n print(f\"Multi-head logits shape: {output_mh['logits'].shape}\")\n print(f\"Multi-head attention shape: {output_mh['attention'].shape}\")\n\n # Test predictions\n preds = model.predict(x)\n print(f\"\\nPredictions shape: {preds.shape}\")\n print(f\"Predictions: {preds}\")\n","structuredPatch":[],"originalFile":null}} | |
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| "content": "\"\"\"\nData Loading and Feature Engineering for TABL\n\nProvides:\n- BybitDataLoader: Load cryptocurrency data from Bybit API\n- prepare_tabl_data: Prepare data for TABL training\n- extract_ohlcv_features: Extract features from OHLCV data\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom typing import Tuple, List, Optional, Dict\nfrom dataclasses import dataclass\nimport requests\nfrom datetime import datetime, timedelta\nimport time\nimport logging\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass Kline:\n \"\"\"Single candlestick data\"\"\"\n timestamp: int\n open: float\n high: float\n low: float\n close: float\n volume: float\n turnover: float\n\n\nclass BybitDataLoader:\n \"\"\"\n Load cryptocurrency data from Bybit API\n\n Example:\n loader = BybitDataLoader()\n df = loader.get_klines(\"BTCUSDT\", \"1h\", 1000)\n \"\"\"\n\n BASE_URL = \"https://api.bybit.com\"\n\n def __init__(self, testnet: bool = False):\n \"\"\"\n Initialize Bybit data loader\n\n Args:\n testnet: Whether to use testnet API\n \"\"\"\n if testnet:\n self.BASE_URL = \"https://api-testnet.bybit.com\"\n\n def get_klines(\n self,\n symbol: str,\n interval: str = \"1h\",\n limit: int = 1000,\n start_time: Optional[int] = None,\n end_time: Optional[int] = None\n ) -> pd.DataFrame:\n \"\"\"\n Get kline/candlestick data\n\n Args:\n symbol: Trading pair (e.g., \"BTCUSDT\")\n interval: Kline interval (1m, 5m, 15m, 30m, 1h, 4h, 1d)\n limit: Number of klines to fetch (max 1000)\n start_time: Start timestamp in milliseconds\n end_time: End timestamp in milliseconds\n\n Returns:\n DataFrame with OHLCV data\n \"\"\"\n endpoint = f\"{self.BASE_URL}/v5/market/kline\"\n\n params = {\n \"category\": \"spot\",\n \"symbol\": symbol,\n \"interval\": self._convert_interval(interval),\n \"limit\": min(limit, 1000)\n }\n\n if start_time:\n params[\"start\"] = start_time\n if end_time:\n params[\"end\"] = end_time\n\n try:\n response = requests.get(endpoint, params=params, timeout=10)\n response.raise_for_status()\n data = response.json()\n\n if data[\"retCode\"] != 0:\n raise ValueError(f\"API error: {data['retMsg']}\")\n\n klines = data[\"result\"][\"list\"]\n\n # Convert to DataFrame\n df = pd.DataFrame(klines, columns=[\n \"timestamp\", \"open\", \"high\", \"low\", \"close\", \"volume\", \"turnover\"\n ])\n\n # Convert types\n df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"].astype(int), unit=\"ms\")\n for col in [\"open\", \"high\", \"low\", \"close\", \"volume\", \"turnover\"]:\n df[col] = df[col].astype(float)\n\n # Sort by timestamp ascending\n df = df.sort_values(\"timestamp\").reset_index(drop=True)\n\n return df\n\n except requests.RequestException as e:\n logger.error(f\"Request failed: {e}\")\n raise\n\n def get_historical_klines(\n self,\n symbol: str,\n interval: str = \"1h\",\n days: int = 30\n ) -> pd.DataFrame:\n \"\"\"\n Get historical klines for a specified number of days\n\n Args:\n symbol: Trading pair\n interval: Kline interval\n days: Number of days of history\n\n Returns:\n DataFrame with OHLCV data\n \"\"\"\n all_data = []\n end_time = int(datetime.now().timestamp() * 1000)\n interval_ms = self._interval_to_ms(interval)\n klines_per_request = 1000\n\n total_klines_needed = int((days * 24 * 60 * 60 * 1000) / interval_ms)\n requests_needed = (total_klines_needed // klines_per_request) + 1\n\n logger.info(f\"Fetching {total_klines_needed} klines for {symbol}...\")\n\n for i in range(requests_needed):\n df = self.get_klines(\n symbol=symbol,\n interval=interval,\n limit=1000,\n end_time=end_time\n )\n\n if df.empty:\n break\n\n all_data.append(df)\n end_time = int(df[\"timestamp\"].iloc[0].timestamp() * 1000) - 1\n\n # Rate limiting\n time.sleep(0.1)\n\n if not all_data:\n return pd.DataFrame()\n\n result = pd.concat(all_data, ignore_index=True)\n result = result.drop_duplicates(subset=[\"timestamp\"]).sort_values(\"timestamp\")\n return result.reset_index(drop=True)\n\n def _convert_interval(self, interval: str) -> str:\n \"\"\"Convert interval string to Bybit format\"\"\"\n mapping = {\n \"1m\": \"1\", \"3m\": \"3\", \"5m\": \"5\", \"15m\": \"15\", \"30m\": \"30\",\n \"1h\": \"60\", \"2h\": \"120\", \"4h\": \"240\", \"6h\": \"360\", \"12h\": \"720\",\n \"1d\": \"D\", \"1w\": \"W\", \"1M\": \"M\"\n }\n return mapping.get(interval, interval)\n\n def _interval_to_ms(self, interval: str) -> int:\n \"\"\"Convert interval string to milliseconds\"\"\"\n mapping = {\n \"1m\": 60000, \"3m\": 180000, \"5m\": 300000, \"15m\": 900000,\n \"30m\": 1800000, \"1h\": 3600000, \"2h\": 7200000, \"4h\": 14400000,\n \"6h\": 21600000, \"12h\": 43200000, \"1d\": 86400000, \"1w\": 604800000\n }\n return mapping.get(interval, 3600000)\n\n\ndef extract_ohlcv_features(\n df: pd.DataFrame,\n include_technical: bool = True\n) -> pd.DataFrame:\n \"\"\"\n Extract features from OHLCV data\n\n Args:\n df: DataFrame with OHLCV columns\n include_technical: Whether to include technical indicators\n\n Returns:\n DataFrame with extracted features\n \"\"\"\n features = pd.DataFrame(index=df.index)\n\n # Price features\n features[\"log_return\"] = np.log(df[\"close\"] / df[\"close\"].shift(1))\n features[\"high_low_range\"] = (df[\"high\"] - df[\"low\"]) / (df[\"close\"] + 1e-8)\n features[\"close_open_diff\"] = (df[\"close\"] - df[\"open\"]) / (df[\"open\"] + 1e-8)\n features[\"hl_position\"] = (df[\"close\"] - df[\"low\"]) / (df[\"high\"] - df[\"low\"] + 1e-8)\n\n # Volume features\n vol_ma_20 = df[\"volume\"].rolling(20).mean()\n features[\"volume_ma_ratio\"] = df[\"volume\"] / (vol_ma_20 + 1e-8)\n features[\"volume_change\"] = df[\"volume\"].pct_change()\n\n # Volatility\n features[\"volatility_20\"] = features[\"log_return\"].rolling(20).std()\n features[\"volatility_5\"] = features[\"log_return\"].rolling(5).std()\n\n if include_technical:\n # RSI\n features[\"rsi_14\"] = compute_rsi(df[\"close\"], 14)\n\n # MACD\n macd, signal = compute_macd(df[\"close\"])\n features[\"macd\"] = macd\n features[\"macd_signal\"] = signal\n features[\"macd_hist\"] = macd - signal\n\n # Bollinger Bands\n bb_upper, bb_lower = compute_bollinger_bands(df[\"close\"])\n features[\"bb_position\"] = (df[\"close\"] - bb_lower) / (bb_upper - bb_lower + 1e-8)\n\n # Momentum\n features[\"momentum_5\"] = df[\"close\"] / df[\"close\"].shift(5) - 1\n features[\"momentum_10\"] = df[\"close\"] / df[\"close\"].shift(10) - 1\n features[\"momentum_20\"] = df[\"close\"] / df[\"close\"].shift(20) - 1\n\n return features\n\n\ndef compute_rsi(prices: pd.Series, period: int = 14) -> pd.Series:\n \"\"\"Compute Relative Strength Index\"\"\"\n delta = prices.diff()\n gain = delta.where(delta > 0, 0).rolling(window=period).mean()\n loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()\n rs = gain / (loss + 1e-8)\n rsi = 100 - (100 / (1 + rs))\n return rsi / 100 # Normalize to [0, 1]\n\n\ndef compute_macd(\n prices: pd.Series,\n fast: int = 12,\n slow: int = 26,\n signal: int = 9\n) -> Tuple[pd.Series, pd.Series]:\n \"\"\"Compute MACD indicator\"\"\"\n exp_fast = prices.ewm(span=fast, adjust=False).mean()\n exp_slow = prices.ewm(span=slow, adjust=False).mean()\n macd = exp_fast - exp_slow\n signal_line = macd.ewm(span=signal, adjust=False).mean()\n # Normalize by price\n return macd / prices, signal_line / prices\n\n\ndef compute_bollinger_bands(\n prices: pd.Series,\n period: int = 20,\n num_std: float = 2.0\n) -> Tuple[pd.Series, pd.Series]:\n \"\"\"Compute Bollinger Bands\"\"\"\n ma = prices.rolling(window=period).mean()\n std = prices.rolling(window=period).std()\n upper = ma + (std * num_std)\n lower = ma - (std * num_std)\n return upper, lower\n\n\ndef prepare_tabl_data(\n df: pd.DataFrame,\n lookback: int = 100,\n horizon: int = 10,\n threshold: float = 0.0002,\n feature_columns: Optional[List[str]] = None\n) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"\n Prepare data for TABL model training\n\n Args:\n df: DataFrame with OHLCV data\n lookback: Number of time steps to look back\n horizon: Prediction horizon\n threshold: Threshold for direction classification\n feature_columns: List of feature columns to use\n\n Returns:\n X: Features (n_samples, lookback, n_features)\n y: Labels (n_samples,) - 0: down, 1: stationary, 2: up\n returns: Actual returns (n_samples,) - for backtesting\n \"\"\"\n # Extract features\n features = extract_ohlcv_features(df)\n\n # Select feature columns\n if feature_columns is None:\n feature_columns = [\n \"log_return\", \"high_low_range\", \"close_open_diff\",\n \"volume_ma_ratio\", \"volatility_20\", \"rsi_14\"\n ]\n\n # Filter to available columns\n available_cols = [c for c in feature_columns if c in features.columns]\n features = features[available_cols]\n\n # Fill NaN values\n features = features.fillna(0)\n\n # Normalize features (z-score)\n features_mean = features.mean()\n features_std = features.std()\n features_norm = (features - features_mean) / (features_std + 1e-8)\n\n # Create sequences\n X, y, returns = [], [], []\n\n for i in range(lookback + 20, len(features_norm) - horizon): # +20 for feature warmup\n # Input sequence\n x_seq = features_norm.iloc[i-lookback:i].values\n\n # Target: future log return\n future_return = np.log(\n df[\"close\"].iloc[i + horizon] / df[\"close\"].iloc[i]\n )\n\n # Classify direction\n if future_return > threshold:\n label = 2 # Up\n elif future_return < -threshold:\n label = 0 # Down\n else:\n label = 1 # Stationary\n\n X.append(x_seq)\n y.append(label)\n returns.append(future_return)\n\n X = np.array(X, dtype=np.float32)\n y = np.array(y, dtype=np.int64)\n returns = np.array(returns, dtype=np.float32)\n\n logger.info(f\"Prepared {len(X)} samples with {X.shape[2]} features\")\n logger.info(f\"Class distribution: down={np.sum(y==0)}, stationary={np.sum(y==1)}, up={np.sum(y==2)}\")\n\n return X, y, returns\n\n\ndef create_train_val_test_split(\n X: np.ndarray,\n y: np.ndarray,\n returns: np.ndarray,\n train_ratio: float = 0.7,\n val_ratio: float = 0.15\n) -> Dict[str, Tuple[np.ndarray, np.ndarray, np.ndarray]]:\n \"\"\"\n Split data into train/val/test sets (time-based, no shuffle)\n\n Args:\n X: Features array\n y: Labels array\n returns: Returns array\n train_ratio: Ratio for training set\n val_ratio: Ratio for validation set\n\n Returns:\n Dictionary with 'train', 'val', 'test' splits\n \"\"\"\n n = len(X)\n train_end = int(n * train_ratio)\n val_end = int(n * (train_ratio + val_ratio))\n\n return {\n \"train\": (X[:train_end], y[:train_end], returns[:train_end]),\n \"val\": (X[train_end:val_end], y[train_end:val_end], returns[train_end:val_end]),\n \"test\": (X[val_end:], y[val_end:], returns[val_end:])\n }\n\n\nclass TABLDataset:\n \"\"\"\n PyTorch-compatible dataset for TABL\n\n Example:\n dataset = TABLDataset(X, y)\n loader = DataLoader(dataset, batch_size=32)\n \"\"\"\n\n def __init__(self, X: np.ndarray, y: np.ndarray):\n \"\"\"\n Initialize dataset\n\n Args:\n X: Features (n_samples, seq_len, n_features)\n y: Labels (n_samples,)\n \"\"\"\n self.X = X\n self.y = y\n\n def __len__(self) -> int:\n return len(self.X)\n\n def __getitem__(self, idx: int) -> Tuple[np.ndarray, int]:\n return self.X[idx], self.y[idx]\n\n\nif __name__ == \"__main__\":\n # Test data loading\n print(\"Testing data loading...\")\n\n # Create sample data (simulated since we can't call API in test)\n np.random.seed(42)\n n_samples = 1000\n\n # Simulate OHLCV data\n base_price = 100\n prices = base_price + np.cumsum(np.random.randn(n_samples) * 0.5)\n volumes = np.abs(np.random.randn(n_samples) * 1000 + 5000)\n\n df = pd.DataFrame({\n \"timestamp\": pd.date_range(\"2024-01-01\", periods=n_samples, freq=\"h\"),\n \"open\": prices + np.random.randn(n_samples) * 0.1,\n \"high\": prices + np.abs(np.random.randn(n_samples)) * 0.5,\n \"low\": prices - np.abs(np.random.randn(n_samples)) * 0.5,\n \"close\": prices,\n \"volume\": volumes,\n \"turnover\": prices * volumes\n })\n\n # Test feature extraction\n features = extract_ohlcv_features(df)\n print(f\"Extracted features: {list(features.columns)}\")\n print(f\"Feature shape: {features.shape}\")\n\n # Test data preparation\n X, y, returns = prepare_tabl_data(df, lookback=100, horizon=10)\n print(f\"\\nData shapes: X={X.shape}, y={y.shape}, returns={returns.shape}\")\n\n # Test split\n splits = create_train_val_test_split(X, y, returns)\n for name, (X_s, y_s, r_s) in splits.items():\n print(f\"{name}: X={X_s.shape}, y={y_s.shape}\")\n\n print(\"\\nData loading tests passed!\")\n" | |
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| "content": "\"\"\"\nData Loading and Feature Engineering for TABL\n\nProvides:\n- BybitDataLoader: Load cryptocurrency data from Bybit API\n- prepare_tabl_data: Prepare data for TABL training\n- extract_ohlcv_features: Extract features from OHLCV data\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom typing import Tuple, List, Optional, Dict\nfrom dataclasses import dataclass\nimport requests\nfrom datetime import datetime, timedelta\nimport time\nimport logging\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass Kline:\n \"\"\"Single candlestick data\"\"\"\n timestamp: int\n open: float\n high: float\n low: float\n close: float\n volume: float\n turnover: float\n\n\nclass BybitDataLoader:\n \"\"\"\n Load cryptocurrency data from Bybit API\n\n Example:\n loader = BybitDataLoader()\n df = loader.get_klines(\"BTCUSDT\", \"1h\", 1000)\n \"\"\"\n\n BASE_URL = \"https://api.bybit.com\"\n\n def __init__(self, testnet: bool = False):\n \"\"\"\n Initialize Bybit data loader\n\n Args:\n testnet: Whether to use testnet API\n \"\"\"\n if testnet:\n self.BASE_URL = \"https://api-testnet.bybit.com\"\n\n def get_klines(\n self,\n symbol: str,\n interval: str = \"1h\",\n limit: int = 1000,\n start_time: Optional[int] = None,\n end_time: Optional[int] = None\n ) -> pd.DataFrame:\n \"\"\"\n Get kline/candlestick data\n\n Args:\n symbol: Trading pair (e.g., \"BTCUSDT\")\n interval: Kline interval (1m, 5m, 15m, 30m, 1h, 4h, 1d)\n limit: Number of klines to fetch (max 1000)\n start_time: Start timestamp in milliseconds\n end_time: End timestamp in milliseconds\n\n Returns:\n DataFrame with OHLCV data\n \"\"\"\n endpoint = f\"{self.BASE_URL}/v5/market/kline\"\n\n params = {\n \"category\": \"spot\",\n \"symbol\": symbol,\n \"interval\": self._convert_interval(interval),\n \"limit\": min(limit, 1000)\n }\n\n if start_time:\n params[\"start\"] = start_time\n if end_time:\n params[\"end\"] = end_time\n\n try:\n response = requests.get(endpoint, params=params, timeout=10)\n response.raise_for_status()\n data = response.json()\n\n if data[\"retCode\"] != 0:\n raise ValueError(f\"API error: {data['retMsg']}\")\n\n klines = data[\"result\"][\"list\"]\n\n # Convert to DataFrame\n df = pd.DataFrame(klines, columns=[\n \"timestamp\", \"open\", \"high\", \"low\", \"close\", \"volume\", \"turnover\"\n ])\n\n # Convert types\n df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"].astype(int), unit=\"ms\")\n for col in [\"open\", \"high\", \"low\", \"close\", \"volume\", \"turnover\"]:\n df[col] = df[col].astype(float)\n\n # Sort by timestamp ascending\n df = df.sort_values(\"timestamp\").reset_index(drop=True)\n\n return df\n\n except requests.RequestException as e:\n logger.error(f\"Request failed: {e}\")\n raise\n\n def get_historical_klines(\n self,\n symbol: str,\n interval: str = \"1h\",\n days: int = 30\n ) -> pd.DataFrame:\n \"\"\"\n Get historical klines for a specified number of days\n\n Args:\n symbol: Trading pair\n interval: Kline interval\n days: Number of days of history\n\n Returns:\n DataFrame with OHLCV data\n \"\"\"\n all_data = []\n end_time = int(datetime.now().timestamp() * 1000)\n interval_ms = self._interval_to_ms(interval)\n klines_per_request = 1000\n\n total_klines_needed = int((days * 24 * 60 * 60 * 1000) / interval_ms)\n requests_needed = (total_klines_needed // klines_per_request) + 1\n\n logger.info(f\"Fetching {total_klines_needed} klines for {symbol}...\")\n\n for i in range(requests_needed):\n df = self.get_klines(\n symbol=symbol,\n interval=interval,\n limit=1000,\n end_time=end_time\n )\n\n if df.empty:\n break\n\n all_data.append(df)\n end_time = int(df[\"timestamp\"].iloc[0].timestamp() * 1000) - 1\n\n # Rate limiting\n time.sleep(0.1)\n\n if not all_data:\n return pd.DataFrame()\n\n result = pd.concat(all_data, ignore_index=True)\n result = result.drop_duplicates(subset=[\"timestamp\"]).sort_values(\"timestamp\")\n return result.reset_index(drop=True)\n\n def _convert_interval(self, interval: str) -> str:\n \"\"\"Convert interval string to Bybit format\"\"\"\n mapping = {\n \"1m\": \"1\", \"3m\": \"3\", \"5m\": \"5\", \"15m\": \"15\", \"30m\": \"30\",\n \"1h\": \"60\", \"2h\": \"120\", \"4h\": \"240\", \"6h\": \"360\", \"12h\": \"720\",\n \"1d\": \"D\", \"1w\": \"W\", \"1M\": \"M\"\n }\n return mapping.get(interval, interval)\n\n def _interval_to_ms(self, interval: str) -> int:\n \"\"\"Convert interval string to milliseconds\"\"\"\n mapping = {\n \"1m\": 60000, \"3m\": 180000, \"5m\": 300000, \"15m\": 900000,\n \"30m\": 1800000, \"1h\": 3600000, \"2h\": 7200000, \"4h\": 14400000,\n \"6h\": 21600000, \"12h\": 43200000, \"1d\": 86400000, \"1w\": 604800000\n }\n return mapping.get(interval, 3600000)\n\n\ndef extract_ohlcv_features(\n df: pd.DataFrame,\n include_technical: bool = True\n) -> pd.DataFrame:\n \"\"\"\n Extract features from OHLCV data\n\n Args:\n df: DataFrame with OHLCV columns\n include_technical: Whether to include technical indicators\n\n Returns:\n DataFrame with extracted features\n \"\"\"\n features = pd.DataFrame(index=df.index)\n\n # Price features\n features[\"log_return\"] = np.log(df[\"close\"] / df[\"close\"].shift(1))\n features[\"high_low_range\"] = (df[\"high\"] - df[\"low\"]) / (df[\"close\"] + 1e-8)\n features[\"close_open_diff\"] = (df[\"close\"] - df[\"open\"]) / (df[\"open\"] + 1e-8)\n features[\"hl_position\"] = (df[\"close\"] - df[\"low\"]) / (df[\"high\"] - df[\"low\"] + 1e-8)\n\n # Volume features\n vol_ma_20 = df[\"volume\"].rolling(20).mean()\n features[\"volume_ma_ratio\"] = df[\"volume\"] / (vol_ma_20 + 1e-8)\n features[\"volume_change\"] = df[\"volume\"].pct_change()\n\n # Volatility\n features[\"volatility_20\"] = features[\"log_return\"].rolling(20).std()\n features[\"volatility_5\"] = features[\"log_return\"].rolling(5).std()\n\n if include_technical:\n # RSI\n features[\"rsi_14\"] = compute_rsi(df[\"close\"], 14)\n\n # MACD\n macd, signal = compute_macd(df[\"close\"])\n features[\"macd\"] = macd\n features[\"macd_signal\"] = signal\n features[\"macd_hist\"] = macd - signal\n\n # Bollinger Bands\n bb_upper, bb_lower = compute_bollinger_bands(df[\"close\"])\n features[\"bb_position\"] = (df[\"close\"] - bb_lower) / (bb_upper - bb_lower + 1e-8)\n\n # Momentum\n features[\"momentum_5\"] = df[\"close\"] / df[\"close\"].shift(5) - 1\n features[\"momentum_10\"] = df[\"close\"] / df[\"close\"].shift(10) - 1\n features[\"momentum_20\"] = df[\"close\"] / df[\"close\"].shift(20) - 1\n\n return features\n\n\ndef compute_rsi(prices: pd.Series, period: int = 14) -> pd.Series:\n \"\"\"Compute Relative Strength Index\"\"\"\n delta = prices.diff()\n gain = delta.where(delta > 0, 0).rolling(window=period).mean()\n loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()\n rs = gain / (loss + 1e-8)\n rsi = 100 - (100 / (1 + rs))\n return rsi / 100 # Normalize to [0, 1]\n\n\ndef compute_macd(\n prices: pd.Series,\n fast: int = 12,\n slow: int = 26,\n signal: int = 9\n) -> Tuple[pd.Series, pd.Series]:\n \"\"\"Compute MACD indicator\"\"\"\n exp_fast = prices.ewm(span=fast, adjust=False).mean()\n exp_slow = prices.ewm(span=slow, adjust=False).mean()\n macd = exp_fast - exp_slow\n signal_line = macd.ewm(span=signal, adjust=False).mean()\n # Normalize by price\n return macd / prices, signal_line / prices\n\n\ndef compute_bollinger_bands(\n prices: pd.Series,\n period: int = 20,\n num_std: float = 2.0\n) -> Tuple[pd.Series, pd.Series]:\n \"\"\"Compute Bollinger Bands\"\"\"\n ma = prices.rolling(window=period).mean()\n std = prices.rolling(window=period).std()\n upper = ma + (std * num_std)\n lower = ma - (std * num_std)\n return upper, lower\n\n\ndef prepare_tabl_data(\n df: pd.DataFrame,\n lookback: int = 100,\n horizon: int = 10,\n threshold: float = 0.0002,\n feature_columns: Optional[List[str]] = None\n) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n \"\"\"\n Prepare data for TABL model training\n\n Args:\n df: DataFrame with OHLCV data\n lookback: Number of time steps to look back\n horizon: Prediction horizon\n threshold: Threshold for direction classification\n feature_columns: List of feature columns to use\n\n Returns:\n X: Features (n_samples, lookback, n_features)\n y: Labels (n_samples,) - 0: down, 1: stationary, 2: up\n returns: Actual returns (n_samples,) - for backtesting\n \"\"\"\n # Extract features\n features = extract_ohlcv_features(df)\n\n # Select feature columns\n if feature_columns is None:\n feature_columns = [\n \"log_return\", \"high_low_range\", \"close_open_diff\",\n \"volume_ma_ratio\", \"volatility_20\", \"rsi_14\"\n ]\n\n # Filter to available columns\n available_cols = [c for c in feature_columns if c in features.columns]\n features = features[available_cols]\n\n # Fill NaN values\n features = features.fillna(0)\n\n # Normalize features (z-score)\n features_mean = features.mean()\n features_std = features.std()\n features_norm = (features - features_mean) / (features_std + 1e-8)\n\n # Create sequences\n X, y, returns = [], [], []\n\n for i in range(lookback + 20, len(features_norm) - horizon): # +20 for feature warmup\n # Input sequence\n x_seq = features_norm.iloc[i-lookback:i].values\n\n # Target: future log return\n future_return = np.log(\n df[\"close\"].iloc[i + horizon] / df[\"close\"].iloc[i]\n )\n\n # Classify direction\n if future_return > threshold:\n label = 2 # Up\n elif future_return < -threshold:\n label = 0 # Down\n else:\n label = 1 # Stationary\n\n X.append(x_seq)\n y.append(label)\n returns.append(future_return)\n\n X = np.array(X, dtype=np.float32)\n y = np.array(y, dtype=np.int64)\n returns = np.array(returns, dtype=np.float32)\n\n logger.info(f\"Prepared {len(X)} samples with {X.shape[2]} features\")\n logger.info(f\"Class distribution: down={np.sum(y==0)}, stationary={np.sum(y==1)}, up={np.sum(y==2)}\")\n\n return X, y, returns\n\n\ndef create_train_val_test_split(\n X: np.ndarray,\n y: np.ndarray,\n returns: np.ndarray,\n train_ratio: float = 0.7,\n val_ratio: float = 0.15\n) -> Dict[str, Tuple[np.ndarray, np.ndarray, np.ndarray]]:\n \"\"\"\n Split data into train/val/test sets (time-based, no shuffle)\n\n Args:\n X: Features array\n y: Labels array\n returns: Returns array\n train_ratio: Ratio for training set\n val_ratio: Ratio for validation set\n\n Returns:\n Dictionary with 'train', 'val', 'test' splits\n \"\"\"\n n = len(X)\n train_end = int(n * train_ratio)\n val_end = int(n * (train_ratio + val_ratio))\n\n return {\n \"train\": (X[:train_end], y[:train_end], returns[:train_end]),\n \"val\": (X[train_end:val_end], y[train_end:val_end], returns[train_end:val_end]),\n \"test\": (X[val_end:], y[val_end:], returns[val_end:])\n }\n\n\nclass TABLDataset:\n \"\"\"\n PyTorch-compatible dataset for TABL\n\n Example:\n dataset = TABLDataset(X, y)\n loader = DataLoader(dataset, batch_size=32)\n \"\"\"\n\n def __init__(self, X: np.ndarray, y: np.ndarray):\n \"\"\"\n Initialize dataset\n\n Args:\n X: Features (n_samples, seq_len, n_features)\n y: Labels (n_samples,)\n \"\"\"\n self.X = X\n self.y = y\n\n def __len__(self) -> int:\n return len(self.X)\n\n def __getitem__(self, idx: int) -> Tuple[np.ndarray, int]:\n return self.X[idx], self.y[idx]\n\n\nif __name__ == \"__main__\":\n # Test data loading\n print(\"Testing data loading...\")\n\n # Create sample data (simulated since we can't call API in test)\n np.random.seed(42)\n n_samples = 1000\n\n # Simulate OHLCV data\n base_price = 100\n prices = base_price + np.cumsum(np.random.randn(n_samples) * 0.5)\n volumes = np.abs(np.random.randn(n_samples) * 1000 + 5000)\n\n df = pd.DataFrame({\n \"timestamp\": pd.date_range(\"2024-01-01\", periods=n_samples, freq=\"h\"),\n \"open\": prices + np.random.randn(n_samples) * 0.1,\n \"high\": prices + np.abs(np.random.randn(n_samples)) * 0.5,\n \"low\": prices - np.abs(np.random.randn(n_samples)) * 0.5,\n \"close\": prices,\n \"volume\": volumes,\n \"turnover\": prices * volumes\n })\n\n # Test feature extraction\n features = extract_ohlcv_features(df)\n print(f\"Extracted features: {list(features.columns)}\")\n print(f\"Feature shape: {features.shape}\")\n\n # Test data preparation\n X, y, returns = prepare_tabl_data(df, lookback=100, horizon=10)\n print(f\"\\nData shapes: X={X.shape}, y={y.shape}, returns={returns.shape}\")\n\n # Test split\n splits = create_train_val_test_split(X, y, returns)\n for name, (X_s, y_s, r_s) in splits.items():\n print(f\"{name}: X={X_s.shape}, y={y_s.shape}\")\n\n print(\"\\nData loading tests passed!\")\n", | |
