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January 21, 2026 19:22
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Solution draft log for https://github.com/suenot/machine-learning-for-trading/pull/421
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| # Solve.mjs Log - 2026-01-21T18:44:52.375Z | |
| [2026-01-21T18:44:52.376Z] [INFO] 📁 Log file: /home/hive/solve-2026-01-21T18-44-52-375Z.log | |
| [2026-01-21T18:44:52.377Z] [INFO] (All output will be logged here) | |
| [2026-01-21T18:44:53.058Z] [INFO] | |
| [2026-01-21T18:44:53.059Z] [INFO] 🚀 solve v1.9.0 | |
| [2026-01-21T18:44:53.059Z] [INFO] 🔧 Raw command executed: | |
| [2026-01-21T18:44:53.060Z] [INFO] /home/hive/.nvm/versions/node/v20.20.0/bin/node /home/hive/.bun/bin/solve https://github.com/suenot/machine-learning-for-trading/issues/124 --model opus --attach-logs --verbose --no-tool-check --auto-resume-on-limit-reset --tokens-budget-stats | |
| [2026-01-21T18:44:53.060Z] [INFO] | |
| [2026-01-21T18:44:53.077Z] [INFO] | |
| [2026-01-21T18:44:53.078Z] [WARNING] ⚠️ SECURITY WARNING: --attach-logs is ENABLED | |
| [2026-01-21T18:44:53.079Z] [INFO] | |
| [2026-01-21T18:44:53.079Z] [INFO] This option will upload the complete solution draft log file to the Pull Request. | |
| [2026-01-21T18:44:53.080Z] [INFO] The log may contain sensitive information such as: | |
| [2026-01-21T18:44:53.080Z] [INFO] • API keys, tokens, or secrets | |
| [2026-01-21T18:44:53.080Z] [INFO] • File paths and directory structures | |
| [2026-01-21T18:44:53.080Z] [INFO] • Command outputs and error messages | |
| [2026-01-21T18:44:53.080Z] [INFO] • Internal system information | |
| [2026-01-21T18:44:53.081Z] [INFO] | |
| [2026-01-21T18:44:53.081Z] [INFO] ⚠️ DO NOT use this option with public repositories or if the log | |
| [2026-01-21T18:44:53.081Z] [INFO] might contain sensitive data that should not be shared publicly. | |
| [2026-01-21T18:44:53.081Z] [INFO] | |
| [2026-01-21T18:44:53.081Z] [INFO] Continuing in 5 seconds... (Press Ctrl+C to abort) | |
| [2026-01-21T18:44:53.081Z] [INFO] | |
| [2026-01-21T18:44:58.087Z] [INFO] | |
| [2026-01-21T18:44:58.110Z] [INFO] 💾 Disk space check: 5698MB available (2048MB required) ✅ | |
| [2026-01-21T18:44:58.112Z] [INFO] 🧠 Memory check: 9444MB available, swap: 4095MB (1MB used), total: 13538MB (256MB required) ✅ | |
| [2026-01-21T18:44:58.134Z] [INFO] ⏩ Skipping tool connection validation (dry-run mode or skip-tool-connection-check enabled) | |
| [2026-01-21T18:44:58.135Z] [INFO] ⏩ Skipping GitHub authentication check (dry-run mode or skip-tool-connection-check enabled) | |
| [2026-01-21T18:44:58.135Z] [INFO] 📋 URL validation: | |
| [2026-01-21T18:44:58.135Z] [INFO] Input URL: https://github.com/suenot/machine-learning-for-trading/issues/124 | |
| [2026-01-21T18:44:58.135Z] [INFO] Is Issue URL: true | |
| [2026-01-21T18:44:58.135Z] [INFO] Is PR URL: false | |
| [2026-01-21T18:44:58.136Z] [INFO] 🔍 Checking repository access for auto-fork... | |
| [2026-01-21T18:44:58.978Z] [INFO] Repository visibility: public | |
| [2026-01-21T18:44:58.979Z] [INFO] ✅ Auto-fork: No write access detected, enabling fork mode | |
| [2026-01-21T18:44:58.980Z] [INFO] ✅ Repository access check: Skipped (fork mode enabled) | |
| [2026-01-21T18:44:59.374Z] [INFO] Repository visibility: public | |
| [2026-01-21T18:44:59.375Z] [INFO] Auto-cleanup default: false (repository is public) | |
| [2026-01-21T18:44:59.378Z] [INFO] 🔍 Auto-continue enabled: Checking for existing PRs for issue #124... | |
| [2026-01-21T18:45:00.122Z] [INFO] 🔍 Fork mode: Checking for existing branches in konard/suenot-machine-learning-for-trading... | |
| [2026-01-21T18:45:01.495Z] [INFO] 📝 No existing PRs found for issue #124 - creating new PR | |
| [2026-01-21T18:45:01.499Z] [INFO] 📝 Issue mode: Working with issue #124 | |
| [2026-01-21T18:45:01.509Z] [INFO] | |
| Creating temporary directory: /tmp/gh-issue-solver-1769021101508 | |
| [2026-01-21T18:45:01.511Z] [INFO] | |
| 🍴 Fork mode: ENABLED | |
| [2026-01-21T18:45:01.512Z] [INFO] Checking fork status... | |
| [2026-01-21T18:45:01.973Z] [INFO] 🔍 Detecting fork conflicts... | |
| [2026-01-21T18:45:03.741Z] [INFO] ✅ No fork conflict: Safe to proceed | |
| [2026-01-21T18:45:04.201Z] [INFO] ✅ Fork exists: konard/suenot-machine-learning-for-trading | |
| [2026-01-21T18:45:04.202Z] [INFO] 🔍 Validating fork parent... | |
| [2026-01-21T18:45:04.723Z] [INFO] ✅ Fork parent validated: suenot/machine-learning-for-trading | |
| [2026-01-21T18:45:04.725Z] [INFO] | |
| 📥 Cloning repository: konard/suenot-machine-learning-for-trading | |
| [2026-01-21T18:47:21.480Z] [INFO] ✅ Cloned to: /tmp/gh-issue-solver-1769021101508 | |
| [2026-01-21T18:47:21.548Z] [INFO] 🔗 Setting upstream: suenot/machine-learning-for-trading | |
| [2026-01-21T18:47:21.611Z] [INFO] ℹ️ Upstream exists: Using existing upstream remote | |
| [2026-01-21T18:47:21.612Z] [INFO] 🔄 Fetching upstream... | |
| [2026-01-21T18:47:22.017Z] [INFO] ✅ Upstream fetched: Successfully | |
| [2026-01-21T18:47:22.019Z] [INFO] 🔄 Syncing default branch... | |
| [2026-01-21T18:47:22.531Z] [INFO] ℹ️ Default branch: main | |
| [2026-01-21T18:47:23.326Z] [INFO] ✅ Default branch synced: with upstream/main | |
| [2026-01-21T18:47:23.326Z] [INFO] 🔄 Pushing to fork: main branch | |
| [2026-01-21T18:47:24.279Z] [INFO] ✅ Fork updated: Default branch pushed to fork | |
| [2026-01-21T18:47:24.462Z] [INFO] | |
| 📌 Default branch: main | |
| [2026-01-21T18:47:24.557Z] [INFO] | |
| 🌿 Creating branch: issue-124-ba7373c4f0fd from main (default) | |
| [2026-01-21T18:47:24.637Z] [INFO] 🔍 Verifying: Branch creation... | |
| [2026-01-21T18:47:24.699Z] [INFO] ✅ Branch created: issue-124-ba7373c4f0fd | |
| [2026-01-21T18:47:24.699Z] [INFO] ✅ Current branch: issue-124-ba7373c4f0fd | |
| [2026-01-21T18:47:24.699Z] [INFO] Branch operation: Create new branch | |
| [2026-01-21T18:47:24.699Z] [INFO] Branch verification: Matches expected | |
| [2026-01-21T18:47:24.702Z] [INFO] | |
| 🚀 Auto PR creation: ENABLED | |
| [2026-01-21T18:47:24.703Z] [INFO] Creating: Initial commit and draft PR... | |
| [2026-01-21T18:47:24.703Z] [INFO] | |
| [2026-01-21T18:47:24.772Z] [INFO] Using CLAUDE.md mode (--claude-file=true, --gitkeep-file=false, --auto-gitkeep-file=true) | |
| [2026-01-21T18:47:24.773Z] [INFO] 📝 Creating: CLAUDE.md with task details | |
| [2026-01-21T18:47:24.774Z] [INFO] Issue URL from argv['issue-url']: https://github.com/suenot/machine-learning-for-trading/issues/124 | |
| [2026-01-21T18:47:24.775Z] [INFO] Issue URL from argv._[0]: undefined | |
| [2026-01-21T18:47:24.775Z] [INFO] Final issue URL: https://github.com/suenot/machine-learning-for-trading/issues/124 | |
| [2026-01-21T18:47:24.776Z] [INFO] ✅ File created: CLAUDE.md | |
| [2026-01-21T18:47:24.776Z] [INFO] 📦 Adding file: To git staging | |
| [2026-01-21T18:47:24.953Z] [INFO] Git status after add: A CLAUDE.md | |
| [2026-01-21T18:47:24.954Z] [INFO] 📝 Creating commit: With CLAUDE.md file | |
| [2026-01-21T18:47:25.034Z] [INFO] ✅ Commit created: Successfully with CLAUDE.md | |
| [2026-01-21T18:47:25.035Z] [INFO] Commit output: [issue-124-ba7373c4f0fd 168f0a0d] Initial commit with task details | |
| 1 file changed, 7 insertions(+) | |
| create mode 100644 CLAUDE.md | |
| [2026-01-21T18:47:25.100Z] [INFO] Commit hash: 168f0a0... | |
| [2026-01-21T18:47:25.161Z] [INFO] Latest commit: 168f0a0d Initial commit with task details | |
| [2026-01-21T18:47:25.269Z] [INFO] Git status: clean | |
| [2026-01-21T18:47:25.327Z] [INFO] Remotes: origin https://github.com/konard/suenot-machine-learning-for-trading.git (fetch) | |
| [2026-01-21T18:47:25.391Z] [INFO] Branch info: * issue-124-ba7373c4f0fd 168f0a0d [origin/main: ahead 1] Initial commit with task details | |
| main 687e85eb [origin/main] Merge pull request #414 from konard/issue-118-e5d3ba7544f4 | |
| [2026-01-21T18:47:25.392Z] [INFO] 📤 Pushing branch: To remote repository... | |
| [2026-01-21T18:47:25.392Z] [INFO] Push command: git push -u origin issue-124-ba7373c4f0fd | |
| [2026-01-21T18:47:26.409Z] [INFO] Push exit code: 0 | |
| [2026-01-21T18:47:26.410Z] [INFO] Push output: remote: | |
| remote: Create a pull request for 'issue-124-ba7373c4f0fd' on GitHub by visiting: | |
| remote: https://github.com/konard/suenot-machine-learning-for-trading/pull/new/issue-124-ba7373c4f0fd | |
| remote: | |
| To https://github.com/konard/suenot-machine-learning-for-trading.git | |
| * [new branch] issue-124-ba7373c4f0fd -> issue-124-ba7373c4f0fd | |
| branch 'issue-124-ba7373c4f0fd' set up to track 'origin/issue-124-ba7373c4f0fd'. | |
| [2026-01-21T18:47:26.410Z] [INFO] ✅ Branch pushed: Successfully to remote | |
| [2026-01-21T18:47:26.410Z] [INFO] Push output: remote: | |
| remote: Create a pull request for 'issue-124-ba7373c4f0fd' on GitHub by visiting: | |
| remote: https://github.com/konard/suenot-machine-learning-for-trading/pull/new/issue-124-ba7373c4f0fd | |
| remote: | |
| To https://github.com/konard/suenot-machine-learning-for-trading.git | |
| * [new branch] issue-124-ba7373c4f0fd -> issue-124-ba7373c4f0fd | |
| branch 'issue-124-ba7373c4f0fd' set up to track 'origin/issue-124-ba7373c4f0fd'. | |
| [2026-01-21T18:47:26.410Z] [INFO] Waiting for GitHub to sync... | |
| [2026-01-21T18:47:29.109Z] [INFO] Compare API check: 1 commit(s) ahead of main | |
| [2026-01-21T18:47:29.110Z] [INFO] GitHub compare API ready: 1 commit(s) found | |
| [2026-01-21T18:47:29.508Z] [INFO] Branch verified on GitHub: issue-124-ba7373c4f0fd | |
| [2026-01-21T18:47:29.920Z] [INFO] Remote commit SHA: 168f0a0... | |
| [2026-01-21T18:47:29.920Z] [INFO] 📋 Getting issue: Title from GitHub... | |
| [2026-01-21T18:47:30.340Z] [INFO] Issue title: "Chapter 87: task agnostic trading" | |
| [2026-01-21T18:47:30.342Z] [INFO] 👤 Getting user: Current GitHub account... | |
| [2026-01-21T18:47:30.748Z] [INFO] Current user: konard | |
| [2026-01-21T18:47:31.061Z] [INFO] User is not a collaborator (will skip assignment) | |
| [2026-01-21T18:47:31.063Z] [INFO] User is not a collaborator (will skip assignment) | |
| [2026-01-21T18:47:31.065Z] [INFO] 🔄 Fetching: Latest main branch... | |
| [2026-01-21T18:47:31.489Z] [INFO] ✅ Base updated: Fetched latest main | |
| [2026-01-21T18:47:31.489Z] [INFO] 🔍 Checking: Commits between branches... | |
| [2026-01-21T18:47:31.556Z] [INFO] Commits ahead of origin/main: 1 | |
| [2026-01-21T18:47:31.556Z] [INFO] ✅ Commits found: 1 commit(s) ahead | |
| [2026-01-21T18:47:31.557Z] [INFO] 🔀 Creating PR: Draft pull request... | |
| [2026-01-21T18:47:31.558Z] [INFO] 🎯 Target branch: main (default) | |
| [2026-01-21T18:47:31.558Z] [INFO] PR Title: [WIP] Chapter 87: task agnostic trading | |
| [2026-01-21T18:47:31.559Z] [INFO] Base branch: main | |
| [2026-01-21T18:47:31.559Z] [INFO] Head branch: issue-124-ba7373c4f0fd | |
| [2026-01-21T18:47:31.559Z] [INFO] Assignee: konard | |
| [2026-01-21T18:47:31.561Z] [INFO] PR Body: | |
| ## 🤖 AI-Powered Solution Draft | |
| This pull request is being automatically generated to solve issue suenot/machine-learning-for-trading#124. | |
| ### 📋 Issue Reference | |
| Fixes suenot/machine-learning-for-trading#124 | |
| ### 🚧 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* | |
| [2026-01-21T18:47:31.565Z] [INFO] Command: cd "/tmp/gh-issue-solver-1769021101508" && gh pr create --draft --title "$(cat '/tmp/pr-title-1769021251564.txt')" --body-file "/tmp/pr-body-1769021251564.md" --base main --head konard:issue-124-ba7373c4f0fd --repo suenot/machine-learning-for-trading | |
| [2026-01-21T18:47:33.658Z] [INFO] 🔍 Verifying: PR creation... | |
| [2026-01-21T18:47:34.062Z] [INFO] ✅ Verification: PR exists on GitHub | |
| [2026-01-21T18:47:34.062Z] [INFO] ✅ PR created: #418 | |
| [2026-01-21T18:47:34.063Z] [INFO] 📍 PR URL: https://github.com/suenot/machine-learning-for-trading/pull/418 | |
| [2026-01-21T18:47:34.067Z] [INFO] ℹ️ Note: Could not assign (no permission) | |
| [2026-01-21T18:47:34.068Z] [INFO] 🔗 Linking: Issue #124 to PR #418... | |
| [2026-01-21T18:47:34.464Z] [INFO] Issue node ID: I_kwDOQvqap87j_OFE | |
| [2026-01-21T18:47:34.901Z] [INFO] PR node ID: PR_kwDOQvqap86-hVXU | |
| [2026-01-21T18:47:35.310Z] [INFO] | |
| [2026-01-21T18:47:35.311Z] [WARNING] ⚠️ ISSUE LINK MISSING: PR not linked to issue | |
| [2026-01-21T18:47:35.311Z] [INFO] | |
| [2026-01-21T18:47:35.311Z] [WARNING] The PR was created from a fork but wasn't linked to the issue. | |
| [2026-01-21T18:47:35.311Z] [WARNING] Expected: "Fixes suenot/machine-learning-for-trading#124" in PR body | |
| [2026-01-21T18:47:35.312Z] [INFO] | |
| [2026-01-21T18:47:35.312Z] [WARNING] To fix manually: | |
| [2026-01-21T18:47:35.312Z] [WARNING] 1. Edit the PR description at: https://github.com/suenot/machine-learning-for-trading/pull/418 | |
| [2026-01-21T18:47:35.313Z] [WARNING] 2. Add this line: Fixes suenot/machine-learning-for-trading#124 | |
| [2026-01-21T18:47:35.313Z] [INFO] | |
| [2026-01-21T18:47:35.652Z] [INFO] 👤 Current user: konard | |
| [2026-01-21T18:47:35.653Z] [INFO] | |
| 📊 Comment counting conditions: | |
| [2026-01-21T18:47:35.653Z] [INFO] prNumber: 418 | |
| [2026-01-21T18:47:35.655Z] [INFO] branchName: issue-124-ba7373c4f0fd | |
| [2026-01-21T18:47:35.655Z] [INFO] isContinueMode: false | |
| [2026-01-21T18:47:35.656Z] [INFO] Will count comments: true | |
| [2026-01-21T18:47:35.657Z] [INFO] 💬 Counting comments: Checking for new comments since last commit... | |
| [2026-01-21T18:47:35.658Z] [INFO] PR #418 on branch: issue-124-ba7373c4f0fd | |
| [2026-01-21T18:47:35.658Z] [INFO] Owner/Repo: suenot/machine-learning-for-trading | |
| [2026-01-21T18:47:36.133Z] [INFO] 📅 Last commit time (from API): 2026-01-21T18:47:25.000Z | |
| [2026-01-21T18:47:37.155Z] [INFO] 💬 New PR comments: 0 | |
| [2026-01-21T18:47:37.156Z] [INFO] 💬 New PR review comments: 0 | |
| [2026-01-21T18:47:37.156Z] [INFO] 💬 New issue comments: 0 | |
| [2026-01-21T18:47:37.156Z] [INFO] Total new comments: 0 | |
| [2026-01-21T18:47:37.157Z] [INFO] Comment lines to add: No (saving tokens) | |
| [2026-01-21T18:47:37.159Z] [INFO] PR review comments fetched: 0 | |
| [2026-01-21T18:47:37.159Z] [INFO] PR conversation comments fetched: 0 | |
| [2026-01-21T18:47:37.160Z] [INFO] Total PR comments checked: 0 | |
| [2026-01-21T18:47:40.040Z] [INFO] Feedback info will be added to prompt: | |
| [2026-01-21T18:47:40.041Z] [INFO] - Pull request description was edited after last commit | |
| [2026-01-21T18:47:40.041Z] [INFO] 📅 Getting timestamps: From GitHub servers... | |
| [2026-01-21T18:47:40.424Z] [INFO] 📝 Issue updated: 2026-01-18T13:32:34.000Z | |
| [2026-01-21T18:47:40.728Z] [INFO] 💬 Comments: None found | |
| [2026-01-21T18:47:41.212Z] [INFO] 🔀 Recent PR: 2026-01-21T18:47:32.000Z | |
| [2026-01-21T18:47:41.212Z] [INFO] | |
| ✅ Reference time: 2026-01-21T18:47:32.000Z | |
| [2026-01-21T18:47:41.213Z] [INFO] | |
| 🔍 Checking for uncommitted changes to include as feedback... | |
| [2026-01-21T18:47:41.302Z] [INFO] ✅ No uncommitted changes found | |
| [2026-01-21T18:47:43.802Z] [INFO] 🎭 Playwright MCP detected - enabling browser automation hints | |
| [2026-01-21T18:47:43.807Z] [INFO] | |
| 📝 Final prompt structure: | |
| [2026-01-21T18:47:43.807Z] [INFO] Characters: 432 | |
| [2026-01-21T18:47:43.807Z] [INFO] System prompt characters: 11977 | |
| [2026-01-21T18:47:43.807Z] [INFO] Feedback info: Included | |
| [2026-01-21T18:47:43.809Z] [INFO] | |
| 🤖 Executing Claude: OPUS | |
| [2026-01-21T18:47:43.809Z] [INFO] Model: opus | |
| [2026-01-21T18:47:43.809Z] [INFO] Working directory: /tmp/gh-issue-solver-1769021101508 | |
| [2026-01-21T18:47:43.809Z] [INFO] Branch: issue-124-ba7373c4f0fd | |
| [2026-01-21T18:47:43.809Z] [INFO] Prompt length: 432 chars | |
| [2026-01-21T18:47:43.810Z] [INFO] System prompt length: 11977 chars | |
| [2026-01-21T18:47:43.810Z] [INFO] Feedback info included: Yes (1 lines) | |
| [2026-01-21T18:47:43.875Z] [INFO] 📈 System resources before execution: | |
| [2026-01-21T18:47:43.876Z] [INFO] Memory: MemFree: 4027468 kB | |
| [2026-01-21T18:47:43.876Z] [INFO] Load: 1.92 1.12 0.73 2/562 1087053 | |
| [2026-01-21T18:47:43.876Z] [INFO] | |
| 📝 Raw command: | |
| [2026-01-21T18:47:43.877Z] [INFO] (cd "/tmp/gh-issue-solver-1769021101508" && 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/124 | |
| Your prepared branch: issue-124-ba7373c4f0fd | |
| Your prepared working directory: /tmp/gh-issue-solver-1769021101508 | |
| Your prepared Pull Request: https://github.com/suenot/machine-learning-for-trading/pull/418 | |
| 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-124-ba7373c4f0fd --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/124. | |
| - 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/418/comments --paginate | |
| 2. PR conversation comments (general discussion): gh api repos/suenot/machine-learning-for-trading/issues/418/comments --paginate | |
| 3. PR reviews (approve/request changes): gh api repos/suenot/machine-learning-for-trading/pulls/418/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/124/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 418 --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 418, 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 418. | |
| Workflow and collaboration. | |
| - When you check branch, verify with git branch --show-current. | |
| - When you push, push only to branch issue-124-ba7373c4f0fd. | |
| - When you finish, create a pull request from branch issue-124-ba7373c4f0fd. (Note: PR 418 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-124-ba7373c4f0fd. | |
| - When you mention result, include pull request url or comment url. | |
| - When you need to create pr, remember pr 418 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 .) | |
| [2026-01-21T18:47:43.877Z] [INFO] | |
| [2026-01-21T18:47:43.878Z] [INFO] 📋 User prompt: | |
| [2026-01-21T18:47:43.878Z] [INFO] ---BEGIN USER PROMPT--- | |
| [2026-01-21T18:47:43.878Z] [INFO] Issue to solve: https://github.com/suenot/machine-learning-for-trading/issues/124 | |
| Your prepared branch: issue-124-ba7373c4f0fd | |
| Your prepared working directory: /tmp/gh-issue-solver-1769021101508 | |
| Your prepared Pull Request: https://github.com/suenot/machine-learning-for-trading/pull/418 | |
| Your forked repository: konard/suenot-machine-learning-for-trading | |
| Original repository (upstream): suenot/machine-learning-for-trading | |
| Proceed. | |
| [2026-01-21T18:47:43.878Z] [INFO] ---END USER PROMPT--- | |
| [2026-01-21T18:47:43.878Z] [INFO] | |
| [2026-01-21T18:47:43.879Z] [INFO] 📋 System prompt: | |
| [2026-01-21T18:47:43.879Z] [INFO] ---BEGIN SYSTEM PROMPT--- | |
| [2026-01-21T18:47:43.879Z] [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-124-ba7373c4f0fd --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/124. | |
| - 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/418/comments --paginate | |
| 2. PR conversation comments (general discussion): gh api repos/suenot/machine-learning-for-trading/issues/418/comments --paginate | |
| 3. PR reviews (approve/request changes): gh api repos/suenot/machine-learning-for-trading/pulls/418/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/124/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 418 --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 418, 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 418. | |
| Workflow and collaboration. | |
| - When you check branch, verify with git branch --show-current. | |
| - When you push, push only to branch issue-124-ba7373c4f0fd. | |
| - When you finish, create a pull request from branch issue-124-ba7373c4f0fd. (Note: PR 418 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-124-ba7373c4f0fd. | |
| - When you mention result, include pull request url or comment url. | |
| - When you need to create pr, remember pr 418 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. | |
| [2026-01-21T18:47:43.879Z] [INFO] ---END SYSTEM PROMPT--- | |
| [2026-01-21T18:47:43.879Z] [INFO] | |
| [2026-01-21T18:47:43.880Z] [INFO] 📊 CLAUDE_CODE_MAX_OUTPUT_TOKENS: 64000 | |
| [2026-01-21T18:47:43.880Z] [INFO] 📋 Command details: | |
| [2026-01-21T18:47:43.881Z] [INFO] 📂 Working directory: /tmp/gh-issue-solver-1769021101508 | |
| [2026-01-21T18:47:43.881Z] [INFO] 🌿 Branch: issue-124-ba7373c4f0fd | |
| [2026-01-21T18:47:43.881Z] [INFO] 🤖 Model: Claude OPUS | |
| [2026-01-21T18:47:43.881Z] [INFO] 🍴 Fork: konard/suenot-machine-learning-for-trading | |
| [2026-01-21T18:47:43.881Z] [INFO] | |
| ▶️ Streaming output: | |
| [2026-01-21T18:47:46.492Z] [INFO] { | |
| "type": "system", | |
| "subtype": "init", | |
| "cwd": "/tmp/gh-issue-solver-1769021101508", | |
| "session_id": "8c23df75-76b7-45ba-aa3a-4295879a3b8f", | |
| "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": "2.1.12", | |
| "output_style": "default", | |
| "agents": [ | |
| "Bash", | |
| "general-purpose", | |
| "statusline-setup", | |
| "Explore", | |
| "Plan" | |
| ], | |
| "skills": [], | |
| "plugins": [], | |
| "uuid": "6e345b47-9f2d-4b15-ade2-622f923332ed" | |
| } | |
| [2026-01-21T18:47:46.493Z] [INFO] 📌 Session ID: 8c23df75-76b7-45ba-aa3a-4295879a3b8f | |
| [2026-01-21T18:47:46.494Z] [INFO] 📁 Log renamed to: /home/hive/8c23df75-76b7-45ba-aa3a-4295879a3b8f.log | |
| [2026-01-21T18:47:49.443Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-5-20251101", | |
| "id": "msg_01TYgC5RVjhYFoevg7eyn49B", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "tool_use", | |
| "id": "toolu_01PWkny7YhXjskMPGhepzK2i", | |
| "name": "Bash", | |
| "input": { | |
| "command": "gh issue view https://github.com/suenot/machine-learning-for-trading/issues/124", | |
| "description": "View issue details" | |
| } | |
| } | |
| ], | |
| "stop_reason": null, | |
| "stop_sequence": null, | |
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21 19:47 205_lottery_ticket_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 206_self_distillation_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 207_teacher_student_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 208_progressive_distillation\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 209_data_distillation_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 20_autoencoders_for_conditional_risk_factors\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 210_feature_distillation\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 211_nas_for_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 212_automl_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 213_hyperparameter_optimization\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 214_evolutionary_nas_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 215_differentiable_nas\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 216_one_shot_nas\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 217_hardware_aware_nas\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 218_multi_objective_nas\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 219_darts_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 21_gans_for_synthetic_time_series\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 220_examm_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 221_adversarial_training_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 222_adversarial_attack_detection\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 223_robust_trading_models\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 224_input_perturbation_defense\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 225_certified_robustness\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 226_adversarial_examples_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 227_evasion_attacks_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 228_poisoning_attacks_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 229_model_inversion_finance\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 22_deep_reinforcement_learning\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 230_membership_inference\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 231_vae_factor_model\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 232_factor_vqvae\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 233_rvrae_dynamic_factor\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 234_beta_vae_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 235_vq_vae_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 236_conditional_vae_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 237_hierarchical_vae_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 238_disentangled_vae_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 239_vae_portfolio_generation\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 23_next_steps\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 240_vae_volatility_surface\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 241_finbert_sentiment\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 242_bert_financial_nlp\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 243_roberta_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 244_xlnet_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 245_albert_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 246_electra_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 247_deberta_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 248_longformer_nlp_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 249_bigbird_nlp_finance\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 24_alpha_factor_library\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 250_gpt_financial_analysis\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 251_named_entity_recognition_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 252_relation_extraction_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 253_document_classification_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 254_text_summarization_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 255_question_answering_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 256_aspect_sentiment_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 257_event_extraction_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 258_temporal_reasoning_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 259_multimodal_nlp_trading\ndrwxrwxr-x 4 hive hive 4096 Jan 21 19:47 25_diffusion_models_for_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 260_knowledge_graph_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 261_lob_deep_learning\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 262_market_making_ml\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 263_order_flow_prediction\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 264_price_impact_modeling\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 265_tick_data_forecasting\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 266_trade_classification\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 267_quote_update_prediction\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 268_spread_modeling_ml\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 269_liquidity_prediction\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 26_temporal_fusion_transformers\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 270_microstructure_features\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 271_deeplob_forecasting\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 272_lobframe_benchmark\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 273_lit_transformer_lob\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 274_cnn_lob_prediction\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 275_lstm_lob_forecasting\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 276_attention_lob\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 277_gnn_lob\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 278_variational_lob\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 279_generative_lob\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 27_graph_neural_networks\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 280_reinforcement_lob\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 281_self_supervised_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 282_masked_autoencoder_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 283_mae_pretraining_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 284_bert_pretraining_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 285_gpt_pretraining_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 286_t5_pretraining_finance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 287_clip_multimodal_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 288_data_augmentation_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 289_curriculum_learning_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 28_regime_detection_hmm\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 290_active_learning_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 291_td3_portfolio\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 292_sac_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 293_ppo_portfolio\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 294_a2c_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 295_ddpg_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 296_dqn_portfolio\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 297_rainbow_dqn_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 298_c51_distributional_rl\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 299_iqn_implicit_quantile\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 29_earnings_surprise_prediction\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 300_hierarchical_rl_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 301_model_based_rl_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 302_world_models_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 303_dreamer_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 304_muzero_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 305_inverse_rl_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 306_imitation_learning_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 307_behavioral_cloning_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 308_gail_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 309_reward_shaping_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 30_order_flow_imbalance\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 310_curiosity_driven_trading\ndrwxrwxr-x 2 hive hive 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324_ensemble_uncertainty\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 325_mc_dropout_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 326_deep_ensembles_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 327_bayesian_neural_network\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 328_variational_inference_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 329_probabilistic_forecasting\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 32_cross_asset_momentum\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 330_conformal_prediction\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 331_flow_models_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 332_normalizing_flows_finance\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 333_realnvp_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 334_glow_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 335_neural_spline_flows\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 336_continuous_normalizing_flows\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 337_score_matching_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 338_energy_based_models\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 339_hopfield_networks_trading\ndrwxrwxr-x 4 hive hive 4096 Jan 21 19:47 33_anomaly_detection\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 340_associative_memory_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 341_graph_transformer_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 342_equivariant_gnn_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 343_message_passing_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 344_graph_attention_trading\ndrwxrwxr-x 5 hive hive 4096 Jan 21 19:47 345_heterogeneous_gnn_trading\ndrwxrwxr-x 5 hive hive 4096 Jan 21 19:47 346_temporal_gnn_trading\ndrwxrwxr-x 5 hive hive 4096 Jan 21 19:47 347_dynamic_gnn_trading\ndrwxrwxr-x 5 hive hive 4096 Jan 21 19:47 348_graph_generation_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 349_graph_pooling_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 34_online_learning_adaptive\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 350_subgraph_mining_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 351_wavenet_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 352_tcn_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 353_dilated_convolutions_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 354_inception_time_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 355_resnet_timeseries\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 356_densenet_trading\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 357_efficientnet_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 358_convnext_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 359_depthwise_separable_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 35_multi_agent_rl\ndrwxrwxr-x 5 hive hive 4096 Jan 21 19:47 360_squeeze_excitation_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 361_koopman_operator_trading\ndrwxrwxr-x 4 hive hive 4096 Jan 21 19:47 362_reservoir_computing_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 363_echo_state_networks\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 364_neuromorphic_trading\ndrwxrwxr-x 4 hive hive 4096 Jan 21 19:47 365_spiking_neural_networks\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 36_crypto_dex_arbitrage\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 37_sentiment_momentum_fusion\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 38_neural_ode_portfolio\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 39_conformal_prediction_trading\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 40_options_ml_greeks\ndrwxrwxr-x 3 hive hive 4096 Jan 21 19:47 41_higher_order_transformers\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:47 42_dual_attention_lob\ndrwxrwxr-x 4 hive hive 4096 Jan 21 19:47 43_stockformer_multivariate\ndrwxrwxr-x 4 hive hive 4096 Jan 21 19:47 44_probsparse_attention\ndrwxrwxr-x 4 hive hive 4096 Jan 21 19:47 45_deep_convolutional_transformer\ndrwxrwxr-x 4 hive hive 4096 Jan 21 19:47 46_temporal_attention_networks\ndrwxrwxr-x 4 hive hive 4096 Jan 21 19:47 47_cross_attention_multi_asset\ndrwxrwxr-x 4 hive hive 4096 Jan 21 19:47 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| "content": " 1→# Chapter 87: Task-Agnostic Trading\n 2→\n 3→## Описание\n 4→\n 5→Универсальные модели для различных торговых задач без специфической настройки.\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. **Task-Agnostic Representation Learning**\n 33→ - URL: https://arxiv.org/abs/1907.12157\n 34→ - Год: 2019\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→87_task_agnostic_trading/\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 87: Task-Agnostic Trading\n\n## Описание\n\nУниверсальные модели для различных торговых задач без специфической настройки.\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. **Task-Agnostic Representation Learning**\n - URL: https://arxiv.org/abs/1907.12157\n - Год: 2019\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```\n87_task_agnostic_trading/\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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| [2026-01-21T18:48:09.105Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01X6kcejH54mei6KJw9nFzGh","type":"tool_result","content":" 1→# Chapter 86: Few-Shot Market Prediction\n 2→\n 3→## Overview\n 4→\n 5→Few-Shot Market Prediction addresses the challenge of making accurate predictions on new or unseen markets with minimal historical data. This is particularly crucial in cryptocurrency markets where new trading pairs frequently emerge, and traditional machine learning models require extensive retraining. Using meta-learning techniques, we can adapt quickly to new market conditions with just 5-20 examples.