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| "content": "\"\"\"\nTrading Strategy and Backtesting for TABL\n\nProvides:\n- TABLStrategy: Trading strategy based on TABL model\n- backtest_tabl_strategy: Backtest function\n- calculate_metrics: Calculate performance metrics\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom typing import Dict, List, Optional, Tuple\nfrom dataclasses import dataclass\nimport torch\nimport torch.nn.functional as F\nimport logging\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass TradeResult:\n \"\"\"Single trade result\"\"\"\n entry_time: int\n exit_time: int\n entry_price: float\n exit_price: float\n direction: int # 1: long, -1: short\n pnl: float\n return_pct: float\n\n\n@dataclass\nclass BacktestResult:\n \"\"\"Backtesting results\"\"\"\n total_return: float\n sharpe_ratio: float\n max_drawdown: float\n win_rate: float\n n_trades: int\n avg_trade_return: float\n profit_factor: float\n equity_curve: np.ndarray\n positions: np.ndarray\n returns: np.ndarray\n\n\nclass TABLStrategy:\n \"\"\"\n Trading strategy based on TABL model predictions\n\n Example:\n strategy = TABLStrategy(\n model=trained_model,\n confidence_threshold=0.6,\n max_position=1.0\n )\n signals = strategy.generate_signals(X_test)\n \"\"\"\n\n def __init__(\n self,\n model,\n confidence_threshold: float = 0.6,\n max_position: float = 1.0,\n transaction_cost: float = 0.001,\n use_softmax: bool = True\n ):\n \"\"\"\n Initialize strategy\n\n Args:\n model: Trained TABL model\n confidence_threshold: Minimum probability for taking position\n max_position: Maximum position size (fraction of capital)\n transaction_cost: Transaction cost per trade\n use_softmax: Whether to use softmax probabilities\n \"\"\"\n self.model = model\n self.confidence_threshold = confidence_threshold\n self.max_position = max_position\n self.transaction_cost = transaction_cost\n self.use_softmax = use_softmax\n\n def generate_signals(\n self,\n X: np.ndarray,\n return_confidence: bool = False\n ) -> Tuple[np.ndarray, Optional[np.ndarray]]:\n \"\"\"\n Generate trading signals from model predictions\n\n Args:\n X: Input features (n_samples, seq_len, n_features)\n return_confidence: Whether to return confidence scores\n\n Returns:\n signals: Trading signals (n_samples,) - -1: short, 0: flat, 1: long\n confidence: Confidence scores (n_samples,) - if return_confidence=True\n \"\"\"\n self.model.eval()\n signals = []\n confidences = []\n\n with torch.no_grad():\n # Process in batches\n batch_size = 64\n for i in range(0, len(X), batch_size):\n batch = torch.FloatTensor(X[i:i+batch_size])\n\n output = self.model(batch)\n probs = output['probs'].numpy()\n\n for p in probs:\n # Get prediction and confidence\n pred = np.argmax(p)\n conf = p[pred]\n\n # Generate signal\n if conf >= self.confidence_threshold:\n if pred == 2: # Up\n signal = 1\n elif pred == 0: # Down\n signal = -1\n else: # Stationary\n signal = 0\n else:\n signal = 0 # Not confident enough\n\n signals.append(signal)\n confidences.append(conf)\n\n signals = np.array(signals)\n confidences = np.array(confidences)\n\n if return_confidence:\n return signals, confidences\n return signals, None\n\n def get_attention_insights(\n self,\n X: np.ndarray\n ) -> Dict[str, np.ndarray]:\n \"\"\"\n Get attention weights for interpretability\n\n Args:\n X: Input features (n_samples, seq_len, n_features)\n\n Returns:\n Dictionary with attention weights and analysis\n \"\"\"\n self.model.eval()\n all_attention = []\n\n with torch.no_grad():\n batch_size = 64\n for i in range(0, len(X), batch_size):\n batch = torch.FloatTensor(X[i:i+batch_size])\n output = self.model(batch, return_attention=True)\n\n if output['attention'] is not None:\n all_attention.append(output['attention'].numpy())\n\n if not all_attention:\n return {}\n\n attention = np.concatenate(all_attention, axis=0)\n\n # Compute statistics\n mean_attention = attention.mean(axis=0)\n std_attention = attention.std(axis=0)\n\n # Find most important time steps\n if attention.ndim == 2: # Single head\n important_steps = np.argsort(mean_attention)[-10:][::-1]\n else: # Multi-head\n combined_attention = attention.mean(axis=1)\n important_steps = np.argsort(combined_attention.mean(axis=0))[-10:][::-1]\n\n return {\n 'attention': attention,\n 'mean': mean_attention,\n 'std': std_attention,\n 'important_steps': important_steps\n }\n\n\ndef backtest_tabl_strategy(\n model,\n X: np.ndarray,\n actual_returns: np.ndarray,\n confidence_threshold: float = 0.6,\n transaction_cost: float = 0.001,\n initial_capital: float = 100000.0\n) -> BacktestResult:\n \"\"\"\n Backtest TABL trading strategy\n\n Args:\n model: Trained TABL model\n X: Input features (n_samples, seq_len, n_features)\n actual_returns: Actual returns for each period (n_samples,)\n confidence_threshold: Minimum probability for trading\n transaction_cost: Transaction cost per trade\n initial_capital: Starting capital\n\n Returns:\n BacktestResult with performance metrics\n \"\"\"\n strategy = TABLStrategy(\n model=model,\n confidence_threshold=confidence_threshold,\n transaction_cost=transaction_cost\n )\n\n # Generate signals\n signals, confidences = strategy.generate_signals(X, return_confidence=True)\n\n # Initialize tracking\n capital = initial_capital\n equity_curve = [capital]\n positions = [0]\n realized_returns = []\n\n prev_position = 0\n\n for i in range(len(signals)):\n signal = signals[i]\n actual_ret = actual_returns[i]\n\n # Transaction cost for position changes\n if signal != prev_position:\n cost = transaction_cost * capital\n capital -= cost\n\n # Apply return based on position\n period_return = prev_position * actual_ret\n capital *= (1 + period_return)\n\n # Track\n equity_curve.append(capital)\n positions.append(signal)\n realized_returns.append(period_return)\n\n prev_position = signal\n\n # Calculate metrics\n equity_curve = np.array(equity_curve)\n positions = np.array(positions)\n realized_returns = np.array(realized_returns)\n\n metrics = calculate_metrics(equity_curve, realized_returns, positions)\n\n return BacktestResult(\n total_return=metrics['total_return'],\n sharpe_ratio=metrics['sharpe_ratio'],\n max_drawdown=metrics['max_drawdown'],\n win_rate=metrics['win_rate'],\n n_trades=metrics['n_trades'],\n avg_trade_return=metrics['avg_trade_return'],\n profit_factor=metrics['profit_factor'],\n equity_curve=equity_curve,\n positions=positions,\n returns=realized_returns\n )\n\n\ndef calculate_metrics(\n equity_curve: np.ndarray,\n returns: np.ndarray,\n positions: np.ndarray,\n annualization_factor: float = 252 * 24 # Hourly data\n) -> Dict[str, float]:\n \"\"\"\n Calculate performance metrics\n\n Args:\n equity_curve: Equity values over time\n returns: Period returns\n positions: Position history\n annualization_factor: Factor for annualizing returns\n\n Returns:\n Dictionary of performance metrics\n \"\"\"\n # Total return\n total_return = (equity_curve[-1] / equity_curve[0] - 1) * 100\n\n # Sharpe ratio\n if len(returns) > 1 and np.std(returns) > 0:\n sharpe_ratio = np.mean(returns) / np.std(returns) * np.sqrt(annualization_factor)\n else:\n sharpe_ratio = 0\n\n # Maximum drawdown\n peak = np.maximum.accumulate(equity_curve)\n drawdown = (peak - equity_curve) / peak\n max_drawdown = np.max(drawdown) * 100\n\n # Win rate (only count periods with position)\n active_returns = returns[positions[:-1] != 0]\n if len(active_returns) > 0:\n win_rate = np.mean(active_returns > 0) * 100\n else:\n win_rate = 0\n\n # Number of trades (position changes)\n n_trades = np.sum(np.diff(positions) != 0)\n\n # Average trade return\n if len(active_returns) > 0:\n avg_trade_return = np.mean(active_returns) * 100\n else:\n avg_trade_return = 0\n\n # Profit factor\n gains = active_returns[active_returns > 0].sum()\n losses = abs(active_returns[active_returns < 0].sum())\n profit_factor = gains / losses if losses > 0 else np.inf\n\n return {\n 'total_return': total_return,\n 'sharpe_ratio': sharpe_ratio,\n 'max_drawdown': max_drawdown,\n 'win_rate': win_rate,\n 'n_trades': n_trades,\n 'avg_trade_return': avg_trade_return,\n 'profit_factor': profit_factor\n }\n\n\ndef print_backtest_results(result: BacktestResult):\n \"\"\"Print backtest results in a formatted way\"\"\"\n print(\"\\n\" + \"=\" * 50)\n print(\"BACKTEST RESULTS\")\n print(\"=\" * 50)\n print(f\"Total Return: {result.total_return:>10.2f}%\")\n print(f\"Sharpe Ratio: {result.sharpe_ratio:>10.2f}\")\n print(f\"Max Drawdown: {result.max_drawdown:>10.2f}%\")\n print(f\"Win Rate: {result.win_rate:>10.2f}%\")\n print(f\"Number of Trades: {result.n_trades:>10d}\")\n print(f\"Avg Trade Return: {result.avg_trade_return:>10.4f}%\")\n print(f\"Profit Factor: {result.profit_factor:>10.2f}\")\n print(\"=\" * 50)\n\n\ndef compare_with_benchmark(\n result: BacktestResult,\n benchmark_returns: np.ndarray,\n initial_capital: float = 100000.0\n) -> Dict[str, float]:\n \"\"\"\n Compare strategy with buy-and-hold benchmark\n\n Args:\n result: BacktestResult from strategy\n benchmark_returns: Buy-and-hold returns\n initial_capital: Starting capital\n\n Returns:\n Dictionary comparing strategy vs benchmark\n \"\"\"\n # Calculate benchmark equity\n benchmark_equity = initial_capital * np.cumprod(1 + benchmark_returns)\n\n # Benchmark metrics\n benchmark_total = (benchmark_equity[-1] / initial_capital - 1) * 100\n benchmark_sharpe = np.mean(benchmark_returns) / np.std(benchmark_returns) * np.sqrt(252 * 24)\n\n peak = np.maximum.accumulate(benchmark_equity)\n benchmark_dd = np.max((peak - benchmark_equity) / peak) * 100\n\n return {\n 'strategy_return': result.total_return,\n 'benchmark_return': benchmark_total,\n 'excess_return': result.total_return - benchmark_total,\n 'strategy_sharpe': result.sharpe_ratio,\n 'benchmark_sharpe': benchmark_sharpe,\n 'strategy_drawdown': result.max_drawdown,\n 'benchmark_drawdown': benchmark_dd\n }\n\n\nif __name__ == \"__main__\":\n # Test strategy with mock model\n print(\"Testing strategy module...\")\n\n # Create mock data\n np.random.seed(42)\n n_samples = 500\n seq_len = 100\n n_features = 6\n\n X = np.random.randn(n_samples, seq_len, n_features).astype(np.float32)\n actual_returns = np.random.randn(n_samples) * 0.01 # 1% daily vol\n\n # Create mock model\n class MockModel:\n def __init__(self):\n pass\n\n def eval(self):\n pass\n\n def __call__(self, x, return_attention=False):\n batch_size = x.shape[0]\n # Random predictions\n probs = torch.softmax(torch.randn(batch_size, 3), dim=1)\n result = {'probs': probs, 'logits': torch.randn(batch_size, 3)}\n if return_attention:\n result['attention'] = torch.randn(batch_size, 100)\n return result\n\n mock_model = MockModel()\n\n # Test strategy\n strategy = TABLStrategy(mock_model, confidence_threshold=0.5)\n signals, confidences = strategy.generate_signals(X, return_confidence=True)\n print(f\"Generated {len(signals)} signals\")\n print(f\"Signal distribution: long={np.sum(signals==1)}, flat={np.sum(signals==0)}, short={np.sum(signals==-1)}\")\n\n # Test backtest\n result = backtest_tabl_strategy(\n mock_model, X, actual_returns,\n confidence_threshold=0.5,\n transaction_cost=0.001\n )\n\n print_backtest_results(result)\n\n # Test benchmark comparison\n comparison = compare_with_benchmark(result, actual_returns)\n print(\"\\nBenchmark Comparison:\")\n for k, v in comparison.items():\n print(f\" {k}: {v:.2f}\")\n\n print(\"\\nStrategy tests passed!\")\n" | |
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| "content": "\"\"\"\nTrading Strategy and Backtesting for TABL\n\nProvides:\n- TABLStrategy: Trading strategy based on TABL model\n- backtest_tabl_strategy: Backtest function\n- calculate_metrics: Calculate performance metrics\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom typing import Dict, List, Optional, Tuple\nfrom dataclasses import dataclass\nimport torch\nimport torch.nn.functional as F\nimport logging\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass TradeResult:\n \"\"\"Single trade result\"\"\"\n entry_time: int\n exit_time: int\n entry_price: float\n exit_price: float\n direction: int # 1: long, -1: short\n pnl: float\n return_pct: float\n\n\n@dataclass\nclass BacktestResult:\n \"\"\"Backtesting results\"\"\"\n total_return: float\n sharpe_ratio: float\n max_drawdown: float\n win_rate: float\n n_trades: int\n avg_trade_return: float\n profit_factor: float\n equity_curve: np.ndarray\n positions: np.ndarray\n returns: np.ndarray\n\n\nclass TABLStrategy:\n \"\"\"\n Trading strategy based on TABL model predictions\n\n Example:\n strategy = TABLStrategy(\n model=trained_model,\n confidence_threshold=0.6,\n max_position=1.0\n )\n signals = strategy.generate_signals(X_test)\n \"\"\"\n\n def __init__(\n self,\n model,\n confidence_threshold: float = 0.6,\n max_position: float = 1.0,\n transaction_cost: float = 0.001,\n use_softmax: bool = True\n ):\n \"\"\"\n Initialize strategy\n\n Args:\n model: Trained TABL model\n confidence_threshold: Minimum probability for taking position\n max_position: Maximum position size (fraction of capital)\n transaction_cost: Transaction cost per trade\n use_softmax: Whether to use softmax probabilities\n \"\"\"\n self.model = model\n self.confidence_threshold = confidence_threshold\n self.max_position = max_position\n self.transaction_cost = transaction_cost\n self.use_softmax = use_softmax\n\n def generate_signals(\n self,\n X: np.ndarray,\n return_confidence: bool = False\n ) -> Tuple[np.ndarray, Optional[np.ndarray]]:\n \"\"\"\n Generate trading signals from model predictions\n\n Args:\n X: Input features (n_samples, seq_len, n_features)\n return_confidence: Whether to return confidence scores\n\n Returns:\n signals: Trading signals (n_samples,) - -1: short, 0: flat, 1: long\n confidence: Confidence scores (n_samples,) - if return_confidence=True\n \"\"\"\n self.model.eval()\n signals = []\n confidences = []\n\n with torch.no_grad():\n # Process in batches\n batch_size = 64\n for i in range(0, len(X), batch_size):\n batch = torch.FloatTensor(X[i:i+batch_size])\n\n output = self.model(batch)\n probs = output['probs'].numpy()\n\n for p in probs:\n # Get prediction and confidence\n pred = np.argmax(p)\n conf = p[pred]\n\n # Generate signal\n if conf >= self.confidence_threshold:\n if pred == 2: # Up\n signal = 1\n elif pred == 0: # Down\n signal = -1\n else: # Stationary\n signal = 0\n else:\n signal = 0 # Not confident enough\n\n signals.append(signal)\n confidences.append(conf)\n\n signals = np.array(signals)\n confidences = np.array(confidences)\n\n if return_confidence:\n return signals, confidences\n return signals, None\n\n def get_attention_insights(\n self,\n X: np.ndarray\n ) -> Dict[str, np.ndarray]:\n \"\"\"\n Get attention weights for interpretability\n\n Args:\n X: Input features (n_samples, seq_len, n_features)\n\n Returns:\n Dictionary with attention weights and analysis\n \"\"\"\n self.model.eval()\n all_attention = []\n\n with torch.no_grad():\n batch_size = 64\n for i in range(0, len(X), batch_size):\n batch = torch.FloatTensor(X[i:i+batch_size])\n output = self.model(batch, return_attention=True)\n\n if output['attention'] is not None:\n all_attention.append(output['attention'].numpy())\n\n if not all_attention:\n return {}\n\n attention = np.concatenate(all_attention, axis=0)\n\n # Compute statistics\n mean_attention = attention.mean(axis=0)\n std_attention = attention.std(axis=0)\n\n # Find most important time steps\n if attention.ndim == 2: # Single head\n important_steps = np.argsort(mean_attention)[-10:][::-1]\n else: # Multi-head\n combined_attention = attention.mean(axis=1)\n important_steps = np.argsort(combined_attention.mean(axis=0))[-10:][::-1]\n\n return {\n 'attention': attention,\n 'mean': mean_attention,\n 'std': std_attention,\n 'important_steps': important_steps\n }\n\n\ndef backtest_tabl_strategy(\n model,\n X: np.ndarray,\n actual_returns: np.ndarray,\n confidence_threshold: float = 0.6,\n transaction_cost: float = 0.001,\n initial_capital: float = 100000.0\n) -> BacktestResult:\n \"\"\"\n Backtest TABL trading strategy\n\n Args:\n model: Trained TABL model\n X: Input features (n_samples, seq_len, n_features)\n actual_returns: Actual returns for each period (n_samples,)\n confidence_threshold: Minimum probability for trading\n transaction_cost: Transaction cost per trade\n initial_capital: Starting capital\n\n Returns:\n BacktestResult with performance metrics\n \"\"\"\n strategy = TABLStrategy(\n model=model,\n confidence_threshold=confidence_threshold,\n transaction_cost=transaction_cost\n )\n\n # Generate signals\n signals, confidences = strategy.generate_signals(X, return_confidence=True)\n\n # Initialize tracking\n capital = initial_capital\n equity_curve = [capital]\n positions = [0]\n realized_returns = []\n\n prev_position = 0\n\n for i in range(len(signals)):\n signal = signals[i]\n actual_ret = actual_returns[i]\n\n # Transaction cost for position changes\n if signal != prev_position:\n cost = transaction_cost * capital\n capital -= cost\n\n # Apply return based on position\n period_return = prev_position * actual_ret\n capital *= (1 + period_return)\n\n # Track\n equity_curve.append(capital)\n positions.append(signal)\n realized_returns.append(period_return)\n\n prev_position = signal\n\n # Calculate metrics\n equity_curve = np.array(equity_curve)\n positions = np.array(positions)\n realized_returns = np.array(realized_returns)\n\n metrics = calculate_metrics(equity_curve, realized_returns, positions)\n\n return BacktestResult(\n total_return=metrics['total_return'],\n sharpe_ratio=metrics['sharpe_ratio'],\n max_drawdown=metrics['max_drawdown'],\n win_rate=metrics['win_rate'],\n n_trades=metrics['n_trades'],\n avg_trade_return=metrics['avg_trade_return'],\n profit_factor=metrics['profit_factor'],\n equity_curve=equity_curve,\n positions=positions,\n returns=realized_returns\n )\n\n\ndef calculate_metrics(\n equity_curve: np.ndarray,\n returns: np.ndarray,\n positions: np.ndarray,\n annualization_factor: float = 252 * 24 # Hourly data\n) -> Dict[str, float]:\n \"\"\"\n Calculate performance metrics\n\n Args:\n equity_curve: Equity values over time\n returns: Period returns\n positions: Position history\n annualization_factor: Factor for annualizing returns\n\n Returns:\n Dictionary of performance metrics\n \"\"\"\n # Total return\n total_return = (equity_curve[-1] / equity_curve[0] - 1) * 100\n\n # Sharpe ratio\n if len(returns) > 1 and np.std(returns) > 0:\n sharpe_ratio = np.mean(returns) / np.std(returns) * np.sqrt(annualization_factor)\n else:\n sharpe_ratio = 0\n\n # Maximum drawdown\n peak = np.maximum.accumulate(equity_curve)\n drawdown = (peak - equity_curve) / peak\n max_drawdown = np.max(drawdown) * 100\n\n # Win rate (only count periods with position)\n active_returns = returns[positions[:-1] != 0]\n if len(active_returns) > 0:\n win_rate = np.mean(active_returns > 0) * 100\n else:\n win_rate = 0\n\n # Number of trades (position changes)\n n_trades = np.sum(np.diff(positions) != 0)\n\n # Average trade return\n if len(active_returns) > 0:\n avg_trade_return = np.mean(active_returns) * 100\n else:\n avg_trade_return = 0\n\n # Profit factor\n gains = active_returns[active_returns > 0].sum()\n losses = abs(active_returns[active_returns < 0].sum())\n profit_factor = gains / losses if losses > 0 else np.inf\n\n return {\n 'total_return': total_return,\n 'sharpe_ratio': sharpe_ratio,\n 'max_drawdown': max_drawdown,\n 'win_rate': win_rate,\n 'n_trades': n_trades,\n 'avg_trade_return': avg_trade_return,\n 'profit_factor': profit_factor\n }\n\n\ndef print_backtest_results(result: BacktestResult):\n \"\"\"Print backtest results in a formatted way\"\"\"\n print(\"\\n\" + \"=\" * 50)\n print(\"BACKTEST RESULTS\")\n print(\"=\" * 50)\n print(f\"Total Return: {result.total_return:>10.2f}%\")\n print(f\"Sharpe Ratio: {result.sharpe_ratio:>10.2f}\")\n print(f\"Max Drawdown: {result.max_drawdown:>10.2f}%\")\n print(f\"Win Rate: {result.win_rate:>10.2f}%\")\n print(f\"Number of Trades: {result.n_trades:>10d}\")\n print(f\"Avg Trade Return: {result.avg_trade_return:>10.4f}%\")\n print(f\"Profit Factor: {result.profit_factor:>10.2f}\")\n print(\"=\" * 50)\n\n\ndef compare_with_benchmark(\n result: BacktestResult,\n benchmark_returns: np.ndarray,\n initial_capital: float = 100000.0\n) -> Dict[str, float]:\n \"\"\"\n Compare strategy with buy-and-hold benchmark\n\n Args:\n result: BacktestResult from strategy\n benchmark_returns: Buy-and-hold returns\n initial_capital: Starting capital\n\n Returns:\n Dictionary comparing strategy vs benchmark\n \"\"\"\n # Calculate benchmark equity\n benchmark_equity = initial_capital * np.cumprod(1 + benchmark_returns)\n\n # Benchmark metrics\n benchmark_total = (benchmark_equity[-1] / initial_capital - 1) * 100\n benchmark_sharpe = np.mean(benchmark_returns) / np.std(benchmark_returns) * np.sqrt(252 * 24)\n\n peak = np.maximum.accumulate(benchmark_equity)\n benchmark_dd = np.max((peak - benchmark_equity) / peak) * 100\n\n return {\n 'strategy_return': result.total_return,\n 'benchmark_return': benchmark_total,\n 'excess_return': result.total_return - benchmark_total,\n 'strategy_sharpe': result.sharpe_ratio,\n 'benchmark_sharpe': benchmark_sharpe,\n 'strategy_drawdown': result.max_drawdown,\n 'benchmark_drawdown': benchmark_dd\n }\n\n\nif __name__ == \"__main__\":\n # Test strategy with mock model\n print(\"Testing strategy module...\")\n\n # Create mock data\n np.random.seed(42)\n n_samples = 500\n seq_len = 100\n n_features = 6\n\n X = np.random.randn(n_samples, seq_len, n_features).astype(np.float32)\n actual_returns = np.random.randn(n_samples) * 0.01 # 1% daily vol\n\n # Create mock model\n class MockModel:\n def __init__(self):\n pass\n\n def eval(self):\n pass\n\n def __call__(self, x, return_attention=False):\n batch_size = x.shape[0]\n # Random predictions\n probs = torch.softmax(torch.randn(batch_size, 3), dim=1)\n result = {'probs': probs, 'logits': torch.randn(batch_size, 3)}\n if return_attention:\n result['attention'] = torch.randn(batch_size, 100)\n return result\n\n mock_model = MockModel()\n\n # Test strategy\n strategy = TABLStrategy(mock_model, confidence_threshold=0.5)\n signals, confidences = strategy.generate_signals(X, return_confidence=True)\n print(f\"Generated {len(signals)} signals\")\n print(f\"Signal distribution: long={np.sum(signals==1)}, flat={np.sum(signals==0)}, short={np.sum(signals==-1)}\")\n\n # Test backtest\n result = backtest_tabl_strategy(\n mock_model, X, actual_returns,\n confidence_threshold=0.5,\n transaction_cost=0.001\n )\n\n print_backtest_results(result)\n\n # Test benchmark comparison\n comparison = compare_with_benchmark(result, actual_returns)\n print(\"\\nBenchmark Comparison:\")\n for k, v in comparison.items():\n print(f\" {k}: {v:.2f}\")\n\n print(\"\\nStrategy tests passed!\")\n", | |
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| "content": "\"\"\"\nExample Usage of TABL for Financial Time-Series Prediction\n\nThis script demonstrates:\n1. Data loading and preparation\n2. Model training\n3. Prediction and evaluation\n4. Attention visualization\n5. Backtesting\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader, TensorDataset\nimport matplotlib.pyplot as plt\nfrom typing import Dict, Tuple\nimport logging\n\nfrom model import TABLModel, TABLConfig\nfrom data import (\n BybitDataLoader,\n prepare_tabl_data,\n extract_ohlcv_features,\n create_train_val_test_split\n)\nfrom strategy import (\n TABLStrategy,\n backtest_tabl_strategy,\n print_backtest_results\n)\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\ndef create_sample_data(n_samples: int = 2000) -> pd.DataFrame:\n \"\"\"Create sample OHLCV data for testing\"\"\"\n np.random.seed(42)\n\n # Simulate price with trend and noise\n base_price = 100\n trend = np.linspace(0, 20, n_samples)\n noise = np.cumsum(np.random.randn(n_samples) * 0.5)\n prices = base_price + trend + noise\n\n # Add some patterns\n cycle = 5 * np.sin(np.linspace(0, 20 * np.pi, n_samples))\n prices += cycle\n\n # Volume correlated with volatility\n returns = np.diff(prices, prepend=prices[0]) / prices\n volatility = pd.Series(returns).rolling(10).std().fillna(0.01).values\n base_volume = 10000\n volumes = base_volume * (1 + 5 * volatility + np.random.randn(n_samples) * 0.2)\n volumes = np.abs(volumes)\n\n df = pd.DataFrame({\n \"timestamp\": pd.date_range(\"2024-01-01\", periods=n_samples, freq=\"h\"),\n \"open\": prices + np.random.randn(n_samples) * 0.1,\n \"high\": prices + np.abs(np.random.randn(n_samples)) * 0.5,\n \"low\": prices - np.abs(np.random.randn(n_samples)) * 0.5,\n \"close\": prices,\n \"volume\": volumes,\n \"turnover\": prices * volumes\n })\n\n # Ensure high >= close >= low\n df[\"high\"] = df[[\"open\", \"high\", \"close\"]].max(axis=1)\n df[\"low\"] = df[[\"open\", \"low\", \"close\"]].min(axis=1)\n\n return df\n\n\ndef train_model(\n model: TABLModel,\n train_loader: DataLoader,\n val_loader: DataLoader,\n epochs: int = 50,\n lr: float = 0.001,\n patience: int = 10\n) -> Dict:\n \"\"\"\n Train TABL model\n\n Args:\n model: TABLModel instance\n train_loader: Training data loader\n val_loader: Validation data loader\n epochs: Number of training epochs\n lr: Learning rate\n patience: Early stopping patience\n\n Returns:\n Dictionary with training history\n \"\"\"\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = model.to(device)\n\n criterion = nn.CrossEntropyLoss()\n optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=1e-5)\n scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n optimizer, mode='max', factor=0.5, patience=5\n )\n\n best_val_acc = 0\n best_model_state = None\n patience_counter = 0\n\n history = {\n 'train_loss': [],\n 'train_acc': [],\n 'val_loss': [],\n 'val_acc': []\n }\n\n for epoch in range(epochs):\n # Training\n model.train()\n train_loss = 0\n train_correct = 0\n train_total = 0\n\n for batch_x, batch_y in train_loader:\n batch_x = batch_x.to(device)\n batch_y = batch_y.to(device)\n\n optimizer.zero_grad()\n output = model(batch_x)\n loss = criterion(output['logits'], batch_y)\n\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n optimizer.step()\n\n train_loss += loss.item()\n preds = output['logits'].argmax(dim=1)\n train_correct += (preds == batch_y).sum().item()\n train_total += batch_y.size(0)\n\n train_loss /= len(train_loader)\n train_acc = train_correct / train_total\n\n # Validation\n model.eval()\n val_loss = 0\n val_correct = 0\n val_total = 0\n\n with torch.no_grad():\n for batch_x, batch_y in val_loader:\n batch_x = batch_x.to(device)\n batch_y = batch_y.to(device)\n\n output = model(batch_x)\n loss = criterion(output['logits'], batch_y)\n\n val_loss += loss.item()\n preds = output['logits'].argmax(dim=1)\n val_correct += (preds == batch_y).sum().item()\n val_total += batch_y.size(0)\n\n val_loss /= len(val_loader)\n val_acc = val_correct / val_total\n\n # Learning rate scheduling\n scheduler.step(val_acc)\n\n # Track history\n history['train_loss'].append(train_loss)\n history['train_acc'].append(train_acc)\n history['val_loss'].append(val_loss)\n history['val_acc'].append(val_acc)\n\n # Early stopping\n if val_acc > best_val_acc:\n best_val_acc = val_acc\n best_model_state = model.state_dict().copy()\n patience_counter = 0\n else:\n patience_counter += 1\n\n logger.info(\n f\"Epoch {epoch+1}/{epochs} - \"\n f\"Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}, \"\n f\"Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}\"\n )\n\n if patience_counter >= patience:\n logger.info(f\"Early stopping at epoch {epoch+1}\")\n break\n\n # Restore best model\n if best_model_state is not None:\n model.load_state_dict(best_model_state)\n\n return history\n\n\ndef visualize_attention(\n model: TABLModel,\n X: np.ndarray,\n sample_idx: int = 0\n):\n \"\"\"Visualize attention weights for a sample\"\"\"\n model.eval()\n device = next(model.parameters()).device\n\n with torch.no_grad():\n x = torch.FloatTensor(X[sample_idx:sample_idx+1]).to(device)\n output = model(x, return_attention=True)\n\n if output['attention'] is None:\n logger.warning(\"Model doesn't return attention weights\")\n return\n\n attention = output['attention'].cpu().numpy().squeeze()\n\n # Plot\n fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n\n # Attention weights\n if attention.ndim == 1:\n axes[0].bar(range(len(attention)), attention)\n axes[0].set_xlabel('Time Step')\n axes[0].set_ylabel('Attention Weight')\n axes[0].set_title('Temporal Attention Weights')\n else:\n # Multi-head: show heatmap\n import seaborn as sns\n sns.heatmap(attention, cmap='Blues', ax=axes[0])\n axes[0].set_xlabel('Time Step')\n axes[0].set_ylabel('Attention Head')\n axes[0].set_title('Multi-Head Attention Weights')\n\n # Input features\n axes[1].plot(X[sample_idx])\n axes[1].set_xlabel('Time Step')\n axes[1].set_ylabel('Feature Value')\n axes[1].set_title('Input Features')\n axes[1].legend([f'Feature {i}' for i in range(X.shape[2])], loc='best')\n\n plt.tight_layout()\n plt.savefig('attention_visualization.png', dpi=150)\n plt.show()\n\n\ndef plot_training_history(history: Dict):\n \"\"\"Plot training history\"\"\"\n fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n\n # Loss\n axes[0].plot(history['train_loss'], label='Train')\n axes[0].plot(history['val_loss'], label='Validation')\n axes[0].set_xlabel('Epoch')\n axes[0].set_ylabel('Loss')\n axes[0].set_title('Training Loss')\n axes[0].legend()\n\n # Accuracy\n axes[1].plot(history['train_acc'], label='Train')\n axes[1].plot(history['val_acc'], label='Validation')\n axes[1].set_xlabel('Epoch')\n axes[1].set_ylabel('Accuracy')\n axes[1].set_title('Training Accuracy')\n axes[1].legend()\n\n plt.tight_layout()\n plt.savefig('training_history.png', dpi=150)\n plt.show()\n\n\ndef plot_backtest_results(result):\n \"\"\"Plot backtest results\"\"\"\n fig, axes = plt.subplots(2, 1, figsize=(12, 8))\n\n # Equity curve\n axes[0].plot(result.equity_curve)\n axes[0].set_xlabel('Time')\n axes[0].set_ylabel('Equity')\n axes[0].set_title('Equity Curve')\n axes[0].grid(True, alpha=0.3)\n\n # Position history\n axes[1].plot(result.positions, drawstyle='steps-post')\n axes[1].set_xlabel('Time')\n axes[1].set_ylabel('Position')\n axes[1].set_title('Position History')\n axes[1].set_yticks([-1, 0, 1])\n axes[1].set_yticklabels(['Short', 'Flat', 'Long'])\n axes[1].grid(True, alpha=0.3)\n\n plt.tight_layout()\n plt.savefig('backtest_results.png', dpi=150)\n plt.show()\n\n\ndef main():\n \"\"\"Main function demonstrating TABL usage\"\"\"\n logger.info(\"=\" * 60)\n logger.info(\"TABL: Temporal Attention-Augmented Bilinear Network\")\n logger.info(\"=\" * 60)\n\n # 1. Create or load data\n logger.info(\"\\n1. Creating sample data...\")\n df = create_sample_data(n_samples=3000)\n logger.info(f\"Data shape: {df.shape}\")\n\n # 2. Prepare features and labels\n logger.info(\"\\n2. Preparing features and labels...\")\n X, y, returns = prepare_tabl_data(\n df,\n lookback=100,\n horizon=10,\n threshold=0.001\n )\n logger.info(f\"X shape: {X.shape}, y shape: {y.shape}\")\n\n # 3. Split data\n logger.info(\"\\n3. Splitting data...\")\n splits = create_train_val_test_split(X, y, returns)\n\n X_train, y_train, ret_train = splits['train']\n X_val, y_val, ret_val = splits['val']\n X_test, y_test, ret_test = splits['test']\n\n logger.info(f\"Train: {len(X_train)}, Val: {len(X_val)}, Test: {len(X_test)}\")\n\n # 4. Create data loaders\n train_dataset = TensorDataset(\n torch.FloatTensor(X_train),\n torch.LongTensor(y_train)\n )\n val_dataset = TensorDataset(\n torch.FloatTensor(X_val),\n torch.LongTensor(y_val)\n )\n test_dataset = TensorDataset(\n torch.FloatTensor(X_test),\n torch.LongTensor(y_test)\n )\n\n train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n val_loader = DataLoader(val_dataset, batch_size=32)\n test_loader = DataLoader(test_dataset, batch_size=32)\n\n # 5. Create model\n logger.info(\"\\n4. Creating TABL model...\")\n config = TABLConfig(\n seq_len=X.shape[1],\n input_dim=X.shape[2],\n hidden_T=20,\n hidden_D=32,\n attention_dim=64,\n n_classes=3,\n use_multihead=True,\n n_heads=4,\n dropout=0.2\n )\n\n model = TABLModel(config)\n logger.info(f\"Model parameters: {sum(p.numel() for p in model.parameters()):,}\")\n\n # 6. Train model\n logger.info(\"\\n5. Training model...\")\n history = train_model(\n model,\n train_loader,\n val_loader,\n epochs=30,\n lr=0.001,\n patience=10\n )\n\n # 7. Evaluate on test set\n logger.info(\"\\n6. Evaluating on test set...\")\n model.eval()\n device = next(model.parameters()).device\n\n test_correct = 0\n test_total = 0\n all_preds = []\n all_probs = []\n\n with torch.no_grad():\n for batch_x, batch_y in test_loader:\n batch_x = batch_x.to(device)\n batch_y = batch_y.to(device)\n\n output = model(batch_x)\n preds = output['logits'].argmax(dim=1)\n\n test_correct += (preds == batch_y).sum().item()\n test_total += batch_y.size(0)\n\n all_preds.extend(preds.cpu().numpy())\n all_probs.extend(output['probs'].cpu().numpy())\n\n test_acc = test_correct / test_total\n logger.info(f\"Test Accuracy: {test_acc:.4f}\")\n\n # 8. Backtest\n logger.info(\"\\n7. Running backtest...\")\n result = backtest_tabl_strategy(\n model,\n X_test,\n ret_test,\n confidence_threshold=0.5,\n transaction_cost=0.001\n )\n print_backtest_results(result)\n\n # 9. Visualizations (comment out if no display)\n try:\n logger.info(\"\\n8. Creating visualizations...\")\n plot_training_history(history)\n visualize_attention(model, X_test)\n plot_backtest_results(result)\n except Exception as e:\n logger.warning(f\"Could not create visualizations: {e}\")\n\n logger.info(\"\\n\" + \"=\" * 60)\n logger.info(\"TABL demonstration complete!\")\n logger.info(\"=\" * 60)\n\n return model, result\n\n\nif __name__ == \"__main__\":\n model, result = main()\n" | |
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| "content": "\"\"\"\nExample Usage of TABL for Financial Time-Series Prediction\n\nThis script demonstrates:\n1. Data loading and preparation\n2. Model training\n3. Prediction and evaluation\n4. Attention visualization\n5. Backtesting\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader, TensorDataset\nimport matplotlib.pyplot as plt\nfrom typing import Dict, Tuple\nimport logging\n\nfrom model import TABLModel, TABLConfig\nfrom data import (\n BybitDataLoader,\n prepare_tabl_data,\n extract_ohlcv_features,\n create_train_val_test_split\n)\nfrom strategy import (\n TABLStrategy,\n backtest_tabl_strategy,\n print_backtest_results\n)\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\ndef create_sample_data(n_samples: int = 2000) -> pd.DataFrame:\n \"\"\"Create sample OHLCV data for testing\"\"\"\n np.random.seed(42)\n\n # Simulate price with trend and noise\n base_price = 100\n trend = np.linspace(0, 20, n_samples)\n noise = np.cumsum(np.random.randn(n_samples) * 0.5)\n prices = base_price + trend + noise\n\n # Add some patterns\n cycle = 5 * np.sin(np.linspace(0, 20 * np.pi, n_samples))\n prices += cycle\n\n # Volume correlated with volatility\n returns = np.diff(prices, prepend=prices[0]) / prices\n volatility = pd.Series(returns).rolling(10).std().fillna(0.01).values\n base_volume = 10000\n volumes = base_volume * (1 + 5 * volatility + np.random.randn(n_samples) * 0.2)\n volumes = np.abs(volumes)\n\n df = pd.DataFrame({\n \"timestamp\": pd.date_range(\"2024-01-01\", periods=n_samples, freq=\"h\"),\n \"open\": prices + np.random.randn(n_samples) * 0.1,\n \"high\": prices + np.abs(np.random.randn(n_samples)) * 0.5,\n \"low\": prices - np.abs(np.random.randn(n_samples)) * 0.5,\n \"close\": prices,\n \"volume\": volumes,\n \"turnover\": prices * volumes\n })\n\n # Ensure high >= close >= low\n df[\"high\"] = df[[\"open\", \"high\", \"close\"]].max(axis=1)\n df[\"low\"] = df[[\"open\", \"low\", \"close\"]].min(axis=1)\n\n return df\n\n\ndef train_model(\n model: TABLModel,\n train_loader: DataLoader,\n val_loader: DataLoader,\n epochs: int = 50,\n lr: float = 0.001,\n patience: int = 10\n) -> Dict:\n \"\"\"\n Train TABL model\n\n Args:\n model: TABLModel instance\n train_loader: Training data loader\n val_loader: Validation data loader\n epochs: Number of training epochs\n lr: Learning rate\n patience: Early stopping patience\n\n Returns:\n Dictionary with training history\n \"\"\"\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = model.to(device)\n\n criterion = nn.CrossEntropyLoss()\n optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=1e-5)\n scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n optimizer, mode='max', factor=0.5, patience=5\n )\n\n best_val_acc = 0\n best_model_state = None\n patience_counter = 0\n\n history = {\n 'train_loss': [],\n 'train_acc': [],\n 'val_loss': [],\n 'val_acc': []\n }\n\n for epoch in range(epochs):\n # Training\n model.train()\n train_loss = 0\n train_correct = 0\n train_total = 0\n\n for batch_x, batch_y in train_loader:\n batch_x = batch_x.to(device)\n batch_y = batch_y.to(device)\n\n optimizer.zero_grad()\n output = model(batch_x)\n loss = criterion(output['logits'], batch_y)\n\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n optimizer.step()\n\n train_loss += loss.item()\n preds = output['logits'].argmax(dim=1)\n train_correct += (preds == batch_y).sum().item()\n train_total += batch_y.size(0)\n\n train_loss /= len(train_loader)\n train_acc = train_correct / train_total\n\n # Validation\n model.eval()\n val_loss = 0\n val_correct = 0\n val_total = 0\n\n with torch.no_grad():\n for batch_x, batch_y in val_loader:\n batch_x = batch_x.to(device)\n batch_y = batch_y.to(device)\n\n output = model(batch_x)\n loss = criterion(output['logits'], batch_y)\n\n val_loss += loss.item()\n preds = output['logits'].argmax(dim=1)\n val_correct += (preds == batch_y).sum().item()\n val_total += batch_y.size(0)\n\n val_loss /= len(val_loader)\n val_acc = val_correct / val_total\n\n # Learning rate scheduling\n scheduler.step(val_acc)\n\n # Track history\n history['train_loss'].append(train_loss)\n history['train_acc'].append(train_acc)\n history['val_loss'].append(val_loss)\n history['val_acc'].append(val_acc)\n\n # Early stopping\n if val_acc > best_val_acc:\n best_val_acc = val_acc\n best_model_state = model.state_dict().copy()\n patience_counter = 0\n else:\n patience_counter += 1\n\n logger.info(\n f\"Epoch {epoch+1}/{epochs} - \"\n f\"Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f}, \"\n f\"Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}\"\n )\n\n if patience_counter >= patience:\n logger.info(f\"Early stopping at epoch {epoch+1}\")\n break\n\n # Restore best model\n if best_model_state is not None:\n model.load_state_dict(best_model_state)\n\n return history\n\n\ndef visualize_attention(\n model: TABLModel,\n X: np.ndarray,\n sample_idx: int = 0\n):\n \"\"\"Visualize attention weights for a sample\"\"\"\n model.eval()\n device = next(model.parameters()).device\n\n with torch.no_grad():\n x = torch.FloatTensor(X[sample_idx:sample_idx+1]).to(device)\n output = model(x, return_attention=True)\n\n if output['attention'] is None:\n logger.warning(\"Model doesn't return attention weights\")\n return\n\n attention = output['attention'].cpu().numpy().squeeze()\n\n # Plot\n fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n\n # Attention weights\n if attention.ndim == 1:\n axes[0].bar(range(len(attention)), attention)\n axes[0].set_xlabel('Time Step')\n axes[0].set_ylabel('Attention Weight')\n axes[0].set_title('Temporal Attention Weights')\n else:\n # Multi-head: show heatmap\n import seaborn as sns\n sns.heatmap(attention, cmap='Blues', ax=axes[0])\n axes[0].set_xlabel('Time Step')\n axes[0].set_ylabel('Attention Head')\n axes[0].set_title('Multi-Head Attention Weights')\n\n # Input features\n axes[1].plot(X[sample_idx])\n axes[1].set_xlabel('Time Step')\n axes[1].set_ylabel('Feature Value')\n axes[1].set_title('Input Features')\n axes[1].legend([f'Feature {i}' for i in range(X.shape[2])], loc='best')\n\n plt.tight_layout()\n plt.savefig('attention_visualization.png', dpi=150)\n plt.show()\n\n\ndef plot_training_history(history: Dict):\n \"\"\"Plot training history\"\"\"\n fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n\n # Loss\n axes[0].plot(history['train_loss'], label='Train')\n axes[0].plot(history['val_loss'], label='Validation')\n axes[0].set_xlabel('Epoch')\n axes[0].set_ylabel('Loss')\n axes[0].set_title('Training Loss')\n axes[0].legend()\n\n # Accuracy\n axes[1].plot(history['train_acc'], label='Train')\n axes[1].plot(history['val_acc'], label='Validation')\n axes[1].set_xlabel('Epoch')\n axes[1].set_ylabel('Accuracy')\n axes[1].set_title('Training Accuracy')\n axes[1].legend()\n\n plt.tight_layout()\n plt.savefig('training_history.png', dpi=150)\n plt.show()\n\n\ndef plot_backtest_results(result):\n \"\"\"Plot backtest results\"\"\"\n fig, axes = plt.subplots(2, 1, figsize=(12, 8))\n\n # Equity curve\n axes[0].plot(result.equity_curve)\n axes[0].set_xlabel('Time')\n axes[0].set_ylabel('Equity')\n axes[0].set_title('Equity Curve')\n axes[0].grid(True, alpha=0.3)\n\n # Position history\n axes[1].plot(result.positions, drawstyle='steps-post')\n axes[1].set_xlabel('Time')\n axes[1].set_ylabel('Position')\n axes[1].set_title('Position History')\n axes[1].set_yticks([-1, 0, 1])\n axes[1].set_yticklabels(['Short', 'Flat', 'Long'])\n axes[1].grid(True, alpha=0.3)\n\n plt.tight_layout()\n plt.savefig('backtest_results.png', dpi=150)\n plt.show()\n\n\ndef main():\n \"\"\"Main function demonstrating TABL usage\"\"\"\n logger.info(\"=\" * 60)\n logger.info(\"TABL: Temporal Attention-Augmented Bilinear Network\")\n logger.info(\"=\" * 60)\n\n # 1. Create or load data\n logger.info(\"\\n1. Creating sample data...\")\n df = create_sample_data(n_samples=3000)\n logger.info(f\"Data shape: {df.shape}\")\n\n # 2. Prepare features and labels\n logger.info(\"\\n2. Preparing features and labels...\")\n X, y, returns = prepare_tabl_data(\n df,\n lookback=100,\n horizon=10,\n threshold=0.001\n )\n logger.info(f\"X shape: {X.shape}, y shape: {y.shape}\")\n\n # 3. Split data\n logger.info(\"\\n3. Splitting data...\")\n splits = create_train_val_test_split(X, y, returns)\n\n X_train, y_train, ret_train = splits['train']\n X_val, y_val, ret_val = splits['val']\n X_test, y_test, ret_test = splits['test']\n\n logger.info(f\"Train: {len(X_train)}, Val: {len(X_val)}, Test: {len(X_test)}\")\n\n # 4. Create data loaders\n train_dataset = TensorDataset(\n torch.FloatTensor(X_train),\n torch.LongTensor(y_train)\n )\n val_dataset = TensorDataset(\n torch.FloatTensor(X_val),\n torch.LongTensor(y_val)\n )\n test_dataset = TensorDataset(\n torch.FloatTensor(X_test),\n torch.LongTensor(y_test)\n )\n\n train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n val_loader = DataLoader(val_dataset, batch_size=32)\n test_loader = DataLoader(test_dataset, batch_size=32)\n\n # 5. Create model\n logger.info(\"\\n4. Creating TABL model...\")\n config = TABLConfig(\n seq_len=X.shape[1],\n input_dim=X.shape[2],\n hidden_T=20,\n hidden_D=32,\n attention_dim=64,\n n_classes=3,\n use_multihead=True,\n n_heads=4,\n dropout=0.2\n )\n\n model = TABLModel(config)\n logger.info(f\"Model parameters: {sum(p.numel() for p in model.parameters()):,}\")\n\n # 6. Train model\n logger.info(\"\\n5. Training model...\")\n history = train_model(\n model,\n train_loader,\n val_loader,\n epochs=30,\n lr=0.001,\n patience=10\n )\n\n # 7. Evaluate on test set\n logger.info(\"\\n6. Evaluating on test set...\")\n model.eval()\n device = next(model.parameters()).device\n\n test_correct = 0\n test_total = 0\n all_preds = []\n all_probs = []\n\n with torch.no_grad():\n for batch_x, batch_y in test_loader:\n batch_x = batch_x.to(device)\n batch_y = batch_y.to(device)\n\n output = model(batch_x)\n preds = output['logits'].argmax(dim=1)\n\n test_correct += (preds == batch_y).sum().item()\n test_total += batch_y.size(0)\n\n all_preds.extend(preds.cpu().numpy())\n all_probs.extend(output['probs'].cpu().numpy())\n\n test_acc = test_correct / test_total\n logger.info(f\"Test Accuracy: {test_acc:.4f}\")\n\n # 8. Backtest\n logger.info(\"\\n7. Running backtest...\")\n result = backtest_tabl_strategy(\n model,\n X_test,\n ret_test,\n confidence_threshold=0.5,\n transaction_cost=0.001\n )\n print_backtest_results(result)\n\n # 9. Visualizations (comment out if no display)\n try:\n logger.info(\"\\n8. Creating visualizations...\")\n plot_training_history(history)\n visualize_attention(model, X_test)\n plot_backtest_results(result)\n except Exception as e:\n logger.warning(f\"Could not create visualizations: {e}\")\n\n logger.info(\"\\n\" + \"=\" * 60)\n logger.info(\"TABL demonstration complete!\")\n logger.info(\"=\" * 60)\n\n return model, result\n\n\nif __name__ == \"__main__\":\n model, result = main()\n", | |
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| "content": "# TABL: Temporal Attention-Augmented Bilinear Network\n# Requirements for Python implementation\n\n# Deep Learning\ntorch>=2.0.0\nnumpy>=1.24.0\npandas>=2.0.0\n\n# HTTP requests (for Bybit API)\nrequests>=2.28.0\n\n# Visualization (optional)\nmatplotlib>=3.7.0\nseaborn>=0.12.0\n\n# Scientific computing\nscipy>=1.10.0\n\n# Logging\ntqdm>=4.65.0\n\n# Optional: for advanced features\n# scikit-learn>=1.2.0 # For metrics\n# ta>=0.10.0 # Technical analysis library\n" | |
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| "content": "# TABL: Temporal Attention-Augmented Bilinear Network\n# Requirements for Python implementation\n\n# Deep Learning\ntorch>=2.0.0\nnumpy>=1.24.0\npandas>=2.0.0\n\n# HTTP requests (for Bybit API)\nrequests>=2.28.0\n\n# Visualization (optional)\nmatplotlib>=3.7.0\nseaborn>=0.12.0\n\n# Scientific computing\nscipy>=1.10.0\n\n# Logging\ntqdm>=4.65.0\n\n# Optional: for advanced features\n# scikit-learn>=1.2.0 # For metrics\n# ta>=0.10.0 # Technical analysis library\n", | |
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| "content": "# TABL Rust Implementation\n\nRust implementation of the Temporal Attention-Augmented Bilinear Network (TABL) for financial time-series prediction using Bybit cryptocurrency data.\n\n## Features\n\n- High-performance TABL model implementation\n- Bybit API client for real-time and historical data\n- Feature engineering for OHLCV data\n- Backtesting framework\n- Multi-head attention support\n\n## Project Structure\n\n```\nrust/\n├── Cargo.toml\n├── README.md\n├── src/\n│ ├── lib.rs # Main library exports\n│ ├── api/ # Bybit API client\n│ │ ├── mod.rs\n│ │ ├── client.rs # HTTP client\n│ │ └── types.rs # API types\n│ ├── data/ # Data processing\n│ │ ├── mod.rs\n│ │ ├── loader.rs # Data loading\n│ │ ├── features.rs # Feature engineering\n│ │ └── dataset.rs # Dataset utilities\n│ ├── model/ # TABL architecture\n│ │ ├── mod.rs\n│ │ ├── bilinear.rs # Bilinear layer\n│ │ ├── attention.rs # Temporal attention\n│ │ └── tabl.rs # Complete TABL model\n│ └── strategy/ # Trading strategy\n│ ├── mod.rs\n│ ├── signals.rs # Signal generation\n│ └── backtest.rs # Backtesting engine\n└── examples/\n ├── fetch_data.rs # Download Bybit data\n ├── train.rs # Train model\n └── backtest.rs # Run backtest\n```\n\n## Quick Start\n\n```bash\n# Build the project\ncargo build --release\n\n# Fetch data from Bybit\ncargo run --example fetch_data -- --symbols BTCUSDT,ETHUSDT --interval 1h --days 30\n\n# Train model\ncargo run --example train -- --epochs 100 --batch-size 32\n\n# Run backtest\ncargo run --example backtest -- --start 2024-01-01 --end 2024-12-31\n```\n\n## Usage\n\n### As a Library\n\n```rust\nuse tabl::{BybitClient, TABLConfig, TABLModel, DataLoader};\n\n#[tokio::main]\nasync fn main() -> anyhow::Result<()> {\n // Fetch data\n let client = BybitClient::new();\n let klines = client.get_klines(\"BTCUSDT\", \"1h\", 1000).await?;\n\n // Prepare features\n let loader = DataLoader::new();\n let (x, y) = loader.prepare_tabl_data(&klines, 100, 10)?;\n\n // Create model\n let config = TABLConfig {\n seq_len: 100,\n input_dim: 6,\n hidden_t: 20,\n hidden_d: 32,\n ..Default::default()\n };\n let model = TABLModel::new(config);\n\n // Make predictions\n let predictions = model.predict(&x)?;\n\n Ok(())\n}\n```\n\n## Configuration\n\n### TABLConfig\n\n| Parameter | Default | Description |\n|-----------|---------|-------------|\n| `seq_len` | 100 | Input sequence length |\n| `input_dim` | 6 | Number of input features |\n| `hidden_t` | 20 | Compressed temporal dimension |\n| `hidden_d` | 32 | Compressed feature dimension |\n| `attention_dim` | 64 | Attention hidden dimension |\n| `n_heads` | 4 | Number of attention heads |\n| `n_classes` | 3 | Output classes (up/stationary/down) |\n| `dropout` | 0.2 | Dropout rate |\n\n## Performance\n\nThe Rust implementation offers:\n- **Memory efficient**: Zero-copy operations where possible\n- **Fast inference**: Optimized matrix operations via ndarray\n- **Parallel processing**: Multi-threaded data preparation\n- **Production ready**: Error handling and logging\n\n## License\n\nMIT\n" | |
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| "content": "# TABL Rust Implementation\n\nRust implementation of the Temporal Attention-Augmented Bilinear Network (TABL) for financial time-series prediction using Bybit cryptocurrency data.\n\n## Features\n\n- High-performance TABL model implementation\n- Bybit API client for real-time and historical data\n- Feature engineering for OHLCV data\n- Backtesting framework\n- Multi-head attention support\n\n## Project Structure\n\n```\nrust/\n├── Cargo.toml\n├── README.md\n├── src/\n│ ├── lib.rs # Main library exports\n│ ├── api/ # Bybit API client\n│ │ ├── mod.rs\n│ │ ├── client.rs # HTTP client\n│ │ └── types.rs # API types\n│ ├── data/ # Data processing\n│ │ ├── mod.rs\n│ │ ├── loader.rs # Data loading\n│ │ ├── features.rs # Feature engineering\n│ │ └── dataset.rs # Dataset utilities\n│ ├── model/ # TABL architecture\n│ │ ├── mod.rs\n│ │ ├── bilinear.rs # Bilinear layer\n│ │ ├── attention.rs # Temporal attention\n│ │ └── tabl.rs # Complete TABL model\n│ └── strategy/ # Trading strategy\n│ ├── mod.rs\n│ ├── signals.rs # Signal generation\n│ └── backtest.rs # Backtesting engine\n└── examples/\n ├── fetch_data.rs # Download Bybit data\n ├── train.rs # Train model\n └── backtest.rs # Run backtest\n```\n\n## Quick Start\n\n```bash\n# Build the project\ncargo build --release\n\n# Fetch data from Bybit\ncargo run --example fetch_data -- --symbols BTCUSDT,ETHUSDT --interval 1h --days 30\n\n# Train model\ncargo run --example train -- --epochs 100 --batch-size 32\n\n# Run backtest\ncargo run --example backtest -- --start 2024-01-01 --end 2024-12-31\n```\n\n## Usage\n\n### As a Library\n\n```rust\nuse tabl::{BybitClient, TABLConfig, TABLModel, DataLoader};\n\n#[tokio::main]\nasync fn main() -> anyhow::Result<()> {\n // Fetch data\n let client = BybitClient::new();\n let klines = client.get_klines(\"BTCUSDT\", \"1h\", 1000).await?;\n\n // Prepare features\n let loader = DataLoader::new();\n let (x, y) = loader.prepare_tabl_data(&klines, 100, 10)?;\n\n // Create model\n let config = TABLConfig {\n seq_len: 100,\n input_dim: 6,\n hidden_t: 20,\n hidden_d: 32,\n ..Default::default()\n };\n let model = TABLModel::new(config);\n\n // Make predictions\n let predictions = model.predict(&x)?;\n\n Ok(())\n}\n```\n\n## Configuration\n\n### TABLConfig\n\n| Parameter | Default | Description |\n|-----------|---------|-------------|\n| `seq_len` | 100 | Input sequence length |\n| `input_dim` | 6 | Number of input features |\n| `hidden_t` | 20 | Compressed temporal dimension |\n| `hidden_d` | 32 | Compressed feature dimension |\n| `attention_dim` | 64 | Attention hidden dimension |\n| `n_heads` | 4 | Number of attention heads |\n| `n_classes` | 3 | Output classes (up/stationary/down) |\n| `dropout` | 0.2 | Dropout rate |\n\n## Performance\n\nThe Rust implementation offers:\n- **Memory efficient**: Zero-copy operations where possible\n- **Fast inference**: Optimized matrix operations via ndarray\n- **Parallel processing**: Multi-threaded data preparation\n- **Production ready**: Error handling and logging\n\n## License\n\nMIT\n", | |
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| "content": "//! # TABL\n//!\n//! Temporal Attention-Augmented Bilinear Network for financial time-series prediction\n//! using cryptocurrency data from Bybit.\n//!\n//! ## Features\n//!\n//! - Temporal attention mechanism for focusing on important time steps\n//! - Bilinear projection for efficient dimensionality reduction\n//! - Multi-head attention support\n//! - Bybit API integration\n//!\n//! ## Modules\n//!\n//! - `api` - Bybit API client\n//! - `data` - Data loading and feature engineering\n//! - `model` - TABL model architecture\n//! - `strategy` - Trading strategy and backtesting\n//!\n//! ## Example\n//!\n//! ```no_run\n//! use tabl::{BybitClient, TABLConfig, TABLModel, DataLoader};\n//!\n//! #[tokio::main]\n//! async fn main() -> anyhow::Result<()> {\n//! // Fetch data\n//! let client = BybitClient::new();\n//! let klines = client.get_klines(\"BTCUSDT\", \"1h\", 1000).await?;\n//!\n//! // Prepare data\n//! let loader = DataLoader::new();\n//! let (x, y) = loader.prepare_tabl_data(&klines, 100, 10)?;\n//!\n//! // Create and use model\n//! let config = TABLConfig::default();\n//! let model = TABLModel::new(config);\n//! let predictions = model.predict(&x)?;\n//!\n//! Ok(())\n//! }\n//! ```\n\npub mod api;\npub mod data;\npub mod model;\npub mod strategy;\n\n// Re-exports for convenience\npub use api::{BybitClient, BybitError, Kline, OrderBook};\npub use data::{DataLoader, Features, prepare_features};\npub use model::{TABLConfig, TABLModel, BilinearLayer, TemporalAttention, AttentionType};\npub use strategy::{BacktestResult, SignalGenerator, TABLStrategy};\n\n/// Library version\npub const VERSION: &str = env!(\"CARGO_PKG_VERSION\");\n\n/// Default configuration values\npub mod defaults {\n /// Input sequence length\n pub const SEQ_LEN: usize = 100;\n\n /// Number of input features\n pub const INPUT_DIM: usize = 6;\n\n /// Compressed temporal dimension\n pub const HIDDEN_T: usize = 20;\n\n /// Compressed feature dimension\n pub const HIDDEN_D: usize = 32;\n\n /// Attention hidden dimension\n pub const ATTENTION_DIM: usize = 64;\n\n /// Number of attention heads\n pub const N_HEADS: usize = 4;\n\n /// Number of output classes\n pub const N_CLASSES: usize = 3;\n\n /// Dropout rate\n pub const DROPOUT: f64 = 0.2;\n\n /// Learning rate\n pub const LEARNING_RATE: f64 = 0.001;\n\n /// Batch size\n pub const BATCH_SIZE: usize = 32;\n\n /// Number of epochs\n pub const EPOCHS: usize = 100;\n\n /// Prediction horizon\n pub const HORIZON: usize = 10;\n\n /// Classification threshold\n pub const THRESHOLD: f64 = 0.0002;\n}\n" | |
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| "content": "//! # TABL\n//!\n//! Temporal Attention-Augmented Bilinear Network for financial time-series prediction\n//! using cryptocurrency data from Bybit.\n//!\n//! ## Features\n//!\n//! - Temporal attention mechanism for focusing on important time steps\n//! - Bilinear projection for efficient dimensionality reduction\n//! - Multi-head attention support\n//! - Bybit API integration\n//!\n//! ## Modules\n//!\n//! - `api` - Bybit API client\n//! - `data` - Data loading and feature engineering\n//! - `model` - TABL model architecture\n//! - `strategy` - Trading strategy and backtesting\n//!\n//! ## Example\n//!\n//! ```no_run\n//! use tabl::{BybitClient, TABLConfig, TABLModel, DataLoader};\n//!\n//! #[tokio::main]\n//! async fn main() -> anyhow::Result<()> {\n//! // Fetch data\n//! let client = BybitClient::new();\n//! let klines = client.get_klines(\"BTCUSDT\", \"1h\", 1000).await?;\n//!\n//! // Prepare data\n//! let loader = DataLoader::new();\n//! let (x, y) = loader.prepare_tabl_data(&klines, 100, 10)?;\n//!\n//! // Create and use model\n//! let config = TABLConfig::default();\n//! let model = TABLModel::new(config);\n//! let predictions = model.predict(&x)?;\n//!\n//! Ok(())\n//! }\n//! ```\n\npub mod api;\npub mod data;\npub mod model;\npub mod strategy;\n\n// Re-exports for convenience\npub use api::{BybitClient, BybitError, Kline, OrderBook};\npub use data::{DataLoader, Features, prepare_features};\npub use model::{TABLConfig, TABLModel, BilinearLayer, TemporalAttention, AttentionType};\npub use strategy::{BacktestResult, SignalGenerator, TABLStrategy};\n\n/// Library version\npub const VERSION: &str = env!(\"CARGO_PKG_VERSION\");\n\n/// Default configuration values\npub mod defaults {\n /// Input sequence length\n pub const SEQ_LEN: usize = 100;\n\n /// Number of input features\n pub const INPUT_DIM: usize = 6;\n\n /// Compressed temporal dimension\n pub const HIDDEN_T: usize = 20;\n\n /// Compressed feature dimension\n pub const HIDDEN_D: usize = 32;\n\n /// Attention hidden dimension\n pub const ATTENTION_DIM: usize = 64;\n\n /// Number of attention heads\n pub const N_HEADS: usize = 4;\n\n /// Number of output classes\n pub const N_CLASSES: usize = 3;\n\n /// Dropout rate\n pub const DROPOUT: f64 = 0.2;\n\n /// Learning rate\n pub const LEARNING_RATE: f64 = 0.001;\n\n /// Batch size\n pub const BATCH_SIZE: usize = 32;\n\n /// Number of epochs\n pub const EPOCHS: usize = 100;\n\n /// Prediction horizon\n pub const HORIZON: usize = 10;\n\n /// Classification threshold\n pub const THRESHOLD: f64 = 0.0002;\n}\n", | |