\n 6→\n 7→## Table of Contents\n 8→\n 9→1. [Introduction](#introduction)\n 10→2. [Theoretical Foundation](#theoretical-foundation)\n 11→3. [Few-Shot Learning Approaches](#few-shot-learning-approaches)\n 12→4. [Architecture Design](#architecture-design)\n 13→5. [Application to Market Prediction](#application-to-market-prediction)\n 14→6. [Implementation Strategy](#implementation-strategy)\n 15→7. [Bybit Integration](#bybit-integration)\n 16→8. [Risk Management](#risk-management)\n 17→9. [Performance Metrics](#performance-metrics)\n 18→10. [References](#references)\n 19→\n 20→---\n 21→\n 22→## Introduction\n 23→\n 24→Traditional machine learning models for market prediction face a critical limitation: they require thousands of labeled examples for each market or asset to achieve reasonable accuracy. This becomes problematic when:\n 25→\n 26→- **New Assets Emerge**: New cryptocurrencies, tokens, or trading pairs are listed daily\n 27→- **Market Dynamics Shift**: The statistical properties of markets change over time\n 28→- **Cross-Market Transfer**: Knowledge from one market may not directly apply to another\n 29→- **Limited Historical Data**: Some market conditions (crashes, squeezes) have few historical examples\n 30→\n 31→### The Few-Shot Learning Solution\n 32→\n 33→```\n 34→┌─────────────────────────────────────────────────────────────────────────────┐\n 35→│ Traditional ML vs Few-Shot Learning │\n 36→├─────────────────────────────────────────────────────────────────────────────┤\n 37→│ │\n 38→│ TRADITIONAL ML: │\n 39→│ ─────────────── │\n 40→│ Training: 10,000+ samples per asset │\n 41→│ Problem: New asset listed → Need weeks of data │\n 42→│ Result: Cannot trade profitably during early adoption phase │\n 43→│ │\n 44→│ ┌─────────────────────────────────────────────────────────────────────┐ │\n 45→│ │ BTC │████████████████████████████████│ 50,000 samples │ │\n 46→│ │ ETH │████████████████████████████████│ 45,000 samples │ │\n 47→│ │ NEW! │██ │ 500 samples ❌ Not enough │ │\n 48→│ └─────────────────────────────────────────────────────────────────────┘ │\n 49→│ │\n 50→│ FEW-SHOT LEARNING: │\n 51→│ ────────────────── │\n 52→│ Training: Learn \"how to learn\" from many assets │\n 53→│ Inference: Adapt to new asset with 5-20 examples │\n 54→│ Result: Trade profitably within hours of listing │\n 55→│ │\n 56→│ ┌─────────────────────────────────────────────────────────────────────┐ │\n 57→│ │ Meta-train on: BTC, ETH, SOL, AVAX, DOT, LINK, UNI, ... │ │\n 58→│ │ Meta-test on: NEW_TOKEN with just 10 examples ✓ │ │\n 59→│ └─────────────────────────────────────────────────────────────────────┘ │\n 60→│ │\n 61→└─────────────────────────────────────────────────────────────────────────────┘\n 62→```\n 63→\n 64→### Key Benefits for Trading\n 65→\n 66→| Aspect | Traditional ML | Few-Shot Learning |\n 67→|--------|---------------|-------------------|\n 68→| New asset adaptation | Weeks to months | Hours |\n 69→| Data requirements | 10,000+ samples | 5-20 samples |\n 70→| Cross-market transfer | Limited | Excellent |\n 71→| Regime adaptation | Slow retraining | Fast adaptation |\n 72→| Model maintenance | High | Low |\n 73→| Market coverage | Limited to trained assets | Expandable on-the-fly |\n 74→\n 75→## Theoretical Foundation\n 76→\n 77→### Meta-Learning Framework\n 78→\n 79→Few-shot market prediction is built on meta-learning, also known as \"learning to learn.\" Instead of training a model for a specific prediction task, we train a model that can quickly adapt to new tasks.\n 80→\n 81→### Mathematical Formulation\n 82→\n 83→**Task Distribution**: Let $p(\\mathcal{T})$ be a distribution over trading tasks (e.g., different assets, timeframes, or prediction targets).\n 84→\n 85→**Support Set**: For each task $\\mathcal{T}_i$, we have a support set $\\mathcal{S}_i = \\{(x_j, y_j)\\}_{j=1}^K$ with $K$ examples (K-shot).\n 86→\n 87→**Query Set**: We evaluate on a query set $\\mathcal{Q}_i = \\{(x_j, y_j)\\}_{j=1}^Q$.\n 88→\n 89→**Meta-Learning Objective**:\n 90→\n 91→$$\\min_\\theta \\mathbb{E}_{\\mathcal{T}_i \\sim p(\\mathcal{T})} \\left[ \\mathcal{L}(\\mathcal{Q}_i; f_{\\theta'_i}) \\right]$$\n 92→\n 93→where $\\theta'_i = \\text{adapt}(\\theta, \\mathcal{S}_i)$ represents the adapted parameters for task $i$.\n 94→\n 95→### Episodic Training\n 96→\n 97→```\n 98→┌────────────────────────────────────────────────────────────────────────────┐\n 99→│ Episodic Training for Market Prediction │\n 100→├────────────────────────────────────────────────────────────────────────────┤\n 101→│ │\n 102→│ Episode Generation: │\n 103→│ ────────────────── │\n 104→│ │\n 105→│ Step 1: Sample N tasks (e.g., N different assets) │\n 106→│ ┌─────────────────────────────────────────────────────────────────────┐ │\n 107→│ │ Task 1: BTC price direction prediction │ │\n 108→│ │ Task 2: ETH volatility forecasting │ │\n 109→│ │ Task 3: SOL trend classification │ │\n 110→│ │ Task 4: AVAX regime detection │ │\n 111→│ │ Task 5: DOT support/resistance prediction │ │\n 112→│ └─────────────────────────────────────────────────────────────────────┘ │\n 113→│ │\n 114→│ Step 2: For each task, create support and query sets │\n 115→│ ┌─────────────────────────────────────────────────────────────────────┐ │\n 116→│ │ BTC Task: │ │\n 117→│ │ Support: [Day 1-5 features → labels] (5-shot) │ │\n 118→│ │ Query: [Day 6-10 features → labels] (5 query examples) │ │\n 119→│ │ │ │\n 120→│ │ ETH Task: │ │\n 121→│ │ Support: [Day 1-5 features → labels] (5-shot) │ │\n 122→│ │ Query: [Day 6-10 features → labels] (5 query examples) │ │\n 123→│ └─────────────────────────────────────────────────────────────────────┘ │\n 124→│ │\n 125→│ Step 3: Meta-learning update │\n 126→│ • Adapt model to each task using support set │\n 127→│ • Evaluate on query set │\n 128→│ • Update meta-parameters to improve adaptation │\n 129→│ │\n 130→└────────────────────────────────────────────────────────────────────────────┘\n 131→```\n 132→\n 133→### N-way K-shot Problem Formulation\n 134→\n 135→For market prediction, we typically frame problems as:\n 136→\n 137→- **N-way Classification**: Classify into N categories (e.g., Up/Down/Sideways)\n 138→- **K-shot**: Use K examples per category in the support set\n 139→- **Regression**: Predict continuous values (price, volatility)\n 140→\n 141→Common configurations for trading:\n 142→- 3-way 5-shot: Up/Down/Sideways with 5 examples each\n 143→- 5-way 10-shot: Five market regimes with 10 examples each\n 144→- 1-shot regression: Predict returns with 1 reference example\n 145→\n 146→## Few-Shot Learning Approaches\n 147→\n 148→### 1. Metric-Based Methods\n 149→\n 150→Learn a metric space where similar market conditions cluster together.\n 151→\n 152→```\n 153→┌────────────────────────────────────────────────────────────────────────────┐\n 154→│ Metric-Based Few-Shot Learning │\n 155→├────────────────────────────────────────────────────────────────────────────┤\n 156→│ │\n 157→│ Core Idea: Learn embeddings where distance = similarity │\n 158→│ │\n 159→│ Embedding Space Visualization: │\n 160→│ │\n 161→│ ↑ Dimension 2 │\n 162→│ │ │\n 163→│ │ ★ Query (new asset) │\n 164→│ │ / │\n 165→│ │ / distance │\n 166→│ │ / │\n 167→│ ●─●─●────●────────● │\n 168→│ Support examples │ │\n 169→│ (same class) │ │\n 170→│ │ ○──○──○ │\n 171→│ │ Support examples │\n 172→│ │ (different class) │\n 173→│ └────────────────────────→ Dimension 1 │\n 174→│ │\n 175→│ Methods: │\n 176→│ ───────── │\n 177→│ • Siamese Networks: Learn pairwise similarity │\n 178→│ • Prototypical Networks: Compare to class prototypes (centroids) │\n 179→│ • Matching Networks: Attention-weighted similarity │\n 180→│ • Relation Networks: Learn similarity function with neural network │\n 181→│ │\n 182→└────────────────────────────────────────────────────────────────────────────┘\n 183→```\n 184→\n 185→### 2. Optimization-Based Methods (MAML Family)\n 186→\n 187→Learn initial parameters that can be quickly fine-tuned.\n 188→\n 189→```\n 190→┌────────────────────────────────────────────────────────────────────────────┐\n 191→│ Model-Agnostic Meta-Learning (MAML) │\n 192→├────────────────────────────────────────────────────────────────────────────┤\n 193→│ │\n 194→│ Core Idea: Find initialization θ that adapts quickly │\n 195→│ │\n 196→│ ┌─────────────────────────────────────────────────────────────────────┐ │\n 197→│ │ │ │\n 198→│ │ θ (initial parameters) │ │\n 199→│ │ │ │ │\n 200→│ │ ┌─────┼─────┬─────────┐ │ │\n 201→│ │ │ │ │ │ │ │\n 202→│ │ ↓ ↓ ↓ ↓ │ │\n 203→│ │ θ'₁ θ'₂ θ'₃ ... θ'ₙ (task-specific params) │ │\n 204→│ │ │ │ │ │ │ │\n 205→│ │ Task1 Task2 Task3 TaskN (inner loop: few gradient steps) │ │\n 206→│ │ │ │ │ │ │ │\n 207→│ │ └─────┴─────┴─────────┘ │ │\n 208→│ │ │ │ │\n 209→│ │ ↓ │ │\n 210→│ │ Meta-update θ (outer loop: optimize for fast adaptation) │ │\n 211→│ │ │ │\n 212→│ └─────────────────────────────────────────────────────────────────────┘ │\n 213→│ │\n 214→│ Algorithm: │\n 215→│ ────────── │\n 216→│ 1. Sample batch of tasks │\n 217→│ 2. For each task: │\n 218→│ - Adapt θ → θ' using support set (1-5 gradient steps) │\n 219→│ - Compute loss on query set with θ' │\n 220→│ 3. Update θ using sum of query losses │\n 221→│ │\n 222→└────────────────────────────────────────────────────────────────────────────┘\n 223→```\n 224→\n 225→### 3. Hybrid Approach for Market Prediction\n 226→\n 227→```\n 228→┌────────────────────────────────────────────────────────────────────────────┐\n 229→│ Hybrid Few-Shot Market Predictor │\n 230→├────────────────────────────────────────────────────────────────────────────┤\n 231→│ | |
| [2026-01-21T18:48:09.109Z] [INFO] │\n 232→│ Input: Market Features │\n 233→│ ┌─────────────────────────────────────────────────────────────────────┐ │\n 234→│ │ Price features: returns, volatility, momentum │ │\n 235→│ │ Volume features: volume, VWAP, order flow │ │\n 236→│ │ Technical indicators: RSI, MACD, Bollinger Bands │ │\n 237→│ │ Crypto-specific: funding rate, OI, liquidations │ │\n 238→│ └─────────────────────────────────────────────────────────────────────┘ │\n 239→│ │ │\n 240→│ ↓ │\n 241→│ ┌─────────────────────────────────────────────────────────────────────┐ │\n 242→│ │ Temporal Encoder (shared across tasks) │ │\n 243→│ │ ─────────────────────────────────────── │ │\n 244→│ │ • 1D-CNN for local patterns │ │\n 245→│ │ • LSTM/Transformer for sequence modeling │ │\n 246→│ │ • Attention mechanism for important features │ │\n 247→│ └─────────────────────────────────────────────────────────────────────┘ │\n 248→│ │ │\n 249→│ ↓ │\n 250→│ ┌────────────────────────┴────────────────────────┐ │\n 251→│ │ │ │\n 252→│ ↓ ↓ │\n 253→│ Metric-Based Path Optimization-Based Path │\n 254→│ ┌────────────────────┐ ┌────────────────────┐ │\n 255→│ │ Prototype Matching │ │ MAML Adaptation │ │\n 256→│ │ (fast, no grad) │ │ (accurate, grad) │ │\n 257→│ └────────────────────┘ └────────────────────┘ │\n 258→│ │ │ │\n 259→│ └───────────────┬──────────────────────────┘ │\n 260→│ │ │\n 261→│ ↓ │\n 262→│ ┌─────────────────────────────────────────────────────────────────────┐ │\n 263→│ │ Ensemble / Confidence-Weighted Output │ │\n 264→│ │ ───────────────────────────────────── │ │\n 265→│ │ High confidence: Use metric-based (faster) │ │\n 266→│ │ Low confidence: Use optimization-based (more accurate) │ │\n 267→│ └─────────────────────────────────────────────────────────────────────┘ │\n 268→│ │ │\n 269→│ ↓ │\n 270→│ Output: Prediction + Confidence Score │\n 271→│ │\n 272→└────────────────────────────────────────────────────────────────────────────┘\n 273→```\n 274→\n 275→## Architecture Design\n 276→\n 277→### Feature Extraction Module\n 278→\n 279→```rust\n 280→/// Market feature types for few-shot learning\n 281→#[derive(Debug, Clone)]\n 282→pub struct MarketFeatures {\n 283→ /// Price-based features\n 284→ pub price_features: PriceFeatures,\n 285→ /// Volume-based features\n 286→ pub volume_features: VolumeFeatures,\n 287→ /// Technical indicators\n 288→ pub technical_features: TechnicalFeatures,\n 289→ /// Crypto-specific features\n 290→ pub crypto_features: CryptoFeatures,\n 291→}\n 292→\n 293→#[derive(Debug, Clone)]\n 294→pub struct PriceFeatures {\n 295→ /// Returns over multiple horizons\n 296→ pub returns: Vec<f32>, // [1m, 5m, 15m, 1h, 4h, 24h]\n 297→ /// Logarithmic returns\n 298→ pub log_returns: Vec<f32>,\n 299→ /// Realized volatility\n 300→ pub volatility: Vec<f32>, // Multiple windows\n 301→ /// High-low range normalized\n 302→ pub range: f32,\n 303→ /// Close position in range [0, 1]\n 304→ pub close_position: f32,\n 305→ /// Price momentum\n 306→ pub momentum: Vec<f32>,\n 307→}\n 308→\n 309→#[derive(Debug, Clone)]\n 310→pub struct VolumeFeatures {\n 311→ /// Normalized volume\n 312→ pub volume_normalized: f32,\n 313→ /// Volume change rate\n 314→ pub volume_change: f32,\n 315→ /// Buy/Sell volume ratio\n 316→ pub buy_sell_ratio: f32,\n 317→ /// Volume-weighted average price deviation\n 318→ pub vwap_deviation: f32,\n 319→ /// Volume profile features\n 320→ pub volume_profile: Vec<f32>,\n 321→}\n 322→\n 323→#[derive(Debug, Clone)]\n 324→pub struct TechnicalFeatures {\n 325→ /// RSI values at different periods\n 326→ pub rsi: Vec<f32>,\n 327→ /// MACD line, signal, histogram\n 328→ pub macd: [f32; 3],\n 329→ /// Bollinger Band position\n 330→ pub bb_position: f32,\n 331→ /// Moving average crossover signals\n 332→ pub ma_signals: Vec<f32>,\n 333→ /// ATR normalized\n 334→ pub atr: f32,\n 335→}\n 336→\n 337→#[derive(Debug, Clone)]\n 338→pub struct CryptoFeatures {\n 339→ /// Funding rate (for perpetuals)\n 340→ pub funding_rate: f32,\n 341→ /// Open interest change\n 342→ pub oi_change: f32,\n 343→ /// Long/Short ratio\n 344→ pub long_short_ratio: f32,\n 345→ /// Recent liquidation volume\n 346→ pub liquidation_volume: f32,\n 347→ /// Spot-Futures basis\n 348→ pub basis: f32,\n 349→}\n 350→```\n 351→\n 352→### Embedding Network Architecture\n 353→\n 354→```rust\n 355→/// Configuration for the embedding network\n 356→#[derive(Debug, Clone)]\n 357→pub struct EmbeddingConfig {\n 358→ /// Input feature dimension\n 359→ pub input_dim: usize,\n 360→ /// Sequence length (number of time steps)\n 361→ pub seq_length: usize,\n 362→ /// Hidden dimension for temporal encoder\n 363→ pub hidden_dim: usize,\n 364→ /// Output embedding dimension\n 365→ pub embedding_dim: usize,\n 366→ /// Number of attention heads\n 367→ pub num_heads: usize,\n 368→ /// Dropout rate\n 369→ pub dropout: f32,\n 370→}\n 371→\n 372→impl Default for EmbeddingConfig {\n 373→ fn default() -> Self {\n 374→ Self {\n 375→ input_dim: 48, // Total feature count\n 376→ seq_length: 96, // 96 time steps (e.g., 96 hours)\n 377→ hidden_dim: 256,\n 378→ embedding_dim: 128,\n 379→ num_heads: 8,\n 380→ dropout: 0.1,\n 381→ }\n 382→ }\n 383→}\n 384→\n 385→/// Embedding network structure\n 386→pub struct EmbeddingNetwork {\n 387→ config: EmbeddingConfig,\n 388→ // Temporal convolution layers\n 389→ conv_layers: Vec<Conv1dLayer>,\n 390→ // Transformer/LSTM encoder\n 391→ temporal_encoder: TemporalEncoder,\n 392→ // Projection head\n 393→ projection: ProjectionHead,\n 394→}\n 395→\n 396→impl EmbeddingNetwork {\n 397→ /// Forward pass through the embedding network\n 398→ pub fn forward(&self, x: &Array3<f32>) -> Array2<f32> {\n 399→ // x shape: (batch, seq_length, features)\n 400→\n 401→ // 1. Temporal convolutions for local patterns\n 402→ let conv_out = self.apply_conv_layers(x);\n 403→\n 404→ // 2. Temporal encoding (LSTM or Transformer)\n 405→ let temporal_out = self.temporal_encoder.forward(&conv_out);\n 406→\n 407→ // 3. Project to embedding space\n 408→ let embeddings = self.projection.forward(&temporal_out);\n 409→\n 410→ // 4. L2 normalize embeddings\n 411→ self.l2_normalize(&embeddings)\n 412→ }\n 413→\n 414→ fn l2_normalize(&self, x: &Array2<f32>) -> Array2<f32> {\n 415→ let norms = x.mapv(|v| v * v)\n 416→ .sum_axis(Axis(1))\n 417→ .mapv(f32::sqrt);\n 418→\n 419→ x / &norms.insert_axis(Axis(1))\n 420→ }\n 421→}\n 422→```\n 423→\n 424→### Few-Shot Predictor Module\n 425→\n 426→```rust\n 427→/// Few-shot market predictor configuration\n 428→#[derive(Debug, Clone)]\n 429→pub struct FewShotConfig {\n 430→ /// Number of classes/categories\n 431→ pub n_way: usize,\n 432→ /// Number of support examples per class\n 433→ pub k_shot: usize,\n 434→ /// Number of query examples for evaluation\n 435→ pub n_query: usize,\n 436→ /// Embedding configuration\n 437→ pub embedding_config: EmbeddingConfig,\n 438→ /// Distance metric type\n 439→ pub distance_type: DistanceType,\n 440→ /// Temperature for softmax\n 441→ pub temperature: f32,\n 442→ /// Whether to use MAML adaptation\n 443→ pub use_maml: bool,\n 444→ /// MAML inner loop learning rate\n 445→ pub maml_inner_lr: f32,\n 446→ /// MAML inner loop steps\n 447→ pub maml_inner_steps: usize,\n 448→}\n 449→\n 450→impl Default for FewShotConfig {\n 451→ fn default() -> Self {\n 452→ Self {\n 453→ n_way: 3, // Up, Down, Sideways\n 454→ k_shot: 5, // 5 examples per class\n 455→ n_query: 15, // 15 query examples\n 456→ embedding_config: EmbeddingConfig::default(),\n 457→ distance_type: DistanceType::SquaredEuclidean,\n 458→ temperature: 1.0,\n 459→ use_maml: false, // Start with metric-based\n 460→ maml_inner_lr: 0.01,\n 461→ maml_inner_steps: 5,\n 462→ }\n 463→ }\n 464→}\n 465→\n 466→/// Distance metric types\n 467→#[derive(Debug, Clone, Copy)]\n 468→pub enum DistanceType {\n 469→ Euclidean,\n 470→ SquaredEuclidean,\n 471→ Cosine,\n 472→ Learned, // Relation network style\n 473→}\n 474→\n 475→/// Prediction result with confidence\n 476→#[derive(Debug, Clone)]\n 477→pub struct PredictionResult {\n 478→ /// Predicted class/category\n 479→ pub prediction: usize,\n 480→ /// Class probabilities\n 481→ pub probabilities: Vec<f32>,\n 482→ /// Confidence score (max probability)\n 483→ pub confidence: f32,\n 484→ /// Distance to each prototype\n 485→ pub distances: Vec<f32>,\n 486→}\n 487→```\n 488→\n 489→## Application to Market Prediction\n 490→\n 491→### Prediction Tasks\n 492→\n 493→```\n 494→┌────────────────────────────────────────────────────────────────────────────┐\n 495→│ Few-Shot Market Prediction Tasks │\n 496→├────────────────────────────────────────────────────────────────────────────┤\n 497→│ │\n 498→│ Task Type 1: DIRECTION PREDICTION (Classification) │\n 499→│ ───────────────────────────────────────────────── │\n 500→│ Predict: Price direction in next N periods │\n 501→│ Classes: [Up, Down, Sideways] │\n 502→│ N-way: 3 │\n 503→│ K-shot: 5-10 │\n 504→│ │\n 505→│ Support Set Example: │\n 506→│ ┌────────────────────────────────────────────────────────────────────┐ │\n 507→│ │ Class \"Up\": │ │\n 508→│ │ • Period 1: features → return +2.3% │ │\n 509→│ │ • Period 2: features → return +1.8% │ │\n 510→│ │ • Period 3: features → return +3.1% │ │\n 511→│ │ ... │ │\n 512→│ │ Class \"Down\": │ │\n 513→│ │ • Period 1: features → return -2.1% │ │\n 514→│ │ • Period 2: features → return -1.5% │ │\n 515→│ │ ... │ │\n 516→│ └────────────────────────────────────────────────────────────────────┘ │\n 517→│ │\n 518→│ Task Type 2: REGIME DETECTION (Classification) │\n 519→│ ───────────────────────────────────────────── │\n 520→│ Predict: Current market regime │\n 521→│ Classes: [Bull, Bear, Sideways, High_Vol, Low_Vol] │\n 522→│ N-way: 5 │\n 523→│ K-shot: 5-10 │\n 524→│ │\n 525→│ Task Type 3: VOLATILITY FORECAST (Regression) │\n 526→│ ───────────────────────────────────────────── │\n 527→│ Predict: Future realized volatility │\n 