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| "text": "This session is being continued from a previous conversation that ran out of context. The summary below covers the earlier portion of the conversation.\n\nAnalysis:\nLet me analyze the conversation chronologically:\n\n1. **Initial Request**: The user asked me to solve GitHub issue #83 for the repository `suenot/machine-learning-for-trading`. The issue is about creating Chapter 46: Temporal Attention Networks. The user provided:\n - Issue URL: https://github.com/suenot/machine-learning-for-trading/issues/83\n - Branch: issue-83-e34794bc7991\n - Working directory: /tmp/gh-issue-solver-1768769379407\n - PR: https://github.com/suenot/machine-learning-for-trading/pull/377\n - Forked repo: konard/suenot-machine-learning-for-trading\n\n2. **Issue Requirements** (from GitHub issue #83):\n - Create README.md (English main content)\n - Create README.ru.md (Russian translation)\n - Create readme.simple.md (simple explanation for beginners, English)\n - Create readme.simple.ru.md (simple explanation, Russian)\n - Include working code examples in both Python AND Rust\n - Use both stock market and cryptocurrency data (Bybit exchange)\n - Code must be modular\n - All examples/code/data must stay WITHIN the chapter folder only\n\n3. **Research Phase**:\n - Viewed the issue details\n - Examined existing chapter structure (chapter 43 as reference)\n - Read the README.specify.md file for chapter 46\n - Searched for information on Temporal Attention Networks and TABL (Temporal Attention-Augmented Bilinear Network)\n\n4. **Files Created**:\n - README.md - comprehensive English documentation on TABL\n - README.ru.md - Russian translation\n - readme.simple.md - simplified explanation with analogies\n - readme.simple.ru.md - Russian simple explanation\n - Python implementation (5 files):\n - python/__init__.py\n - python/model.py (TABL model with BilinearLayer, TemporalAttention, MultiHeadTABL)\n - python/data.py (BybitDataLoader, feature engineering)\n - python/strategy.py (backtesting framework)\n - python/example_usage.py\n - python/requirements.txt\n - Rust implementation (started):\n - rust/Cargo.toml\n - rust/README.md\n - rust/src/lib.rs\n\n5. **Task Tracking** (TodoWrite):\n - README.md - completed\n - README.ru.md - completed\n - readme.simple.md - completed\n - readme.simple.ru.md - completed\n - Python implementation - completed\n - Rust implementation - in_progress\n - Test implementations - pending\n - Commit and push - pending\n - Finalize PR - pending\n\n6. **Current State**: I was in the middle of creating the Rust implementation. I had created the directory structure, Cargo.toml, README.md, and src/lib.rs. I still needed to create:\n - src/api/mod.rs, client.rs, types.rs\n - src/data/mod.rs, loader.rs, features.rs, dataset.rs\n - src/model/mod.rs, bilinear.rs, attention.rs, tabl.rs\n - src/strategy/mod.rs, signals.rs, backtest.rs\n - examples/fetch_data.rs, train.rs, backtest.rs\n\nNo errors were encountered. The user has not provided any feedback yet - they only gave the initial task.\n\nSummary:\n1. Primary Request and Intent:\n The user requested solving GitHub issue #83 for the `suenot/machine-learning-for-trading` repository. The task is to create Chapter 46: Temporal Attention Networks. Requirements include:\n - Creating README.md (English) and README.ru.md (Russian) main documentation\n - Creating readme.simple.md (English) and readme.simple.ru.md (Russian) simplified explanations\n - Implementing working code examples in both Python AND Rust\n - Using stock market and cryptocurrency data (specifically Bybit exchange)\n - Keeping all code modular and contained within the chapter folder only\n - The final output should be marked as \"DONE\"\n\n2. Key Technical Concepts:\n - TABL (Temporal Attention-Augmented Bilinear Network) - specialized attention for financial time-series\n - Bilinear projection: H = σ(W₁ · X · W₂ + b) for dimensionality reduction\n - Temporal Attention mechanism: α = softmax(w · tanh(U · X^T)) for focusing on important time steps\n - Multi-head attention for capturing different temporal patterns\n - Limit Order Book (LOB) prediction\n - OHLCV feature engineering (log returns, RSI, MACD, Bollinger Bands)\n - Bybit cryptocurrency exchange API integration\n - Backtesting framework for trading strategies\n - PyTorch neural network implementation\n - Rust ndarray-based numerical computing\n\n3. Files and Code Sections:\n \n - **46_temporal_attention_networks/README.md**\n - Main English documentation explaining TABL architecture\n - Contains architecture diagrams, code examples, comparison tables\n - References papers: arxiv.org/abs/1712.00975\n\n - **46_temporal_attention_networks/README.ru.md**\n - Complete Russian translation of README.md\n\n - **46_temporal_attention_networks/readme.simple.md**\n - Simplified explanation using analogies (movie watching, school scenarios)\n - Explains temporal attention like a \"spotlight operator\"\n\n - **46_temporal_attention_networks/readme.simple.ru.md**\n - Russian translation of simplified explanation\n\n - **46_temporal_attention_networks/python/__init__.py**\n - Module exports for TABLConfig, TABLModel, MultiHeadTABL, etc.\n\n - **46_temporal_attention_networks/python/model.py**\n - Core TABL implementation with PyTorch\n - Key classes: BilinearLayer, TemporalAttention, TABL, MultiHeadTABL, TABLModel\n ```python\n class BilinearLayer(nn.Module):\n def __init__(self, T_in, T_out, D_in, D_out, dropout=0.1, use_batch_norm=True):\n # Temporal projection: W1 ∈ ℝ^(T_out×T_in)\n # Feature projection: W2 ∈ ℝ^(D_in×D_out)\n \n class TemporalAttention(nn.Module):\n def forward(self, x, mask=None):\n # α = softmax(w · tanh(U · X^T))\n # context = X^T · α\n ```\n\n - **46_temporal_attention_networks/python/data.py**\n - BybitDataLoader class for API integration\n - Feature extraction: extract_ohlcv_features(), compute_rsi(), compute_macd()\n - Data preparation: prepare_tabl_data()\n\n - **46_temporal_attention_networks/python/strategy.py**\n - TABLStrategy class for signal generation\n - backtest_tabl_strategy() function\n - calculate_metrics() for Sharpe ratio, max drawdown, win rate\n\n - **46_temporal_attention_networks/python/example_usage.py**\n - Complete demonstration script\n - Functions: create_sample_data(), train_model(), visualize_attention()\n\n - **46_temporal_attention_networks/python/requirements.txt**\n - torch>=2.0.0, numpy, pandas, requests, matplotlib, seaborn\n\n - **46_temporal_attention_networks/rust/Cargo.toml**\n - Dependencies: reqwest, tokio, ndarray, nalgebra, chrono, etc.\n\n - **46_temporal_attention_networks/rust/README.md**\n - Rust implementation documentation\n\n - **46_temporal_attention_networks/rust/src/lib.rs**\n - Main library exports and default configuration\n ```rust\n pub mod api;\n pub mod data;\n pub mod model;\n pub mod strategy;\n \n pub mod defaults {\n pub const SEQ_LEN: usize = 100;\n pub const HIDDEN_T: usize = 20;\n // ...\n }\n ```\n\n4. Errors and fixes:\n - No errors were encountered during implementation\n\n5. Problem Solving:\n - Researched Temporal Attention Networks and found TABL as the key architecture\n - Used chapter 43 (Stockformer) as a reference for file structure\n - Designed modular Python and Rust implementations following the existing pattern\n\n6. All user messages:\n - \"Issue to solve: https://github.com/suenot/machine-learning-for-trading/issues/83\nYour prepared branch: issue-83-e34794bc7991\nYour prepared working directory: /tmp/gh-issue-solver-1768769379407\nYour prepared Pull Request: https://github.com/suenot/machine-learning-for-trading/pull/377\nYour forked repository: konard/suenot-machine-learning-for-trading\nOriginal repository (upstream): suenot/machine-learning-for-trading\n\nProceed.\"\n\n7. Pending Tasks:\n - Complete Rust implementation (src/api/*, src/data/*, src/model/*, src/strategy/*, examples/*)\n - Test both Python and Rust implementations\n - Commit and push changes to the branch\n - Finalize PR and verify CI passes\n - Mark task as DONE\n\n8. Current Work:\n I was creating the Rust implementation for TABL. I had completed:\n - rust/Cargo.toml with all dependencies\n - rust/README.md with documentation\n - rust/src/lib.rs with module structure and defaults\n \n Directory structure created but not yet populated:\n - rust/src/api/\n - rust/src/data/\n - rust/src/model/\n - rust/src/strategy/\n - rust/examples/\n\n9. Optional Next Step:\n Continue creating the Rust implementation files. The next files to create are:\n - src/api/mod.rs, client.rs, types.rs (Bybit API client)\n - src/data/mod.rs, loader.rs, features.rs, dataset.rs (data processing)\n - src/model/mod.rs, bilinear.rs, attention.rs, tabl.rs (TABL model)\n - src/strategy/mod.rs, signals.rs, backtest.rs (trading strategy)\n - examples/fetch_data.rs, train.rs, backtest.rs\n\n Task tracking shows: \"Create Rust implementation\" status: \"in_progress\"\n\nIf you need specific details from before compaction (like exact code snippets, error messages, or content you generated), read the full transcript at: /home/hive/.claude/projects/-tmp-gh-issue-solver-1768769379407/77018388-5fda-4d3e-acd4-e8399840434b.jsonl\nPlease continue the conversation from where we left it off without asking the user any further questions. Continue with the last task that you were asked to work on." | |
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| "text": "I'll continue with the Rust implementation. Let me create the remaining source files for the TABL library." | |
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| "content": "//! Bybit API client module\n//!\n//! This module provides a client for interacting with the Bybit cryptocurrency exchange API.\n\nmod client;\nmod types;\n\npub use client::BybitClient;\npub use types::{BybitError, Kline, OrderBook, OrderBookLevel};\n" | |
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| "content": "//! Type definitions for Bybit API responses\n\nuse serde::{Deserialize, Serialize};\nuse thiserror::Error;\n\n/// Errors that can occur when interacting with the Bybit API\n#[derive(Error, Debug)]\npub enum BybitError {\n #[error(\"HTTP request failed: {0}\")]\n RequestError(#[from] reqwest::Error),\n\n #[error(\"JSON parsing failed: {0}\")]\n ParseError(#[from] serde_json::Error),\n\n #[error(\"API error: {code} - {message}\")]\n ApiError { code: i32, message: String },\n\n #[error(\"Invalid response format\")]\n InvalidResponse,\n\n #[error(\"Rate limit exceeded\")]\n RateLimitExceeded,\n}\n\n/// OHLCV candlestick data\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct Kline {\n /// Start time of the candle (Unix timestamp in milliseconds)\n pub start_time: i64,\n /// Opening price\n pub open: f64,\n /// Highest price\n pub high: f64,\n /// Lowest price\n pub low: f64,\n /// Closing price\n pub close: f64,\n /// Trading volume\n pub volume: f64,\n /// Turnover (quote volume)\n pub turnover: f64,\n}\n\nimpl Kline {\n /// Calculate typical price (HLC average)\n pub fn typical_price(&self) -> f64 {\n (self.high + self.low + self.close) / 3.0\n }\n\n /// Calculate price range\n pub fn range(&self) -> f64 {\n self.high - self.low\n }\n\n /// Check if this is a bullish candle\n pub fn is_bullish(&self) -> bool {\n self.close > self.open\n }\n\n /// Calculate body size\n pub fn body_size(&self) -> f64 {\n (self.close - self.open).abs()\n }\n}\n\n/// Order book level with price and quantity\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct OrderBookLevel {\n pub price: f64,\n pub quantity: f64,\n}\n\n/// Order book snapshot\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct OrderBook {\n /// Symbol (e.g., \"BTCUSDT\")\n pub symbol: String,\n /// Timestamp\n pub timestamp: i64,\n /// Bid levels (sorted by price descending)\n pub bids: Vec<OrderBookLevel>,\n /// Ask levels (sorted by price ascending)\n pub asks: Vec<OrderBookLevel>,\n}\n\nimpl OrderBook {\n /// Calculate mid price\n pub fn mid_price(&self) -> Option<f64> {\n if self.bids.is_empty() || self.asks.is_empty() {\n return None;\n }\n Some((self.bids[0].price + self.asks[0].price) / 2.0)\n }\n\n /// Calculate bid-ask spread\n pub fn spread(&self) -> Option<f64> {\n if self.bids.is_empty() || self.asks.is_empty() {\n return None;\n }\n Some(self.asks[0].price - self.bids[0].price)\n }\n\n /// Calculate order book imbalance\n pub fn imbalance(&self, depth: usize) -> f64 {\n let bid_volume: f64 = self.bids.iter().take(depth).map(|l| l.quantity).sum();\n let ask_volume: f64 = self.asks.iter().take(depth).map(|l| l.quantity).sum();\n let total = bid_volume + ask_volume;\n if total == 0.0 {\n 0.0\n } else {\n (bid_volume - ask_volume) / total\n }\n }\n\n /// Calculate weighted average price for bids\n pub fn vwap_bid(&self, depth: usize) -> Option<f64> {\n let levels: Vec<_> = self.bids.iter().take(depth).collect();\n if levels.is_empty() {\n return None;\n }\n let total_volume: f64 = levels.iter().map(|l| l.quantity).sum();\n if total_volume == 0.0 {\n return None;\n }\n let weighted_sum: f64 = levels.iter().map(|l| l.price * l.quantity).sum();\n Some(weighted_sum / total_volume)\n }\n\n /// Calculate weighted average price for asks\n pub fn vwap_ask(&self, depth: usize) -> Option<f64> {\n let levels: Vec<_> = self.asks.iter().take(depth).collect();\n if levels.is_empty() {\n return None;\n }\n let total_volume: f64 = levels.iter().map(|l| l.quantity).sum();\n if total_volume == 0.0 {\n return None;\n }\n let weighted_sum: f64 = levels.iter().map(|l| l.price * l.quantity).sum();\n Some(weighted_sum / total_volume)\n }\n}\n\n/// Raw API response wrapper\n#[derive(Debug, Deserialize)]\npub(crate) struct ApiResponse<T> {\n #[serde(rename = \"retCode\")]\n pub ret_code: i32,\n #[serde(rename = \"retMsg\")]\n pub ret_msg: String,\n pub result: T,\n}\n\n/// Kline list result\n#[derive(Debug, Deserialize)]\npub(crate) struct KlineResult {\n pub symbol: String,\n pub category: String,\n pub list: Vec<Vec<String>>,\n}\n\n/// Order book result\n#[derive(Debug, Deserialize)]\npub(crate) struct OrderBookResult {\n pub s: String,\n pub b: Vec<Vec<String>>,\n pub a: Vec<Vec<String>>,\n pub ts: i64,\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_kline_calculations() {\n let kline = Kline {\n start_time: 1000000,\n open: 100.0,\n high: 110.0,\n low: 95.0,\n close: 105.0,\n volume: 1000.0,\n turnover: 100000.0,\n };\n\n assert!((kline.typical_price() - 103.333).abs() < 0.01);\n assert_eq!(kline.range(), 15.0);\n assert!(kline.is_bullish());\n assert_eq!(kline.body_size(), 5.0);\n }\n\n #[test]\n fn test_orderbook_calculations() {\n let orderbook = OrderBook {\n symbol: \"BTCUSDT\".to_string(),\n timestamp: 1000000,\n bids: vec![\n OrderBookLevel { price: 99.0, quantity: 10.0 },\n OrderBookLevel { price: 98.0, quantity: 20.0 },\n ],\n asks: vec![\n OrderBookLevel { price: 101.0, quantity: 15.0 },\n OrderBookLevel { price: 102.0, quantity: 25.0 },\n ],\n };\n\n assert_eq!(orderbook.mid_price(), Some(100.0));\n assert_eq!(orderbook.spread(), Some(2.0));\n\n // Imbalance: (30 - 40) / 70 = -0.1428...\n let imbalance = orderbook.imbalance(2);\n assert!((imbalance - (-0.1428)).abs() < 0.01);\n }\n}\n" | |
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| "content": "//! Bybit API client implementation\n\nuse reqwest::Client;\nuse std::time::Duration;\n\nuse super::types::{ApiResponse, BybitError, Kline, KlineResult, OrderBook, OrderBookLevel, OrderBookResult};\n\n/// Bybit API client\npub struct BybitClient {\n client: Client,\n base_url: String,\n}\n\nimpl Default for BybitClient {\n fn default() -> Self {\n Self::new()\n }\n}\n\nimpl BybitClient {\n /// Create a new Bybit client\n pub fn new() -> Self {\n Self {\n client: Client::builder()\n .timeout(Duration::from_secs(30))\n .build()\n .expect(\"Failed to create HTTP client\"),\n base_url: \"https://api.bybit.com\".to_string(),\n }\n }\n\n /// Create a client with a custom base URL (for testing)\n pub fn with_base_url(base_url: &str) -> Self {\n Self {\n client: Client::builder()\n .timeout(Duration::from_secs(30))\n .build()\n .expect(\"Failed to create HTTP client\"),\n base_url: base_url.to_string(),\n }\n }\n\n /// Fetch OHLCV candlestick data\n ///\n /// # Arguments\n /// * `symbol` - Trading pair symbol (e.g., \"BTCUSDT\")\n /// * `interval` - Kline interval (1, 3, 5, 15, 30, 60, 120, 240, 360, 720, D, M, W)\n /// * `limit` - Number of candles to fetch (max 1000)\n ///\n /// # Example\n /// ```no_run\n /// use tabl::BybitClient;\n ///\n /// #[tokio::main]\n /// async fn main() {\n /// let client = BybitClient::new();\n /// let klines = client.get_klines(\"BTCUSDT\", \"1h\", 100).await.unwrap();\n /// println!(\"Fetched {} candles\", klines.len());\n /// }\n /// ```\n pub async fn get_klines(\n &self,\n symbol: &str,\n interval: &str,\n limit: usize,\n ) -> Result<Vec<Kline>, BybitError> {\n let url = format!(\n \"{}/v5/market/kline?category=spot&symbol={}&interval={}&limit={}\",\n self.base_url,\n symbol,\n interval,\n limit.min(1000)\n );\n\n let response = self.client.get(&url).send().await?;\n let api_response: ApiResponse<KlineResult> = response.json().await?;\n\n if api_response.ret_code != 0 {\n return Err(BybitError::ApiError {\n code: api_response.ret_code,\n message: api_response.ret_msg,\n });\n }\n\n let klines: Result<Vec<Kline>, BybitError> = api_response\n .result\n .list\n .iter()\n .map(|row| {\n if row.len() < 7 {\n return Err(BybitError::InvalidResponse);\n }\n Ok(Kline {\n start_time: row[0].parse().map_err(|_| BybitError::InvalidResponse)?,\n open: row[1].parse().map_err(|_| BybitError::InvalidResponse)?,\n high: row[2].parse().map_err(|_| BybitError::InvalidResponse)?,\n low: row[3].parse().map_err(|_| BybitError::InvalidResponse)?,\n close: row[4].parse().map_err(|_| BybitError::InvalidResponse)?,\n volume: row[5].parse().map_err(|_| BybitError::InvalidResponse)?,\n turnover: row[6].parse().map_err(|_| BybitError::InvalidResponse)?,\n })\n })\n .collect();\n\n // Bybit returns data in reverse chronological order, so we reverse it\n let mut klines = klines?;\n klines.reverse();\n Ok(klines)\n }\n\n /// Fetch order book snapshot\n ///\n /// # Arguments\n /// * `symbol` - Trading pair symbol (e.g., \"BTCUSDT\")\n /// * `limit` - Order book depth (max 500)\n pub async fn get_orderbook(\n &self,\n symbol: &str,\n limit: usize,\n ) -> Result<OrderBook, BybitError> {\n let url = format!(\n \"{}/v5/market/orderbook?category=spot&symbol={}&limit={}\",\n self.base_url,\n symbol,\n limit.min(500)\n );\n\n let response = self.client.get(&url).send().await?;\n let api_response: ApiResponse<OrderBookResult> = response.json().await?;\n\n if api_response.ret_code != 0 {\n return Err(BybitError::ApiError {\n code: api_response.ret_code,\n message: api_response.ret_msg,\n });\n }\n\n let bids: Result<Vec<OrderBookLevel>, BybitError> = api_response\n .result\n .b\n .iter()\n .map(|level| {\n if level.len() < 2 {\n return Err(BybitError::InvalidResponse);\n }\n Ok(OrderBookLevel {\n price: level[0].parse().map_err(|_| BybitError::InvalidResponse)?,\n quantity: level[1].parse().map_err(|_| BybitError::InvalidResponse)?,\n })\n })\n .collect();\n\n let asks: Result<Vec<OrderBookLevel>, BybitError> = api_response\n .result\n .a\n .iter()\n .map(|level| {\n if level.len() < 2 {\n return Err(BybitError::InvalidResponse);\n }\n Ok(OrderBookLevel {\n price: level[0].parse().map_err(|_| BybitError::InvalidResponse)?,\n quantity: level[1].parse().map_err(|_| BybitError::InvalidResponse)?,\n })\n })\n .collect();\n\n Ok(OrderBook {\n symbol: api_response.result.s,\n timestamp: api_response.result.ts,\n bids: bids?,\n asks: asks?,\n })\n }\n\n /// Fetch multiple symbols' klines in parallel\n pub async fn get_klines_multi(\n &self,\n symbols: &[&str],\n interval: &str,\n limit: usize,\n ) -> Vec<Result<(String, Vec<Kline>), BybitError>> {\n let futures: Vec<_> = symbols\n .iter()\n .map(|symbol| {\n let symbol = symbol.to_string();\n let client = self.clone();\n let interval = interval.to_string();\n async move {\n let klines = client.get_klines(&symbol, &interval, limit).await?;\n Ok((symbol, klines))\n }\n })\n .collect();\n\n futures::future::join_all(futures).await\n }\n}\n\nimpl Clone for BybitClient {\n fn clone(&self) -> Self {\n Self {\n client: self.client.clone(),\n base_url: self.base_url.clone(),\n }\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_client_creation() {\n let client = BybitClient::new();\n assert_eq!(client.base_url, \"https://api.bybit.com\");\n }\n\n #[test]\n fn test_client_with_custom_url() {\n let client = BybitClient::with_base_url(\"https://custom.api.com\");\n assert_eq!(client.base_url, \"https://custom.api.com\");\n }\n}\n" | |
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| "content": "//! Bybit API client module\n//!\n//! This module provides a client for interacting with the Bybit cryptocurrency exchange API.\n\nmod client;\nmod types;\n\npub use client::BybitClient;\npub use types::{BybitError, Kline, OrderBook, OrderBookLevel};\n", | |
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| "content": "//! Type definitions for Bybit API responses\n\nuse serde::{Deserialize, Serialize};\nuse thiserror::Error;\n\n/// Errors that can occur when interacting with the Bybit API\n#[derive(Error, Debug)]\npub enum BybitError {\n #[error(\"HTTP request failed: {0}\")]\n RequestError(#[from] reqwest::Error),\n\n #[error(\"JSON parsing failed: {0}\")]\n ParseError(#[from] serde_json::Error),\n\n #[error(\"API error: {code} - {message}\")]\n ApiError { code: i32, message: String },\n\n #[error(\"Invalid response format\")]\n InvalidResponse,\n\n #[error(\"Rate limit exceeded\")]\n RateLimitExceeded,\n}\n\n/// OHLCV candlestick data\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct Kline {\n /// Start time of the candle (Unix timestamp in milliseconds)\n pub start_time: i64,\n /// Opening price\n pub open: f64,\n /// Highest price\n pub high: f64,\n /// Lowest price\n pub low: f64,\n /// Closing price\n pub close: f64,\n /// Trading volume\n pub volume: f64,\n /// Turnover (quote volume)\n pub turnover: f64,\n}\n\nimpl Kline {\n /// Calculate typical price (HLC average)\n pub fn typical_price(&self) -> f64 {\n (self.high + self.low + self.close) / 3.0\n }\n\n /// Calculate price range\n pub fn range(&self) -> f64 {\n self.high - self.low\n }\n\n /// Check if this is a bullish candle\n pub fn is_bullish(&self) -> bool {\n self.close > self.open\n }\n\n /// Calculate body size\n pub fn body_size(&self) -> f64 {\n (self.close - self.open).abs()\n }\n}\n\n/// Order book level with price and quantity\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct OrderBookLevel {\n pub price: f64,\n pub quantity: f64,\n}\n\n/// Order book snapshot\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct OrderBook {\n /// Symbol (e.g., \"BTCUSDT\")\n pub symbol: String,\n /// Timestamp\n pub timestamp: i64,\n /// Bid levels (sorted by price descending)\n pub bids: Vec<OrderBookLevel>,\n /// Ask levels (sorted by price ascending)\n pub asks: Vec<OrderBookLevel>,\n}\n\nimpl OrderBook {\n /// Calculate mid price\n pub fn mid_price(&self) -> Option<f64> {\n if self.bids.is_empty() || self.asks.is_empty() {\n return None;\n }\n Some((self.bids[0].price + self.asks[0].price) / 2.0)\n }\n\n /// Calculate bid-ask spread\n pub fn spread(&self) -> Option<f64> {\n if self.bids.is_empty() || self.asks.is_empty() {\n return None;\n }\n Some(self.asks[0].price - self.bids[0].price)\n }\n\n /// Calculate order book imbalance\n pub fn imbalance(&self, depth: usize) -> f64 {\n let bid_volume: f64 = self.bids.iter().take(depth).map(|l| l.quantity).sum();\n let ask_volume: f64 = self.asks.iter().take(depth).map(|l| l.quantity).sum();\n let total = bid_volume + ask_volume;\n if total == 0.0 {\n 0.0\n } else {\n (bid_volume - ask_volume) / total\n }\n }\n\n /// Calculate weighted average price for bids\n pub fn vwap_bid(&self, depth: usize) -> Option<f64> {\n let levels: Vec<_> = self.bids.iter().take(depth).collect();\n if levels.is_empty() {\n return None;\n }\n let total_volume: f64 = levels.iter().map(|l| l.quantity).sum();\n if total_volume == 0.0 {\n return None;\n }\n let weighted_sum: f64 = levels.iter().map(|l| l.price * l.quantity).sum();\n Some(weighted_sum / total_volume)\n }\n\n /// Calculate weighted average price for asks\n pub fn vwap_ask(&self, depth: usize) -> Option<f64> {\n let levels: Vec<_> = self.asks.iter().take(depth).collect();\n if levels.is_empty() {\n return None;\n }\n let total_volume: f64 = levels.iter().map(|l| l.quantity).sum();\n if total_volume == 0.0 {\n return None;\n }\n let weighted_sum: f64 = levels.iter().map(|l| l.price * l.quantity).sum();\n Some(weighted_sum / total_volume)\n }\n}\n\n/// Raw API response wrapper\n#[derive(Debug, Deserialize)]\npub(crate) struct ApiResponse<T> {\n #[serde(rename = \"retCode\")]\n pub ret_code: i32,\n #[serde(rename = \"retMsg\")]\n pub ret_msg: String,\n pub result: T,\n}\n\n/// Kline list result\n#[derive(Debug, Deserialize)]\npub(crate) struct KlineResult {\n pub symbol: String,\n pub category: String,\n pub list: Vec<Vec<String>>,\n}\n\n/// Order book result\n#[derive(Debug, Deserialize)]\npub(crate) struct OrderBookResult {\n pub s: String,\n pub b: Vec<Vec<String>>,\n pub a: Vec<Vec<String>>,\n pub ts: i64,\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_kline_calculations() {\n let kline = Kline {\n start_time: 1000000,\n open: 100.0,\n high: 110.0,\n low: 95.0,\n close: 105.0,\n volume: 1000.0,\n turnover: 100000.0,\n };\n\n assert!((kline.typical_price() - 103.333).abs() < 0.01);\n assert_eq!(kline.range(), 15.0);\n assert!(kline.is_bullish());\n assert_eq!(kline.body_size(), 5.0);\n }\n\n #[test]\n fn test_orderbook_calculations() {\n let orderbook = OrderBook {\n symbol: \"BTCUSDT\".to_string(),\n timestamp: 1000000,\n bids: vec![\n OrderBookLevel { price: 99.0, quantity: 10.0 },\n OrderBookLevel { price: 98.0, quantity: 20.0 },\n ],\n asks: vec![\n OrderBookLevel { price: 101.0, quantity: 15.0 },\n OrderBookLevel { price: 102.0, quantity: 25.0 },\n ],\n };\n\n assert_eq!(orderbook.mid_price(), Some(100.0));\n assert_eq!(orderbook.spread(), Some(2.0));\n\n // Imbalance: (30 - 40) / 70 = -0.1428...\n let imbalance = orderbook.imbalance(2);\n assert!((imbalance - (-0.1428)).abs() < 0.01);\n }\n}\n", | |
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| "content": "//! Bybit API client implementation\n\nuse reqwest::Client;\nuse std::time::Duration;\n\nuse super::types::{ApiResponse, BybitError, Kline, KlineResult, OrderBook, OrderBookLevel, OrderBookResult};\n\n/// Bybit API client\npub struct BybitClient {\n client: Client,\n base_url: String,\n}\n\nimpl Default for BybitClient {\n fn default() -> Self {\n Self::new()\n }\n}\n\nimpl BybitClient {\n /// Create a new Bybit client\n pub fn new() -> Self {\n Self {\n client: Client::builder()\n .timeout(Duration::from_secs(30))\n .build()\n .expect(\"Failed to create HTTP client\"),\n base_url: \"https://api.bybit.com\".to_string(),\n }\n }\n\n /// Create a client with a custom base URL (for testing)\n pub fn with_base_url(base_url: &str) -> Self {\n Self {\n client: Client::builder()\n .timeout(Duration::from_secs(30))\n .build()\n .expect(\"Failed to create HTTP client\"),\n base_url: base_url.to_string(),\n }\n }\n\n /// Fetch OHLCV candlestick data\n ///\n /// # Arguments\n /// * `symbol` - Trading pair symbol (e.g., \"BTCUSDT\")\n /// * `interval` - Kline interval (1, 3, 5, 15, 30, 60, 120, 240, 360, 720, D, M, W)\n /// * `limit` - Number of candles to fetch (max 1000)\n ///\n /// # Example\n /// ```no_run\n /// use tabl::BybitClient;\n ///\n /// #[tokio::main]\n /// async fn main() {\n /// let client = BybitClient::new();\n /// let klines = client.get_klines(\"BTCUSDT\", \"1h\", 100).await.unwrap();\n /// println!(\"Fetched {} candles\", klines.len());\n /// }\n /// ```\n pub async fn get_klines(\n &self,\n symbol: &str,\n interval: &str,\n limit: usize,\n ) -> Result<Vec<Kline>, BybitError> {\n let url = format!(\n \"{}/v5/market/kline?category=spot&symbol={}&interval={}&limit={}\",\n self.base_url,\n symbol,\n interval,\n limit.min(1000)\n );\n\n let response = self.client.get(&url).send().await?;\n let api_response: ApiResponse<KlineResult> = response.json().await?;\n\n if api_response.ret_code != 0 {\n return Err(BybitError::ApiError {\n code: api_response.ret_code,\n message: api_response.ret_msg,\n });\n }\n\n let klines: Result<Vec<Kline>, BybitError> = api_response\n .result\n .list\n .iter()\n .map(|row| {\n if row.len() < 7 {\n return Err(BybitError::InvalidResponse);\n }\n Ok(Kline {\n start_time: row[0].parse().map_err(|_| BybitError::InvalidResponse)?,\n open: row[1].parse().map_err(|_| BybitError::InvalidResponse)?,\n high: row[2].parse().map_err(|_| BybitError::InvalidResponse)?,\n low: row[3].parse().map_err(|_| BybitError::InvalidResponse)?,\n close: row[4].parse().map_err(|_| BybitError::InvalidResponse)?,\n volume: row[5].parse().map_err(|_| BybitError::InvalidResponse)?,\n turnover: row[6].parse().map_err(|_| BybitError::InvalidResponse)?,\n })\n })\n .collect();\n\n // Bybit returns data in reverse chronological order, so we reverse it\n let mut klines = klines?;\n klines.reverse();\n Ok(klines)\n }\n\n /// Fetch order book snapshot\n ///\n /// # Arguments\n /// * `symbol` - Trading pair symbol (e.g., \"BTCUSDT\")\n /// * `limit` - Order book depth (max 500)\n pub async fn get_orderbook(\n &self,\n symbol: &str,\n limit: usize,\n ) -> Result<OrderBook, BybitError> {\n let url = format!(\n \"{}/v5/market/orderbook?category=spot&symbol={}&limit={}\",\n self.base_url,\n symbol,\n limit.min(500)\n );\n\n let response = self.client.get(&url).send().await?;\n let api_response: ApiResponse<OrderBookResult> = response.json().await?;\n\n if api_response.ret_code != 0 {\n return Err(BybitError::ApiError {\n code: api_response.ret_code,\n message: api_response.ret_msg,\n });\n }\n\n let bids: Result<Vec<OrderBookLevel>, BybitError> = api_response\n .result\n .b\n .iter()\n .map(|level| {\n if level.len() < 2 {\n return Err(BybitError::InvalidResponse);\n }\n Ok(OrderBookLevel {\n price: level[0].parse().map_err(|_| BybitError::InvalidResponse)?,\n quantity: level[1].parse().map_err(|_| BybitError::InvalidResponse)?,\n })\n })\n .collect();\n\n let asks: Result<Vec<OrderBookLevel>, BybitError> = api_response\n .result\n .a\n .iter()\n .map(|level| {\n if level.len() < 2 {\n return Err(BybitError::InvalidResponse);\n }\n Ok(OrderBookLevel {\n price: level[0].parse().map_err(|_| BybitError::InvalidResponse)?,\n quantity: level[1].parse().map_err(|_| BybitError::InvalidResponse)?,\n })\n })\n .collect();\n\n Ok(OrderBook {\n symbol: api_response.result.s,\n timestamp: api_response.result.ts,\n bids: bids?,\n asks: asks?,\n })\n }\n\n /// Fetch multiple symbols' klines in parallel\n pub async fn get_klines_multi(\n &self,\n symbols: &[&str],\n interval: &str,\n limit: usize,\n ) -> Vec<Result<(String, Vec<Kline>), BybitError>> {\n let futures: Vec<_> = symbols\n .iter()\n .map(|symbol| {\n let symbol = symbol.to_string();\n let client = self.clone();\n let interval = interval.to_string();\n async move {\n let klines = client.get_klines(&symbol, &interval, limit).await?;\n Ok((symbol, klines))\n }\n })\n .collect();\n\n futures::future::join_all(futures).await\n }\n}\n\nimpl Clone for BybitClient {\n fn clone(&self) -> Self {\n Self {\n client: self.client.clone(),\n base_url: self.base_url.clone(),\n }\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_client_creation() {\n let client = BybitClient::new();\n assert_eq!(client.base_url, \"https://api.bybit.com\");\n }\n\n #[test]\n fn test_client_with_custom_url() {\n let client = BybitClient::with_base_url(\"https://custom.api.com\");\n assert_eq!(client.base_url, \"https://custom.api.com\");\n }\n}\n", | |