528→│ Output: Continuous value │\n 529→│ K-shot: 5-20 reference periods │\n 530→│ │\n 531→│ Task Type 4: SUPPORT/RESISTANCE (Classification) │\n 532→│ ───────────────────────────────────────────────── │\n 533→│ Predict: Will price bounce or break? │\n 534→│ Classes: [Bounce, Break] │\n 535→│ N-way: 2 │\n 536→│ K-shot: 5-10 │\n 537→│ │\n 538→│ Task Type 5: CROSS-ASSET TRANSFER │\n 539→│ ───────────────────────────────── │\n 540→│ Train on: BTC, ETH, SOL, AVAX │\n 541→│ Adapt to: New token with 5 examples │\n 542→│ Use Case: Trade new listings profitably │\n 543→│ │\n 544→└────────────────────────────────────────────────────────────────────────────┘\n 545→```\n 546→\n 547→### Trading Strategy Integration\n 548→\n 549→```\n 550→┌────────────────────────────────────────────────────────────────────────────┐\n 551→│ Few-Shot Trading Strategy Pipeline │\n 552→├────────────────────────────────────────────────────────────────────────────┤\n 553→│ │\n 554→│ Phase 1: SUPPORT SET CONSTRUCTION (Online) │\n 555→│ ─────────────────────────────────────────── │\n 556→│ • Monitor recent market data for asset │\n 557→│ • Label recent periods based on realized outcomes │\n 558→│ • Create support set with balanced class representation │\n 559→│ • Update support set as new data arrives (sliding window) │\n 560→│ │\n 561→│ ┌────────────────────────────────────────────────────────────────────┐ │\n 562→│ │ Rolling Support Set for BTC: │ │\n 563→│ │ ┌──────────────────────────────────────────────────────────────┐ │ │\n 564→│ │ │ t-48h │ t-47h │ ... │ t-25h │ ... │ t-1h │ Current │ │ │\n 565→│ │ │ Up │ Up │ │ Down │ │ Side │ ? │ │ │\n 566→│ │ └──────────────────────────────────────────────────────────────┘ │ │\n 567→│ │ Support: Last 48 labeled periods │ │\n 568→│ │ Query: Current period (to predict) │ │\n 569→│ └────────────────────────────────────────────────────────────────────┘ │\n 570→│ │\n 571→│ Phase 2: PREDICTION (Real-time) │\n 572→│ ──────────────────────────────── │\n 573→│ • Extract features for current period │\n 574→│ • Compute embedding │\n 575→│ • Compare to prototypes/adapted model │\n 576→│ • Generate prediction with confidence │\n 577→│ │\n 578→│ Phase 3: SIGNAL GENERATION │\n 579→│ ────────────────────────── │\n 580→│ ┌────────────────────────────────────────────────────────────────────┐ │\n 581→│ │ Prediction → Trading Signal Mapping │ │\n 582→│ │ │ │\n 583→│ │ UP (conf > 0.7) → LONG signal, size = conf × base_size │ │\n 584→│ │ DOWN (conf > 0.7) → SHORT signal, size = conf × base_size │ │\n 585→│ │ SIDEWAYS → No trade or range-bound strategy │ │\n 586→│ │ Low confidence → Reduce position size or skip │ │\n 587→│ └────────────────────────────────────────────────────────────────────┘ │\n 588→│ │\n 589→│ Phase 4: POSITION MANAGEMENT │\n 590→│ ──────────────────────────── │\n 591→│ • Enter position based on signal │\n 592→│ • Set stop-loss and take-profit based on regime │\n 593→│ • Monitor prediction confidence continuously │\n 594→│ • Exit when: target hit, stop hit, or confidence drops │\n 595→│ │\n 596→└────────────────────────────────────────────────────────────────────────────┘\n 597→```\n 598→\n 599→### New Asset Adaptation Protocol\n 600→\n 601→```\n 602→┌────────────────────────────────────────────────────────────────────────────┐\n 603→│ New Asset Adaptation Protocol │\n 604→├────────────────────────────────────────────────────────────────────────────┤\n 605→│ │\n 606→│ When a new asset is listed (e.g., new token on Bybit): │\n 607→│ │\n 608→│ Hour 0-1: DATA COLLECTION │\n 609→│ ───────────────────────── │\n 610→│ • Start collecting OHLCV data │\n 611→│ • Monitor order book dynamics │\n 612→│ • Record funding rates (if perpetual) │\n 613→│ │\n 614→│ Hour 1-4: INITIAL SUPPORT SET │\n 615→│ ────────────────────────────── │\n 616→│ • Label first periods based on realized returns │\n 617→│ • Create minimal support set (3-5 examples per class) │\n 618→│ • Use conservative predictions (high confidence threshold) │\n 619→│ │\n 620→│ Hour 4-24: ACTIVE LEARNING │\n 621→│ ────────────────────────── │\n 622→│ • Expand support set with new labeled examples │\n 623→│ • Lower confidence threshold as data accumulates │\n 624→│ • Start small position sizing │\n 625→│ │\n 626→│ After 24h: FULL ADAPTATION │\n 627→│ ────────────────────────── │\n 628→│ • Support set reaches target size (20-50 examples) │\n 629→│ • Full position sizing enabled │\n 630→│ • Continuous support set updates (sliding window) │\n 631→│ │\n 632→│ ┌────────────────────────────────────────────────────────────────────┐ │\n 633→│ │ Confidence Threshold Schedule │ │\n 634→│ │ │ │\n 635→│ │ 100% ───────────────────────────────────────── │ │\n 636→│ │ 95% ▓▓▓▓▓ │ │\n 637→│ │ 90% ▓▓▓▓▓▓▓▓▓ │ │\n 638→│ │ 85% ▓▓▓▓▓▓ │ │\n 639→│ │ 80% ▓▓▓▓▓▓▓▓ │ │\n 640→│ │ 75% ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ │ │\n 641→│ │ 70% ▓▓▓▓▓▓▓▓▓▓▓ │ │\n 642→│ │ ────┬────┬────┬────┬────┬────┬────┬────┬────┬────→ Hours │ │\n 643→│ │ 1 4 8 12 16 20 24 36 48 72 │ │\n 644→│ └────────────────────────────────────────────────────────────────────┘ │\n 645→│ │\n 646→└────────────────────────────────────────────────────────────────────────────┘\n 647→```\n 648→\n 649→## Implementation Strategy\n 650→\n 651→### Module Structure\n 652→\n 653→```\n 654→86_few_shot_market_prediction/\n 655→├── Cargo.toml\n 656→├── README.md # Main documentation (English)\n 657→├── README.ru.md # Russian translation\n 658→├── readme.simple.md # Simplified explanation\n 659→├── readme.simple.ru.md # Simplified Russian\n 660→├── src/\n 661→│ ├── lib.rs # Library root\n 662→│ ├── config.rs # Configuration types\n 663→│ ├── features/\n 664→│ │ ├── mod.rs # Feature module\n 665→│ │ ├── extractor.rs # Feature extraction\n 666→│ │ ├── normalizer.rs # Feature normalization\n 667→│ │ └── types.rs # Feature types\n 668→│ ├── embedding/\n 669→│ │ ├── mod.rs # Embedding module\n 670→│ │ ├── network.rs # Embedding network\n 671→│ │ ├── temporal.rs # Temporal encoding\n 672→│ │ └── attention.rs # Attention mechanisms\n 673→│ ├── fewshot/\n 674→│ │ ├── mod.rs # Few-shot module\n 675→│ │ ├── prototypical.rs # Prototypical networks\n 676→│ │ ├── maml.rs # MAML implementation\n 677→│ │ ├── episode.rs # Episode generation\n 678→│ │ └── predictor.rs # Prediction interface\n 679→│ ├── data/\n 680→│ │ ├── mod.rs # Data module\n 681→│ │ ├── bybit.rs # Bybit API client\n 682→│ │ ├── dataset.rs # Dataset handling\n 683→│ │ └── support_set.rs # Support set management\n 684→│ ├── trading/\n 685→│ │ ├── mod.rs # Trading module\n 686→│ │ ├── signals.rs # Signal generation\n 687→│ │ ├── strategy.rs # Trading strategy\n 688→│ │ └── risk.rs # Risk management\n 689→│ └── utils/\n 690→│ ├── mod.rs # Utilities\n 691→│ ├── metrics.rs # Evaluation metrics\n 692→│ └── math.rs # Math utilities\n 693→├── examples/\n 694→│ ├── basic_fewshot.rs # Basic few-shot example\n 695→│ ├── direction_prediction.rs # Direction prediction demo\n 696→│ ├── new_asset_trading.rs # New asset adaptation\n 697→│ └── backtest.rs # Backtesting example\n 698→├── python/\n 699→│ ├── few_shot_predictor.py # PyTorch implementation\n 700→│ ├── train.py # Training script\n 701→│ ├── evaluate.py # Evaluation script\n 702→│ └── requirements.txt # Python dependencies\n 703→└── tests/\n 704→ ├── integration.rs # Integration tests\n 705→ └── unit_tests.rs # Unit tests\n 706→```\n 707→\n 708→### Core Implementation (Rust)\n 709→\n 710→```rust\n 711→//! Few-Shot Market Prediction Core Module\n 712→\n 713→use ndarray::{Array1, Array2, Array3, Axis};\n 714→use std::collections::HashMap;\n 715→\n 716→/// Market prediction categories\n 717→#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]\n 718→pub enum PredictionCategory {\n 719→ /// Price expected to increase\n 720→ Up,\n 721→ /// Price expected to decrease\n 722→ Down,\n 723→ /// Price expected to remain stable\n 724→ Sideways,\n 725→}\n 726→\n 727→impl PredictionCategory {\n 728→ /// Convert to trading bias\n 729→ pub fn to_trading_bias(&self) -> TradingBias {\n 730→ match self {\n 731→ Self::Up => TradingBias::Long,\n 732→ Self::Down => TradingBias::Short,\n 733→ Self::Sideways => TradingBias::Neutral,\n 734→ }\n 735→ }\n 736→\n 737→ /// Get all categories\n 738→ pub fn all() -> Vec<Self> {\n 739→ vec![Self::Up, Self::Down, Self::Sideways]\n 740→ }\n 741→}\n 742→\n 743→/// Trading bias based on prediction\n 744→#[derive(Debug, Clone, Copy)]\n 745→pub enum TradingBias {\n 746→ Long,\n 747→ Short,\n 748→ Neutral,\n 749→}\n 750→\n 751→/// Support set for few-shot learning\n 752→#[derive(Debug, Clone)]\n 753→pub struct SupportSet {\n 754→ /// Features for each example: (n_examples, seq_len, feature_dim)\n 755→ pub features: Array3<f32>,\n 756→ /// Labels for each example\n 757→ pub labels: Vec<PredictionCategory>,\n 758→ /// Timestamps for each example\n 759→ pub timestamps: Vec<u64>,\n 760→ /// Maximum examples per class\n 761→ max_per_class: usize,\n 762→}\n 763→\n 764→impl SupportSet {\n 765→ /// Create a new empty support set\n 766→ pub fn new(max_per_class: usize) -> Self {\n 767→ Self {\n 768→ features: Array3::zeros((0, 0, 0)),\n 769→ labels: Vec::new(),\n 770→ timestamps: Vec::new(),\n 771→ max_per_class,\n 772→ }\n 773→ }\n 774→\n 775→ /// Add a new example to the support set\n 776→ pub fn add_example(\n 777→ &mut self,\n 778→ features: Array2<f32>,\n 779→ label: PredictionCategory,\n 780→ timestamp: u64,\n 781→ ) {\n 782→ // Count current examples for this class\n 783→ let class_count = self.labels.iter()\n 784→ .filter(|&l| *l == label)\n 785→ .count();\n 786→\n 787→ // If at max capacity, remove oldest example of this class\n 788→ if class_count >= self.max_per_class {\n 789→ self.remove_oldest_of_class(label);\n 790→ }\n 791→\n 792→ // Add new example\n 793→ self.labels.push(label);\n 794→ self.timestamps.push(timestamp);\n 795→\n 796→ // Concatenate features\n 797→ // (Implementation depends on actual array handling)\n 798→ }\n 799→\n 800→ /// Remove the oldest example of a given class\n 801→ fn remove_oldest_of_class(&mut self, class: PredictionCategory) {\n 802→ if let Some(idx) = self.labels.iter()\n 803→ .enumerate()\n 804→ .filter(|(_, l)| **l == class)\n 805→ .min_by_key(|(i, _)| self.timestamps[*i])\n 806→ .map(|(i, _)| i)\n 807→ {\n 808→ self.labels.remove(idx);\n 809→ self.timestamps.remove(idx);\n 810→ // Remove from features array\n 811→ }\n 812→ }\n 813→\n 814→ /// Get examples for a specific class\n 815→ pub fn get_class_examples(&self, class: PredictionCategory) -> Vec<usize> {\n 816→ self.labels.iter()\n 817→ .enumerate()\n 818→ .filter(|(_, l)| **l == class)\n 819→ .map(|(i, _)| i)\n 820→ .collect()\n 821→ }\n 822→\n 823→ /// Check if support set is ready for prediction\n 824→ pub fn is_ready(&self, min_per_class: usize) -> bool {\n 825→ PredictionCategory::all().iter().all(|class| {\n 826→ self.get_class_examples(*class).len() >= min_per_class\n 827→ })\n 828→ }\n 829→}\n 830→\n 831→/// Few-shot market predictor\n 832→pub struct FewShotPredictor {\n 833→ /// Configuration\n 834→ config: FewShotConfig,\n 835→ /// Embedding network\n 836→ embedding_network: EmbeddingNetwork,\n 837→ /// Current support set\n 838→ support_set: SupportSet,\n 839→ /// Cached prototypes for each class\n 840→ prototypes: HashMap<PredictionCategory, Array1<f32>>,\n 841→}\n 842→\n 843→impl FewShotPredictor {\n 844→ /// Create a new predictor\n 845→ pub fn new(config: FewShotConfig) -> Self {\n 846→ Self {\n 847→ embedding_network: EmbeddingNetwork::new(config.embedding_config.clone()),\n 848→ support_set: SupportSet::new(config.k_shot * 2), // 2x buffer\n 849→ prototypes: HashMap::new(),\n 850→ config,\n 851→ }\n 852→ }\n 853→\n 854→ /// Update support set with new labeled example\n 855→ pub fn update_support_set(\n 856→ &mut self,\n 857→ features: Array2<f32>,\n 858→ label: PredictionCategory,\n 859→ timestamp: u64,\n 860→ ) {\n 861→ self.support_set.add_example(features, label, timestamp);\n 862→ self.update_prototypes();\n 863→ }\n 864→\n 865→ /// Update prototypes from current support set\n 866→ fn update_prototypes(&mut self) {\n 867→ self.prototypes.clear();\n 868→\n 869→ for class in PredictionCategory::all() {\n 870→ let indices = self.support_set.get_class_examples(class);\n 871→ if indices.is_empty() {\n 872→ continue;\n 873→ }\n 874→\n 875→ // Get embeddings for all examples of this class\n 876→ // Compute prototype as mean embedding\n 877→ // (Simplified - actual implementation would batch process)\n 878→ }\n 879→ }\n 880→\n 881→ /// Make prediction for new query\n 882→ pub fn predict(&self, query_features: &Array2<f32>) -> PredictionResult {\n 883→ // 1. Embed query\n 884→ let query_embedding = self.embedding_network.forward_single(query_features);\n 885→\n 886→ // 2. Compute distances to all prototypes\n 887→ let mut distances = HashMap::new();\n 888→ for (class, prototype) in &self.prototypes {\n 889→ let dist = self.compute_distance(&query_embedding, prototype);\n 890→ distances.insert(*class, dist);\n 891→ }\n 892→\n 893→ // 3. Convert distances to probabilities\n 894→ let (probabilities, prediction, confidence) =\n 895→ self.distances_to_probabilities(&distances);\n 896→\n 897→ PredictionResult {\n 898→ prediction: prediction as usize,\n 899→ probabilities,\n 900→ confidence,\n 901→ distances: distances.values().cloned().collect(),\n 902→ }\n 903→ }\n 904→\n 905→ /// Compute distance between two embeddings\n 906→ fn compute_distance(&self, a: &Array1<f32>, b: &Array1<f32>) -> f32 {\n 907→ match self.config.distance_type {\n 908→ DistanceType::Euclidean => {\n 909→ (a - b).mapv(|x| x * x).sum().sqrt()\n 910→ }\n 911→ DistanceType::SquaredEuclidean => {\n 912→ (a - b).mapv(|x| x * x).sum()\n 913→ }\n 914→ DistanceType::Cosine => {\n 915→ let dot = a.dot(b);\n 916→ let norm_a = a.mapv(|x| x * x).sum().sqrt();\n 917→ let norm_b = b.mapv(|x| x * x).sum().sqrt();\n 918→ 1.0 - (dot / (norm_a * norm_b))\n 919→ }\n 920→ DistanceType::Learned => {\n 921→ // Would use a learned relation network\n 922→ unimplemented!(\"Learned distance requires relation network\")\n 923→ }\n 924→ }\n 925→ }\n 926→\n 927→ /// Convert distances to probabilities via softmax\n 928→ fn distances_to_probabilities(\n 929→ &self,\n 930→ distances: &HashMap<PredictionCategory, f32>,\n 931→ ) -> (Vec<f32>, PredictionCategory, f32) {\n 932→ let classes: Vec<_> = distances.keys().cloned().collect();\n 933→ let neg_distances: Vec<f32> = classes.iter()\n 934→ .map(|c| -distances[c] / self.config.temperature)\n 935→ .collect();\n 936→\n 937→ // Softmax\n 938→ let max_val = neg_distances.iter().cloned().fold(f32::NEG_INFINITY, f32::max);\n 939→ let exp_vals: Vec<f32> = neg_distances.iter()\n 940→ .map(|x| (x - max_val).exp())\n 941→ .collect();\n 942→ let sum_exp: f32 = exp_vals.iter().sum();\n 943→ let probabilities: Vec<f32> = exp_vals.iter()\n 944→ .map(|x| x / sum_exp)\n 945→ .collect();\n 946→\n 947→ // Find prediction (class with highest probability)\n 948→ let (max_idx, &max_prob) = probabilities.iter()\n 949→ .enumerate()\n 950→ .max_by(|a, b| a.1.partial_cmp(b.1).unwrap())\n 951→ .unwrap();\n 952→\n 953→ (probabilities, classes[max_idx], max_prob)\n 954→ }\n 955→}\n 956→```\n 957→\n 958→## Bybit Integration\n 959→\n 960→### Data Collection Pipeline\n 961→\n 962→```\n 963→┌────────────────────────────────────────────────────────────────────────────┐\n 964→│ Bybit Data Pipeline for Few-Shot Learning │\n 965→├────────────────────────────────────────────────────────────────────────────┤\n 966→│ │\n 967→│ 1. HISTORICAL DATA (Meta-Training) │\n 968→│ ────────────────────────────────── │\n 969→│ Purpose: Train the base model on many assets │\n 970→│ │\n 971→│ API Endpoints: │\n 972→│ • GET /v5/market/kline → OHLCV candles for multiple symbols │\n 973→│ • GET /v5/market/tickers → Current market state │\n 974→│ • GET /v5/market/funding/history → Funding rate history │\n 975→│ • GET /v5/market/open-interest → Open interest history │\n 976→│ │\n 977→│ Data Collection Strategy: │\n 978→│ ┌────────────────────────────────────────────────────────────────────┐ │\n 979→│ │ Symbols: BTC, ETH, SOL, AVAX, DOT, LINK, UNI, ATOM, ... │ │\n 980→│ │ Timeframe: 1h candles │ │\n 981→│ │ History: 1 year per symbol │ │\n 982→│ │ Task sampling: Different windows as different tasks │ │\n 983→│ └────────────────────────────────────────────────────────────────────┘ │\n 984→│ │\n 985→│ 2. REAL-TIME DATA (Inference/Adaptation) │\n 986→│ ──────────────────────────────────────── │\n 987→│ Purpose: Build support sets and make predictions │\n 988→│ │\n 989→│ WebSocket Subscriptions: │\n 990→│ • kline.1.{symbol} → 1-minute candles for feature updates │\n 991→│ • ticker.{symbol} → Real-time price for signal execution │\n 992→│ • liquidation.{symbol} → Liquidation events for features │\n 993→│ │\n 994→│ ┌────────────────────────────────────────────────────────────────────┐ │\n 995→│ │ Real-time Loop: │ │\n 996→│ │ 1. Receive new candle via WebSocket │ │\n 997→│ │ 2. Update feature buffer │ │\n 998→│ │ 3. Every hour: Label previous period, update support set │ │\n 999→│ │ 4. Make prediction for next period │ │\n 1000→│ │ 5. Generate trading signal if confidence > threshold │ │\n 1001→│ └────────────────────────────────────────────────────────────────────┘ │\n 1002→│ │\n 1003→│ 3. ORDER EXECUTION │\n 1004→│ ────────────────── │\n 1005→│ • POST /v5/order/create → Place market/limit orders │\n 1006→│ • GET /v5/position/list → Check current positions │\n 1007→│ • POST /v5/order/cancel → Cancel pending orders │\n 1008→│ │\n 1009→└────────────────────────────────────────────────────────────────────────────┘\n 1010→```\n 1011→\n 1012→### Bybit Client Implementation\n 1013→\n 1014→```rust\n 1015→use reqwest::Client;\n 1016→use serde::{Deserialize, Serialize};\n 1017→use tokio_tungstenite::WebSocketStream;\n 1018→\n 1019→/// Bybit API client for few-shot learning\n 1020→pub struct BybitClient {\n 1021→ /// HTTP client for REST API\n 1022→ http_client: Client,\n 1023→ /// API key (optional for public endpoints)\n 1024→ api_key: Option<String>,\n 1025→ /// API secret\n 1026→ api_secret: Option<String>,\n 1027→ /// Base URL for REST API\n 1028→ base_url: String,\n 1029→ /// WebSocket URL\n 1030→ ws_url: String,\n 1031→}\n 1032→\n 1033→impl BybitClient {\n 1034→ /// Create a new Bybit client\n 1035→ pub fn new(api_key: Option<String>, api_secret: Option<String>) -> Self {\n 1036→ Self {\n 1037→ http_client: Client::new(),\n 1038→ api_key,\n 1039→ api_secret,\n 1040→ base_url: \"https://api.bybit.com\".to_string(),\n 1041→ ws_url: \"wss://stream.bybit.com/v5/public/linear\".to_string(),\n 1042→ }\n 1043→ }\n 1044→\n 1045→ /// Fetch historical klines for meta-training\n 1046→ pub async fn get_klines(\n 1047→ &self,\n 1048→ symbol: &str,\n 1049→ interval: &str,\n 1050→ start_time: u64,\n 1051→ end_time: u64,\n 1052→ limit: u32,\n 1053→ ) -> Result<Vec<Kline>, BybitError> {\n 1054→ let url = format!