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| "text": "Now let me create the data module:" | |
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| "content": "//! Data loading and feature engineering module\n//!\n//! This module provides functionality for:\n//! - Loading and processing market data\n//! - Computing technical indicators\n//! - Preparing data for TABL model\n\nmod features;\nmod loader;\n\npub use features::{prepare_features, Features};\npub use loader::DataLoader;\n" | |
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| "content": "//! Feature engineering for TABL model\n\nuse crate::api::Kline;\nuse ndarray::{Array1, Array2, Axis};\n\n/// Container for computed features\n#[derive(Debug, Clone)]\npub struct Features {\n /// Feature matrix [time_steps, n_features]\n pub data: Array2<f64>,\n /// Feature names\n pub names: Vec<String>,\n /// Timestamps corresponding to each row\n pub timestamps: Vec<i64>,\n}\n\nimpl Features {\n /// Get the number of time steps\n pub fn len(&self) -> usize {\n self.data.nrows()\n }\n\n /// Check if features are empty\n pub fn is_empty(&self) -> bool {\n self.data.is_empty()\n }\n\n /// Get the number of features\n pub fn n_features(&self) -> usize {\n self.data.ncols()\n }\n\n /// Normalize features to zero mean and unit variance\n pub fn normalize(&mut self) {\n for col_idx in 0..self.data.ncols() {\n let col = self.data.column(col_idx);\n let mean = col.mean().unwrap_or(0.0);\n let std = col.std(0.0);\n\n if std > 1e-8 {\n for row_idx in 0..self.data.nrows() {\n self.data[[row_idx, col_idx]] = (self.data[[row_idx, col_idx]] - mean) / std;\n }\n }\n }\n }\n\n /// Get a slice of features for a time window\n pub fn window(&self, start: usize, length: usize) -> Option<Array2<f64>> {\n if start + length > self.len() {\n return None;\n }\n Some(self.data.slice(ndarray::s![start..start + length, ..]).to_owned())\n }\n}\n\n/// Compute log returns\nfn compute_log_returns(prices: &[f64]) -> Vec<f64> {\n if prices.len() < 2 {\n return vec![];\n }\n\n prices\n .windows(2)\n .map(|w| {\n if w[0] > 0.0 {\n (w[1] / w[0]).ln()\n } else {\n 0.0\n }\n })\n .collect()\n}\n\n/// Compute RSI (Relative Strength Index)\nfn compute_rsi(prices: &[f64], period: usize) -> Vec<f64> {\n if prices.len() < period + 1 {\n return vec![50.0; prices.len()];\n }\n\n let mut rsi = vec![50.0; period];\n let mut avg_gain = 0.0;\n let mut avg_loss = 0.0;\n\n // Initial averages\n for i in 1..=period {\n let change = prices[i] - prices[i - 1];\n if change > 0.0 {\n avg_gain += change;\n } else {\n avg_loss += change.abs();\n }\n }\n avg_gain /= period as f64;\n avg_loss /= period as f64;\n\n // Calculate first RSI\n let rs = if avg_loss > 0.0 { avg_gain / avg_loss } else { 100.0 };\n rsi.push(100.0 - 100.0 / (1.0 + rs));\n\n // Subsequent values using exponential smoothing\n for i in (period + 1)..prices.len() {\n let change = prices[i] - prices[i - 1];\n let gain = if change > 0.0 { change } else { 0.0 };\n let loss = if change < 0.0 { change.abs() } else { 0.0 };\n\n avg_gain = (avg_gain * (period as f64 - 1.0) + gain) / period as f64;\n avg_loss = (avg_loss * (period as f64 - 1.0) + loss) / period as f64;\n\n let rs = if avg_loss > 0.0 { avg_gain / avg_loss } else { 100.0 };\n rsi.push(100.0 - 100.0 / (1.0 + rs));\n }\n\n rsi\n}\n\n/// Compute MACD (Moving Average Convergence Divergence)\nfn compute_macd(prices: &[f64], fast: usize, slow: usize, signal: usize) -> (Vec<f64>, Vec<f64>) {\n let ema_fast = compute_ema(prices, fast);\n let ema_slow = compute_ema(prices, slow);\n\n let macd_line: Vec<f64> = ema_fast\n .iter()\n .zip(ema_slow.iter())\n .map(|(f, s)| f - s)\n .collect();\n\n let signal_line = compute_ema(&macd_line, signal);\n\n (macd_line, signal_line)\n}\n\n/// Compute Exponential Moving Average\nfn compute_ema(data: &[f64], period: usize) -> Vec<f64> {\n if data.is_empty() || period == 0 {\n return vec![];\n }\n\n let mut ema = vec![0.0; data.len()];\n let multiplier = 2.0 / (period as f64 + 1.0);\n\n // Start with SMA\n let sma: f64 = data.iter().take(period).sum::<f64>() / period as f64;\n ema[period - 1] = sma;\n\n // Calculate EMA\n for i in period..data.len() {\n ema[i] = (data[i] - ema[i - 1]) * multiplier + ema[i - 1];\n }\n\n // Fill initial values\n for i in 0..period - 1 {\n ema[i] = data[..=i].iter().sum::<f64>() / (i + 1) as f64;\n }\n\n ema\n}\n\n/// Compute Bollinger Bands\nfn compute_bollinger_bands(prices: &[f64], period: usize, num_std: f64) -> (Vec<f64>, Vec<f64>, Vec<f64>) {\n let mut upper = vec![0.0; prices.len()];\n let mut middle = vec![0.0; prices.len()];\n let mut lower = vec![0.0; prices.len()];\n\n for i in 0..prices.len() {\n let start = if i >= period { i - period + 1 } else { 0 };\n let window = &prices[start..=i];\n\n let mean = window.iter().sum::<f64>() / window.len() as f64;\n let variance = window.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / window.len() as f64;\n let std = variance.sqrt();\n\n middle[i] = mean;\n upper[i] = mean + num_std * std;\n lower[i] = mean - num_std * std;\n }\n\n (upper, middle, lower)\n}\n\n/// Compute ATR (Average True Range)\nfn compute_atr(klines: &[Kline], period: usize) -> Vec<f64> {\n if klines.is_empty() {\n return vec![];\n }\n\n let mut tr = vec![0.0; klines.len()];\n\n // First TR is just high - low\n tr[0] = klines[0].high - klines[0].low;\n\n // Subsequent TRs\n for i in 1..klines.len() {\n let high_low = klines[i].high - klines[i].low;\n let high_close = (klines[i].high - klines[i - 1].close).abs();\n let low_close = (klines[i].low - klines[i - 1].close).abs();\n tr[i] = high_low.max(high_close).max(low_close);\n }\n\n // Compute ATR as EMA of TR\n compute_ema(&tr, period)\n}\n\n/// Compute volume-related features\nfn compute_volume_features(klines: &[Kline], period: usize) -> (Vec<f64>, Vec<f64>) {\n let volumes: Vec<f64> = klines.iter().map(|k| k.volume).collect();\n\n // Volume SMA\n let volume_sma = compute_sma(&volumes, period);\n\n // Volume ratio (current / average)\n let volume_ratio: Vec<f64> = volumes\n .iter()\n .zip(volume_sma.iter())\n .map(|(v, sma)| if *sma > 0.0 { v / sma } else { 1.0 })\n .collect();\n\n (volume_sma, volume_ratio)\n}\n\n/// Compute Simple Moving Average\nfn compute_sma(data: &[f64], period: usize) -> Vec<f64> {\n let mut sma = vec![0.0; data.len()];\n\n for i in 0..data.len() {\n let start = if i >= period { i - period + 1 } else { 0 };\n let window = &data[start..=i];\n sma[i] = window.iter().sum::<f64>() / window.len() as f64;\n }\n\n sma\n}\n\n/// Prepare features from kline data\n///\n/// # Arguments\n/// * `klines` - Vector of OHLCV candlestick data\n///\n/// # Returns\n/// * `Features` struct containing computed features\npub fn prepare_features(klines: &[Kline]) -> Features {\n if klines.is_empty() {\n return Features {\n data: Array2::zeros((0, 0)),\n names: vec![],\n timestamps: vec![],\n };\n }\n\n let close_prices: Vec<f64> = klines.iter().map(|k| k.close).collect();\n let timestamps: Vec<i64> = klines.iter().map(|k| k.start_time).collect();\n\n // Compute features\n let log_returns = compute_log_returns(&close_prices);\n let rsi = compute_rsi(&close_prices, 14);\n let (macd_line, signal_line) = compute_macd(&close_prices, 12, 26, 9);\n let (bb_upper, bb_middle, bb_lower) = compute_bollinger_bands(&close_prices, 20, 2.0);\n let atr = compute_atr(klines, 14);\n let (_, volume_ratio) = compute_volume_features(klines, 20);\n\n // Pad log_returns to match length (first value is 0)\n let mut padded_returns = vec![0.0];\n padded_returns.extend(log_returns);\n\n // Bollinger Band position (where price is relative to bands)\n let bb_position: Vec<f64> = close_prices\n .iter()\n .enumerate()\n .map(|(i, p)| {\n let range = bb_upper[i] - bb_lower[i];\n if range > 0.0 {\n (p - bb_lower[i]) / range\n } else {\n 0.5\n }\n })\n .collect();\n\n // MACD histogram\n let macd_histogram: Vec<f64> = macd_line\n .iter()\n .zip(signal_line.iter())\n .map(|(m, s)| m - s)\n .collect();\n\n // Build feature matrix\n let n_samples = klines.len();\n let n_features = 6;\n let mut data = Array2::zeros((n_samples, n_features));\n\n for i in 0..n_samples {\n data[[i, 0]] = padded_returns[i];\n data[[i, 1]] = rsi[i] / 100.0; // Normalize RSI to [0, 1]\n data[[i, 2]] = macd_histogram[i];\n data[[i, 3]] = bb_position[i];\n data[[i, 4]] = atr[i];\n data[[i, 5]] = volume_ratio[i];\n }\n\n let names = vec![\n \"log_return\".to_string(),\n \"rsi\".to_string(),\n \"macd_histogram\".to_string(),\n \"bb_position\".to_string(),\n \"atr\".to_string(),\n \"volume_ratio\".to_string(),\n ];\n\n Features {\n data,\n names,\n timestamps,\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n fn create_test_klines(n: usize) -> Vec<Kline> {\n (0..n)\n .map(|i| Kline {\n start_time: i as i64 * 3600000,\n open: 100.0 + (i as f64 * 0.1).sin(),\n high: 101.0 + (i as f64 * 0.1).sin(),\n low: 99.0 + (i as f64 * 0.1).sin(),\n close: 100.5 + (i as f64 * 0.1).sin(),\n volume: 1000.0 + (i as f64 * 10.0),\n turnover: 100000.0,\n })\n .collect()\n }\n\n #[test]\n fn test_prepare_features() {\n let klines = create_test_klines(100);\n let features = prepare_features(&klines);\n\n assert_eq!(features.len(), 100);\n assert_eq!(features.n_features(), 6);\n assert_eq!(features.names.len(), 6);\n }\n\n #[test]\n fn test_compute_rsi() {\n let prices: Vec<f64> = (0..50).map(|i| 100.0 + (i as f64 * 0.1).sin() * 10.0).collect();\n let rsi = compute_rsi(&prices, 14);\n\n assert_eq!(rsi.len(), prices.len());\n assert!(rsi.iter().all(|&r| r >= 0.0 && r <= 100.0));\n }\n\n #[test]\n fn test_compute_ema() {\n let data = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0];\n let ema = compute_ema(&data, 3);\n\n assert_eq!(ema.len(), data.len());\n // EMA should be close to recent values\n assert!(ema[9] > ema[0]);\n }\n\n #[test]\n fn test_features_window() {\n let klines = create_test_klines(100);\n let features = prepare_features(&klines);\n\n let window = features.window(10, 20);\n assert!(window.is_some());\n let window = window.unwrap();\n assert_eq!(window.nrows(), 20);\n assert_eq!(window.ncols(), 6);\n }\n\n #[test]\n fn test_features_normalize() {\n let klines = create_test_klines(100);\n let mut features = prepare_features(&klines);\n features.normalize();\n\n // Check that each column has approximately zero mean\n for col_idx in 0..features.n_features() {\n let col = features.data.column(col_idx);\n let mean = col.mean().unwrap();\n assert!(mean.abs() < 1e-10, \"Column {} mean: {}\", col_idx, mean);\n }\n }\n}\n" | |
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| "content": "//! Data loader for TABL model\n\nuse crate::api::{BybitClient, Kline};\nuse crate::data::features::{prepare_features, Features};\nuse ndarray::{Array2, Array3};\n\n/// Data loader for preparing TABL training data\npub struct DataLoader {\n /// Sequence length for input windows\n seq_len: usize,\n /// Prediction horizon\n horizon: usize,\n /// Classification threshold for labeling\n threshold: f64,\n}\n\nimpl Default for DataLoader {\n fn default() -> Self {\n Self::new()\n }\n}\n\nimpl DataLoader {\n /// Create a new data loader with default parameters\n pub fn new() -> Self {\n Self {\n seq_len: 100,\n horizon: 10,\n threshold: 0.0002,\n }\n }\n\n /// Create a data loader with custom parameters\n pub fn with_params(seq_len: usize, horizon: usize, threshold: f64) -> Self {\n Self {\n seq_len,\n horizon,\n threshold,\n }\n }\n\n /// Set the sequence length\n pub fn seq_len(mut self, seq_len: usize) -> Self {\n self.seq_len = seq_len;\n self\n }\n\n /// Set the prediction horizon\n pub fn horizon(mut self, horizon: usize) -> Self {\n self.horizon = horizon;\n self\n }\n\n /// Set the classification threshold\n pub fn threshold(mut self, threshold: f64) -> Self {\n self.threshold = threshold;\n self\n }\n\n /// Prepare data for TABL from klines\n ///\n /// # Returns\n /// * `(X, y)` - Feature tensor [n_samples, seq_len, n_features] and labels\n pub fn prepare_tabl_data(&self, klines: &[Kline]) -> anyhow::Result<(Array3<f64>, Vec<i32>)> {\n if klines.len() < self.seq_len + self.horizon {\n anyhow::bail!(\n \"Not enough data: need at least {} candles, got {}\",\n self.seq_len + self.horizon,\n klines.len()\n );\n }\n\n // Compute features\n let mut features = prepare_features(klines);\n features.normalize();\n\n // Create labels based on future returns\n let close_prices: Vec<f64> = klines.iter().map(|k| k.close).collect();\n let labels = self.create_labels(&close_prices);\n\n // Create sliding windows\n let n_samples = klines.len() - self.seq_len - self.horizon + 1;\n let n_features = features.n_features();\n\n let mut x = Array3::zeros((n_samples, self.seq_len, n_features));\n let mut y = Vec::with_capacity(n_samples);\n\n for i in 0..n_samples {\n // Copy feature window\n for t in 0..self.seq_len {\n for f in 0..n_features {\n x[[i, t, f]] = features.data[[i + t, f]];\n }\n }\n // Get label (at the end of the sequence)\n y.push(labels[i + self.seq_len - 1]);\n }\n\n Ok((x, y))\n }\n\n /// Create classification labels based on future returns\n fn create_labels(&self, prices: &[f64]) -> Vec<i32> {\n let mut labels = vec![1; prices.len()]; // Default to \"hold\"\n\n for i in 0..prices.len() - self.horizon {\n let current_price = prices[i];\n let future_price = prices[i + self.horizon];\n let return_pct = (future_price - current_price) / current_price;\n\n labels[i] = if return_pct > self.threshold {\n 2 // Up\n } else if return_pct < -self.threshold {\n 0 // Down\n } else {\n 1 // Hold\n };\n }\n\n labels\n }\n\n /// Load data from Bybit API and prepare for TABL\n pub async fn load_from_bybit(\n &self,\n client: &BybitClient,\n symbol: &str,\n interval: &str,\n limit: usize,\n ) -> anyhow::Result<(Array3<f64>, Vec<i32>)> {\n let klines = client.get_klines(symbol, interval, limit).await?;\n self.prepare_tabl_data(&klines)\n }\n\n /// Split data into train/validation/test sets\n pub fn train_val_test_split(\n &self,\n x: &Array3<f64>,\n y: &[i32],\n train_ratio: f64,\n val_ratio: f64,\n ) -> (\n (Array3<f64>, Vec<i32>),\n (Array3<f64>, Vec<i32>),\n (Array3<f64>, Vec<i32>),\n ) {\n let n_samples = x.shape()[0];\n let train_end = (n_samples as f64 * train_ratio) as usize;\n let val_end = (n_samples as f64 * (train_ratio + val_ratio)) as usize;\n\n let x_train = x.slice(ndarray::s![..train_end, .., ..]).to_owned();\n let y_train = y[..train_end].to_vec();\n\n let x_val = x.slice(ndarray::s![train_end..val_end, .., ..]).to_owned();\n let y_val = y[train_end..val_end].to_vec();\n\n let x_test = x.slice(ndarray::s![val_end.., .., ..]).to_owned();\n let y_test = y[val_end..].to_vec();\n\n ((x_train, y_train), (x_val, y_val), (x_test, y_test))\n }\n\n /// Create batches for training\n pub fn create_batches(\n x: &Array3<f64>,\n y: &[i32],\n batch_size: usize,\n ) -> Vec<(Array3<f64>, Vec<i32>)> {\n let n_samples = x.shape()[0];\n let n_batches = (n_samples + batch_size - 1) / batch_size;\n\n (0..n_batches)\n .map(|i| {\n let start = i * batch_size;\n let end = (start + batch_size).min(n_samples);\n\n let x_batch = x.slice(ndarray::s![start..end, .., ..]).to_owned();\n let y_batch = y[start..end].to_vec();\n\n (x_batch, y_batch)\n })\n .collect()\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n fn create_test_klines(n: usize) -> Vec<Kline> {\n (0..n)\n .map(|i| {\n let price = 100.0 + (i as f64 * 0.05).sin() * 5.0;\n Kline {\n start_time: i as i64 * 3600000,\n open: price,\n high: price + 1.0,\n low: price - 1.0,\n close: price + (i as f64 * 0.1).sin(),\n volume: 1000.0 + (i as f64 * 10.0),\n turnover: 100000.0,\n }\n })\n .collect()\n }\n\n #[test]\n fn test_prepare_tabl_data() {\n let klines = create_test_klines(200);\n let loader = DataLoader::new();\n let result = loader.prepare_tabl_data(&klines);\n\n assert!(result.is_ok());\n let (x, y) = result.unwrap();\n\n // Check dimensions\n assert_eq!(x.shape()[1], 100); // seq_len\n assert_eq!(x.shape()[2], 6); // n_features\n assert_eq!(x.shape()[0], y.len());\n }\n\n #[test]\n fn test_create_labels() {\n let prices = vec![100.0, 100.1, 99.9, 100.2, 100.3, 100.0, 99.8, 100.5, 100.4, 100.2];\n let loader = DataLoader::with_params(3, 2, 0.001);\n let labels = loader.create_labels(&prices);\n\n assert_eq!(labels.len(), prices.len());\n // Labels should be 0, 1, or 2\n assert!(labels.iter().all(|&l| l >= 0 && l <= 2));\n }\n\n #[test]\n fn test_train_val_test_split() {\n let klines = create_test_klines(300);\n let loader = DataLoader::with_params(50, 5, 0.0002);\n let (x, y) = loader.prepare_tabl_data(&klines).unwrap();\n\n let ((x_train, y_train), (x_val, y_val), (x_test, y_test)) =\n loader.train_val_test_split(&x, &y, 0.7, 0.15);\n\n let total = y_train.len() + y_val.len() + y_test.len();\n assert_eq!(total, y.len());\n\n // Roughly check proportions\n let train_ratio = y_train.len() as f64 / total as f64;\n assert!(train_ratio > 0.6 && train_ratio < 0.8);\n }\n\n #[test]\n fn test_create_batches() {\n let klines = create_test_klines(200);\n let loader = DataLoader::new();\n let (x, y) = loader.prepare_tabl_data(&klines).unwrap();\n\n let batches = DataLoader::create_batches(&x, &y, 16);\n\n // Check that all samples are covered\n let total_samples: usize = batches.iter().map(|(_, y)| y.len()).sum();\n assert_eq!(total_samples, y.len());\n }\n}\n" | |
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| "content": "//! Data loading and feature engineering module\n//!\n//! This module provides functionality for:\n//! - Loading and processing market data\n//! - Computing technical indicators\n//! - Preparing data for TABL model\n\nmod features;\nmod loader;\n\npub use features::{prepare_features, Features};\npub use loader::DataLoader;\n", | |
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| "content": "//! Feature engineering for TABL model\n\nuse crate::api::Kline;\nuse ndarray::{Array1, Array2, Axis};\n\n/// Container for computed features\n#[derive(Debug, Clone)]\npub struct Features {\n /// Feature matrix [time_steps, n_features]\n pub data: Array2<f64>,\n /// Feature names\n pub names: Vec<String>,\n /// Timestamps corresponding to each row\n pub timestamps: Vec<i64>,\n}\n\nimpl Features {\n /// Get the number of time steps\n pub fn len(&self) -> usize {\n self.data.nrows()\n }\n\n /// Check if features are empty\n pub fn is_empty(&self) -> bool {\n self.data.is_empty()\n }\n\n /// Get the number of features\n pub fn n_features(&self) -> usize {\n self.data.ncols()\n }\n\n /// Normalize features to zero mean and unit variance\n pub fn normalize(&mut self) {\n for col_idx in 0..self.data.ncols() {\n let col = self.data.column(col_idx);\n let mean = col.mean().unwrap_or(0.0);\n let std = col.std(0.0);\n\n if std > 1e-8 {\n for row_idx in 0..self.data.nrows() {\n self.data[[row_idx, col_idx]] = (self.data[[row_idx, col_idx]] - mean) / std;\n }\n }\n }\n }\n\n /// Get a slice of features for a time window\n pub fn window(&self, start: usize, length: usize) -> Option<Array2<f64>> {\n if start + length > self.len() {\n return None;\n }\n Some(self.data.slice(ndarray::s![start..start + length, ..]).to_owned())\n }\n}\n\n/// Compute log returns\nfn compute_log_returns(prices: &[f64]) -> Vec<f64> {\n if prices.len() < 2 {\n return vec![];\n }\n\n prices\n .windows(2)\n .map(|w| {\n if w[0] > 0.0 {\n (w[1] / w[0]).ln()\n } else {\n 0.0\n }\n })\n .collect()\n}\n\n/// Compute RSI (Relative Strength Index)\nfn compute_rsi(prices: &[f64], period: usize) -> Vec<f64> {\n if prices.len() < period + 1 {\n return vec![50.0; prices.len()];\n }\n\n let mut rsi = vec![50.0; period];\n let mut avg_gain = 0.0;\n let mut avg_loss = 0.0;\n\n // Initial averages\n for i in 1..=period {\n let change = prices[i] - prices[i - 1];\n if change > 0.0 {\n avg_gain += change;\n } else {\n avg_loss += change.abs();\n }\n }\n avg_gain /= period as f64;\n avg_loss /= period as f64;\n\n // Calculate first RSI\n let rs = if avg_loss > 0.0 { avg_gain / avg_loss } else { 100.0 };\n rsi.push(100.0 - 100.0 / (1.0 + rs));\n\n // Subsequent values using exponential smoothing\n for i in (period + 1)..prices.len() {\n let change = prices[i] - prices[i - 1];\n let gain = if change > 0.0 { change } else { 0.0 };\n let loss = if change < 0.0 { change.abs() } else { 0.0 };\n\n avg_gain = (avg_gain * (period as f64 - 1.0) + gain) / period as f64;\n avg_loss = (avg_loss * (period as f64 - 1.0) + loss) / period as f64;\n\n let rs = if avg_loss > 0.0 { avg_gain / avg_loss } else { 100.0 };\n rsi.push(100.0 - 100.0 / (1.0 + rs));\n }\n\n rsi\n}\n\n/// Compute MACD (Moving Average Convergence Divergence)\nfn compute_macd(prices: &[f64], fast: usize, slow: usize, signal: usize) -> (Vec<f64>, Vec<f64>) {\n let ema_fast = compute_ema(prices, fast);\n let ema_slow = compute_ema(prices, slow);\n\n let macd_line: Vec<f64> = ema_fast\n .iter()\n .zip(ema_slow.iter())\n .map(|(f, s)| f - s)\n .collect();\n\n let signal_line = compute_ema(&macd_line, signal);\n\n (macd_line, signal_line)\n}\n\n/// Compute Exponential Moving Average\nfn compute_ema(data: &[f64], period: usize) -> Vec<f64> {\n if data.is_empty() || period == 0 {\n return vec![];\n }\n\n let mut ema = vec![0.0; data.len()];\n let multiplier = 2.0 / (period as f64 + 1.0);\n\n // Start with SMA\n let sma: f64 = data.iter().take(period).sum::<f64>() / period as f64;\n ema[period - 1] = sma;\n\n // Calculate EMA\n for i in period..data.len() {\n ema[i] = (data[i] - ema[i - 1]) * multiplier + ema[i - 1];\n }\n\n // Fill initial values\n for i in 0..period - 1 {\n ema[i] = data[..=i].iter().sum::<f64>() / (i + 1) as f64;\n }\n\n ema\n}\n\n/// Compute Bollinger Bands\nfn compute_bollinger_bands(prices: &[f64], period: usize, num_std: f64) -> (Vec<f64>, Vec<f64>, Vec<f64>) {\n let mut upper = vec![0.0; prices.len()];\n let mut middle = vec![0.0; prices.len()];\n let mut lower = vec![0.0; prices.len()];\n\n for i in 0..prices.len() {\n let start = if i >= period { i - period + 1 } else { 0 };\n let window = &prices[start..=i];\n\n let mean = window.iter().sum::<f64>() / window.len() as f64;\n let variance = window.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / window.len() as f64;\n let std = variance.sqrt();\n\n middle[i] = mean;\n upper[i] = mean + num_std * std;\n lower[i] = mean - num_std * std;\n }\n\n (upper, middle, lower)\n}\n\n/// Compute ATR (Average True Range)\nfn compute_atr(klines: &[Kline], period: usize) -> Vec<f64> {\n if klines.is_empty() {\n return vec![];\n }\n\n let mut tr = vec![0.0; klines.len()];\n\n // First TR is just high - low\n tr[0] = klines[0].high - klines[0].low;\n\n // Subsequent TRs\n for i in 1..klines.len() {\n let high_low = klines[i].high - klines[i].low;\n let high_close = (klines[i].high - klines[i - 1].close).abs();\n let low_close = (klines[i].low - klines[i - 1].close).abs();\n tr[i] = high_low.max(high_close).max(low_close);\n }\n\n // Compute ATR as EMA of TR\n compute_ema(&tr, period)\n}\n\n/// Compute volume-related features\nfn compute_volume_features(klines: &[Kline], period: usize) -> (Vec<f64>, Vec<f64>) {\n let volumes: Vec<f64> = klines.iter().map(|k| k.volume).collect();\n\n // Volume SMA\n let volume_sma = compute_sma(&volumes, period);\n\n // Volume ratio (current / average)\n let volume_ratio: Vec<f64> = volumes\n .iter()\n .zip(volume_sma.iter())\n .map(|(v, sma)| if *sma > 0.0 { v / sma } else { 1.0 })\n .collect();\n\n (volume_sma, volume_ratio)\n}\n\n/// Compute Simple Moving Average\nfn compute_sma(data: &[f64], period: usize) -> Vec<f64> {\n let mut sma = vec![0.0; data.len()];\n\n for i in 0..data.len() {\n let start = if i >= period { i - period + 1 } else { 0 };\n let window = &data[start..=i];\n sma[i] = window.iter().sum::<f64>() / window.len() as f64;\n }\n\n sma\n}\n\n/// Prepare features from kline data\n///\n/// # Arguments\n/// * `klines` - Vector of OHLCV candlestick data\n///\n/// # Returns\n/// * `Features` struct containing computed features\npub fn prepare_features(klines: &[Kline]) -> Features {\n if klines.is_empty() {\n return Features {\n data: Array2::zeros((0, 0)),\n names: vec![],\n timestamps: vec![],\n };\n }\n\n let close_prices: Vec<f64> = klines.iter().map(|k| k.close).collect();\n let timestamps: Vec<i64> = klines.iter().map(|k| k.start_time).collect();\n\n // Compute features\n let log_returns = compute_log_returns(&close_prices);\n let rsi = compute_rsi(&close_prices, 14);\n let (macd_line, signal_line) = compute_macd(&close_prices, 12, 26, 9);\n let (bb_upper, bb_middle, bb_lower) = compute_bollinger_bands(&close_prices, 20, 2.0);\n let atr = compute_atr(klines, 14);\n let (_, volume_ratio) = compute_volume_features(klines, 20);\n\n // Pad log_returns to match length (first value is 0)\n let mut padded_returns = vec![0.0];\n padded_returns.extend(log_returns);\n\n // Bollinger Band position (where price is relative to bands)\n let bb_position: Vec<f64> = close_prices\n .iter()\n .enumerate()\n .map(|(i, p)| {\n let range = bb_upper[i] - bb_lower[i];\n if range > 0.0 {\n (p - bb_lower[i]) / range\n } else {\n 0.5\n }\n })\n .collect();\n\n // MACD histogram\n let macd_histogram: Vec<f64> = macd_line\n .iter()\n .zip(signal_line.iter())\n .map(|(m, s)| m - s)\n .collect();\n\n // Build feature matrix\n let n_samples = klines.len();\n let n_features = 6;\n let mut data = Array2::zeros((n_samples, n_features));\n\n for i in 0..n_samples {\n data[[i, 0]] = padded_returns[i];\n data[[i, 1]] = rsi[i] / 100.0; // Normalize RSI to [0, 1]\n data[[i, 2]] = macd_histogram[i];\n data[[i, 3]] = bb_position[i];\n data[[i, 4]] = atr[i];\n data[[i, 5]] = volume_ratio[i];\n }\n\n let names = vec![\n \"log_return\".to_string(),\n \"rsi\".to_string(),\n \"macd_histogram\".to_string(),\n \"bb_position\".to_string(),\n \"atr\".to_string(),\n \"volume_ratio\".to_string(),\n ];\n\n Features {\n data,\n names,\n timestamps,\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n fn create_test_klines(n: usize) -> Vec<Kline> {\n (0..n)\n .map(|i| Kline {\n start_time: i as i64 * 3600000,\n open: 100.0 + (i as f64 * 0.1).sin(),\n high: 101.0 + (i as f64 * 0.1).sin(),\n low: 99.0 + (i as f64 * 0.1).sin(),\n close: 100.5 + (i as f64 * 0.1).sin(),\n volume: 1000.0 + (i as f64 * 10.0),\n turnover: 100000.0,\n })\n .collect()\n }\n\n #[test]\n fn test_prepare_features() {\n let klines = create_test_klines(100);\n let features = prepare_features(&klines);\n\n assert_eq!(features.len(), 100);\n assert_eq!(features.n_features(), 6);\n assert_eq!(features.names.len(), 6);\n }\n\n #[test]\n fn test_compute_rsi() {\n let prices: Vec<f64> = (0..50).map(|i| 100.0 + (i as f64 * 0.1).sin() * 10.0).collect();\n let rsi = compute_rsi(&prices, 14);\n\n assert_eq!(rsi.len(), prices.len());\n assert!(rsi.iter().all(|&r| r >= 0.0 && r <= 100.0));\n }\n\n #[test]\n fn test_compute_ema() {\n let data = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0];\n let ema = compute_ema(&data, 3);\n\n assert_eq!(ema.len(), data.len());\n // EMA should be close to recent values\n assert!(ema[9] > ema[0]);\n }\n\n #[test]\n fn test_features_window() {\n let klines = create_test_klines(100);\n let features = prepare_features(&klines);\n\n let window = features.window(10, 20);\n assert!(window.is_some());\n let window = window.unwrap();\n assert_eq!(window.nrows(), 20);\n assert_eq!(window.ncols(), 6);\n }\n\n #[test]\n fn test_features_normalize() {\n let klines = create_test_klines(100);\n let mut features = prepare_features(&klines);\n features.normalize();\n\n // Check that each column has approximately zero mean\n for col_idx in 0..features.n_features() {\n let col = features.data.column(col_idx);\n let mean = col.mean().unwrap();\n assert!(mean.abs() < 1e-10, \"Column {} mean: {}\", col_idx, mean);\n }\n }\n}\n", | |