(\n 1055→ \"{}/v5/market/kline?category=linear&symbol={}&interval={}&start={}&end={}&limit={}\",\n 1056→ self.base_url, symbol, interval, start_time, end_time, limit\n 1057→ );\n 1058→\n 1059→ let response: KlineResponse = self.http_client\n 1060→ .get(&url)\n 1061→ .send()\n 1062→ .await?\n 1063→ .json()\n 1064→ .await?;\n 1065→\n 1066→ Ok(response.result.list.into_iter().map(Kline::from).collect())\n 1067→ }\n 1068→\n 1069→ /// Fetch funding rate history\n 1070→ pub async fn get_funding_history(\n 1071→ &self,\n 1072→ symbol: &str,\n 1073→ start_time: u64,\n 1074→ end_time: u64,\n 1075→ ) -> Result<Vec<FundingRate>, BybitError> {\n 1076→ let url = format!(\n 1077→ \"{}/v5/market/funding/history?category=linear&symbol={}&startTime={}&endTime={}\",\n 1078→ self.base_url, symbol, start_time, end_time\n 1079→ );\n 1080→\n 1081→ let response: FundingResponse = self.http_client\n 1082→ .get(&url)\n 1083→ .send()\n 1084→ .await?\n 1085→ .json()\n 1086→ .await?;\n 1087→\n 1088→ Ok(response.result.list)\n 1089→ }\n 1090→\n 1091→ /// Subscribe to real-time kline updates\n 1092→ pub async fn subscribe_klines(\n 1093→ &self,\n 1094→ symbols: &[&str],\n 1095→ interval: &str,\n 1096→ ) -> Result<WebSocketStream<impl tokio::io::AsyncRead + tokio::io::AsyncWrite>, BybitError> {\n 1097→ use tokio_tungstenite::connect_async;\n 1098→\n 1099→ let (ws_stream, _) = connect_async(&self.ws_url).await?;\n 1100→\n 1101→ // Send subscription message\n 1102→ let topics: Vec<String> = symbols.iter()\n 1103→ .map(|s| format!(\"kline.{}.{}\", interval, s))\n 1104→ .collect();\n 1105→\n 1106→ let subscribe_msg = serde_json::json!({\n 1107→ \"op\": \"subscribe\",\n 1108→ \"args\": topics\n 1109→ });\n 1110→\n 1111→ // ... send subscription and return stream\n 1112→\n 1113→ Ok(ws_stream)\n 1114→ }\n 1115→}\n 1116→\n 1117→/// Kline (candlestick) data\n 1118→#[derive(Debug, Clone, Deserialize, Serialize)]\n 1119→pub struct Kline {\n 1120→ pub timestamp: u64,\n 1121→ pub open: f64,\n 1122→ pub high: f64,\n 1123→ pub low: f64,\n 1124→ pub close: f64,\n 1125→ pub volume: f64,\n 1126→ pub turnover: f64,\n 1127→}\n 1128→\n 1129→/// Funding rate data\n 1130→#[derive(Debug, Clone, Deserialize, Serialize)]\n 1131→pub struct FundingRate {\n 1132→ pub symbol: String,\n 1133→ pub funding_rate: f64,\n 1134→ pub funding_rate_timestamp: u64,\n 1135→}\n 1136→```\n 1137→\n 1138→## Risk Management\n 1139→\n 1140→### Confidence-Based Position Sizing\n 1141→\n 1142→```\n 1143→┌────────────────────────────────────────────────────────────────────────────┐\n 1144→│ Risk Management for Few-Shot Trading │\n 1145→├────────────────────────────────────────────────────────────────────────────┤\n 1146→│ │\n 1147→│ 1. CONFIDENCE-BASED POSITION SIZING │\n 1148→│ ───────────────────────────────────── │\n 1149→│ │\n 1150→│ Position Size = Base Size × Confidence × Regime Multiplier │\n 1151→│ │\n 1152→│ ┌────────────────────────────────────────────────────────────────────┐ │\n 1153→│ │ Confidence Thresholds: │ │\n 1154→│ │ │ │\n 1155→│ │ Confidence < 0.50: NO TRADE (too uncertain) │ │\n 1156→│ │ Confidence 0.50-0.65: 25% of base size │ │\n 1157→│ │ Confidence 0.65-0.75: 50% of base size │ │\n 1158→│ │ Confidence 0.75-0.85: 75% of base size │ │\n 1159→│ │ Confidence 0.85-0.95: 100% of base size │ │\n 1160→│ │ Confidence > 0.95: 100% of base size (cap to avoid overfit) │ │\n 1161→│ └────────────────────────────────────────────────────────────────────┘ │\n 1162→│ │\n 1163→│ 2. SUPPORT SET QUALITY ASSESSMENT │\n 1164→│ ───────────────────────────────── │\n 1165→│ │\n 1166→│ Quality Score = f(class_balance, recency, diversity) │\n 1167→│ │\n 1168→│ ┌────────────────────────────────────────────────────────────────────┐ │\n 1169→│ │ Quality Factors: │ │\n 1170→│ │ │ │\n 1171→│ │ • Class Balance: All classes should have similar # examples │ │\n 1172→│ │ • Recency: Recent examples should be weighted higher │ │\n 1173→│ │ • Diversity: Examples should cover different market conditions │ │\n 1174→│ │ • Consistency: Similar examples should have similar labels │ │\n 1175→│ └────────────────────────────────────────────────────────────────────┘ │\n 1176→│ │\n 1177→│ 3. STOP-LOSS AND TAKE-PROFIT │\n 1178→│ ──────────────────────────── │\n 1179→│ │\n 1180→│ ┌─────────────────────────────────────────────────────────────────┐ │\n 1181→│ │ Prediction │ Stop-Loss │ Take-Profit │ Max Hold Time │ │\n 1182→│ │────────────────┼───────────┼─────────────┼──────────────────────│ │\n 1183→│ │ UP (high conf)│ -2.0% │ +3.0% │ 4 hours │ │\n 1184→│ │ UP (med conf) │ -1.5% │ +2.0% │ 2 hours │ │\n 1185→│ │ DOWN (high) │ -2.0% │ +3.0% │ 4 hours │ │\n 1186→│ │ DOWN (med) │ -1.5% │ +2.0% │ 2 hours │ │\n 1187→│ │ SIDEWAYS │ -1.0% │ +1.0% │ 1 hour │ │\n 1188→│ └─────────────────────────────────────────────────────────────────┘ │\n 1189→│ │\n 1190→│ 4. CIRCUIT BREAKERS │\n 1191→│ ─────────────────── │\n 1192→│ │\n 1193→│ Automatic position closure when: │\n 1194→│ • Confidence drops below 0.40 during position hold │\n 1195→│ • Prediction changes direction (UP → DOWN or vice versa) │\n 1196→│ • Support set becomes stale (>4h since last update) │\n 1197→│ • Daily loss exceeds 3% of capital │\n 1198→│ • 5 consecutive losing trades │\n 1199→│ │\n 1200→│ 5. NEW ASSET RISK SCALING │\n 1201→│ ────────────────────────── │\n 1202→│ │\n 1203→│ ┌────────────────────────────────────────────────────────────────────┐ │\n 1204→│ │ Hours Since Listing │ Max Position Size │ │\n 1205→│ │──────────────────────┼──────────────────────────────────────────── │ │\n 1206→│ │ 0-4 hours │ 10% of normal │ │\n 1207→│ │ 4-12 hours │ 25% of normal │ │\n 1208→│ │ 12-24 hours │ 50% of normal │ │\n 1209→│ │ 24-48 hours │ 75% of normal │ │\n 1210→│ │ 48+ hours │ 100% of normal │ │\n 1211→│ └────────────────────────────────────────────────────────────────────┘ │\n 1212→│ │\n 1213→└────────────────────────────────────────────────────────────────────────────┘\n 1214→```\n 1215→\n 1216→### Risk Parameters Implementation\n 1217→\n 1218→```rust\n 1219→/// Risk parameters for few-shot trading\n 1220→#[derive(Debug, Clone)]\n 1221→pub struct RiskParameters {\n 1222→ /// Base position size as fraction of capital\n 1223→ pub base_position_size: f32,\n 1224→ /// Maximum position size multiplier\n 1225→ pub max_position_multiplier: f32,\n 1226→ /// Minimum confidence to trade\n 1227→ pub min_confidence: f32,\n 1228→ /// Stop-loss percentage\n 1229→ pub stop_loss_pct: f32,\n 1230→ /// Take-profit percentage\n 1231→ pub take_profit_pct: f32,\n 1232→ /// Maximum holding time in seconds\n 1233→ pub max_hold_time: u64,\n 1234→ /// Daily loss limit as fraction of capital\n 1235→ pub daily_loss_limit: f32,\n 1236→ /// Maximum consecutive losses before pause\n 1237→ pub max_consecutive_losses: usize,\n 1238→}\n 1239→\n 1240→impl Default for RiskParameters {\n 1241→ fn default() -> Self {\n 1242→ Self {\n 1243→ base_position_size: 0.02, // 2% of capital per trade\n 1244→ max_position_multiplier: 3.0, // Maximum 6% per trad | |
| [2026-01-21T18:48:09.115Z] [INFO] e\n 1245→ min_confidence: 0.50,\n 1246→ stop_loss_pct: 0.02, // 2% stop loss\n 1247→ take_profit_pct: 0.03, // 3% take profit\n 1248→ max_hold_time: 4 * 3600, // 4 hours\n 1249→ daily_loss_limit: 0.03, // 3% daily loss limit\n 1250→ max_consecutive_losses: 5,\n 1251→ }\n 1252→ }\n 1253→}\n 1254→\n 1255→impl RiskParameters {\n 1256→ /// Calculate position size based on confidence\n 1257→ pub fn calculate_position_size(&self, confidence: f32, capital: f32) -> f32 {\n 1258→ if confidence < self.min_confidence {\n 1259→ return 0.0;\n 1260→ }\n 1261→\n 1262→ // Scale position size with confidence\n 1263→ let confidence_multiplier = match confidence {\n 1264→ c if c < 0.65 => 0.25,\n 1265→ c if c < 0.75 => 0.50,\n 1266→ c if c < 0.85 => 0.75,\n 1267→ _ => 1.00,\n 1268→ };\n 1269→\n 1270→ let position_size = self.base_position_size\n 1271→ * confidence_multiplier\n 1272→ * self.max_position_multiplier;\n 1273→\n 1274→ capital * position_size.min(self.base_position_size * self.max_position_multiplier)\n 1275→ }\n 1276→\n 1277→ /// Scale parameters for new assets\n 1278→ pub fn scale_for_new_asset(&self, hours_since_listing: f32) -> Self {\n 1279→ let scale_factor = match hours_since_listing {\n 1280→ h if h < 4.0 => 0.10,\n 1281→ h if h < 12.0 => 0.25,\n 1282→ h if h < 24.0 => 0.50,\n 1283→ h if h < 48.0 => 0.75,\n 1284→ _ => 1.00,\n 1285→ };\n 1286→\n 1287→ Self {\n 1288→ base_position_size: self.base_position_size * scale_factor,\n 1289→ min_confidence: self.min_confidence + (1.0 - scale_factor) * 0.2,\n 1290→ stop_loss_pct: self.stop_loss_pct * (1.0 + (1.0 - scale_factor) * 0.5),\n 1291→ ..*self\n 1292→ }\n 1293→ }\n 1294→}\n 1295→```\n 1296→\n 1297→## Performance Metrics\n 1298→\n 1299→### Model Evaluation Metrics\n 1300→\n 1301→| Metric | Description | Target |\n 1302→|--------|-------------|--------|\n 1303→| **Few-Shot Accuracy** | Classification accuracy with K examples | > 65% |\n 1304→| **1-Shot Accuracy** | Accuracy with single example per class | > 55% |\n 1305→| **5-Shot Accuracy** | Accuracy with 5 examples per class | > 70% |\n 1306→| **Cross-Asset Transfer** | Accuracy on unseen assets | > 60% |\n 1307→| **Adaptation Speed** | Episodes needed to reach target accuracy | < 10 |\n 1308→| **Confidence Calibration** | Reliability of confidence scores | ECE < 0.10 |\n 1309→\n 1310→### Trading Performance Metrics\n 1311→\n 1312→| Metric | Description | Target |\n 1313→|--------|-------------|--------|\n 1314→| **Sharpe Ratio** | Risk-adjusted returns | > 2.0 |\n 1315→| **Sortino Ratio** | Downside risk-adjusted returns | > 2.5 |\n 1316→| **Max Drawdown** | Largest peak-to-trough decline | < 15% |\n 1317→| **Win Rate** | Percentage of profitable trades | > 55% |\n 1318→| **Profit Factor** | Gross profit / Gross loss | > 1.5 |\n 1319→| **New Asset ROI** | Returns on newly listed assets | > 10% monthly |\n 1320→\n 1321→### Latency Budget\n 1322→\n 1323→```\n 1324→┌─────────────────────────────────────────────────┐\n 1325→│ Latency Requirements │\n 1326→├─────────────────────────────────────────────────┤\n 1327→│ Feature Extraction: < 5ms │\n 1328→│ Embedding Computation: < 15ms │\n 1329→│ Prototype Distance: < 3ms │\n 1330→│ Prediction: < 2ms │\n 1331→├─────────────────────────────────────────────────┤\n 1332→│ Total Inference: < 25ms │\n 1333→├─────────────────────────────────────────────────┤\n 1334→│ Support Set Update: < 50ms │\n 1335→│ MAML Adaptation (if used): < 500ms │\n 1336→└─────────────────────────────────────────────────┘\n 1337→```\n 1338→\n 1339→## References\n 1340→\n 1341→1. **Short-Term Stock Price-Trend Prediction Using Meta-Learning**\n 1342→ - URL: https://arxiv.org/abs/2105.13599\n 1343→ - Year: 2021\n 1344→\n 1345→2. **Prototypical Networks for Few-shot Learning**\n 1346→ - Snell, J., Swersky, K., & Zemel, R. (2017)\n 1347→ - URL: https://arxiv.org/abs/1703.05175\n 1348→\n 1349→3. **Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks**\n 1350→ - Finn, C., Abbeel, P., & Levine, S. (2017)\n 1351→ - URL: https://arxiv.org/abs/1703.03400\n 1352→\n 1353→4. **Matching Networks for One Shot Learning**\n 1354→ - Vinyals, O., et al. (2016)\n 1355→ - URL: https://arxiv.org/abs/1606.04080\n 1356→\n 1357→5. **Meta-Learning: A Survey**\n 1358→ - Hospedales, T., et al. (2020)\n 1359→ - URL: https://arxiv.org/abs/2004.05439\n 1360→\n 1361→6. **Few-Shot Learning for Time Series Forecasting**\n 1362→ - Recent advances in applying meta-learning to temporal data\n 1363→\n 1364→7. **Cross-Asset Learning in Financial Markets**\n 1365→ - Transfer learning approaches for multi-asset trading\n 1366→\n 1367→---\n 1368→\n 1369→## Next Steps\n 1370→\n 1371→- [Simple Explanation](readme.simple.md) - Beginner-friendly version\n 1372→- [Russian Version](README.ru.md) - Russian translation\n 1373→- [Run Examples](examples/) - Working Rust code\n 1374→- [Python Implementation](python/) - PyTorch reference implementation\n 1375→\n 1376→---\n 1377→\n 1378→*Chapter 86 of Machine Learning for Trading*\n 1379→\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":"8c23df75-76b7-45ba-aa3a-4295879a3b8f","uuid":"0f10b02c-2c42-4113-9687-f0910d38889b","tool_use_result":{"type":"text","file":{"filePath":"/tmp/gh-issue-solver-1769021101508/86_few_shot_market_prediction/README.md","content":"# Chapter 86: Few-Shot Market Prediction\n\n## Overview\n\nFew-Shot Market Prediction addresses the challenge of making accurate predictions on new or unseen markets with minimal historical data. This is particularly crucial in cryptocurrency markets where new trading pairs frequently emerge, and traditional machine learning models require extensive retraining. Using meta-learning techniques, we can adapt quickly to new market conditions with just 5-20 examples.\n\n## Table of Contents\n\n1. [Introduction](#introduction)\n2. [Theoretical Foundation](#theoretical-foundation)\n3. [Few-Shot Learning Approaches](#few-shot-learning-approaches)\n4. [Architecture Design](#architecture-design)\n5. [Application to Market Prediction](#application-to-market-prediction)\n6. [Implementation Strategy](#implementation-strategy)\n7. [Bybit Integration](#bybit-integration)\n8. [Risk Management](#risk-management)\n9. [Performance Metrics](#performance-metrics)\n10. [References](#references)\n\n---\n\n## Introduction\n\nTraditional machine learning models for market prediction face a critical limitation: they require thousands of labeled examples for each market or asset to achieve reasonable accuracy. This becomes problematic when:\n\n- **New Assets Emerge**: New cryptocurrencies, tokens, or trading pairs are listed daily\n- **Market Dynamics Shift**: The statistical properties of markets change over time\n- **Cross-Market Transfer**: Knowledge from one market may not directly apply to another\n- **Limited Historical Data**: Some market conditions (crashes, squeezes) have few historical examples\n\n### The Few-Shot Learning Solution\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ Traditional ML vs Few-Shot Learning │\n├─────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ TRADITIONAL ML: │\n│ ─────────────── │\n│ Training: 10,000+ samples per asset │\n│ Problem: New asset listed → Need weeks of data │\n│ Result: Cannot trade profitably during early adoption phase │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ BTC │████████████████████████████████│ 50,000 samples │ │\n│ │ ETH │████████████████████████████████│ 45,000 samples │ │\n│ │ NEW! │██ │ 500 samples ❌ Not enough │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ FEW-SHOT LEARNING: │\n│ ────────────────── │\n│ Training: Learn \"how to learn\" from many assets │\n│ Inference: Adapt to new asset with 5-20 examples │\n│ Result: Trade profitably within hours of listing │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Meta-train on: BTC, ETH, SOL, AVAX, DOT, LINK, UNI, ... │ │\n│ │ Meta-test on: NEW_TOKEN with just 10 examples ✓ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n└─────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Key Benefits for Trading\n\n| Aspect | Traditional ML | Few-Shot Learning |\n|--------|---------------|-------------------|\n| New asset adaptation | Weeks to months | Hours |\n| Data requirements | 10,000+ samples | 5-20 samples |\n| Cross-market transfer | Limited | Excellent |\n| Regime adaptation | Slow retraining | Fast adaptation |\n| Model maintenance | High | Low |\n| Market coverage | Limited to trained assets | Expandable on-the-fly |\n\n## Theoretical Foundation\n\n### Meta-Learning Framework\n\nFew-shot market prediction is built on meta-learning, also known as \"learning to learn.\" Instead of training a model for a specific prediction task, we train a model that can quickly adapt to new tasks.\n\n### Mathematical Formulation\n\n**Task Distribution**: Let $p(\\mathcal{T})$ be a distribution over trading tasks (e.g., different assets, timeframes, or prediction targets).\n\n**Support Set**: For each task $\\mathcal{T}_i$, we have a support set $\\mathcal{S}_i = \\{(x_j, y_j)\\}_{j=1}^K$ with $K$ examples (K-shot).\n\n**Query Set**: We evaluate on a query set $\\mathcal{Q}_i = \\{(x_j, y_j)\\}_{j=1}^Q$.\n\n**Meta-Learning Objective**:\n\n$$\\min_\\theta \\mathbb{E}_{\\mathcal{T}_i \\sim p(\\mathcal{T})} \\left[ \\mathcal{L}(\\mathcal{Q}_i; f_{\\theta'_i}) \\right]$$\n\nwhere $\\theta'_i = \\text{adapt}(\\theta, \\mathcal{S}_i)$ represents the adapted parameters for task $i$.\n\n### Episodic Training\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Episodic Training for Market Prediction │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Episode Generation: │\n│ ────────────────── │\n│ │\n│ Step 1: Sample N tasks (e.g., N different assets) │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Task 1: BTC price direction prediction │ │\n│ │ Task 2: ETH volatility forecasting │ │\n│ │ Task 3: SOL trend classification │ │\n│ │ Task 4: AVAX regime detection │ │\n│ │ Task 5: DOT support/resistance prediction │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ Step 2: For each task, create support and query sets │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ BTC Task: │ │\n│ │ Support: [Day 1-5 features → labels] (5-shot) │ │\n│ │ Query: [Day 6-10 features → labels] (5 query examples) │ │\n│ │ │ │\n│ │ ETH Task: │ │\n│ │ Support: [Day 1-5 features → labels] (5-shot) │ │\n│ │ Query: [Day 6-10 features → labels] (5 query examples) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ Step 3: Meta-learning update │\n│ • Adapt model to each task using support set │\n│ • Evaluate on query set │\n│ • Update meta-parameters to improve adaptation │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### N-way K-shot Problem Formulation\n\nFor market prediction, we typically frame problems as:\n\n- **N-way Classification**: Classify into N categories (e.g., Up/Down/Sideways)\n- **K-shot**: Use K examples per category in the support set\n- **Regression**: Predict continuous values (price, volatility)\n\nCommon configurations for trading:\n- 3-way 5-shot: Up/Down/Sideways with 5 examples each\n- 5-way 10-shot: Five market regimes with 10 examples each\n- 1-shot regression: Predict returns with 1 reference example\n\n## Few-Shot Learning Approaches\n\n### 1. Metric-Based Methods\n\nLearn a metric space where similar market conditions cluster together.\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Metric-Based Few-Shot Learning │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Core Idea: Learn embeddings where distance = similarity │\n│ │\n│ Embedding Space Visualization: │\n│ │\n│ ↑ Dimension 2 │\n│ │ │\n│ │ ★ Query (new asset) │\n│ │ / │\n│ │ / distance │\n│ │ / │\n│ ●─●─●────●────────● │\n│ Support examples │ │\n│ (same class) │ │\n│ │ ○──○──○ │\n│ │ Support examples │\n│ │ (different class) │\n│ └────────────────────────→ Dimension 1 │\n│ │\n│ Methods: │\n│ ───────── │\n│ • Siamese Networks: Learn pairwise similarity │\n│ • Prototypical Networks: Compare to class prototypes (centroids) │\n│ • Matching Networks: Attention-weighted similarity │\n│ • Relation Networks: Learn similarity function with neural network │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### 2. Optimization-Based Methods (MAML Family)\n\nLearn initial parameters that can be quickly fine-tuned.\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Model-Agnostic Meta-Learning (MAML) │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Core Idea: Find initialization θ that adapts quickly │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ │ │\n│ │ θ (initial parameters) │ │\n│ │ │ │ │\n│ │ ┌─────┼─────┬─────────┐ │ │\n│ │ │ │ │ │ │ │\n│ │ ↓ ↓ ↓ ↓ │ │\n│ │ θ'₁ θ'₂ θ'₃ ... θ'ₙ (task-specific params) │ │\n│ │ │ │ │ │ │ │\n│ │ Task1 Task2 Task3 TaskN (inner loop: few gradient steps) │ │\n│ │ │ │ │ │ │ │\n│ │ └─────┴─────┴─────────┘ │ │\n│ │ │ │ │\n│ │ ↓ │ │\n│ │ Meta-update θ (outer loop: optimize for fast adaptation) │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ Algorithm: │\n│ ────────── │\n│ 1. Sample batch of tasks │\n│ 2. For each task: │\n│ - Adapt θ → θ' using support set (1-5 gradient steps) │\n│ - Compute loss on query set with θ' │\n│ 3. Update θ using sum of query losses │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### 3. Hybrid Approach for Market Prediction\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Hybrid Few-Shot Market Predictor │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Input: Market Features │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Price features: returns, volatility, momentum │ │\n│ │ Volume features: volume, VWAP, order flow │ │\n│ │ Technical indicators: RSI, MACD, Bollinger Bands │ │\n│ │ Crypto-specific: funding rate, OI, liquidations │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ↓ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Temporal Encoder (shared across tasks) │ │\n│ │ ─────────────────────────────────────── │ │\n│ │ • 1D-CNN for local patterns │ │\n│ │ • LSTM/Transformer for sequence modeling │ │\n│ │ • Attention mechanism for important features │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ↓ │\n│ ┌────────────────────────┴────────────────────────┐ │\n│ │ │ │\n│ ↓ ↓ │\n│ Metric-Based Path Optimization-Based Path │\n│ ┌────────────────────┐ ┌────────────────────┐ │\n│ │ Prototype Matching │ │ MAML Adaptation │ │\n│ │ (fast, no grad) │ │ (accurate, grad) │ │\n│ └────────────────────┘ └────────────────────┘ │\n│ │ │ │\n│ └───────────────┬──────────────────────────┘ │\n│ │ │\n│ ↓ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Ensemble / Confidence-Weighted Output │ │\n│ │ ───────────────────────────────────── │ │\n│ │ High confidence: Use metric-based (faster) │ │\n│ │ Low confidence: Use optimization-based (more accurate) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ↓ │\n│ Output: Prediction + Confidence Score │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Architecture Design\n\n### Feature Extraction Module\n\n```rust\n/// Market feature types for few-shot learning\n#[derive(Debug, Clone)]\npub struct MarketFeatures {\n /// Price-based features\n pub price_features: PriceFeatures,\n /// Volume-based features\n pub volume_features: VolumeFeatures,\n /// Technical indicators\n pub technical_features: TechnicalFeatures,\n /// Crypto-specific features\n pub crypto_features: CryptoFeatures,\n}\n\n#[derive(Debug, Clone)]\npub struct PriceFeatures {\n /// Returns over multiple horizons\n pub returns: Vec<f32>, // [1m, 5m, 15m, 1h, 4h, 24h]\n /// Logarithmic returns\n pub log_returns: Vec<f32>,\n /// Realized volatility\n pub volatility: Vec<f32>, // Multiple windows\n /// High-low range normalized\n pub range: f32,\n /// Close position in range [0, 1]\n pub close_position: f32,\n /// Price momentum\n pub momentum: Vec<f32>,\n}\n\n#[derive(Debug, Clone)]\npub struct VolumeFeatures {\n /// Normalized volume\n pub volume_normalized: f32,\n /// Volume change rate\n pub volume_change: f32,\n /// Buy/Sell volume ratio\n pub buy_sell_ratio: f32,\n /// Volume-weighted average price deviation\n pub vwap_deviation: f32,\n /// Volume profile features\n pub volume_profile: Vec<f32>,\n}\n\n#[derive(Debug, Clone)]\npub struct TechnicalFeatures {\n /// RSI values at different periods\n pub rsi: Vec<f32>,\n /// MACD line, signal, histogram\n pub macd: [f32; 3],\n /// Bollinger Band position\n pub bb_position: f32,\n /// Moving average crossover signals\n pub ma_signals: Vec<f32>,\n /// ATR normalized\n pub atr: f32,\n}\n\n#[derive(Debug, Clone)]\npub struct CryptoFeatures {\n /// Funding rate (for perpetuals)\n pub funding_rate: f32,\n /// Open interest change\n pub oi_change: f32,\n /// Long/Short ratio\n pub long_short_ratio: f32,\n /// Recent liquidation volume\n pub liquidation_volume: f32,\n /// Spot-Futures basis\n pub basis: f32,\n}\n```\n\n### Embedding Network Architecture\n\n```rust\n/// Configuration for the embedding network\n#[derive(Debug, Clone)]\npub struct EmbeddingConfig {\n /// Input feature dimension\n pub input_dim: usize,\n /// Sequence length (number of time steps)\n pub seq_length: usize,\n /// Hidden dimension for temporal encoder\n pub hidden_dim: usize,\n /// Output embedding dimension\n pub embedding_dim: usize,\n /// Number of attention heads\n pub num_heads: usize,\n /// Dropout rate\n pub dropout: f32,\n}\n\nimpl Default for EmbeddingConfig {\n fn default() -> Self {\n Self {\n input_dim: 48, // Total feature count\n seq_length: 96, // 96 time steps (e.g., 96 hours)\n hidden_dim: 256,\n embedding_dim: 128,\n num_heads: 8,\n dropout: 0.1,\n }\n }\n}\n\n/// Embedding network structure\npub struct EmbeddingNetwork {\n config: EmbeddingConfig,\n // Temporal convolution layers\n conv_layers: Vec<Conv1dLayer>,\n // Transformer/LSTM encoder\n temporal_encoder: TemporalEncoder,\n // Projection head\n projection: ProjectionHead,\n}\n\nimpl EmbeddingNetwork {\n /// Forward pass through the embedding network\n pub fn forward(&self, x: &Array3<f32>) -> Array2<f32> {\n // x shape: (batch, seq_length, features)\n\n // 1. Temporal convolutions for local patterns\n let conv_out = self.apply_conv_layers(x);\n\n // 2. Temporal encoding (LSTM or Transformer)\n let temporal_out = self.temporal_encoder.forward(&conv_out);\n\n // 3. Project to embedding space\n let embeddings = self.projection.forward(&temporal_out);\n\n // 4. L2 normalize embeddings\n self.l2_normalize(&embeddings)\n }\n\n fn l2_normalize(&self, x: &Array2<f32>) -> Array2<f32> {\n let norms = x.mapv(|v| v * v)\n .sum_axis(Axis(1))\n .mapv(f32::sqrt);\n\n x / &norms.insert_axis(Axis(1))\n }\n}\n```\n\n### Few-Shot Predictor Module\n\n```rust\n/// Few-shot market predictor configuration\n#[derive(Debug, Clone)]\npub struct FewShotConfig {\n /// Number of classes/categories\n pub n_way: usize,\n /// Number of support examples per class\n pub k_shot: usize,\n /// Number of query examples for evaluation\n pub n_query: usize,\n /// Embedding configuration\n pub embedding_config: EmbeddingConfig,\n /// Distance metric type\n pub distance_type: DistanceType,\n /// Temperature for softmax\n pub temperature: f32,\n /// Whether to use MAML adaptation\n pub use_maml: bool,\n /// MAML inner loop learning rate\n pub maml_inner_lr: f32,\n /// MAML inner loop steps\n pub maml_inner_steps: usize,\n}\n\nimpl Default for FewShotConfig {\n fn default() -> Self {\n Self {\n n_way: 3, // Up, Down, Sideways\n k_shot: 5, // 5 examples per class\n n_query: 15, // 15 query examples\n embedding_config: EmbeddingConfig::default(),\n distance_type: DistanceType::SquaredEuclidean,\n temperature: 1.0,\n use_maml: false, // Start with metric-based\n maml_inner_lr: 0.01,\n maml_inner_steps: 5,\n }\n }\n}\n\n/// Distance metric types\n#[derive(Debug, Clone, Copy)]\npub enum DistanceType {\n Euclidean,\n SquaredEuclidean,\n Cosine,\n Learned, // Relation network style\n}\n\n/// Prediction result with confidence\n#[derive(Debug, Clone)]\npub struct PredictionResult {\n /// Predicted class/category\n pub prediction: usize,\n /// Class probabilities\n pub probabilities: Vec<f32>,\n /// Confidence score (max probability)\n pub confidence: f32,\n /// Distance to each prototype\n pub distances: Vec<f32>,\n}\n```\n\n## Application to Market Prediction\n\n### Prediction Tasks\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Few-Shot Market Prediction Tasks │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Task Type 1: DIRECTION PREDICTION (Classification) │\n│ ───────────────────────────────────────────────── │\n│ Predict: Price direction in next N periods │\n│ Classes: [Up, Down, Sideways] │\n│ N-way: 3 │\n│ K-shot: 5-10 │\n│ │\n│ Support Set Example: │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Class \"Up\": │ │\n│ │ • Period 1: features → return +2.3% │ │\n│ │ • Period 2: features → return +1.8% │ │\n│ │ • Period 3: features → return +3.1% │ │\n│ │ ... │ │\n│ │ Class \"Down\": │ │\n│ │ • Period 1: features → return -2.1% │ │\n│ │ • Period 2: features → return -1.5% │ │\n│ │ ... │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ Task Type 2: REGIME DETECTION (Classification) │\n│ ───────────────────────────────────────────── │\n│ Predict: Current market regime │\n│ Classes: [Bull, Bear, Sideways, High_Vol, Low_Vol] │\n│ N-way: 5 │\n│ K-shot: 5-10 │\n│ │\n│ Task Type 3: VOLATILITY FORECAST (Regression) │\n│ ───────────────────────────────────────────── │\n│ Predict: Future realized volatility │\n│ Output: Continuous value │\n│ K-shot: 5-20 reference periods │\n│ │\n│ Task Type 4: SUPPORT/RESISTANCE (Classification) │\n│ ───────────────────────────────────────────────── │\n│ Predict: Will price bounce or break? │\n│ Classes: [Bounce, Break] │\n│ N-way: 2 │\n│ K-shot: 5-10 │\n│ │\n│ Task Type 5: CROSS-ASSET TRANSFER │\n│ ───────────────────────────────── │\n│ Train on: BTC, ETH, SOL, AVAX │\n│ Adapt to: New token with 5 examples │\n│ Use Case: Trade new listings profitably │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Trading Strategy Integration\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Few-Shot Trading Strategy Pipeline │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Phase 1: SUPPORT SET CONSTRUCTION (Online) │\n│ ─────────────────────────────────────────── │\n│ • Monitor recent market data for asset │\n│ • Label recent periods based on realized outcomes │\n│ • Create support set with balanced class representation │\n│ • Update support set as new data arrives (sliding window) │\n│ │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Rolling Support Set for BTC: │ │\n│ │ ┌──────────────────────────────────────────────────────────────┐ │ │\n│ │ │ t-48h │ t-47h │ ... │ t-25h │ ... │ t-1h │ Current │ │ │\n│ │ │ Up │ Up │ │ Down │ │ Side │ ? │ │ │\n│ │ └──────────────────────────────────────────────────────────────┘ │ │\n│ │ Support: Last 48 labeled periods │ │\n│ │ Query: Current period (to predict) │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ Phase 2: PREDICTION (Real-time) │\n│ ──────────────────────────────── │\n│ • Extract features for current period │\n│ • Compute embedding │\n│ • Compare to prototypes/adapted model │\n│ • Generate prediction with confidence │\n│ │\n│ Phase 3: SIGNAL GENERATION │\n│ ────────────────────────── │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Prediction → Trading Signal Mapping │ │\n│ │ │ │\n│ │ UP (conf > 0.7) → LONG signal, size = conf × base_size │ │\n│ │ DOWN (conf > 0.7) → SHORT signal, size = conf × base_size │ │\n│ │ SIDEWAYS → No trade or range-bound strategy │ │\n│ │ Low confidence → Reduce position size or skip │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ Phase 4: POSITION MANAGEMENT │\n│ ──────────────────────────── │\n│ • Enter position based on signal │\n│ • Set stop-loss and take-profit based on regime │\n│ • Monitor prediction confidence continuously │\n│ • Exit when: target hit, stop hit, or confidence drops │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### New Asset Adaptation Protocol\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ New Asset Adaptation Protocol │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ When a new asset is listed (e.g., new token on Bybit): │\n│ │\n│ Hour 0-1: DATA COLLECTION │\n│ ───────────────────────── │\n│ • Start collecting OHLCV data │\n│ • Monitor order book dynamics │\n│ • Record funding rates (if perpetual) │\n│ │\n│ Hour 1-4: INITIAL SUPPORT SET │\n│ ────────────────────────────── │\n│ • Label first periods based on realized returns │\n│ • Create minimal support set (3-5 examples per class) │\n│ • Use conservative predictions (high confidence threshold) │\n│ │\n│ Hour 4-24: ACTIVE LEARNING │\n│ ────────────────────────── │\n│ • Expand support set with new labeled examples │\n│ • Lower confidence threshold as data accumulates │\n│ • Start small position sizing │\n│ │\n│ After 24h: FULL ADAPTATION │\n│ ────────────────────────── │\n│ • Support set reaches target size (20-50 examples) │\n│ • Full position sizing enabled │\n│ • Continuous support set updates (sliding window) │\n│ │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Confidence Threshold Schedule │ │\n│ │ │ │\n│ │ 100% ───────────────────────────────────────── │ │\n│ │ 95% ▓▓▓▓▓ │ │\n│ │ 90% ▓▓▓▓▓▓▓▓▓ │ │\n│ │ 85% ▓▓▓▓▓▓ │ │\n│ │ 80% ▓▓▓▓▓▓▓▓ │ │\n│ │ 75% ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ │ │\n│ │ 70% ▓▓▓▓▓▓▓▓▓▓▓ │ │\n│ │ ────┬────┬────┬────┬────┬────┬────┬────┬────┬────→ Hours │ │\n│ │ 1 4 8 12 16 20 24 36 48 72 │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Implementation Strategy\n\n### Module Structure\n\n```\n86_few_shot_market_prediction/\n├── Cargo.toml\n├── README.md # Main documentation (English)\n├── README.ru.md # Russian translation\n├── readme.simple.md # Simplified explanation\n├── readme.simple.ru.md # Simplified Russian\n├── src/\n│ ├── lib.rs # Library root\n│ ├── config.rs # Configuration types\n│ ├── features/\n│ │ ├── mod.rs # Feature module\n│ │ ├── extractor.rs # Feature extraction\n│ │ ├── normalizer.rs # Feature normalization\n│ │ └── types.rs # Feature types\n│ ├── embedding/\n│ │ ├── mod.rs # Embedding module\n│ │ ├── network.rs # Embedding network\n│ │ ├── temporal.rs # Temporal encoding\n│ │ └── attention.rs # Attention mechanisms\n│ ├── fewshot/\n│ │ ├── mod.rs # Few-shot module\n│ │ ├── prototypical.rs # Prototypical networks\n│ │ ├── maml.rs # MAML implementation\n│ │ ├── episode.rs # Episode generation\n│ │ └── predictor.rs # Prediction interface\n│ ├── data/\n│ │ ├── mod.rs # Data module\n│ │ ├── bybit.rs # Bybit API client\n│ │ ├── dataset.rs # Dataset handling\n│ │ └── support_set.rs # Support set management\n│ ├── trading/\n│ │ ├── mod.rs # Trading module\n│ │ ├── signals.rs # Signal generation\n│ │ ├── strategy.rs # Trading strategy\n│ │ └── risk.rs # Risk management\n│ └── utils/\n│ ├── mod.rs # Utilities\n│ ├── metrics.rs # Evaluation metrics\n│ └── math.rs # Math utilities\n├── examples/\n│ ├── basic_fewshot.rs # Basic few-shot example\n│ ├── direction_prediction.rs # Direction prediction demo\n│ ├── new_asset_trading.rs # New asset adaptation\n│ └── backtest.rs # Backtesting example\n├── python/\n│ ├── few_shot_predictor.py # PyTorch implementation\n│ ├── train.py # Training script\n│ ├── evaluate.py # Evaluation script\n│ └── requirements.txt # Python dependencies\n└── tests/\n ├── integration.rs # Integration tests\n └── unit_tests.rs # Unit tests\n```\n\n### Core Implementation (Rust)\n\n```rust\n//! Few-Shot Market Prediction Core Module\n\nuse ndarray::{Array1, Array2, Array3, Axis};\nuse std::collections::HashMap;\n\n/// Market prediction categories\n#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]\npub enum PredictionCategory {\n /// Price expected to increase\n Up,\n /// Price expected to decrease\n Down,\n /// Price expected to remain stable\n Sideways,\n}\n\nimpl PredictionCategory {\n /// Convert to trading bias\n pub fn to_trading_bias(&self) -> TradingBias {\n match self {\n Self::Up => TradingBias::Long,\n Self::Down => TradingBias::Short,\n Self::Sideways => TradingBias::Neutral,\n }\n }\n\n /// Get all categories\n pub fn all() -> Vec<Self> {\n vec![Self::Up, Self::Down, Self::Sideways]\n }\n}\n\n/// Trading bias based on prediction\n#[derive(Debug, Clone, Copy)]\npub enum TradingBias {\n Long,\n Short,\n Neutral,\n}\n\n/// Support set for few-shot learning\n#[derive(Debug, Clone)]\npub struct SupportSet {\n /// Features for each example: (n_examples, seq_len, feature_dim)\n pub features: Array3<f32>,\n /// Labels for each example\n pub labels: Vec<PredictionCategory>,\n /// Timestamps for each example\n pub timestamps: Vec<u64>,\n /// Maximum examples per class\n max_per_class: usize,\n}\n\nimpl SupportSet {\n /// Create a new empty support set\n pub fn new(max_per_class: usize) -> Self {\n Self {\n | |
| [2026-01-21T18:48:09.132Z] [INFO] features: Array3::zeros((0, 0, 0)),\n labels: Vec::new(),\n timestamps: Vec::new(),\n max_per_class,\n }\n }\n\n /// Add a new example to the support set\n pub fn add_example(\n &mut self,\n features: Array2<f32>,\n label: PredictionCategory,\n timestamp: u64,\n ) {\n // Count current examples for this class\n let class_count = self.labels.iter()\n .filter(|&l| *l == label)\n .count();\n\n // If at max capacity, remove oldest example of this class\n if class_count >= self.max_per_class {\n self.remove_oldest_of_class(label);\n }\n\n // Add new example\n self.labels.push(label);\n self.timestamps.push(timestamp);\n\n // Concatenate features\n // (Implementation depends on actual array handling)\n }\n\n /// Remove the oldest example of a given class\n fn remove_oldest_of_class(&mut self, class: PredictionCategory) {\n if let Some(idx) = self.labels.iter()\n .enumerate()\n .filter(|(_, l)| **l == class)\n .min_by_key(|(i, _)| self.timestamps[*i])\n .map(|(i, _)| i)\n {\n self.labels.remove(idx);\n self.timestamps.remove(idx);\n // Remove from features array\n }\n }\n\n /// Get examples for a specific class\n pub fn get_class_examples(&self, class: PredictionCategory) -> Vec<usize> {\n self.labels.iter()\n .enumerate()\n .filter(|(_, l)| **l == class)\n .map(|(i, _)| i)\n .collect()\n }\n\n /// Check if support set is ready for prediction\n pub fn is_ready(&self, min_per_class: usize) -> bool {\n PredictionCategory::all().iter().all(|class| {\n self.get_class_examples(*class).len() >= min_per_class\n })\n }\n}\n\n/// Few-shot market predictor\npub struct FewShotPredictor {\n /// Confi | |