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| "content": "//! Data loader for TABL model\n\nuse crate::api::{BybitClient, Kline};\nuse crate::data::features::{prepare_features, Features};\nuse ndarray::{Array2, Array3};\n\n/// Data loader for preparing TABL training data\npub struct DataLoader {\n /// Sequence length for input windows\n seq_len: usize,\n /// Prediction horizon\n horizon: usize,\n /// Classification threshold for labeling\n threshold: f64,\n}\n\nimpl Default for DataLoader {\n fn default() -> Self {\n Self::new()\n }\n}\n\nimpl DataLoader {\n /// Create a new data loader with default parameters\n pub fn new() -> Self {\n Self {\n seq_len: 100,\n horizon: 10,\n threshold: 0.0002,\n }\n }\n\n /// Create a data loader with custom parameters\n pub fn with_params(seq_len: usize, horizon: usize, threshold: f64) -> Self {\n Self {\n seq_len,\n horizon,\n threshold,\n }\n }\n\n /// Set the sequence length\n pub fn seq_len(mut self, seq_len: usize) -> Self {\n self.seq_len = seq_len;\n self\n }\n\n /// Set the prediction horizon\n pub fn horizon(mut self, horizon: usize) -> Self {\n self.horizon = horizon;\n self\n }\n\n /// Set the classification threshold\n pub fn threshold(mut self, threshold: f64) -> Self {\n self.threshold = threshold;\n self\n }\n\n /// Prepare data for TABL from klines\n ///\n /// # Returns\n /// * `(X, y)` - Feature tensor [n_samples, seq_len, n_features] and labels\n pub fn prepare_tabl_data(&self, klines: &[Kline]) -> anyhow::Result<(Array3<f64>, Vec<i32>)> {\n if klines.len() < self.seq_len + self.horizon {\n anyhow::bail!(\n \"Not enough data: need at least {} candles, got {}\",\n self.seq_len + self.horizon,\n klines.len()\n );\n }\n\n // Compute features\n let mut features = prepare_features(klines);\n features.normalize();\n\n // Create labels based on future returns\n let close_prices: Vec<f64> = klines.iter().map(|k| k.close).collect();\n let labels = self.create_labels(&close_prices);\n\n // Create sliding windows\n let n_samples = klines.len() - self.seq_len - self.horizon + 1;\n let n_features = features.n_features();\n\n let mut x = Array3::zeros((n_samples, self.seq_len, n_features));\n let mut y = Vec::with_capacity(n_samples);\n\n for i in 0..n_samples {\n // Copy feature window\n for t in 0..self.seq_len {\n for f in 0..n_features {\n x[[i, t, f]] = features.data[[i + t, f]];\n }\n }\n // Get label (at the end of the sequence)\n y.push(labels[i + self.seq_len - 1]);\n }\n\n Ok((x, y))\n }\n\n /// Create classification labels based on future returns\n fn create_labels(&self, prices: &[f64]) -> Vec<i32> {\n let mut labels = vec![1; prices.len()]; // Default to \"hold\"\n\n for i in 0..prices.len() - self.horizon {\n let current_price = prices[i];\n let future_price = prices[i + self.horizon];\n let return_pct = (future_price - current_price) / current_price;\n\n labels[i] = if return_pct > self.threshold {\n 2 // Up\n } else if return_pct < -self.threshold {\n 0 // Down\n } else {\n 1 // Hold\n };\n }\n\n labels\n }\n\n /// Load data from Bybit API and prepare for TABL\n pub async fn load_from_bybit(\n &self,\n client: &BybitClient,\n symbol: &str,\n interval: &str,\n limit: usize,\n ) -> anyhow::Result<(Array3<f64>, Vec<i32>)> {\n let klines = client.get_klines(symbol, interval, limit).await?;\n self.prepare_tabl_data(&klines)\n }\n\n /// Split data into train/validation/test sets\n pub fn train_val_test_split(\n &self,\n x: &Array3<f64>,\n y: &[i32],\n train_ratio: f64,\n val_ratio: f64,\n ) -> (\n (Array3<f64>, Vec<i32>),\n (Array3<f64>, Vec<i32>),\n (Array3<f64>, Vec<i32>),\n ) {\n let n_samples = x.shape()[0];\n let train_end = (n_samples as f64 * train_ratio) as usize;\n let val_end = (n_samples as f64 * (train_ratio + val_ratio)) as usize;\n\n let x_train = x.slice(ndarray::s![..train_end, .., ..]).to_owned();\n let y_train = y[..train_end].to_vec();\n\n let x_val = x.slice(ndarray::s![train_end..val_end, .., ..]).to_owned();\n let y_val = y[train_end..val_end].to_vec();\n\n let x_test = x.slice(ndarray::s![val_end.., .., ..]).to_owned();\n let y_test = y[val_end..].to_vec();\n\n ((x_train, y_train), (x_val, y_val), (x_test, y_test))\n }\n\n /// Create batches for training\n pub fn create_batches(\n x: &Array3<f64>,\n y: &[i32],\n batch_size: usize,\n ) -> Vec<(Array3<f64>, Vec<i32>)> {\n let n_samples = x.shape()[0];\n let n_batches = (n_samples + batch_size - 1) / batch_size;\n\n (0..n_batches)\n .map(|i| {\n let start = i * batch_size;\n let end = (start + batch_size).min(n_samples);\n\n let x_batch = x.slice(ndarray::s![start..end, .., ..]).to_owned();\n let y_batch = y[start..end].to_vec();\n\n (x_batch, y_batch)\n })\n .collect()\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n fn create_test_klines(n: usize) -> Vec<Kline> {\n (0..n)\n .map(|i| {\n let price = 100.0 + (i as f64 * 0.05).sin() * 5.0;\n Kline {\n start_time: i as i64 * 3600000,\n open: price,\n high: price + 1.0,\n low: price - 1.0,\n close: price + (i as f64 * 0.1).sin(),\n volume: 1000.0 + (i as f64 * 10.0),\n turnover: 100000.0,\n }\n })\n .collect()\n }\n\n #[test]\n fn test_prepare_tabl_data() {\n let klines = create_test_klines(200);\n let loader = DataLoader::new();\n let result = loader.prepare_tabl_data(&klines);\n\n assert!(result.is_ok());\n let (x, y) = result.unwrap();\n\n // Check dimensions\n assert_eq!(x.shape()[1], 100); // seq_len\n assert_eq!(x.shape()[2], 6); // n_features\n assert_eq!(x.shape()[0], y.len());\n }\n\n #[test]\n fn test_create_labels() {\n let prices = vec![100.0, 100.1, 99.9, 100.2, 100.3, 100.0, 99.8, 100.5, 100.4, 100.2];\n let loader = DataLoader::with_params(3, 2, 0.001);\n let labels = loader.create_labels(&prices);\n\n assert_eq!(labels.len(), prices.len());\n // Labels should be 0, 1, or 2\n assert!(labels.iter().all(|&l| l >= 0 && l <= 2));\n }\n\n #[test]\n fn test_train_val_test_split() {\n let klines = create_test_klines(300);\n let loader = DataLoader::with_params(50, 5, 0.0002);\n let (x, y) = loader.prepare_tabl_data(&klines).unwrap();\n\n let ((x_train, y_train), (x_val, y_val), (x_test, y_test)) =\n loader.train_val_test_split(&x, &y, 0.7, 0.15);\n\n let total = y_train.len() + y_val.len() + y_test.len();\n assert_eq!(total, y.len());\n\n // Roughly check proportions\n let train_ratio = y_train.len() as f64 / total as f64;\n assert!(train_ratio > 0.6 && train_ratio < 0.8);\n }\n\n #[test]\n fn test_create_batches() {\n let klines = create_test_klines(200);\n let loader = DataLoader::new();\n let (x, y) = loader.prepare_tabl_data(&klines).unwrap();\n\n let batches = DataLoader::create_batches(&x, &y, 16);\n\n // Check that all samples are covered\n let total_samples: usize = batches.iter().map(|(_, y)| y.len()).sum();\n assert_eq!(total_samples, y.len());\n }\n}\n", | |
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| [2026-01-18T21:08:07.466Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-5-20251101", | |
| "id": "msg_01Nt3BAwAbvdZB5r5UCj2vpY", | |
| "type": "message", | |
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| "content": [ | |
| { | |
| "type": "text", | |
| "text": "Now let me create the model module with bilinear and attention layers:" | |
| } | |
| ], | |
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| } | |
| [2026-01-18T21:08:09.486Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-5-20251101", | |
| "id": "msg_01Nt3BAwAbvdZB5r5UCj2vpY", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "tool_use", | |
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| "content": "//! TABL model module\n//!\n//! This module provides the core TABL (Temporal Attention-Augmented Bilinear Network) implementation.\n\nmod attention;\nmod bilinear;\nmod tabl;\n\npub use attention::{AttentionType, TemporalAttention};\npub use bilinear::BilinearLayer;\npub use tabl::{TABLConfig, TABLModel};\n" | |
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| "content": "//! Bilinear projection layer for temporal data\n\nuse ndarray::{Array1, Array2, Axis};\nuse rand::Rng;\nuse rand_distr::Normal;\n\n/// Bilinear layer for joint temporal-feature projection\n///\n/// Performs the operation: H = σ(W₁ · X · W₂ + b)\n/// - W₁ projects along temporal dimension\n/// - W₂ projects along feature dimension\n#[derive(Debug, Clone)]\npub struct BilinearLayer {\n /// Temporal projection matrix [T_out, T_in]\n w1: Array2<f64>,\n /// Feature projection matrix [D_in, D_out]\n w2: Array2<f64>,\n /// Bias term [T_out, D_out]\n bias: Array2<f64>,\n /// Input temporal dimension\n t_in: usize,\n /// Output temporal dimension\n t_out: usize,\n /// Input feature dimension\n d_in: usize,\n /// Output feature dimension\n d_out: usize,\n /// Dropout rate\n dropout: f64,\n /// Use batch normalization\n use_batch_norm: bool,\n /// Running mean for batch norm\n running_mean: Option<Array2<f64>>,\n /// Running variance for batch norm\n running_var: Option<Array2<f64>>,\n}\n\nimpl BilinearLayer {\n /// Create a new bilinear layer\n ///\n /// # Arguments\n /// * `t_in` - Input temporal dimension\n /// * `t_out` - Output temporal dimension\n /// * `d_in` - Input feature dimension\n /// * `d_out` - Output feature dimension\n /// * `dropout` - Dropout rate (0.0 - 1.0)\n /// * `use_batch_norm` - Whether to use batch normalization\n pub fn new(\n t_in: usize,\n t_out: usize,\n d_in: usize,\n d_out: usize,\n dropout: f64,\n use_batch_norm: bool,\n ) -> Self {\n let mut rng = rand::thread_rng();\n\n // Xavier/Glorot initialization\n let w1_std = (2.0 / (t_in + t_out) as f64).sqrt();\n let w2_std = (2.0 / (d_in + d_out) as f64).sqrt();\n\n let w1_dist = Normal::new(0.0, w1_std).unwrap();\n let w2_dist = Normal::new(0.0, w2_std).unwrap();\n\n let w1 = Array2::from_shape_fn((t_out, t_in), |_| rng.sample(w1_dist));\n let w2 = Array2::from_shape_fn((d_in, d_out), |_| rng.sample(w2_dist));\n let bias = Array2::zeros((t_out, d_out));\n\n let (running_mean, running_var) = if use_batch_norm {\n (\n Some(Array2::zeros((t_out, d_out))),\n Some(Array2::ones((t_out, d_out))),\n )\n } else {\n (None, None)\n };\n\n Self {\n w1,\n w2,\n bias,\n t_in,\n t_out,\n d_in,\n d_out,\n dropout,\n use_batch_norm,\n running_mean,\n running_var,\n }\n }\n\n /// Forward pass\n ///\n /// # Arguments\n /// * `x` - Input tensor [T_in, D_in]\n /// * `training` - Whether in training mode (applies dropout)\n ///\n /// # Returns\n /// * Output tensor [T_out, D_out]\n pub fn forward(&self, x: &Array2<f64>, training: bool) -> Array2<f64> {\n assert_eq!(x.nrows(), self.t_in, \"Temporal dimension mismatch\");\n assert_eq!(x.ncols(), self.d_in, \"Feature dimension mismatch\");\n\n // H = W₁ · X · W₂ + b\n let h1 = self.w1.dot(x); // [T_out, D_in]\n let mut h = h1.dot(&self.w2); // [T_out, D_out]\n h = &h + &self.bias;\n\n // Batch normalization (simplified - per-sample)\n if self.use_batch_norm {\n if let (Some(running_mean), Some(running_var)) = (&self.running_mean, &self.running_var) {\n h = (&h - running_mean) / (running_var.mapv(|v| (v + 1e-5).sqrt()));\n }\n }\n\n // ReLU activation\n h.mapv_inplace(|v| v.max(0.0));\n\n // Dropout during training\n if training && self.dropout > 0.0 {\n let mut rng = rand::thread_rng();\n h.mapv_inplace(|v| {\n if rng.gen::<f64>() < self.dropout {\n 0.0\n } else {\n v / (1.0 - self.dropout)\n }\n });\n }\n\n h\n }\n\n /// Forward pass for a batch\n ///\n /// # Arguments\n /// * `batch` - Batch of inputs [batch_size, T_in, D_in]\n /// * `training` - Whether in training mode\n ///\n /// # Returns\n /// * Batch of outputs [batch_size, T_out, D_out]\n pub fn forward_batch(\n &self,\n batch: &ndarray::Array3<f64>,\n training: bool,\n ) -> ndarray::Array3<f64> {\n let batch_size = batch.shape()[0];\n let mut output = ndarray::Array3::zeros((batch_size, self.t_out, self.d_out));\n\n for i in 0..batch_size {\n let x = batch.slice(ndarray::s![i, .., ..]).to_owned();\n let y = self.forward(&x, training);\n output.slice_mut(ndarray::s![i, .., ..]).assign(&y);\n }\n\n output\n }\n\n /// Get the output shape\n pub fn output_shape(&self) -> (usize, usize) {\n (self.t_out, self.d_out)\n }\n\n /// Get the number of parameters\n pub fn num_params(&self) -> usize {\n self.w1.len() + self.w2.len() + self.bias.len()\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_bilinear_forward() {\n let layer = BilinearLayer::new(100, 20, 6, 32, 0.1, false);\n let x = Array2::from_shape_fn((100, 6), |_| rand::random::<f64>());\n\n let y = layer.forward(&x, false);\n\n assert_eq!(y.shape(), &[20, 32]);\n }\n\n #[test]\n fn test_bilinear_batch() {\n let layer = BilinearLayer::new(50, 10, 4, 16, 0.0, false);\n let batch = ndarray::Array3::from_shape_fn((8, 50, 4), |_| rand::random::<f64>());\n\n let output = layer.forward_batch(&batch, false);\n\n assert_eq!(output.shape(), &[8, 10, 16]);\n }\n\n #[test]\n fn test_bilinear_with_batch_norm() {\n let layer = BilinearLayer::new(100, 20, 6, 32, 0.1, true);\n let x = Array2::from_shape_fn((100, 6), |_| rand::random::<f64>());\n\n let y = layer.forward(&x, false);\n\n assert_eq!(y.shape(), &[20, 32]);\n }\n\n #[test]\n fn test_num_params() {\n let layer = BilinearLayer::new(100, 20, 6, 32, 0.1, false);\n let expected = 100 * 20 + 6 * 32 + 20 * 32;\n assert_eq!(layer.num_params(), expected);\n }\n}\n" | |
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| "content": "//! Temporal attention mechanism\n\nuse ndarray::{Array1, Array2, Axis};\nuse rand::Rng;\nuse rand_distr::Normal;\n\n/// Type of attention mechanism\n#[derive(Debug, Clone, Copy, PartialEq)]\npub enum AttentionType {\n /// Additive attention (Bahdanau style)\n Additive,\n /// Scaled dot-product attention\n ScaledDotProduct,\n /// Multi-head attention\n MultiHead,\n}\n\n/// Temporal attention layer\n///\n/// Computes attention weights over time steps:\n/// α = softmax(w · tanh(U · X^T))\n#[derive(Debug, Clone)]\npub struct TemporalAttention {\n /// Hidden dimension for attention computation\n hidden_dim: usize,\n /// Input dimension\n input_dim: usize,\n /// Query projection [hidden_dim, input_dim]\n u: Array2<f64>,\n /// Attention weights [hidden_dim]\n w: Array1<f64>,\n /// Attention type\n attention_type: AttentionType,\n /// Number of heads (for multi-head attention)\n n_heads: usize,\n /// Head dimension\n head_dim: usize,\n /// Query projections for multi-head [n_heads, head_dim, input_dim]\n wq: Option<Vec<Array2<f64>>>,\n /// Key projections for multi-head [n_heads, head_dim, input_dim]\n wk: Option<Vec<Array2<f64>>>,\n /// Value projections for multi-head [n_heads, head_dim, input_dim]\n wv: Option<Vec<Array2<f64>>>,\n /// Output projection [input_dim, n_heads * head_dim]\n wo: Option<Array2<f64>>,\n}\n\nimpl TemporalAttention {\n /// Create a new temporal attention layer\n ///\n /// # Arguments\n /// * `input_dim` - Dimension of input features\n /// * `hidden_dim` - Hidden dimension for attention computation\n /// * `attention_type` - Type of attention mechanism\n pub fn new(input_dim: usize, hidden_dim: usize, attention_type: AttentionType) -> Self {\n Self::with_heads(input_dim, hidden_dim, attention_type, 1)\n }\n\n /// Create a multi-head attention layer\n ///\n /// # Arguments\n /// * `input_dim` - Dimension of input features\n /// * `hidden_dim` - Hidden dimension for attention computation\n /// * `attention_type` - Type of attention mechanism\n /// * `n_heads` - Number of attention heads\n pub fn with_heads(\n input_dim: usize,\n hidden_dim: usize,\n attention_type: AttentionType,\n n_heads: usize,\n ) -> Self {\n let mut rng = rand::thread_rng();\n let std = (2.0 / (input_dim + hidden_dim) as f64).sqrt();\n let dist = Normal::new(0.0, std).unwrap();\n\n let u = Array2::from_shape_fn((hidden_dim, input_dim), |_| rng.sample(dist));\n let w = Array1::from_shape_fn(hidden_dim, |_| rng.sample(dist));\n\n let (wq, wk, wv, wo, head_dim) = if attention_type == AttentionType::MultiHead && n_heads > 1 {\n let head_dim = input_dim / n_heads;\n let head_std = (2.0 / (input_dim + head_dim) as f64).sqrt();\n let head_dist = Normal::new(0.0, head_std).unwrap();\n\n let wq: Vec<Array2<f64>> = (0..n_heads)\n .map(|_| Array2::from_shape_fn((head_dim, input_dim), |_| rng.sample(head_dist)))\n .collect();\n let wk: Vec<Array2<f64>> = (0..n_heads)\n .map(|_| Array2::from_shape_fn((head_dim, input_dim), |_| rng.sample(head_dist)))\n .collect();\n let wv: Vec<Array2<f64>> = (0..n_heads)\n .map(|_| Array2::from_shape_fn((head_dim, input_dim), |_| rng.sample(head_dist)))\n .collect();\n let wo = Array2::from_shape_fn((input_dim, n_heads * head_dim), |_| rng.sample(head_dist));\n\n (Some(wq), Some(wk), Some(wv), Some(wo), head_dim)\n } else {\n (None, None, None, None, hidden_dim)\n };\n\n Self {\n hidden_dim,\n input_dim,\n u,\n w,\n attention_type,\n n_heads,\n head_dim,\n wq,\n wk,\n wv,\n wo,\n }\n }\n\n /// Compute softmax over a vector\n fn softmax(x: &Array1<f64>) -> Array1<f64> {\n let max_val = x.iter().cloned().fold(f64::NEG_INFINITY, f64::max);\n let exp_x = x.mapv(|v| (v - max_val).exp());\n let sum: f64 = exp_x.sum();\n exp_x / sum\n }\n\n /// Forward pass with additive attention\n ///\n /// # Arguments\n /// * `x` - Input tensor [T, D]\n /// * `mask` - Optional mask [T] where true values are masked out\n ///\n /// # Returns\n /// * `(context, attention_weights)` - Context vector [D] and attention weights [T]\n fn forward_additive(\n &self,\n x: &Array2<f64>,\n mask: Option<&Array1<bool>>,\n ) -> (Array1<f64>, Array1<f64>) {\n let t = x.nrows();\n\n // U · X^T -> [hidden_dim, T]\n let projected = self.u.dot(&x.t());\n\n // tanh(U · X^T) -> [hidden_dim, T]\n let activated = projected.mapv(|v| v.tanh());\n\n // w · tanh(U · X^T) -> [T]\n let mut scores = self.w.dot(&activated);\n\n // Apply mask if provided\n if let Some(m) = mask {\n for (i, &masked) in m.iter().enumerate() {\n if masked {\n scores[i] = f64::NEG_INFINITY;\n }\n }\n }\n\n // softmax -> attention weights [T]\n let attention_weights = Self::softmax(&scores);\n\n // Context = X^T · α -> [D]\n let context = x.t().dot(&attention_weights);\n\n (context, attention_weights)\n }\n\n /// Forward pass with scaled dot-product attention\n fn forward_scaled_dot_product(\n &self,\n x: &Array2<f64>,\n mask: Option<&Array1<bool>>,\n ) -> (Array1<f64>, Array1<f64>) {\n let t = x.nrows();\n let d = x.ncols();\n\n // Use the last time step as query\n let query = x.row(t - 1).to_owned();\n\n // Compute dot products: Q · K^T / sqrt(d)\n let scale = (d as f64).sqrt();\n let mut scores = Array1::zeros(t);\n\n for i in 0..t {\n let key = x.row(i);\n scores[i] = query.dot(&key) / scale;\n }\n\n // Apply mask\n if let Some(m) = mask {\n for (i, &masked) in m.iter().enumerate() {\n if masked {\n scores[i] = f64::NEG_INFINITY;\n }\n }\n }\n\n let attention_weights = Self::softmax(&scores);\n\n // Weighted sum of values\n let mut context = Array1::zeros(d);\n for i in 0..t {\n context = &context + &(x.row(i).to_owned() * attention_weights[i]);\n }\n\n (context, attention_weights)\n }\n\n /// Forward pass with multi-head attention\n fn forward_multi_head(\n &self,\n x: &Array2<f64>,\n mask: Option<&Array1<bool>>,\n ) -> (Array1<f64>, Array1<f64>) {\n let t = x.nrows();\n\n if self.wq.is_none() || self.wk.is_none() || self.wv.is_none() || self.wo.is_none() {\n // Fall back to additive if multi-head weights aren't set\n return self.forward_additive(x, mask);\n }\n\n let wq = self.wq.as_ref().unwrap();\n let wk = self.wk.as_ref().unwrap();\n let wv = self.wv.as_ref().unwrap();\n let wo = self.wo.as_ref().unwrap();\n\n // Compute attention for each head\n let mut head_outputs = Vec::with_capacity(self.n_heads);\n let mut all_weights = Array1::zeros(t);\n\n for head in 0..self.n_heads {\n // Project to query, key, value spaces\n let q = wq[head].dot(&x.row(t - 1).t()); // [head_dim]\n let scale = (self.head_dim as f64).sqrt();\n\n let mut scores = Array1::zeros(t);\n for i in 0..t {\n let k = wk[head].dot(&x.row(i).t()); // [head_dim]\n scores[i] = q.dot(&k) / scale;\n }\n\n // Apply mask\n if let Some(m) = mask {\n for (i, &masked) in m.iter().enumerate() {\n if masked {\n scores[i] = f64::NEG_INFINITY;\n }\n }\n }\n\n let weights = Self::softmax(&scores);\n all_weights = &all_weights + &weights;\n\n // Compute weighted value\n let mut head_output = Array1::zeros(self.head_dim);\n for i in 0..t {\n let v = wv[head].dot(&x.row(i).t()); // [head_dim]\n head_output = &head_output + &(v * weights[i]);\n }\n\n head_outputs.push(head_output);\n }\n\n // Concatenate heads\n let mut concat = Array1::zeros(self.n_heads * self.head_dim);\n for (head, output) in head_outputs.iter().enumerate() {\n for (i, &val) in output.iter().enumerate() {\n concat[head * self.head_dim + i] = val;\n }\n }\n\n // Output projection\n let context = wo.dot(&concat);\n\n // Average attention weights\n all_weights = all_weights / self.n_heads as f64;\n\n (context, all_weights)\n }\n\n /// Forward pass\n ///\n /// # Arguments\n /// * `x` - Input tensor [T, D]\n /// * `mask` - Optional mask [T] where true values are masked out\n ///\n /// # Returns\n /// * `(context, attention_weights)` - Context vector [D] and attention weights [T]\n pub fn forward(\n &self,\n x: &Array2<f64>,\n mask: Option<&Array1<bool>>,\n ) -> (Array1<f64>, Array1<f64>) {\n match self.attention_type {\n AttentionType::Additive => self.forward_additive(x, mask),\n AttentionType::ScaledDotProduct => self.forward_scaled_dot_product(x, mask),\n AttentionType::MultiHead => self.forward_multi_head(x, mask),\n }\n }\n\n /// Forward pass for a batch\n ///\n /// # Arguments\n /// * `batch` - Batch of inputs [batch_size, T, D]\n /// * `masks` - Optional masks [batch_size, T]\n ///\n /// # Returns\n /// * `(contexts, attention_weights)` - [batch_size, D] and [batch_size, T]\n pub fn forward_batch(\n &self,\n batch: &ndarray::Array3<f64>,\n masks: Option<&ndarray::Array2<bool>>,\n ) -> (Array2<f64>, Array2<f64>) {\n let batch_size = batch.shape()[0];\n let t = batch.shape()[1];\n let d = batch.shape()[2];\n\n let mut contexts = Array2::zeros((batch_size, d));\n let mut all_weights = Array2::zeros((batch_size, t));\n\n for i in 0..batch_size {\n let x = batch.slice(ndarray::s![i, .., ..]).to_owned();\n let mask = masks.map(|m| m.row(i).to_owned());\n\n let (context, weights) = self.forward(&x, mask.as_ref());\n\n contexts.row_mut(i).assign(&context);\n all_weights.row_mut(i).assign(&weights);\n }\n\n (contexts, all_weights)\n }\n\n /// Get the number of parameters\n pub fn num_params(&self) -> usize {\n let base = self.u.len() + self.w.len();\n\n if let (Some(wq), Some(wk), Some(wv), Some(wo)) = (&self.wq, &self.wk, &self.wv, &self.wo) {\n let multi_head_params: usize = wq.iter().map(|w| w.len()).sum::<usize>()\n + wk.iter().map(|w| w.len()).sum::<usize>()\n + wv.iter().map(|w| w.len()).sum::<usize>()\n + wo.len();\n base + multi_head_params\n } else {\n base\n }\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_additive_attention() {\n let attn = TemporalAttention::new(32, 64, AttentionType::Additive);\n let x = Array2::from_shape_fn((100, 32), |_| rand::random::<f64>());\n\n let (context, weights) = attn.forward(&x, None);\n\n assert_eq!(context.len(), 32);\n assert_eq!(weights.len(), 100);\n\n // Weights should sum to 1\n let sum: f64 = weights.sum();\n assert!((sum - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn test_scaled_dot_product_attention() {\n let attn = TemporalAttention::new(32, 64, AttentionType::ScaledDotProduct);\n let x = Array2::from_shape_fn((100, 32), |_| rand::random::<f64>());\n\n let (context, weights) = attn.forward(&x, None);\n\n assert_eq!(context.len(), 32);\n assert_eq!(weights.len(), 100);\n\n // Weights should sum to 1\n let sum: f64 = weights.sum();\n assert!((sum - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn test_multi_head_attention() {\n let attn = TemporalAttention::with_heads(32, 64, AttentionType::MultiHead, 4);\n let x = Array2::from_shape_fn((100, 32), |_| rand::random::<f64>());\n\n let (context, weights) = attn.forward(&x, None);\n\n assert_eq!(context.len(), 32);\n assert_eq!(weights.len(), 100);\n\n // Weights should sum to 1\n let sum: f64 = weights.sum();\n assert!((sum - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn test_attention_with_mask() {\n let attn = TemporalAttention::new(32, 64, AttentionType::Additive);\n let x = Array2::from_shape_fn((10, 32), |_| rand::random::<f64>());\n\n // Mask out first 5 time steps\n let mut mask = Array1::from_elem(10, false);\n for i in 0..5 {\n mask[i] = true;\n }\n\n let (_, weights) = attn.forward(&x, Some(&mask));\n\n // Masked positions should have ~0 weight\n for i in 0..5 {\n assert!(weights[i] < 1e-6);\n }\n\n // Remaining weights should sum to ~1\n let sum: f64 = weights.iter().skip(5).sum();\n assert!((sum - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn test_batch_forward() {\n let attn = TemporalAttention::new(32, 64, AttentionType::Additive);\n let batch = ndarray::Array3::from_shape_fn((16, 100, 32), |_| rand::random::<f64>());\n\n let (contexts, weights) = attn.forward_batch(&batch, None);\n\n assert_eq!(contexts.shape(), &[16, 32]);\n assert_eq!(weights.shape(), &[16, 100]);\n }\n}\n" | |