| [2026-01-21T18:48:09.134Z] [INFO] guration\n config: FewShotConfig,\n /// Embedding network\n embedding_network: EmbeddingNetwork,\n /// Current support set\n support_set: SupportSet,\n /// Cached prototypes for each class\n prototypes: HashMap<PredictionCategory, Array1<f32>>,\n}\n\nimpl FewShotPredictor {\n /// Create a new predictor\n pub fn new(config: FewShotConfig) -> Self {\n Self {\n embedding_network: EmbeddingNetwork::new(config.embedding_config.clone()),\n support_set: SupportSet::new(config.k_shot * 2), // 2x buffer\n prototypes: HashMap::new(),\n config,\n }\n }\n\n /// Update support set with new labeled example\n pub fn update_support_set(\n &mut self,\n features: Array2<f32>,\n label: PredictionCategory,\n timestamp: u64,\n ) {\n self.support_set.add_example(features, label, timestamp);\n self.update_prototypes();\n }\n\n /// Update prototypes from current support set\n fn update_prototypes(&mut self) {\n self.prototypes.clear();\n\n for class in PredictionCategory::all() {\n let indices = self.support_set.get_class_examples(class);\n if indices.is_empty() {\n continue;\n }\n\n // Get embeddings for all examples of this class\n // Compute prototype as mean embedding\n // (Simplified - actual implementation would batch process)\n }\n }\n\n /// Make prediction for new query\n pub fn predict(&self, query_features: &Array2<f32>) -> PredictionResult {\n // 1. Embed query\n let query_embedding = self.embedding_network.forward_single(query_features);\n\n // 2. Compute distances to all prototypes\n let mut distances = HashMap::new();\n for (class, prototype) in &self.prototypes {\n let dist = self.compute_distance(&query_embedding, prototype);\n distances.insert(*class, dist);\n }\n\n // 3. Convert distances to probabilities\n let (probabilities, prediction, confidence) =\n self.distances_to_probabilities(&distances);\n\n PredictionResult {\n prediction: prediction as usize,\n probabilities,\n confidence,\n distances: distances.values().cloned().collect(),\n }\n }\n\n /// Compute distance between two embeddings\n fn compute_distance(&self, a: &Array1<f32>, b: &Array1<f32>) -> f32 {\n match self.config.distance_type {\n DistanceType::Euclidean => {\n (a - b).mapv(|x| x * x).sum().sqrt()\n }\n DistanceType::SquaredEuclidean => {\n (a - b).mapv(|x| x * x).sum()\n }\n DistanceType::Cosine => {\n let dot = a.dot(b);\n let norm_a = a.mapv(|x| x * x).sum().sqrt();\n let norm_b = b.mapv(|x| x * x).sum().sqrt();\n 1.0 - (dot / (norm_a * norm_b))\n }\n DistanceType::Learned => {\n // Would use a learned relation network\n unimplemented!(\"Learned distance requires relation network\")\n }\n }\n }\n\n /// Convert distances to probabilities via softmax\n fn distances_to_probabilities(\n &self,\n distances: &HashMap<PredictionCategory, f32>,\n ) -> (Vec<f32>, PredictionCategory, f32) {\n let classes: Vec<_> = distances.keys().cloned().collect();\n let neg_distances: Vec<f32> = classes.iter()\n .map(|c| -distances[c] / self.config.temperature)\n .collect();\n\n // Softmax\n let max_val = neg_distances.iter().cloned().fold(f32::NEG_INFINITY, f32::max);\n let exp_vals: Vec<f32> = neg_distances.iter()\n .map(|x| (x - max_val).exp())\n .collect();\n let sum_exp: f32 = exp_vals.iter().sum();\n let probabilities: Vec<f32> = exp_vals.iter()\n .map(|x| x / sum_exp)\n .collect();\n\n // Find prediction (class with highest probability)\n let (max_idx, &max_prob) = probabilities.iter()\n .enumerate()\n .max_by(|a, b| a.1.partial_cmp(b.1).unwrap())\n .unwrap();\n\n (probabilities, classes[max_idx], max_prob)\n }\n}\n```\n\n## Bybit Integration\n\n### Data Collection Pipeline\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Bybit Data Pipeline for Few-Shot Learning │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. HISTORICAL DATA (Meta-Training) │\n│ ────────────────────────────────── │\n│ Purpose: Train the base model on many assets │\n│ │\n│ API Endpoints: │\n│ • GET /v5/market/kline → OHLCV candles for multiple symbols │\n│ • GET /v5/market/tickers → Current market state │\n│ • GET /v5/market/funding/history → Funding rate history │\n│ • GET /v5/market/open-interest → Open interest history │\n│ │\n│ Data Collection Strategy: │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Symbols: BTC, ETH, SOL, AVAX, DOT, LINK, UNI, ATOM, ... │ │\n│ │ Timeframe: 1h candles │ │\n│ │ History: 1 year per symbol │ │\n│ │ Task sampling: Different windows as different tasks │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 2. REAL-TIME DATA (Inference/Adaptation) │\n│ ──────────────────────────────────────── │\n│ Purpose: Build support sets and make predictions │\n│ │\n│ WebSocket Subscriptions: │\n│ • kline.1.{symbol} → 1-minute candles for feature updates │\n│ • ticker.{symbol} → Real-time price for signal execution │\n│ • liquidation.{symbol} → Liquidation events for features │\n│ │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Real-time Loop: │ │\n│ │ 1. Receive new candle via WebSocket │ │\n│ │ 2. Update feature buffer │ │\n│ │ 3. Every hour: Label previous period, update support set │ │\n│ │ 4. Make prediction for next period │ │\n│ │ 5. Generate trading signal if confidence > threshold │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 3. ORDER EXECUTION │\n│ ────────────────── │\n│ • POST /v5/order/create → Place market/limit orders │\n│ • GET /v5/position/list → Check current positions │\n│ • POST /v5/order/cancel → Cancel pending orders │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Bybit Client Implementation\n\n```rust\nuse reqwest::Client;\nuse serde::{Deserialize, Serialize};\nuse tokio_tungstenite::WebSocketStream;\n\n/// Bybit API client for few-shot learning\npub struct BybitClient {\n /// HTTP client for REST API\n http_client: Client,\n /// API key (optional for public endpoints)\n api_key: Option<String>,\n /// API secret\n api_secret: Option<String>,\n /// Base URL for REST API\n base_url: String,\n /// WebSocket URL\n ws_url: String,\n}\n\nimpl BybitClient {\n /// Create a new Bybit client\n pub fn new(api_key: Option<String>, api_secret: Option<String>) -> Self {\n Self {\n http_client: Client::new(),\n api_key,\n api_secret,\n base_url: \"https://api.bybit.com\".to_string(),\n ws_url: \"wss://stream.bybit.com/v5/public/linear\".to_string(),\n }\n }\n\n /// Fetch historical klines for meta-training\n pub async fn get_klines(\n &self,\n symbol: &str,\n interval: &str,\n start_time: u64,\n end_time: u64,\n limit: u32,\n ) -> Result<Vec<Kline>, BybitError> {\n let url = format!(\n \"{}/v5/market/kline?category=linear&symbol={}&interval={}&start={}&end={}&limit={}\",\n self.base_url, symbol, interval, start_time, end_time, limit\n );\n\n let response: KlineResponse = self.http_client\n .get(&url)\n .send()\n .await?\n .json()\n .await?;\n\n Ok(response.result.list.into_iter().map(Kline::from).collect())\n }\n\n /// Fetch funding rate history\n pub async fn get_funding_history(\n &self,\n symbol: &str,\n start_time: u64,\n end_time: u64,\n ) -> Result<Vec<FundingRate>, BybitError> {\n let url = format!(\n \"{}/v5/market/funding/history?category=linear&symbol={}&startTime={}&endTime={}\",\n self.base_url, symbol, start_time, end_time\n );\n\n let response: FundingResponse = self.http_client\n .get(&url)\n .send()\n .await?\n .json()\n .await?;\n\n Ok(response.result.list)\n }\n\n /// Subscribe to real-time kline updates\n pub async fn subscribe_klines(\n &self,\n symbols: &[&str],\n interval: &str,\n ) -> Result<WebSocketStream<impl tokio::io::AsyncRead + tokio::io::AsyncWrite>, BybitError> {\n use tokio_tungstenite::connect_async;\n\n let (ws_stream, _) = connect_async(&self.ws_url).await?;\n\n // Send subscription message\n let topics: Vec<String> = symbols.iter()\n .map(|s| format!(\"kline.{}.{}\", interval, s))\n .collect();\n\n let subscribe_msg = serde_json::json!({\n \"op\": \"subscribe\",\n \"args\": topics\n });\n\n // ... send subscription and return stream\n\n Ok(ws_stream)\n }\n}\n\n/// Kline (candlestick) data\n#[derive(Debug, Clone, Deserialize, Serialize)]\npub struct Kline {\n pub timestamp: u64,\n pub open: f64,\n pub high: f64,\n pub low: f64,\n pub close: f64,\n pub volume: f64,\n pub turnover: f64,\n}\n\n/// Funding rate data\n#[derive(Debug, Clone, Deserialize, Serialize)]\npub struct FundingRate {\n pub symbol: String,\n pub funding_rate: f64,\n pub funding_rate_timestamp: u64,\n}\n```\n\n## Risk Management\n\n### Confidence-Based Position Sizing\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Risk Management for Few-Shot Trading │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. CONFIDENCE-BASED POSITION SIZING │\n│ ───────────────────────────────────── │\n│ │\n│ Position Size = Base Size × Confidence × Regime Multiplier │\n│ │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Confidence Thresholds: │ │\n│ │ │ │\n│ │ Confidence < 0.50: NO TRADE (too uncertain) │ │\n│ │ Confidence 0.50-0.65: 25% of base size │ │\n│ │ Confidence 0.65-0.75: 50% of base size │ │\n│ │ Confidence 0.75-0.85: 75% of base size │ │\n│ │ Confidence 0.85-0.95: 100% of base size │ │\n│ │ Confidence > 0.95: 100% of base size (cap to avoid overfit) │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 2. SUPPORT SET QUALITY ASSESSMENT │\n│ ───────────────────────────────── │\n│ │\n│ Quality Score = f(class_balance, recency, diversity) │\n│ │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Quality Factors: │ │\n│ │ │ │\n│ │ • Class Balance: All classes should have similar # examples │ │\n│ │ • Recency: Recent examples should be weighted higher │ │\n│ │ • Diversity: Examples should cover different market conditions │ │\n│ │ • Consistency: Similar examples should have similar labels │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 3. STOP-LOSS AND TAKE-PROFIT │\n│ ──────────────────────────── │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────┐ │\n│ │ Prediction │ Stop-Loss │ Take-Profit │ Max Hold Time │ │\n│ │────────────────┼───────────┼─────────────┼──────────────────────│ │\n│ │ UP (high conf)│ -2.0% │ +3.0% │ 4 hours │ │\n│ │ UP (med conf) │ -1.5% │ +2.0% │ 2 hours │ │\n│ │ DOWN (high) │ -2.0% │ +3.0% │ 4 hours │ │\n│ │ DOWN (med) │ -1.5% │ +2.0% │ 2 hours │ │\n│ │ SIDEWAYS │ -1.0% │ +1.0% │ 1 hour │ │\n│ └─────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 4. CIRCUIT BREAKERS │\n│ ─────────────────── │\n│ │\n│ Automatic position closure when: │\n│ • Confidence drops below 0.40 during position hold │\n│ • Prediction changes direction (UP → DOWN or vice versa) │\n│ • Support set becomes stale (>4h since last update) │\n│ • Daily loss exceeds 3% of capital │\n│ • 5 consecutive losing trades │\n│ │\n│ 5. NEW ASSET RISK SCALING │\n│ ────────────────────────── │\n│ │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Hours Since Listing │ Max Position Size │ │\n│ │──────────────────────┼──────────────────────────────────────────── │ │\n│ │ 0-4 hours │ 10% of normal │ │\n│ │ 4-12 hours │ 25% of normal │ │\n│ │ 12-24 hours │ 50% of normal │ │\n│ │ 24-48 hours │ 75% of normal │ │\n│ │ 48+ hours │ 100% of normal │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Risk Parameters Implementation\n\n```rust\n/// Risk parameters for few-shot trading\n#[derive(Debug, Clone)]\npub struct RiskParameters {\n /// Base position size as fraction of capital\n pub base_position_size: f32,\n /// Maximum position size multiplier\n pub max_position_multiplier: f32,\n /// Minimum confidence to trade\n pub min_confidence: f32,\n /// Stop-loss percentage\n pub stop_loss_pct: f32,\n /// Take-profit percentage\n pub take_profit_pct: f32,\n /// Maximum holding time in seconds\n pub max_hold_time: u64,\n /// Daily loss limit as fraction of capital\n pub daily_loss_limit: f32,\n /// Maximum consecutive losses before pause\n pub max_consecutive_losses: usize,\n}\n\nimpl Default for RiskParameters {\n fn default() -> Self {\n Self {\n base_position_size: 0.02, // 2% of capital per trade\n max_position_multiplier: 3.0, // Maximum 6% per trade\n min_confidence: 0.50,\n stop_loss_pct: 0.02, // 2% stop loss\n take_profit_pct: 0.03, // 3% take profit\n max_hold_time: 4 * 3600, // 4 hours\n daily_loss_limit: 0.03, // 3% daily loss limit\n max_consecutive_losses: 5,\n }\n }\n}\n\nimpl RiskParameters {\n /// Calculate position size based on confidence\n pub fn calculate_position_size(&self, confidence: f32, capital: f32) -> f32 {\n if confidence < self.min_confidence {\n return 0.0;\n }\n\n // Scale position size with confidence\n let confidence_multiplier = match confidence {\n c if c < 0.65 => 0.25,\n c if c < 0.75 => 0.50,\n c if c < 0.85 => 0.75,\n _ => 1.00,\n };\n\n let position_size = self.base_position_size\n * confidence_multiplier\n * self.max_position_multiplier;\n\n capital * position_size.min(self.base_position_size * self.max_position_multiplier)\n }\n\n /// Scale parameters for new assets\n pub fn scale_for_new_asset(&self, hours_since_listing: f32) -> Self {\n let scale_factor = match hours_since_listing {\n h if h < 4.0 => 0.10,\n h if h < 12.0 => 0.25,\n h if h < 24.0 => 0.50,\n h if h < 48.0 => 0.75,\n _ => 1.00,\n };\n\n Self {\n base_position_size: self.base_position_size * scale_factor,\n min_confidence: self.min_confidence + (1.0 - scale_factor) * 0.2,\n stop_loss_pct: self.stop_loss_pct * (1.0 + (1.0 - scale_factor) * 0.5),\n ..*self\n }\n }\n}\n```\n\n## Performance Metrics\n\n### Model Evaluation Metrics\n\n| Metric | Description | Target |\n|--------|-------------|--------|\n| **Few-Shot Accuracy** | Classification accuracy with K examples | > 65% |\n| **1-Shot Accuracy** | Accuracy with single example per class | > 55% |\n| **5-Shot Accuracy** | Accuracy with 5 examples per class | > 70% |\n| **Cross-Asset Transfer** | Accuracy on unseen assets | > 60% |\n| **Adaptation Speed** | Episodes needed to reach target accuracy | < 10 |\n| **Confidence Calibration** | Reliability of confidence scores | ECE < 0.10 |\n\n### Trading Performance Metrics\n\n| Metric | Description | Target |\n|--------|-------------|--------|\n| **Sharpe Ratio** | Risk-adjusted returns | > 2.0 |\n| **Sortino Ratio** | Downside risk-adjusted returns | > 2.5 |\n| **Max Drawdown** | Largest peak-to-trough decline | < 15% |\n| **Win Rate** | Percentage of profitable trades | > 55% |\n| **Profit Factor** | Gross profit / Gross loss | > 1.5 |\n| **New Asset ROI** | Returns on newly listed assets | > 10% monthly |\n\n### Latency Budget\n\n```\n┌─────────────────────────────────────────────────┐\n│ Latency Requirements │\n├─────────────────────────────────────────────────┤\n│ Feature Extraction: < 5ms │\n│ Embedding Computation: < 15ms │\n│ Prototype Distance: < 3ms │\n│ Prediction: < 2ms │\n├─────────────────────────────────────────────────┤\n│ Total Inference: < 25ms │\n├─────────────────────────────────────────────────┤\n│ Support Set Update: < 50ms │\n│ MAML Adaptation (if used): < 500ms │\n└─────────────────────────────────────────────────┘\n```\n\n## References\n\n1. **Short-Term Stock Price-Trend Prediction Using Meta-Learning**\n - URL: https://arxiv.org/abs/2105.13599\n - Year: 2021\n\n2. **Prototypical Networks for Few-shot Learning**\n - Snell, J., Swersky, K., & Zemel, R. (2017)\n - URL: https://arxiv.org/abs/1703.05175\n\n3. **Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks**\n - Finn, C., Abbeel, P., & Levine, S. (2017)\n - URL: https://arxiv.org/abs/1703.03400\n\n4. **Matching Networks for One Shot Learning**\n - Vinyals, O., et al. (2016)\n - URL: https://arxiv.org/abs/1606.04080\n\n5. **Meta-Learning: A Survey**\n - Hospedales, T., et al. (2020)\n - URL: https://arxiv.org/abs/2004.05439\n\n6. **Few-Shot Learning for Time Series Forecasting**\n - Recent advances in applying meta-learning to temporal data\n\n7. **Cross-Asset Learning in Financial Markets**\n - Transfer learning approaches for multi-asset trading\n\n---\n\n## Next Steps\n\n- [Simple Explanation](readme.simple.md) - Beginner-friendly version\n- [Russian Version](README.ru.md) - Russian translation\n- [Run Examples](examples/) - Working Rust code\n- [Python Implementation](python/) - PyTorch reference implementation\n\n---\n\n*Chapter 86 of Machine Learning for Trading*\n","numLines":1379,"startLine":1,"totalLines":1379}}} | |