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| "content": "//! TABL (Temporal Attention-Augmented Bilinear Network) model\n\nuse ndarray::{Array1, Array2, Array3, Axis};\nuse rand::Rng;\nuse rand_distr::Normal;\n\nuse super::attention::{AttentionType, TemporalAttention};\nuse super::bilinear::BilinearLayer;\nuse crate::defaults;\n\n/// Configuration for TABL model\n#[derive(Debug, Clone)]\npub struct TABLConfig {\n /// Input sequence length (T)\n pub seq_len: usize,\n /// Number of input features (D)\n pub input_dim: usize,\n /// Compressed temporal dimension (T')\n pub hidden_t: usize,\n /// Compressed feature dimension (D')\n pub hidden_d: usize,\n /// Attention hidden dimension\n pub attention_dim: usize,\n /// Number of attention heads\n pub n_heads: usize,\n /// Number of output classes\n pub n_classes: usize,\n /// Dropout rate\n pub dropout: f64,\n /// Attention type\n pub attention_type: AttentionType,\n}\n\nimpl Default for TABLConfig {\n fn default() -> Self {\n Self {\n seq_len: defaults::SEQ_LEN,\n input_dim: defaults::INPUT_DIM,\n hidden_t: defaults::HIDDEN_T,\n hidden_d: defaults::HIDDEN_D,\n attention_dim: defaults::ATTENTION_DIM,\n n_heads: defaults::N_HEADS,\n n_classes: defaults::N_CLASSES,\n dropout: defaults::DROPOUT,\n attention_type: AttentionType::Additive,\n }\n }\n}\n\nimpl TABLConfig {\n /// Create a new configuration with custom parameters\n pub fn new(\n seq_len: usize,\n input_dim: usize,\n hidden_t: usize,\n hidden_d: usize,\n n_classes: usize,\n ) -> Self {\n Self {\n seq_len,\n input_dim,\n hidden_t,\n hidden_d,\n attention_dim: hidden_d * 2,\n n_heads: 4,\n n_classes,\n dropout: 0.2,\n attention_type: AttentionType::Additive,\n }\n }\n\n /// Set the attention type\n pub fn with_attention_type(mut self, attention_type: AttentionType) -> Self {\n self.attention_type = attention_type;\n self\n }\n\n /// Set the number of attention heads\n pub fn with_heads(mut self, n_heads: usize) -> Self {\n self.n_heads = n_heads;\n self\n }\n\n /// Set the dropout rate\n pub fn with_dropout(mut self, dropout: f64) -> Self {\n self.dropout = dropout;\n self\n }\n}\n\n/// TABL (Temporal Attention-Augmented Bilinear Network) model\n#[derive(Debug)]\npub struct TABLModel {\n /// Model configuration\n config: TABLConfig,\n /// Bilinear projection layer\n bilinear: BilinearLayer,\n /// Temporal attention layer\n attention: TemporalAttention,\n /// Output layer weights [n_classes, hidden_d]\n output_weights: Array2<f64>,\n /// Output layer bias [n_classes]\n output_bias: Array1<f64>,\n}\n\nimpl TABLModel {\n /// Create a new TABL model\n pub fn new(config: TABLConfig) -> Self {\n let bilinear = BilinearLayer::new(\n config.seq_len,\n config.hidden_t,\n config.input_dim,\n config.hidden_d,\n config.dropout,\n true,\n );\n\n let attention = TemporalAttention::with_heads(\n config.hidden_d,\n config.attention_dim,\n config.attention_type,\n config.n_heads,\n );\n\n // Initialize output layer\n let mut rng = rand::thread_rng();\n let std = (2.0 / (config.hidden_d + config.n_classes) as f64).sqrt();\n let dist = Normal::new(0.0, std).unwrap();\n\n let output_weights = Array2::from_shape_fn(\n (config.n_classes, config.hidden_d),\n |_| rng.sample(dist),\n );\n let output_bias = Array1::zeros(config.n_classes);\n\n Self {\n config,\n bilinear,\n attention,\n output_weights,\n output_bias,\n }\n }\n\n /// Forward pass for a single sample\n ///\n /// # Arguments\n /// * `x` - Input tensor [seq_len, input_dim]\n /// * `training` - Whether in training mode\n ///\n /// # Returns\n /// * `(logits, attention_weights)` - Class logits and attention weights\n pub fn forward(\n &self,\n x: &Array2<f64>,\n training: bool,\n ) -> (Array1<f64>, Array1<f64>) {\n // Bilinear projection: [seq_len, input_dim] -> [hidden_t, hidden_d]\n let h = self.bilinear.forward(x, training);\n\n // Temporal attention: [hidden_t, hidden_d] -> [hidden_d]\n let (context, attention_weights) = self.attention.forward(&h, None);\n\n // Output layer: [hidden_d] -> [n_classes]\n let logits = self.output_weights.dot(&context) + &self.output_bias;\n\n (logits, attention_weights)\n }\n\n /// Forward pass for a batch\n ///\n /// # Arguments\n /// * `batch` - Batch of inputs [batch_size, seq_len, input_dim]\n /// * `training` - Whether in training mode\n ///\n /// # Returns\n /// * `(logits, attention_weights)` - [batch_size, n_classes] and [batch_size, hidden_t]\n pub fn forward_batch(\n &self,\n batch: &Array3<f64>,\n training: bool,\n ) -> (Array2<f64>, Array2<f64>) {\n let batch_size = batch.shape()[0];\n\n let mut logits = Array2::zeros((batch_size, self.config.n_classes));\n let mut all_weights = Array2::zeros((batch_size, self.config.hidden_t));\n\n for i in 0..batch_size {\n let x = batch.slice(ndarray::s![i, .., ..]).to_owned();\n let (sample_logits, weights) = self.forward(&x, training);\n\n logits.row_mut(i).assign(&sample_logits);\n all_weights.row_mut(i).assign(&weights);\n }\n\n (logits, all_weights)\n }\n\n /// Compute softmax probabilities from logits\n pub fn softmax(logits: &Array1<f64>) -> Array1<f64> {\n let max_val = logits.iter().cloned().fold(f64::NEG_INFINITY, f64::max);\n let exp_logits = logits.mapv(|v| (v - max_val).exp());\n let sum: f64 = exp_logits.sum();\n exp_logits / sum\n }\n\n /// Predict class probabilities for a single sample\n pub fn predict_proba(&self, x: &Array2<f64>) -> Array1<f64> {\n let (logits, _) = self.forward(x, false);\n Self::softmax(&logits)\n }\n\n /// Predict class for a single sample\n pub fn predict(&self, x: &Array2<f64>) -> usize {\n let proba = self.predict_proba(x);\n proba\n .iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .map(|(i, _)| i)\n .unwrap_or(0)\n }\n\n /// Predict classes for a batch\n pub fn predict_batch(&self, batch: &Array3<f64>) -> Vec<usize> {\n let (logits, _) = self.forward_batch(batch, false);\n\n (0..logits.nrows())\n .map(|i| {\n let row = logits.row(i);\n row.iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .map(|(i, _)| i)\n .unwrap_or(0)\n })\n .collect()\n }\n\n /// Compute cross-entropy loss\n pub fn compute_loss(&self, logits: &Array2<f64>, targets: &[i32]) -> f64 {\n let batch_size = logits.nrows();\n let mut total_loss = 0.0;\n\n for i in 0..batch_size {\n let row = logits.row(i);\n let proba = Self::softmax(&row.to_owned());\n let target = targets[i] as usize;\n\n // Cross-entropy: -log(p[target])\n let p = proba[target].max(1e-10);\n total_loss -= p.ln();\n }\n\n total_loss / batch_size as f64\n }\n\n /// Compute accuracy\n pub fn compute_accuracy(&self, predictions: &[usize], targets: &[i32]) -> f64 {\n let correct = predictions\n .iter()\n .zip(targets.iter())\n .filter(|(&pred, &target)| pred == target as usize)\n .count();\n\n correct as f64 / predictions.len() as f64\n }\n\n /// Get the model configuration\n pub fn config(&self) -> &TABLConfig {\n &self.config\n }\n\n /// Get the total number of parameters\n pub fn num_params(&self) -> usize {\n self.bilinear.num_params()\n + self.attention.num_params()\n + self.output_weights.len()\n + self.output_bias.len()\n }\n\n /// Get attention weights for interpretability\n pub fn get_attention_weights(&self, x: &Array2<f64>) -> Array1<f64> {\n let h = self.bilinear.forward(x, false);\n let (_, attention_weights) = self.attention.forward(&h, None);\n attention_weights\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_tabl_forward() {\n let config = TABLConfig::default();\n let model = TABLModel::new(config.clone());\n\n let x = Array2::from_shape_fn(\n (config.seq_len, config.input_dim),\n |_| rand::random::<f64>(),\n );\n\n let (logits, weights) = model.forward(&x, false);\n\n assert_eq!(logits.len(), config.n_classes);\n assert_eq!(weights.len(), config.hidden_t);\n }\n\n #[test]\n fn test_tabl_batch() {\n let config = TABLConfig::new(50, 6, 10, 16, 3);\n let model = TABLModel::new(config.clone());\n\n let batch = Array3::from_shape_fn(\n (16, config.seq_len, config.input_dim),\n |_| rand::random::<f64>(),\n );\n\n let (logits, weights) = model.forward_batch(&batch, false);\n\n assert_eq!(logits.shape(), &[16, 3]);\n assert_eq!(weights.shape(), &[16, 10]);\n }\n\n #[test]\n fn test_tabl_predict() {\n let config = TABLConfig::new(50, 6, 10, 16, 3);\n let model = TABLModel::new(config.clone());\n\n let x = Array2::from_shape_fn(\n (config.seq_len, config.input_dim),\n |_| rand::random::<f64>(),\n );\n\n let pred = model.predict(&x);\n assert!(pred < 3);\n\n let proba = model.predict_proba(&x);\n assert_eq!(proba.len(), 3);\n\n // Probabilities should sum to 1\n let sum: f64 = proba.sum();\n assert!((sum - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn test_tabl_loss() {\n let config = TABLConfig::new(50, 6, 10, 16, 3);\n let model = TABLModel::new(config);\n\n let logits = Array2::from_shape_fn((4, 3), |_| rand::random::<f64>());\n let targets = vec![0, 1, 2, 1];\n\n let loss = model.compute_loss(&logits, &targets);\n assert!(loss.is_finite());\n assert!(loss > 0.0);\n }\n\n #[test]\n fn test_tabl_accuracy() {\n let config = TABLConfig::new(50, 6, 10, 16, 3);\n let model = TABLModel::new(config);\n\n let predictions = vec![0, 1, 2, 1];\n let targets = vec![0, 1, 0, 1];\n\n let accuracy = model.compute_accuracy(&predictions, &targets);\n assert!((accuracy - 0.75).abs() < 1e-6);\n }\n\n #[test]\n fn test_tabl_with_multi_head_attention() {\n let config = TABLConfig::new(50, 6, 10, 16, 3)\n .with_attention_type(AttentionType::MultiHead)\n .with_heads(4);\n\n let model = TABLModel::new(config.clone());\n\n let x = Array2::from_shape_fn(\n (config.seq_len, config.input_dim),\n |_| rand::random::<f64>(),\n );\n\n let (logits, _) = model.forward(&x, false);\n assert_eq!(logits.len(), 3);\n }\n\n #[test]\n fn test_num_params() {\n let config = TABLConfig::new(50, 6, 10, 16, 3);\n let model = TABLModel::new(config);\n\n let params = model.num_params();\n assert!(params > 0);\n }\n}\n" | |
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| "content": "//! TABL model module\n//!\n//! This module provides the core TABL (Temporal Attention-Augmented Bilinear Network) implementation.\n\nmod attention;\nmod bilinear;\nmod tabl;\n\npub use attention::{AttentionType, TemporalAttention};\npub use bilinear::BilinearLayer;\npub use tabl::{TABLConfig, TABLModel};\n", | |
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| "content": "//! Bilinear projection layer for temporal data\n\nuse ndarray::{Array1, Array2, Axis};\nuse rand::Rng;\nuse rand_distr::Normal;\n\n/// Bilinear layer for joint temporal-feature projection\n///\n/// Performs the operation: H = σ(W₁ · X · W₂ + b)\n/// - W₁ projects along temporal dimension\n/// - W₂ projects along feature dimension\n#[derive(Debug, Clone)]\npub struct BilinearLayer {\n /// Temporal projection matrix [T_out, T_in]\n w1: Array2<f64>,\n /// Feature projection matrix [D_in, D_out]\n w2: Array2<f64>,\n /// Bias term [T_out, D_out]\n bias: Array2<f64>,\n /// Input temporal dimension\n t_in: usize,\n /// Output temporal dimension\n t_out: usize,\n /// Input feature dimension\n d_in: usize,\n /// Output feature dimension\n d_out: usize,\n /// Dropout rate\n dropout: f64,\n /// Use batch normalization\n use_batch_norm: bool,\n /// Running mean for batch norm\n running_mean: Option<Array2<f64>>,\n /// Running variance for batch norm\n running_var: Option<Array2<f64>>,\n}\n\nimpl BilinearLayer {\n /// Create a new bilinear layer\n ///\n /// # Arguments\n /// * `t_in` - Input temporal dimension\n /// * `t_out` - Output temporal dimension\n /// * `d_in` - Input feature dimension\n /// * `d_out` - Output feature dimension\n /// * `dropout` - Dropout rate (0.0 - 1.0)\n /// * `use_batch_norm` - Whether to use batch normalization\n pub fn new(\n t_in: usize,\n t_out: usize,\n d_in: usize,\n d_out: usize,\n dropout: f64,\n use_batch_norm: bool,\n ) -> Self {\n let mut rng = rand::thread_rng();\n\n // Xavier/Glorot initialization\n let w1_std = (2.0 / (t_in + t_out) as f64).sqrt();\n let w2_std = (2.0 / (d_in + d_out) as f64).sqrt();\n\n let w1_dist = Normal::new(0.0, w1_std).unwrap();\n let w2_dist = Normal::new(0.0, w2_std).unwrap();\n\n let w1 = Array2::from_shape_fn((t_out, t_in), |_| rng.sample(w1_dist));\n let w2 = Array2::from_shape_fn((d_in, d_out), |_| rng.sample(w2_dist));\n let bias = Array2::zeros((t_out, d_out));\n\n let (running_mean, running_var) = if use_batch_norm {\n (\n Some(Array2::zeros((t_out, d_out))),\n Some(Array2::ones((t_out, d_out))),\n )\n } else {\n (None, None)\n };\n\n Self {\n w1,\n w2,\n bias,\n t_in,\n t_out,\n d_in,\n d_out,\n dropout,\n use_batch_norm,\n running_mean,\n running_var,\n }\n }\n\n /// Forward pass\n ///\n /// # Arguments\n /// * `x` - Input tensor [T_in, D_in]\n /// * `training` - Whether in training mode (applies dropout)\n ///\n /// # Returns\n /// * Output tensor [T_out, D_out]\n pub fn forward(&self, x: &Array2<f64>, training: bool) -> Array2<f64> {\n assert_eq!(x.nrows(), self.t_in, \"Temporal dimension mismatch\");\n assert_eq!(x.ncols(), self.d_in, \"Feature dimension mismatch\");\n\n // H = W₁ · X · W₂ + b\n let h1 = self.w1.dot(x); // [T_out, D_in]\n let mut h = h1.dot(&self.w2); // [T_out, D_out]\n h = &h + &self.bias;\n\n // Batch normalization (simplified - per-sample)\n if self.use_batch_norm {\n if let (Some(running_mean), Some(running_var)) = (&self.running_mean, &self.running_var) {\n h = (&h - running_mean) / (running_var.mapv(|v| (v + 1e-5).sqrt()));\n }\n }\n\n // ReLU activation\n h.mapv_inplace(|v| v.max(0.0));\n\n // Dropout during training\n if training && self.dropout > 0.0 {\n let mut rng = rand::thread_rng();\n h.mapv_inplace(|v| {\n if rng.gen::<f64>() < self.dropout {\n 0.0\n } else {\n v / (1.0 - self.dropout)\n }\n });\n }\n\n h\n }\n\n /// Forward pass for a batch\n ///\n /// # Arguments\n /// * `batch` - Batch of inputs [batch_size, T_in, D_in]\n /// * `training` - Whether in training mode\n ///\n /// # Returns\n /// * Batch of outputs [batch_size, T_out, D_out]\n pub fn forward_batch(\n &self,\n batch: &ndarray::Array3<f64>,\n training: bool,\n ) -> ndarray::Array3<f64> {\n let batch_size = batch.shape()[0];\n let mut output = ndarray::Array3::zeros((batch_size, self.t_out, self.d_out));\n\n for i in 0..batch_size {\n let x = batch.slice(ndarray::s![i, .., ..]).to_owned();\n let y = self.forward(&x, training);\n output.slice_mut(ndarray::s![i, .., ..]).assign(&y);\n }\n\n output\n }\n\n /// Get the output shape\n pub fn output_shape(&self) -> (usize, usize) {\n (self.t_out, self.d_out)\n }\n\n /// Get the number of parameters\n pub fn num_params(&self) -> usize {\n self.w1.len() + self.w2.len() + self.bias.len()\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_bilinear_forward() {\n let layer = BilinearLayer::new(100, 20, 6, 32, 0.1, false);\n let x = Array2::from_shape_fn((100, 6), |_| rand::random::<f64>());\n\n let y = layer.forward(&x, false);\n\n assert_eq!(y.shape(), &[20, 32]);\n }\n\n #[test]\n fn test_bilinear_batch() {\n let layer = BilinearLayer::new(50, 10, 4, 16, 0.0, false);\n let batch = ndarray::Array3::from_shape_fn((8, 50, 4), |_| rand::random::<f64>());\n\n let output = layer.forward_batch(&batch, false);\n\n assert_eq!(output.shape(), &[8, 10, 16]);\n }\n\n #[test]\n fn test_bilinear_with_batch_norm() {\n let layer = BilinearLayer::new(100, 20, 6, 32, 0.1, true);\n let x = Array2::from_shape_fn((100, 6), |_| rand::random::<f64>());\n\n let y = layer.forward(&x, false);\n\n assert_eq!(y.shape(), &[20, 32]);\n }\n\n #[test]\n fn test_num_params() {\n let layer = BilinearLayer::new(100, 20, 6, 32, 0.1, false);\n let expected = 100 * 20 + 6 * 32 + 20 * 32;\n assert_eq!(layer.num_params(), expected);\n }\n}\n", | |
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| "content": "//! Temporal attention mechanism\n\nuse ndarray::{Array1, Array2, Axis};\nuse rand::Rng;\nuse rand_distr::Normal;\n\n/// Type of attention mechanism\n#[derive(Debug, Clone, Copy, PartialEq)]\npub enum AttentionType {\n /// Additive attention (Bahdanau style)\n Additive,\n /// Scaled dot-product attention\n ScaledDotProduct,\n /// Multi-head attention\n MultiHead,\n}\n\n/// Temporal attention layer\n///\n/// Computes attention weights over time steps:\n/// α = softmax(w · tanh(U · X^T))\n#[derive(Debug, Clone)]\npub struct TemporalAttention {\n /// Hidden dimension for attention computation\n hidden_dim: usize,\n /// Input dimension\n input_dim: usize,\n /// Query projection [hidden_dim, input_dim]\n u: Array2<f64>,\n /// Attention weights [hidden_dim]\n w: Array1<f64>,\n /// Attention type\n attention_type: AttentionType,\n /// Number of heads (for multi-head attention)\n n_heads: usize,\n /// Head dimension\n head_dim: usize,\n /// Query projections for multi-head [n_heads, head_dim, input_dim]\n wq: Option<Vec<Array2<f64>>>,\n /// Key projections for multi-head [n_heads, head_dim, input_dim]\n wk: Option<Vec<Array2<f64>>>,\n /// Value projections for multi-head [n_heads, head_dim, input_dim]\n wv: Option<Vec<Array2<f64>>>,\n /// Output projection [input_dim, n_heads * head_dim]\n wo: Option<Array2<f64>>,\n}\n\nimpl TemporalAttention {\n /// Create a new temporal attention layer\n ///\n /// # Arguments\n /// * `input_dim` - Dimension of input features\n /// * `hidden_dim` - Hidden dimension for attention computation\n /// * `attention_type` - Type of attention mechanism\n pub fn new(input_dim: usize, hidden_dim: usize, attention_type: AttentionType) -> Self {\n Self::with_heads(input_dim, hidden_dim, attention_type, 1)\n }\n\n /// Create a multi-head attention layer\n ///\n /// # Arguments\n /// * `input_dim` - Dimension of input features\n /// * `hidden_dim` - Hidden dimension for attention computation\n /// * `attention_type` - Type of attention mechanism\n /// * `n_heads` - Number of attention heads\n pub fn with_heads(\n input_dim: usize,\n hidden_dim: usize,\n attention_type: AttentionType,\n n_heads: usize,\n ) -> Self {\n let mut rng = rand::thread_rng();\n let std = (2.0 / (input_dim + hidden_dim) as f64).sqrt();\n let dist = Normal::new(0.0, std).unwrap();\n\n let u = Array2::from_shape_fn((hidden_dim, input_dim), |_| rng.sample(dist));\n let w = Array1::from_shape_fn(hidden_dim, |_| rng.sample(dist));\n\n let (wq, wk, wv, wo, head_dim) = if attention_type == AttentionType::MultiHead && n_heads > 1 {\n let head_dim = input_dim / n_heads;\n let head_std = (2.0 / (input_dim + head_dim) as f64).sqrt();\n let head_dist = Normal::new(0.0, head_std).unwrap();\n\n let wq: Vec<Array2<f64>> = (0..n_heads)\n .map(|_| Array2::from_shape_fn((head_dim, input_dim), |_| rng.sample(head_dist)))\n .collect();\n let wk: Vec<Array2<f64>> = (0..n_heads)\n .map(|_| Array2::from_shape_fn((head_dim, input_dim), |_| rng.sample(head_dist)))\n .collect();\n let wv: Vec<Array2<f64>> = (0..n_heads)\n .map(|_| Array2::from_shape_fn((head_dim, input_dim), |_| rng.sample(head_dist)))\n .collect();\n let wo = Array2::from_shape_fn((input_dim, n_heads * head_dim), |_| rng.sample(head_dist));\n\n (Some(wq), Some(wk), Some(wv), Some(wo), head_dim)\n } else {\n (None, None, None, None, hidden_dim)\n };\n\n Self {\n hidden_dim,\n input_dim,\n u,\n w,\n attention_type,\n n_heads,\n head_dim,\n wq,\n wk,\n wv,\n wo,\n }\n }\n\n /// Compute softmax over a vector\n fn softmax(x: &Array1<f64>) -> Array1<f64> {\n let max_val = x.iter().cloned().fold(f64::NEG_INFINITY, f64::max);\n let exp_x = x.mapv(|v| (v - max_val).exp());\n let sum: f64 = exp_x.sum();\n exp_x / sum\n }\n\n /// Forward pass with additive attention\n ///\n /// # Arguments\n /// * `x` - Input tensor [T, D]\n /// * `mask` - Optional mask [T] where true values are masked out\n ///\n /// # Returns\n /// * `(context, attention_weights)` - Context vector [D] and attention weights [T]\n fn forward_additive(\n &self,\n x: &Array2<f64>,\n mask: Option<&Array1<bool>>,\n ) -> (Array1<f64>, Array1<f64>) {\n let t = x.nrows();\n\n // U · X^T -> [hidden_dim, T]\n let projected = self.u.dot(&x.t());\n\n // tanh(U · X^T) -> [hidden_dim, T]\n let activated = projected.mapv(|v| v.tanh());\n\n // w · tanh(U · X^T) -> [T]\n let mut scores = self.w.dot(&activated);\n\n // Apply mask if provided\n if let Some(m) = mask {\n for (i, &masked) in m.iter().enumerate() {\n if masked {\n scores[i] = f64::NEG_INFINITY;\n }\n }\n }\n\n // softmax -> attention weights [T]\n let attention_weights = Self::softmax(&scores);\n\n // Context = X^T · α -> [D]\n let context = x.t().dot(&attention_weights);\n\n (context, attention_weights)\n }\n\n /// Forward pass with scaled dot-product attention\n fn forward_scaled_dot_product(\n &self,\n x: &Array2<f64>,\n mask: Option<&Array1<bool>>,\n ) -> (Array1<f64>, Array1<f64>) {\n let t = x.nrows();\n let d = x.ncols();\n\n // Use the last time step as query\n let query = x.row(t - 1).to_owned();\n\n // Compute dot products: Q · K^T / sqrt(d)\n let scale = (d as f64).sqrt();\n let mut scores = Array1::zeros(t);\n\n for i in 0..t {\n let key = x.row(i);\n scores[i] = query.dot(&key) / scale;\n }\n\n // Apply mask\n if let Some(m) = mask {\n for (i, &masked) in m.iter().enumerate() {\n if masked {\n scores[i] = f64::NEG_INFINITY;\n }\n }\n }\n\n let attention_weights = Self::softmax(&scores);\n\n // Weighted sum of values\n let mut context = Array1::zeros(d);\n for i in 0..t {\n context = &context + &(x.row(i).to_owned() * attention_weights[i]);\n }\n\n (context, attention_weights)\n }\n\n /// Forward pass with multi-head attention\n fn forward_multi_head(\n &self,\n x: &Array2<f64>,\n mask: Option<&Array1<bool>>,\n ) -> (Array1<f64>, Array1<f64>) {\n let t = x.nrows();\n\n if self.wq.is_none() || self.wk.is_none() || self.wv.is_none() || self.wo.is_none() {\n // Fall back to additive if multi-head weights aren't set\n return self.forward_additive(x, mask);\n }\n\n let wq = self.wq.as_ref().unwrap();\n let wk = self.wk.as_ref().unwrap();\n let wv = self.wv.as_ref().unwrap();\n let wo = self.wo.as_ref().unwrap();\n\n // Compute attention for each head\n let mut head_outputs = Vec::with_capacity(self.n_heads);\n let mut all_weights = Array1::zeros(t);\n\n for head in 0..self.n_heads {\n // Project to query, key, value spaces\n let q = wq[head].dot(&x.row(t - 1).t()); // [head_dim]\n let scale = (self.head_dim as f64).sqrt();\n\n let mut scores = Array1::zeros(t);\n for i in 0..t {\n let k = wk[head].dot(&x.row(i).t()); // [head_dim]\n scores[i] = q.dot(&k) / scale;\n }\n\n // Apply mask\n if let Some(m) = mask {\n for (i, &masked) in m.iter().enumerate() {\n if masked {\n scores[i] = f64::NEG_INFINITY;\n }\n }\n }\n\n let weights = Self::softmax(&scores);\n all_weights = &all_weights + &weights;\n\n // Compute weighted value\n let mut head_output = Array1::zeros(self.head_dim);\n for i in 0..t {\n let v = wv[head].dot(&x.row(i).t()); // [head_dim]\n head_output = &head_output + &(v * weights[i]);\n }\n\n head_outputs.push(head_output);\n }\n\n // Concatenate heads\n let mut concat = Array1::zeros(self.n_heads * self.head_dim);\n for (head, output) in head_outputs.iter().enumerate() {\n for (i, &val) in output.iter().enumerate() {\n concat[head * self.head_dim + i] = val;\n }\n }\n\n // Output projection\n let context = wo.dot(&concat);\n\n // Average attention weights\n all_weights = all_weights / self.n_heads as f64;\n\n (context, all_weights)\n }\n\n /// Forward pass\n ///\n /// # Arguments\n /// * `x` - Input tensor [T, D]\n /// * `mask` - Optional mask [T] where true values are masked out\n ///\n /// # Returns\n /// * `(context, attention_weights)` - Context vector [D] and attention weights [T]\n pub fn forward(\n &self,\n x: &Array2<f64>,\n mask: Option<&Array1<bool>>,\n ) -> (Array1<f64>, Array1<f64>) {\n match self.attention_type {\n AttentionType::Additive => self.forward_additive(x, mask),\n AttentionType::ScaledDotProduct => self.forward_scaled_dot_product(x, mask),\n AttentionType::MultiHead => self.forward_multi_head(x, mask),\n }\n }\n\n /// Forward pass for a batch\n ///\n /// # Arguments\n /// * `batch` - Batch of inputs [batch_size, T, D]\n /// * `masks` - Optional masks [batch_size, T]\n ///\n /// # Returns\n /// * `(contexts, attention_weights)` - [batch_size, D] and [batch_size, T]\n pub fn forward_batch(\n &self,\n batch: &ndarray::Array3<f64>,\n masks: Option<&ndarray::Array2<bool>>,\n ) -> (Array2<f64>, Array2<f64>) {\n let batch_size = batch.shape()[0];\n let t = batch.shape()[1];\n let d = batch.shape()[2];\n\n let mut contexts = Array2::zeros((batch_size, d));\n let mut all_weights = Array2::zeros((batch_size, t));\n\n for i in 0..batch_size {\n let x = batch.slice(ndarray::s![i, .., ..]).to_owned();\n let mask = masks.map(|m| m.row(i).to_owned());\n\n let (context, weights) = self.forward(&x, mask.as_ref());\n\n contexts.row_mut(i).assign(&context);\n all_weights.row_mut(i).assign(&weights);\n }\n\n (contexts, all_weights)\n }\n\n /// Get the number of parameters\n pub fn num_params(&self) -> usize {\n let base = self.u.len() + self.w.len();\n\n if let (Some(wq), Some(wk), Some(wv), Some(wo)) = (&self.wq, &self.wk, &self.wv, &self.wo) {\n let multi_head_params: usize = wq.iter().map(|w| w.len()).sum::<usize>()\n + wk.iter().map(|w| w.len()).sum::<usize>()\n + wv.iter().map(|w| w.len()).sum::<usize>()\n + wo.len();\n base + multi_head_params\n } else {\n base\n }\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_additive_attention() {\n let attn = TemporalAttention::new(32, 64, AttentionType::Additive);\n let x = Array2::from_shape_fn((100, 32), |_| rand::random::<f64>());\n\n let (context, weights) = attn.forward(&x, None);\n\n assert_eq!(context.len(), 32);\n assert_eq!(weights.len(), 100);\n\n // Weights should sum to 1\n let sum: f64 = weights.sum();\n assert!((sum - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn test_scaled_dot_product_attention() {\n let attn = TemporalAttention::new(32, 64, AttentionType::ScaledDotProduct);\n let x = Array2::from_shape_fn((100, 32), |_| rand::random::<f64>());\n\n let (context, weights) = attn.forward(&x, None);\n\n assert_eq!(context.len(), 32);\n assert_eq!(weights.len(), 100);\n\n // Weights should sum to 1\n let sum: f64 = weights.sum();\n assert!((sum - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn test_multi_head_attention() {\n let attn = TemporalAttention::with_heads(32, 64, AttentionType::MultiHead, 4);\n let x = Array2::from_shape_fn((100, 32), |_| rand::random::<f64>());\n\n let (context, weights) = attn.forward(&x, None);\n\n assert_eq!(context.len(), 32);\n assert_eq!(weights.len(), 100);\n\n // Weights should sum to 1\n let sum: f64 = weights.sum();\n assert!((sum - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn test_attention_with_mask() {\n let attn = TemporalAttention::new(32, 64, AttentionType::Additive);\n let x = Array2::from_shape_fn((10, 32), |_| rand::random::<f64>());\n\n // Mask out first 5 time steps\n let mut mask = Array1::from_elem(10, false);\n for i in 0..5 {\n mask[i] = true;\n }\n\n let (_, weights) = attn.forward(&x, Some(&mask));\n\n // Masked positions should have ~0 weight\n for i in 0..5 {\n assert!(weights[i] < 1e-6);\n }\n\n // Remaining weights should sum to ~1\n let sum: f64 = weights.iter().skip(5).sum();\n assert!((sum - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn test_batch_forward() {\n let attn = TemporalAttention::new(32, 64, AttentionType::Additive);\n let batch = ndarray::Array3::from_shape_fn((16, 100, 32), |_| rand::random::<f64>());\n\n let (contexts, weights) = attn.forward_batch(&batch, None);\n\n assert_eq!(contexts.shape(), &[16, 32]);\n assert_eq!(weights.shape(), &[16, 100]);\n }\n}\n", | |
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| "content": "//! TABL (Temporal Attention-Augmented Bilinear Network) model\n\nuse ndarray::{Array1, Array2, Array3, Axis};\nuse rand::Rng;\nuse rand_distr::Normal;\n\nuse super::attention::{AttentionType, TemporalAttention};\nuse super::bilinear::BilinearLayer;\nuse crate::defaults;\n\n/// Configuration for TABL model\n#[derive(Debug, Clone)]\npub struct TABLConfig {\n /// Input sequence length (T)\n pub seq_len: usize,\n /// Number of input features (D)\n pub input_dim: usize,\n /// Compressed temporal dimension (T')\n pub hidden_t: usize,\n /// Compressed feature dimension (D')\n pub hidden_d: usize,\n /// Attention hidden dimension\n pub attention_dim: usize,\n /// Number of attention heads\n pub n_heads: usize,\n /// Number of output classes\n pub n_classes: usize,\n /// Dropout rate\n pub dropout: f64,\n /// Attention type\n pub attention_type: AttentionType,\n}\n\nimpl Default for TABLConfig {\n fn default() -> Self {\n Self {\n seq_len: defaults::SEQ_LEN,\n input_dim: defaults::INPUT_DIM,\n hidden_t: defaults::HIDDEN_T,\n hidden_d: defaults::HIDDEN_D,\n attention_dim: defaults::ATTENTION_DIM,\n n_heads: defaults::N_HEADS,\n n_classes: defaults::N_CLASSES,\n dropout: defaults::DROPOUT,\n attention_type: AttentionType::Additive,\n }\n }\n}\n\nimpl TABLConfig {\n /// Create a new configuration with custom parameters\n pub fn new(\n seq_len: usize,\n input_dim: usize,\n hidden_t: usize,\n hidden_d: usize,\n n_classes: usize,\n ) -> Self {\n Self {\n seq_len,\n input_dim,\n hidden_t,\n hidden_d,\n attention_dim: hidden_d * 2,\n n_heads: 4,\n n_classes,\n dropout: 0.2,\n attention_type: AttentionType::Additive,\n }\n }\n\n /// Set the attention type\n pub fn with_attention_type(mut self, attention_type: AttentionType) -> Self {\n self.attention_type = attention_type;\n self\n }\n\n /// Set the number of attention heads\n pub fn with_heads(mut self, n_heads: usize) -> Self {\n self.n_heads = n_heads;\n self\n }\n\n /// Set the dropout rate\n pub fn with_dropout(mut self, dropout: f64) -> Self {\n self.dropout = dropout;\n self\n }\n}\n\n/// TABL (Temporal Attention-Augmented Bilinear Network) model\n#[derive(Debug)]\npub struct TABLModel {\n /// Model configuration\n config: TABLConfig,\n /// Bilinear projection layer\n bilinear: BilinearLayer,\n /// Temporal attention layer\n attention: TemporalAttention,\n /// Output layer weights [n_classes, hidden_d]\n output_weights: Array2<f64>,\n /// Output layer bias [n_classes]\n output_bias: Array1<f64>,\n}\n\nimpl TABLModel {\n /// Create a new TABL model\n pub fn new(config: TABLConfig) -> Self {\n let bilinear = BilinearLayer::new(\n config.seq_len,\n config.hidden_t,\n config.input_dim,\n config.hidden_d,\n config.dropout,\n true,\n );\n\n let attention = TemporalAttention::with_heads(\n config.hidden_d,\n config.attention_dim,\n config.attention_type,\n config.n_heads,\n );\n\n // Initialize output layer\n let mut rng = rand::thread_rng();\n let std = (2.0 / (config.hidden_d + config.n_classes) as f64).sqrt();\n let dist = Normal::new(0.0, std).unwrap();\n\n let output_weights = Array2::from_shape_fn(\n (config.n_classes, config.hidden_d),\n |_| rng.sample(dist),\n );\n let output_bias = Array1::zeros(config.n_classes);\n\n Self {\n config,\n bilinear,\n attention,\n output_weights,\n output_bias,\n }\n }\n\n /// Forward pass for a single sample\n ///\n /// # Arguments\n /// * `x` - Input tensor [seq_len, input_dim]\n /// * `training` - Whether in training mode\n ///\n /// # Returns\n /// * `(logits, attention_weights)` - Class logits and attention weights\n pub fn forward(\n &self,\n x: &Array2<f64>,\n training: bool,\n ) -> (Array1<f64>, Array1<f64>) {\n // Bilinear projection: [seq_len, input_dim] -> [hidden_t, hidden_d]\n let h = self.bilinear.forward(x, training);\n\n // Temporal attention: [hidden_t, hidden_d] -> [hidden_d]\n let (context, attention_weights) = self.attention.forward(&h, None);\n\n // Output layer: [hidden_d] -> [n_classes]\n let logits = self.output_weights.dot(&context) + &self.output_bias;\n\n (logits, attention_weights)\n }\n\n /// Forward pass for a batch\n ///\n /// # Arguments\n /// * `batch` - Batch of inputs [batch_size, seq_len, input_dim]\n /// * `training` - Whether in training mode\n ///\n /// # Returns\n /// * `(logits, attention_weights)` - [batch_size, n_classes] and [batch_size, hidden_t]\n pub fn forward_batch(\n &self,\n batch: &Array3<f64>,\n training: bool,\n ) -> (Array2<f64>, Array2<f64>) {\n let batch_size = batch.shape()[0];\n\n let mut logits = Array2::zeros((batch_size, self.config.n_classes));\n let mut all_weights = Array2::zeros((batch_size, self.config.hidden_t));\n\n for i in 0..batch_size {\n let x = batch.slice(ndarray::s![i, .., ..]).to_owned();\n let (sample_logits, weights) = self.forward(&x, training);\n\n logits.row_mut(i).assign(&sample_logits);\n all_weights.row_mut(i).assign(&weights);\n }\n\n (logits, all_weights)\n }\n\n /// Compute softmax probabilities from logits\n pub fn softmax(logits: &Array1<f64>) -> Array1<f64> {\n let max_val = logits.iter().cloned().fold(f64::NEG_INFINITY, f64::max);\n let exp_logits = logits.mapv(|v| (v - max_val).exp());\n let sum: f64 = exp_logits.sum();\n exp_logits / sum\n }\n\n /// Predict class probabilities for a single sample\n pub fn predict_proba(&self, x: &Array2<f64>) -> Array1<f64> {\n let (logits, _) = self.forward(x, false);\n Self::softmax(&logits)\n }\n\n /// Predict class for a single sample\n pub fn predict(&self, x: &Array2<f64>) -> usize {\n let proba = self.predict_proba(x);\n proba\n .iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .map(|(i, _)| i)\n .unwrap_or(0)\n }\n\n /// Predict classes for a batch\n pub fn predict_batch(&self, batch: &Array3<f64>) -> Vec<usize> {\n let (logits, _) = self.forward_batch(batch, false);\n\n (0..logits.nrows())\n .map(|i| {\n let row = logits.row(i);\n row.iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .map(|(i, _)| i)\n .unwrap_or(0)\n })\n .collect()\n }\n\n /// Compute cross-entropy loss\n pub fn compute_loss(&self, logits: &Array2<f64>, targets: &[i32]) -> f64 {\n let batch_size = logits.nrows();\n let mut total_loss = 0.0;\n\n for i in 0..batch_size {\n let row = logits.row(i);\n let proba = Self::softmax(&row.to_owned());\n let target = targets[i] as usize;\n\n // Cross-entropy: -log(p[target])\n let p = proba[target].max(1e-10);\n total_loss -= p.ln();\n }\n\n total_loss / batch_size as f64\n }\n\n /// Compute accuracy\n pub fn compute_accuracy(&self, predictions: &[usize], targets: &[i32]) -> f64 {\n let correct = predictions\n .iter()\n .zip(targets.iter())\n .filter(|(&pred, &target)| pred == target as usize)\n .count();\n\n correct as f64 / predictions.len() as f64\n }\n\n /// Get the model configuration\n pub fn config(&self) -> &TABLConfig {\n &self.config\n }\n\n /// Get the total number of parameters\n pub fn num_params(&self) -> usize {\n self.bilinear.num_params()\n + self.attention.num_params()\n + self.output_weights.len()\n + self.output_bias.len()\n }\n\n /// Get attention weights for interpretability\n pub fn get_attention_weights(&self, x: &Array2<f64>) -> Array1<f64> {\n let h = self.bilinear.forward(x, false);\n let (_, attention_weights) = self.attention.forward(&h, None);\n attention_weights\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_tabl_forward() {\n let config = TABLConfig::default();\n let model = TABLModel::new(config.clone());\n\n let x = Array2::from_shape_fn(\n (config.seq_len, config.input_dim),\n |_| rand::random::<f64>(),\n );\n\n let (logits, weights) = model.forward(&x, false);\n\n assert_eq!(logits.len(), config.n_classes);\n assert_eq!(weights.len(), config.hidden_t);\n }\n\n #[test]\n fn test_tabl_batch() {\n let config = TABLConfig::new(50, 6, 10, 16, 3);\n let model = TABLModel::new(config.clone());\n\n let batch = Array3::from_shape_fn(\n (16, config.seq_len, config.input_dim),\n |_| rand::random::<f64>(),\n );\n\n let (logits, weights) = model.forward_batch(&batch, false);\n\n assert_eq!(logits.shape(), &[16, 3]);\n assert_eq!(weights.shape(), &[16, 10]);\n }\n\n #[test]\n fn test_tabl_predict() {\n let config = TABLConfig::new(50, 6, 10, 16, 3);\n let model = TABLModel::new(config.clone());\n\n let x = Array2::from_shape_fn(\n (config.seq_len, config.input_dim),\n |_| rand::random::<f64>(),\n );\n\n let pred = model.predict(&x);\n assert!(pred < 3);\n\n let proba = model.predict_proba(&x);\n assert_eq!(proba.len(), 3);\n\n // Probabilities should sum to 1\n let sum: f64 = proba.sum();\n assert!((sum - 1.0).abs() < 1e-6);\n }\n\n #[test]\n fn test_tabl_loss() {\n let config = TABLConfig::new(50, 6, 10, 16, 3);\n let model = TABLModel::new(config);\n\n let logits = Array2::from_shape_fn((4, 3), |_| rand::random::<f64>());\n let targets = vec![0, 1, 2, 1];\n\n let loss = model.compute_loss(&logits, &targets);\n assert!(loss.is_finite());\n assert!(loss > 0.0);\n }\n\n #[test]\n fn test_tabl_accuracy() {\n let config = TABLConfig::new(50, 6, 10, 16, 3);\n let model = TABLModel::new(config);\n\n let predictions = vec![0, 1, 2, 1];\n let targets = vec![0, 1, 0, 1];\n\n let accuracy = model.compute_accuracy(&predictions, &targets);\n assert!((accuracy - 0.75).abs() < 1e-6);\n }\n\n #[test]\n fn test_tabl_with_multi_head_attention() {\n let config = TABLConfig::new(50, 6, 10, 16, 3)\n .with_attention_type(AttentionType::MultiHead)\n .with_heads(4);\n\n let model = TABLModel::new(config.clone());\n\n let x = Array2::from_shape_fn(\n (config.seq_len, config.input_dim),\n |_| rand::random::<f64>(),\n );\n\n let (logits, _) = model.forward(&x, false);\n assert_eq!(logits.len(), 3);\n }\n\n #[test]\n fn test_num_params() {\n let config = TABLConfig::new(50, 6, 10, 16, 3);\n let model = TABLModel::new(config);\n\n let params = model.num_params();\n assert!(params > 0);\n }\n}\n", | |
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| [2026-01-18T21:09:56.086Z] [INFO] { | |
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| { | |
| "type": "text", | |
| "text": "Now let me create the strategy module:" | |
| } | |
| ], | |
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| "uuid": "86cde5af-99e9-4771-b984-098de37c1cf1" | |
| } | |
| [2026-01-18T21:09:57.785Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-5-20251101", | |
| "id": "msg_01C8eai7HW1Dd9PTYtJRLQet", | |
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| { | |
| "type": "tool_use", | |
| "id": "toolu_01LXSJjCfYoG5miFqA32sEMq", | |
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| "input": { | |
| "file_path": "/tmp/gh-issue-solver-1768769379407/46_temporal_attention_networks/rust/src/strategy/mod.rs", | |
| "content": "//! Trading strategy and backtesting module\n//!\n//! This module provides signal generation and backtesting capabilities.\n\nmod backtest;\nmod signals;\n\npub use backtest::BacktestResult;\npub use signals::{SignalGenerator, TABLStrategy};\n" | |
| } | |
| } | |
| ], | |
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| "uuid": "1eb77754-c800-4dc9-8171-4b0cd93c9c5e" | |
| } | |
| [2026-01-18T21:10:16.659Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-5-20251101", | |
| "id": "msg_01C8eai7HW1Dd9PTYtJRLQet", | |
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| { | |
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| "file_path": "/tmp/gh-issue-solver-1768769379407/46_temporal_attention_networks/rust/src/strategy/signals.rs", | |
| "content": "//! Signal generation for trading strategies\n\nuse crate::model::TABLModel;\nuse ndarray::Array2;\n\n/// Trading signal\n#[derive(Debug, Clone, Copy, PartialEq)]\npub enum Signal {\n /// Buy signal\n Buy,\n /// Sell signal\n Sell,\n /// Hold position\n Hold,\n}\n\nimpl Signal {\n /// Convert to numeric value for calculations\n pub fn to_position(&self) -> f64 {\n match self {\n Signal::Buy => 1.0,\n Signal::Sell => -1.0,\n Signal::Hold => 0.0,\n }\n }\n\n /// Create from class prediction\n pub fn from_class(class: usize) -> Self {\n match class {\n 0 => Signal::Sell,\n 2 => Signal::Buy,\n _ => Signal::Hold,\n }\n }\n}\n\n/// Signal generator trait\npub trait SignalGenerator {\n /// Generate a trading signal from input data\n fn generate_signal(&self, x: &Array2<f64>) -> Signal;\n\n /// Generate signals for a batch\n fn generate_signals(&self, batch: &ndarray::Array3<f64>) -> Vec<Signal>;\n}\n\n/// TABL-based trading strategy\npub struct TABLStrategy {\n /// TABL model for predictions\n model: TABLModel,\n /// Probability threshold for generating signals\n threshold: f64,\n /// Minimum confidence for signal generation\n min_confidence: f64,\n}\n\nimpl TABLStrategy {\n /// Create a new TABL strategy\n pub fn new(model: TABLModel) -> Self {\n Self {\n model,\n threshold: 0.5,\n min_confidence: 0.4,\n }\n }\n\n /// Set the probability threshold\n pub fn with_threshold(mut self, threshold: f64) -> Self {\n self.threshold = threshold;\n self\n }\n\n /// Set the minimum confidence\n pub fn with_min_confidence(mut self, min_confidence: f64) -> Self {\n self.min_confidence = min_confidence;\n self\n }\n\n /// Get the underlying model\n pub fn model(&self) -> &TABLModel {\n &self.model\n }\n\n /// Get attention weights for interpretability\n pub fn get_attention_weights(&self, x: &Array2<f64>) -> ndarray::Array1<f64> {\n self.model.get_attention_weights(x)\n }\n}\n\nimpl SignalGenerator for TABLStrategy {\n fn generate_signal(&self, x: &Array2<f64>) -> Signal {\n let proba = self.model.predict_proba(x);\n\n // Find max probability and corresponding class\n let (max_class, max_prob) = proba\n .iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .unwrap();\n\n // Only generate signal if confidence is high enough\n if *max_prob < self.min_confidence {\n return Signal::Hold;\n }\n\n // Apply threshold for non-hold signals\n match max_class {\n 0 if proba[0] > self.threshold => Signal::Sell,\n 2 if proba[2] > self.threshold => Signal::Buy,\n _ => Signal::Hold,\n }\n }\n\n fn generate_signals(&self, batch: &ndarray::Array3<f64>) -> Vec<Signal> {\n let batch_size = batch.shape()[0];\n\n (0..batch_size)\n .map(|i| {\n let x = batch.slice(ndarray::s![i, .., ..]).to_owned();\n self.generate_signal(&x)\n })\n .collect()\n }\n}\n\n/// Simple threshold-based strategy for comparison\npub struct ThresholdStrategy {\n /// Buy threshold (positive return)\n buy_threshold: f64,\n /// Sell threshold (negative return)\n sell_threshold: f64,\n}\n\nimpl ThresholdStrategy {\n /// Create a new threshold strategy\n pub fn new(buy_threshold: f64, sell_threshold: f64) -> Self {\n Self {\n buy_threshold,\n sell_threshold,\n }\n }\n}\n\nimpl SignalGenerator for ThresholdStrategy {\n fn generate_signal(&self, x: &Array2<f64>) -> Signal {\n // Use the last row's first feature (assumed to be returns)\n let last_return = x[[x.nrows() - 1, 0]];\n\n if last_return > self.buy_threshold {\n Signal::Buy\n } else if last_return < self.sell_threshold {\n Signal::Sell\n } else {\n Signal::Hold\n }\n }\n\n fn generate_signals(&self, batch: &ndarray::Array3<f64>) -> Vec<Signal> {\n let batch_size = batch.shape()[0];\n\n (0..batch_size)\n .map(|i| {\n let x = batch.slice(ndarray::s![i, .., ..]).to_owned();\n self.generate_signal(&x)\n })\n .collect()\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n use crate::model::TABLConfig;\n\n #[test]\n fn test_signal_conversion() {\n assert_eq!(Signal::Buy.to_position(), 1.0);\n assert_eq!(Signal::Sell.to_position(), -1.0);\n assert_eq!(Signal::Hold.to_position(), 0.0);\n }\n\n #[test]\n fn test_signal_from_class() {\n assert_eq!(Signal::from_class(0), Signal::Sell);\n assert_eq!(Signal::from_class(1), Signal::Hold);\n assert_eq!(Signal::from_class(2), Signal::Buy);\n }\n\n #[test]\n fn test_tabl_strategy() {\n let config = TABLConfig::new(50, 6, 10, 16, 3);\n let model = TABLModel::new(config.clone());\n let strategy = TABLStrategy::new(model);\n\n let x = Array2::from_shape_fn(\n (config.seq_len, config.input_dim),\n |_| rand::random::<f64>(),\n );\n\n let signal = strategy.generate_signal(&x);\n assert!(matches!(signal, Signal::Buy | Signal::Sell | Signal::Hold));\n }\n\n #[test]\n fn test_threshold_strategy() {\n let strategy = ThresholdStrategy::new(0.001, -0.001);\n\n // Positive return\n let mut x = Array2::zeros((10, 6));\n x[[9, 0]] = 0.002;\n assert_eq!(strategy.generate_signal(&x), Signal::Buy);\n\n // Negative return\n x[[9, 0]] = -0.002;\n assert_eq!(strategy.generate_signal(&x), Signal::Sell);\n\n // Neutral\n x[[9, 0]] = 0.0005;\n assert_eq!(strategy.generate_signal(&x), Signal::Hold);\n }\n}\n" | |
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| [2026-01-18T21:10:49.060Z] [INFO] { | |
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| { | |
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| "file_path": "/tmp/gh-issue-solver-1768769379407/46_temporal_attention_networks/rust/src/strategy/backtest.rs", | |
| "content": "//! Backtesting framework\n\nuse crate::api::Kline;\nuse crate::strategy::signals::{Signal, SignalGenerator};\nuse ndarray::{Array2, Array3};\n\n/// Results from a backtest\n#[derive(Debug, Clone)]\npub struct BacktestResult {\n /// Total return\n pub total_return: f64,\n /// Annualized return\n pub annualized_return: f64,\n /// Sharpe ratio (annualized)\n pub sharpe_ratio: f64,\n /// Maximum drawdown\n pub max_drawdown: f64,\n /// Win rate (percentage of profitable trades)\n pub win_rate: f64,\n /// Total number of trades\n pub n_trades: usize,\n /// Profit factor (gross profit / gross loss)\n pub profit_factor: f64,\n /// List of signals generated\n pub signals: Vec<Signal>,\n /// Equity curve\n pub equity_curve: Vec<f64>,\n /// Daily returns\n pub returns: Vec<f64>,\n}\n\nimpl BacktestResult {\n /// Print a summary of the backtest results\n pub fn summary(&self) -> String {\n format!(\n \"Backtest Results:\\n\\\n ─────────────────────────\\n\\\n Total Return: {:>10.2}%\\n\\\n Annual Return: {:>10.2}%\\n\\\n Sharpe Ratio: {:>10.2}\\n\\\n Max Drawdown: {:>10.2}%\\n\\\n Win Rate: {:>10.2}%\\n\\\n Trades: {:>10}\\n\\\n Profit Factor: {:>10.2}\",\n self.total_return * 100.0,\n self.annualized_return * 100.0,\n self.sharpe_ratio,\n self.max_drawdown * 100.0,\n self.win_rate * 100.0,\n self.n_trades,\n self.profit_factor,\n )\n }\n}\n\n/// Backtest engine\npub struct BacktestEngine {\n /// Initial capital\n initial_capital: f64,\n /// Transaction cost (as fraction)\n transaction_cost: f64,\n /// Slippage (as fraction)\n slippage: f64,\n /// Risk-free rate for Sharpe ratio calculation\n risk_free_rate: f64,\n /// Trading periods per year (for annualization)\n periods_per_year: f64,\n}\n\nimpl Default for BacktestEngine {\n fn default() -> Self {\n Self::new()\n }\n}\n\nimpl BacktestEngine {\n /// Create a new backtest engine with default parameters\n pub fn new() -> Self {\n Self {\n initial_capital: 10000.0,\n transaction_cost: 0.001, // 0.1%\n slippage: 0.0005, // 0.05%\n risk_free_rate: 0.02, // 2% annual\n periods_per_year: 365.0 * 24.0, // Hourly data\n }\n }\n\n /// Set initial capital\n pub fn with_capital(mut self, capital: f64) -> Self {\n self.initial_capital = capital;\n self\n }\n\n /// Set transaction cost\n pub fn with_transaction_cost(mut self, cost: f64) -> Self {\n self.transaction_cost = cost;\n self\n }\n\n /// Set slippage\n pub fn with_slippage(mut self, slippage: f64) -> Self {\n self.slippage = slippage;\n self\n }\n\n /// Set periods per year\n pub fn with_periods_per_year(mut self, periods: f64) -> Self {\n self.periods_per_year = periods;\n self\n }\n\n /// Run backtest with a signal generator and price data\n pub fn run<S: SignalGenerator>(\n &self,\n strategy: &S,\n data: &Array3<f64>,\n prices: &[f64],\n ) -> BacktestResult {\n let n_samples = data.shape()[0];\n assert_eq!(n_samples, prices.len(), \"Data and prices must have same length\");\n\n // Generate signals\n let signals = strategy.generate_signals(data);\n\n // Calculate returns and equity\n let mut equity = vec![self.initial_capital];\n let mut returns = Vec::with_capacity(n_samples);\n let mut position = 0.0;\n let mut trades = Vec::new();\n let mut current_trade_entry = 0.0;\n\n for i in 1..n_samples {\n let price_return = (prices[i] - prices[i - 1]) / prices[i - 1];\n let signal = signals[i - 1];\n\n // Calculate position change\n let new_position = signal.to_position();\n let position_change = (new_position - position).abs();\n\n // Transaction costs\n let cost = if position_change > 0.0 {\n (self.transaction_cost + self.slippage) * position_change\n } else {\n 0.0\n };\n\n // Calculate return\n let strategy_return = position * price_return - cost;\n returns.push(strategy_return);\n\n // Update equity\n let new_equity = equity.last().unwrap() * (1.0 + strategy_return);\n equity.push(new_equity);\n\n // Track trades\n if position_change > 0.0 && position != 0.0 {\n // Closing a position\n let trade_return = (prices[i] - current_trade_entry) / current_trade_entry * position;\n trades.push(trade_return);\n }\n if new_position != 0.0 && position == 0.0 {\n // Opening a position\n current_trade_entry = prices[i];\n }\n\n position = new_position;\n }\n\n // Close final position if any\n if position != 0.0 {\n let trade_return = (prices[n_samples - 1] - current_trade_entry)\n / current_trade_entry * position;\n trades.push(trade_return);\n }\n\n // Calculate metrics\n let total_return = (equity.last().unwrap() / self.initial_capital) - 1.0;\n let n_periods = returns.len() as f64;\n let annualized_return = (1.0 + total_return).powf(self.periods_per_year / n_periods) - 1.0;\n\n // Sharpe ratio\n let mean_return = returns.iter().sum::<f64>() / n_periods;\n let variance = returns\n .iter()\n .map(|r| (r - mean_return).powi(2))\n .sum::<f64>()\n / n_periods;\n let std = variance.sqrt();\n let risk_free_per_period = self.risk_free_rate / self.periods_per_year;\n let sharpe_ratio = if std > 0.0 {\n (mean_return - risk_free_per_period) / std * self.periods_per_year.sqrt()\n } else {\n 0.0\n };\n\n // Maximum drawdown\n let mut max_equity = self.initial_capital;\n let mut max_drawdown = 0.0;\n for e in &equity {\n max_equity = max_equity.max(*e);\n let drawdown = (max_equity - e) / max_equity;\n max_drawdown = max_drawdown.max(drawdown);\n }\n\n // Win rate and profit factor\n let winning_trades: Vec<_> = trades.iter().filter(|&&r| r > 0.0).collect();\n let losing_trades: Vec<_> = trades.iter().filter(|&&r| r < 0.0).collect();\n\n let win_rate = if !trades.is_empty() {\n winning_trades.len() as f64 / trades.len() as f64\n } else {\n 0.0\n };\n\n let gross_profit: f64 = winning_trades.iter().map(|&&r| r).sum();\n let gross_loss: f64 = losing_trades.iter().map(|&&r| r.abs()).sum();\n let profit_factor = if gross_loss > 0.0 {\n gross_profit / gross_loss\n } else if gross_profit > 0.0 {\n f64::INFINITY\n } else {\n 0.0\n };\n\n BacktestResult {\n total_return,\n annualized_return,\n sharpe_ratio,\n max_drawdown,\n win_rate,\n n_trades: trades.len(),\n profit_factor,\n signals,\n equity_curve: equity,\n returns,\n }\n }\n\n /// Run backtest with kline data\n pub fn run_with_klines<S: SignalGenerator>(\n &self,\n strategy: &S,\n data: &Array3<f64>,\n klines: &[Kline],\n ) -> BacktestResult {\n let prices: Vec<f64> = klines.iter().map(|k| k.close).collect();\n self.run(strategy, data, &prices)\n }\n}\n\n/// Calculate buy-and-hold benchmark\npub fn calculate_buy_and_hold(prices: &[f64]) -> f64 {\n if prices.is_empty() || prices[0] == 0.0 {\n return 0.0;\n }\n (prices.last().unwrap() - prices[0]) / prices[0]\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n use crate::model::{TABLConfig, TABLModel};\n use crate::strategy::signals::TABLStrategy;\n\n #[test]\n fn test_backtest_engine() {\n let config = TABLConfig::new(20, 4, 5, 8, 3);\n let model = TABLModel::new(config.clone());\n let strategy = TABLStrategy::new(model);\n\n // Create synthetic data\n let n_samples = 100;\n let data = Array3::from_shape_fn(\n (n_samples, config.seq_len, config.input_dim),\n |_| rand::random::<f64>(),\n );\n let prices: Vec<f64> = (0..n_samples)\n .map(|i| 100.0 + (i as f64 * 0.01).sin() * 10.0)\n .collect();\n\n let engine = BacktestEngine::new();\n let result = engine.run(&strategy, &data, &prices);\n\n assert_eq!(result.equity_curve.len(), n_samples);\n assert_eq!(result.returns.len(), n_samples - 1);\n assert!(result.max_drawdown >= 0.0 && result.max_drawdown <= 1.0);\n assert!(result.win_rate >= 0.0 && result.win_rate <= 1.0);\n }\n\n #[test]\n fn test_buy_and_hold() {\n let prices = vec![100.0, 105.0, 103.0, 110.0, 115.0];\n let bnh = calculate_buy_and_hold(&prices);\n assert!((bnh - 0.15).abs() < 1e-6);\n }\n\n #[test]\n fn test_backtest_result_summary() {\n let result = BacktestResult {\n total_return: 0.25,\n annualized_return: 0.50,\n sharpe_ratio: 1.5,\n max_drawdown: 0.10,\n win_rate: 0.55,\n n_trades: 50,\n profit_factor: 1.8,\n signals: vec![],\n equity_curve: vec![],\n returns: vec![],\n };\n\n let summary = result.summary();\n assert!(summary.contains(\"25.00%\")); // total return\n assert!(summary.contains(\"50\")); // trades\n }\n}\n" | |
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| "content": "//! Trading strategy and backtesting module\n//!\n//! This module provides signal generation and backtesting capabilities.\n\nmod backtest;\nmod signals;\n\npub use backtest::BacktestResult;\npub use signals::{SignalGenerator, TABLStrategy};\n", | |
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| "content": "//! Signal generation for trading strategies\n\nuse crate::model::TABLModel;\nuse ndarray::Array2;\n\n/// Trading signal\n#[derive(Debug, Clone, Copy, PartialEq)]\npub enum Signal {\n /// Buy signal\n Buy,\n /// Sell signal\n Sell,\n /// Hold position\n Hold,\n}\n\nimpl Signal {\n /// Convert to numeric value for calculations\n pub fn to_position(&self) -> f64 {\n match self {\n Signal::Buy => 1.0,\n Signal::Sell => -1.0,\n Signal::Hold => 0.0,\n }\n }\n\n /// Create from class prediction\n pub fn from_class(class: usize) -> Self {\n match class {\n 0 => Signal::Sell,\n 2 => Signal::Buy,\n _ => Signal::Hold,\n }\n }\n}\n\n/// Signal generator trait\npub trait SignalGenerator {\n /// Generate a trading signal from input data\n fn generate_signal(&self, x: &Array2<f64>) -> Signal;\n\n /// Generate signals for a batch\n fn generate_signals(&self, batch: &ndarray::Array3<f64>) -> Vec<Signal>;\n}\n\n/// TABL-based trading strategy\npub struct TABLStrategy {\n /// TABL model for predictions\n model: TABLModel,\n /// Probability threshold for generating signals\n threshold: f64,\n /// Minimum confidence for signal generation\n min_confidence: f64,\n}\n\nimpl TABLStrategy {\n /// Create a new TABL strategy\n pub fn new(model: TABLModel) -> Self {\n Self {\n model,\n threshold: 0.5,\n min_confidence: 0.4,\n }\n }\n\n /// Set the probability threshold\n pub fn with_threshold(mut self, threshold: f64) -> Self {\n self.threshold = threshold;\n self\n }\n\n /// Set the minimum confidence\n pub fn with_min_confidence(mut self, min_confidence: f64) -> Self {\n self.min_confidence = min_confidence;\n self\n }\n\n /// Get the underlying model\n pub fn model(&self) -> &TABLModel {\n &self.model\n }\n\n /// Get attention weights for interpretability\n pub fn get_attention_weights(&self, x: &Array2<f64>) -> ndarray::Array1<f64> {\n self.model.get_attention_weights(x)\n }\n}\n\nimpl SignalGenerator for TABLStrategy {\n fn generate_signal(&self, x: &Array2<f64>) -> Signal {\n let proba = self.model.predict_proba(x);\n\n // Find max probability and corresponding class\n let (max_class, max_prob) = proba\n .iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .unwrap();\n\n // Only generate signal if confidence is high enough\n if *max_prob < self.min_confidence {\n return Signal::Hold;\n }\n\n // Apply threshold for non-hold signals\n match max_class {\n 0 if proba[0] > self.threshold => Signal::Sell,\n 2 if proba[2] > self.threshold => Signal::Buy,\n _ => Signal::Hold,\n }\n }\n\n fn generate_signals(&self, batch: &ndarray::Array3<f64>) -> Vec<Signal> {\n let batch_size = batch.shape()[0];\n\n (0..batch_size)\n .map(|i| {\n let x = batch.slice(ndarray::s![i, .., ..]).to_owned();\n self.generate_signal(&x)\n })\n .collect()\n }\n}\n\n/// Simple threshold-based strategy for comparison\npub struct ThresholdStrategy {\n /// Buy threshold (positive return)\n buy_threshold: f64,\n /// Sell threshold (negative return)\n sell_threshold: f64,\n}\n\nimpl ThresholdStrategy {\n /// Create a new threshold strategy\n pub fn new(buy_threshold: f64, sell_threshold: f64) -> Self {\n Self {\n buy_threshold,\n sell_threshold,\n }\n }\n}\n\nimpl SignalGenerator for ThresholdStrategy {\n fn generate_signal(&self, x: &Array2<f64>) -> Signal {\n // Use the last row's first feature (assumed to be returns)\n let last_return = x[[x.nrows() - 1, 0]];\n\n if last_return > self.buy_threshold {\n Signal::Buy\n } else if last_return < self.sell_threshold {\n Signal::Sell\n } else {\n Signal::Hold\n }\n }\n\n fn generate_signals(&self, batch: &ndarray::Array3<f64>) -> Vec<Signal> {\n let batch_size = batch.shape()[0];\n\n (0..batch_size)\n .map(|i| {\n let x = batch.slice(ndarray::s![i, .., ..]).to_owned();\n self.generate_signal(&x)\n })\n |
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