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| "content": " 1→# Few-Shot Market Prediction - Simple Explanation\n 2→\n 3→## What is this all about? (The Easiest Explanation)\n 4→\n 5→Imagine you're a **doctor** who needs to diagnose rare diseases:\n 6→\n 7→- **Common cold** - you've seen thousands of patients with this\n 8→- **Flu** - you've also seen many flu cases\n 9→- **Rare tropical disease** - you've only seen **3 patients** in your entire career!\n 10→\n 11→Can you still recognize the rare disease when you see it again? **Yes, you can!** Because you learned to pick out the key symptoms that make it unique, even from just a few cases.\n 12→\n 13→**Few-Shot Learning works exactly like this:**\n 14→1. Learn from a small number of examples (shots)\n 15→2. Build a mental model of what each category looks like\n 16→3. When you see something new, compare it to your mental models\n 17→4. Make a prediction based on similarity!\n 18→\n 19→Now replace diseases with **trading patterns**:\n 20→- **Common cold** = Normal trading day (seen thousands)\n 21→- **Flu** = Regular volatility spike (seen many times)\n 22→- **Rare tropical disease** = Black swan event / New coin pattern (seen only a few times!)\n 23→\n 24→And you have Few-Shot Learning for trading!\n 25→\n 26→---\n 27→\n 28→## The Big Problem We're Solving\n 29→\n 30→### The \"New Coin\" Problem\n 31→\n 32→Imagine you're a trader and a **brand new cryptocurrency** just got listed:\n 33→\n 34→```\n 35→NEW COIN: \"QUANTUM\" just launched on Bybit!\n 36→\n 37→You have:\n 38→• 3 days of price data\n 39→• A few hundred trades to analyze\n 40→• No historical patterns to study\n 41→\n 42→Traditional AI says: \"Sorry, I need 6 months of data!\"\n 43→Few-Shot AI says: \"Let me learn from similar coins I've seen before!\"\n 44→```\n 45→\n 46→### The \"Rare Event\" Problem\n 47→\n 48→```\n 49→FLASH CRASH detected!\n 50→\n 51→Traditional AI: \"I've only seen 5 flash crashes in history.\n 52→ That's not enough data to learn from!\"\n 53→\n 54→Few-Shot AI: \"5 examples? That's plenty!\n 55→ I'll learn what makes them special.\"\n 56→```\n 57→\n 58→---\n 59→\n 60→## Let's Break It Down Step by Step\n 61→\n 62→### Step 1: The Learning-to-Learn Concept\n 63→\n 64→Few-shot learning is about **learning HOW to learn**, not just learning specific patterns.\n 65→\n 66→```\n 67→Traditional AI Learning:\n 68→\n 69→Teacher: \"Study these 10,000 Bitcoin patterns\"\n 70→AI: *memorizes all patterns*\n 71→Teacher: \"What about this new Ethereum pattern?\"\n 72→AI: \"I don't know Ethereum, only Bitcoin!\"\n 73→\n 74→The AI memorized, but didn't learn HOW to learn.\n 75→```\n 76→\n 77→```\n 78→Few-Shot AI Learning:\n 79→\n 80→Teacher: \"Here are 5 examples each of 10 different coins\"\n 81→AI: \"I see... price spikes look similar across coins...\"\n 82→AI: \"Volume patterns have common characteristics...\"\n 83→AI: \"I'm learning the STRUCTURE of patterns!\"\n 84→Teacher: \"Here's a brand new coin with 5 examples\"\n 85→AI: \"Got it! I can apply what I learned about patterns!\"\n 86→\n 87→The AI learned HOW to recognize patterns in general!\n 88→```\n 89→\n 90→### Step 2: What is \"N-way K-shot\"?\n 91→\n 92→This is fancy terminology, but it's simple:\n 93→\n 94→```\n 95→N-way = How many categories to choose from\n 96→K-shot = How many examples per category\n 97→\n 98→Example: 5-way 3-shot\n 99→\n 100→5 categories (ways):\n 101→┌─────────────────────────────────────────┐\n 102→│ 1. STRONG UPTREND │\n 103→│ 2. WEAK UPTREND │\n 104→│ 3. SIDEWAYS │\n 105→│ 4. WEAK DOWNTREND │\n 106→│ 5. CRASH │\n 107→└─────────────────────────────────────────┘\n 108→\n 109→3 examples each (shots):\n 110→┌─────────────────────────────────────────┐\n 111→│ STRONG UPTREND: Example1, Example2, Example3 │\n 112→│ WEAK UPTREND: Example1, Example2, Example3 │\n 113→│ SIDEWAYS: Example1, Example2, Example3 │\n 114→│ ... and so on │\n 115→└─────────────────────────────────────────┘\n 116→\n 117→Total examples needed: 5 × 3 = 15 examples!\n 118→That's all the AI needs to make predictions!\n 119→```\n 120→\n 121→### Step 3: The Support Set and Query\n 122→\n 123→Think of it like a **matching game**:\n 124→\n 125→```\n 126→SUPPORT SET (Reference Cards):\n 127→┌─────┐ ┌─────┐ ┌─────┐\n 128→│ 📈 │ │ 📈 │ │ 📈 │ ← 3 examples of UPTREND\n 129→│ UP │ │ UP │ │ UP │\n 130→└─────┘ └─────┘ └─────┘\n 131→\n 132→┌─────┐ ┌─────┐ ┌─────┐\n 133→│ 📉 │ │ 📉 │ │ 📉 │ ← 3 examples of DOWNTREND\n 134→│DOWN │ │DOWN │ │DOWN │\n 135→└─────┘ └─────┘ └─────┘\n 136→\n 137→QUERY (Unknown Card):\n 138→┌─────┐\n 139→│ ❓ │ ← New market data - which category?\n 140→│ ??? │\n 141→└─────┘\n 142→\n 143→AI compares the query to all support examples\n 144→and finds the closest match!\n 145→```\n 146→\n 147→---\n 148→\n 149→## Real World Analogy: The Language Detective\n 150→\n 151→Imagine you're a **language detective** trying to learn new languages with minimal examples:\n 152→\n 153→### Traditional Language Learning\n 154→\n 155→```\n 156→To learn Spanish:\n 157→- 5 years of classes\n 158→- 10,000 vocabulary words\n 159→- 1,000 hours of practice\n 160→\n 161→To learn French:\n 162→- Start from scratch!\n 163→- Another 5 years of classes\n 164→- Another 10,000 words\n 165→```\n 166→\n 167→### Few-Shot Language Learning\n 168→\n 169→```\n 170→First, you learn HOW languages work:\n 171→- Words have patterns (prefixes, suffixes)\n 172→- Grammar follows rules\n 173→- Similar languages share roots\n 174→\n 175→Now for a new language (Portuguese):\n 176→- You only need 100 example sentences\n 177→- You recognize patterns from Spanish\n 178→- You adapt your existing knowledge\n 179→\n 180→You're not memorizing - you're TRANSFERRING knowledge!\n 181→```\n 182→\n 183→### How This Applies to Trading\n 184→\n 185→```\n 186→Traditional Trading AI:\n 187→┌─────────────────────────────────────────────────┐\n 188→│ To predict Bitcoin: │\n 189→│ - Train on 3 years of BTC data │\n 190→│ - 1 million data points │\n 191→│ │\n 192→│ To predict Ethereum: │\n 193→│ - Start over! Train on 3 years of ETH data │\n 194→│ - Another 1 million data points │\n 195→│ │\n 196→│ New coin listed yesterday? │\n 197→│ - \"Sorry, impossible to predict!\" │\n 198→└─────────────────────────────────────────────────┘\n 199→\n 200→Few-Shot Trading AI:\n 201→┌─────────────────────────────────────────────────┐\n 202→│ First, learn HOW crypto patterns work: │\n 203→│ - Trained on many coins │\n 204→│ - Learned general pattern structures │\n 205→│ │\n 206→│ New coin listed yesterday? │\n 207→│ - \"Give me 5 examples of each pattern type\" │\n 208→│ - \"I'll apply my general knowledge!\" │\n 209→│ - \"Here's my prediction with 73% confidence\" │\n 210→└─────────────────────────────────────────────────┘\n 211→```\n 212→\n 213→---\n 214→\n 215→## Three Ways to Do Few-Shot Learning\n 216→\n 217→### Method 1: Metric-Based (Measuring Similarity)\n 218→\n 219→This is like finding the **closest match**:\n 220→\n 221→```\n 222→How it works:\n 223→\n 224→1. Turn each example into numbers (embedding)\n 225→ \"BTC going up with high volume\" → [0.8, 0.9, 0.3, ...]\n 226→\n 227→2. Calculate average for each category\n 228→ UPTREND average → [0.7, 0.8, 0.4, ...]\n 229→ DOWNTREND average → [0.2, 0.3, 0.6, ...]\n 230→\n 231→3. For new data, find the closest average\n 232→ New data → [0.75, 0.85, 0.35, ...]\n 233→\n 234→ Distance to UPTREND: 0.07 ← Closest!\n 235→ Distance to DOWNTREND: 0.58\n 236→\n 237→ Prediction: UPTREND!\n 238→```\n 239→\n 240→**Example algorithms:**\n 241→- Prototypical Networks (Chapter 83)\n 242→- Siamese Networks\n 243→- Matching Networks\n 244→\n 245→### Method 2: Optimization-Based (MAML)\n 246→\n 247→This is like having a **quick learner brain**:\n 248→\n 249→```\n 250→MAML = Model-Agnostic Meta-Learning\n 251→\n 252→The idea:\n 253→┌─────────────────────────────────────────────────┐\n 254→│ Normal AI: \"I'm optimized for Bitcoin data\" │\n 255→│ \"To learn Ethereum, retrain me!\" │\n 256→│ │\n 257→│ MAML AI: \"I'm optimized to be a quick learner\" │\n 258→│ \"Give me 5 ETH examples...\" │\n 259→│ \"...and 2 quick updates later...\" │\n 260→│ \"I'm now good at ETH too!\" │\n 261→└─────────────────────────────────────────────────┘\n 262→\n 263→MAML finds a \"starting point\" that works well\n 264→for QUICKLY adapting to any new task!\n 265→```\n 266→\n 267→Think of it like this:\n 268→\n 269→```\n 270→Regular athlete: Trained specifically for tennis\n 271→ Needs years to switch to badminton\n 272→\n 273→MAML athlete: Trained to be adaptable\n 274→ Can pick up ANY racket sport in a week\n 275→ Has a \"good starting form\" that adapts\n 276→```\n 277→\n 278→### Method 3: Hybrid (Best of Both)\n 279→\n 280→Combine similarity matching WITH quick adaptation:\n 281→\n 282→```\n 283→Step 1: Use Metric-Based for initial prediction\n 284→ \"Based on similarity, this looks like a crash\"\n 285→ Confidence: 65%\n 286→\n 287→Step 2: Use MAML to fine-tune with recent data\n 288→ \"Let me adjust based on these 5 recent examples\"\n 289→\n 290→Step 3: Final prediction\n 291→ \"This is definitely a crash pattern\"\n 292→ Confidence: 89%\n 293→```\n 294→\n 295→---\n 296→\n 297→## Trading with Few-Shot Learning\n 298→\n 299→### Scenario 1: New Coin Analysis\n 300→\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": "# Few-Shot Market Prediction - Simple Explanation\n\n## What is this all about? (The Easiest Explanation)\n\nImagine you're a **doctor** who needs to diagnose rare diseases:\n\n- **Common cold** - you've seen thousands of patients with this\n- **Flu** - you've also seen many flu cases\n- **Rare tropical disease** - you've only seen **3 patients** in your entire career!\n\nCan you still recognize the rare disease when you see it again? **Yes, you can!** Because you learned to pick out the key symptoms that make it unique, even from just a few cases.\n\n**Few-Shot Learning works exactly like this:**\n1. Learn from a small number of examples (shots)\n2. Build a mental model of what each category looks like\n3. When you see something new, compare it to your mental models\n4. Make a prediction based on similarity!\n\nNow replace diseases with **trading patterns**:\n- **Common cold** = Normal trading day (seen thousands)\n- **Flu** = Regular volatility spike (seen many times)\n- **Rare tropical disease** = Black swan event / New coin pattern (seen only a few times!)\n\nAnd you have Few-Shot Learning for trading!\n\n---\n\n## The Big Problem We're Solving\n\n### The \"New Coin\" Problem\n\nImagine you're a trader and a **brand new cryptocurrency** just got listed:\n\n```\nNEW COIN: \"QUANTUM\" just launched on Bybit!\n\nYou have:\n• 3 days of price data\n• A few hundred trades to analyze\n• No historical patterns to study\n\nTraditional AI says: \"Sorry, I need 6 months of data!\"\nFew-Shot AI says: \"Let me learn from similar coins I've seen before!\"\n```\n\n### The \"Rare Event\" Problem\n\n```\nFLASH CRASH detected!\n\nTraditional AI: \"I've only seen 5 flash crashes in history.\n That's not enough data to learn from!\"\n\nFew-Shot AI: \"5 examples? That's plenty!\n I'll learn what makes them special.\"\n```\n\n---\n\n## Let's Break It Down Step by Step\n\n### Step 1: The Learning-to-Learn Concept\n\nFew-shot learning is about **learning HOW to learn**, not just learning specific patterns.\n\n```\nTraditional AI Learning:\n\nTeacher: \"Study these 10,000 Bitcoin patterns\"\nAI: *memorizes all patterns*\nTeacher: \"What about this new Ethereum pattern?\"\nAI: \"I don't know Ethereum, only Bitcoin!\"\n\nThe AI memorized, but didn't learn HOW to learn.\n```\n\n```\nFew-Shot AI Learning:\n\nTeacher: \"Here are 5 examples each of 10 different coins\"\nAI: \"I see... price spikes look similar across coins...\"\nAI: \"Volume patterns have common characteristics...\"\nAI: \"I'm learning the STRUCTURE of patterns!\"\nTeacher: \"Here's a brand new coin with 5 examples\"\nAI: \"Got it! I can apply what I learned about patterns!\"\n\nThe AI learned HOW to recognize patterns in general!\n```\n\n### Step 2: What is \"N-way K-shot\"?\n\nThis is fancy terminology, but it's simple:\n\n```\nN-way = How many categories to choose from\nK-shot = How many examples per category\n\nExample: 5-way 3-shot\n\n5 categories (ways):\n┌─────────────────────────────────────────┐\n│ 1. STRONG UPTREND │\n│ 2. WEAK UPTREND │\n│ 3. SIDEWAYS │\n│ 4. WEAK DOWNTREND │\n│ 5. CRASH │\n└─────────────────────────────────────────┘\n\n3 examples each (shots):\n┌─────────────────────────────────────────┐\n│ STRONG UPTREND: Example1, Example2, Example3 │\n│ WEAK UPTREND: Example1, Example2, Example3 │\n│ SIDEWAYS: Example1, Example2, Example3 │\n│ ... and so on │\n└─────────────────────────────────────────┘\n\nTotal examples needed: 5 × 3 = 15 examples!\nThat's all the AI needs to make predictions!\n```\n\n### Step 3: The Support Set and Query\n\nThink of it like a **matching game**:\n\n```\nSUPPORT SET (Reference Cards):\n┌─────┐ ┌─────┐ ┌─────┐\n│ 📈 │ │ 📈 │ │ 📈 │ ← 3 examples of UPTREND\n│ UP │ │ UP │ │ UP │\n└─────┘ └─────┘ └─────┘\n\n┌─────┐ ┌─────┐ ┌─────┐\n│ 📉 │ │ 📉 │ │ 📉 │ ← 3 examples of DOWNTREND\n│DOWN │ │DOWN │ │DOWN │\n└─────┘ └─────┘ └─────┘\n\nQUERY (Unknown Card):\n┌─────┐\n│ ❓ │ ← New market data - which category?\n│ ??? │\n└─────┘\n\nAI compares the query to all support examples\nand finds the closest match!\n```\n\n---\n\n## Real World Analogy: The Language Detective\n\nImagine you're a **language detective** trying to learn new languages with minimal examples:\n\n### Traditional Language Learning\n\n```\nTo learn Spanish:\n- 5 years of classes\n- 10,000 vocabulary words\n- 1,000 hours of practice\n\nTo learn French:\n- Start from scratch!\n- Another 5 years of classes\n- Another 10,000 words\n```\n\n### Few-Shot Language Learning\n\n```\nFirst, you learn HOW languages work:\n- Words have patterns (prefixes, suffixes)\n- Grammar follows rules\n- Similar languages share roots\n\nNow for a new language (Portuguese):\n- You only need 100 example sentences\n- You recognize patterns from Spanish\n- You adapt your existing knowledge\n\nYou're not memorizing - you're TRANSFERRING knowledge!\n```\n\n### How This Applies to Trading\n\n```\nTraditional Trading AI:\n┌─────────────────────────────────────────────────┐\n│ To predict Bitcoin: │\n│ - Train on 3 years of BTC data │\n│ - 1 million data points │\n│ │\n│ To predict Ethereum: │\n│ - Start over! Train on 3 years of ETH data │\n│ - Another 1 million data points │\n│ │\n│ New coin listed yesterday? │\n│ - \"Sorry, impossible to predict!\" │\n└─────────────────────────────────────────────────┘\n\nFew-Shot Trading AI:\n┌─────────────────────────────────────────────────┐\n│ First, learn HOW crypto patterns work: │\n│ - Trained on many coins │\n│ - Learned general pattern structures │\n│ │\n│ New coin listed yesterday? │\n│ - \"Give me 5 examples of each pattern type\" │\n│ - \"I'll apply my general knowledge!\" │\n│ - \"Here's my prediction with 73% confidence\" │\n└─────────────────────────────────────────────────┘\n```\n\n---\n\n## Three Ways to Do Few-Shot Learning\n\n### Method 1: Metric-Based (Measuring Similarity)\n\nThis is like finding the **closest match**:\n\n```\nHow it works:\n\n1. Turn each example into numbers (embedding)\n \"BTC going up with high volume\" → [0.8, 0.9, 0.3, ...]\n\n2. Calculate average for each category\n UPTREND average → [0.7, 0.8, 0.4, ...]\n DOWNTREND average → [0.2, 0.3, 0.6, ...]\n\n3. For new data, find the closest average\n New data → [0.75, 0.85, 0.35, ...]\n\n Distance to UPTREND: 0.07 ← Closest!\n Distance to DOWNTREND: 0.58\n\n Prediction: UPTREND!\n```\n\n**Example algorithms:**\n- Prototypical Networks (Chapter 83)\n- Siamese Networks\n- Matching Networks\n\n### Method 2: Optimization-Based (MAML)\n\nThis is like having a **quick learner brain**:\n\n```\nMAML = Model-Agnostic Meta-Learning\n\nThe idea:\n┌─────────────────────────────────────────────────┐\n│ Normal AI: \"I'm optimized for Bitcoin data\" │\n│ \"To learn Ethereum, retrain me!\" │\n│ │\n│ MAML AI: \"I'm optimized to be a quick learner\" │\n│ \"Give me 5 ETH examples...\" │\n│ \"...and 2 quick updates later...\" │\n│ \"I'm now good at ETH too!\" │\n└─────────────────────────────────────────────────┘\n\nMAML finds a \"starting point\" that works well\nfor QUICKLY adapting to any new task!\n```\n\nThink of it like this:\n\n```\nRegular athlete: Trained specifically for tennis\n Needs years to switch to badminton\n\nMAML athlete: Trained to be adaptable\n Can pick up ANY racket sport in a week\n Has a \"good starting form\" that adapts\n```\n\n### Method 3: Hybrid (Best of Both)\n\nCombine similarity matching WITH quick adaptation:\n\n```\nStep 1: Use Metric-Based for initial prediction\n \"Based on similarity, this looks like a crash\"\n Confidence: 65%\n\nStep 2: Use MAML to fine-tune with recent data\n \"Let me adjust based on these 5 recent examples\"\n\nStep 3: Final prediction\n \"This is definitely a crash pattern\"\n Confidence: 89%\n```\n\n---\n\n## Trading with Few-Shot Learning\n\n### Scenario 1: New Coin Analysis\n", | |
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| "prompt": "Summarize the key concepts, methods, and techniques of Task-Agnostic Representation Learning. What are the main ideas, architectures, and how can this be applied to trading/financial data?" | |
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| "content": "# Response\n\nI cannot provide information about Task-Agnostic Representation Learning based on the provided content. The webpage describes a research paper on **\"Semantic Labelling and Learning for Parity Game Solving in LTL Synthesis\"** (arXiv:1907.12157), which focuses on game theory and formal verification—not task-agnostic representation learning.\n\nThe paper's abstract indicates it addresses: \"solving games in the context of LTL synthesis\" through semantic labeling, strategy improvement techniques, and Q-learning applications to parity games. These concepts relate to formal methods and automated synthesis rather than general-purpose representation learning for financial applications.\n\nTo address your question about task-agnostic representation learning and financial applications, you would need to provide source material specifically covering that topic." | |
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| "result": "# Response\n\nI cannot provide information about Task-Agnostic Representation Learning based on the provided content. The webpage describes a research paper on **\"Semantic Labelling and Learning for Parity Game Solving in LTL Synthesis\"** (arXiv:1907.12157), which focuses on game theory and formal verification—not task-agnostic representation learning.\n\nThe paper's abstract indicates it addresses: \"solving games in the context of LTL synthesis\" through semantic labeling, strategy improvement techniques, and Q-learning applications to parity games. These concepts relate to formal methods and automated synthesis rather than general-purpose representation learning for financial applications.\n\nTo address your question about task-agnostic representation learning and financial applications, you would need to provide source material specifically covering that topic.", | |
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| "content": "Web search results for query: \"task agnostic representation learning meta learning finance trading 2024 2025\"\n\nLinks: [{\"title\":\"[2303.02841] Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance\",\"url\":\"https://ar5iv.labs.arxiv.org/html/2303.02841\"},{\"title\":\"(PDF) Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance | Bixing Yan - Academia.edu\",\"url\":\"https://www.academia.edu/108228513/Model_Agnostic_Meta_Learning_for_Natural_Language_Understanding_Tasks_in_Finance\"},{\"title\":\"Trading in Fast-Changing Markets with Meta-Reinforcement ...\",\"url\":\"https://cdn.techscience.cn/files/iasc/2024/TSP_IASC-39-2/TSP_IASC_42762/TSP_IASC_42762.pdf\"},{\"title\":\"FinRL Contests: Benchmarking Data-driven Financial Reinforcement Learning Agents\",\"url\":\"https://arxiv.org/html/2504.02281v3\"},{\"title\":\"Adaptive Event-Driven Labeling: Multi-Scale Causal Framework with Meta-Learning for Financial Time Series | MDPI\",\"url\":\"https://www.mdpi.com/2076-3417/15/24/13204\"},{\"title\":\"Reinforcement Learning in Financial Decision Making: A Systematic Review of Performance, Challenges, and Implementation Strategies\",\"url\":\"https://arxiv.org/html/2512.10913v1\"},{\"title\":\"Meta‐Learning Enhanced Trade Forecasting: A Neural Framework Leveraging Efficient Multicommodity STL Decomposition - Ma - 2024 - International Journal of Intelligent Systems - Wiley Online Library\",\"url\":\"https://onlinelibrary.wiley.com/doi/10.1155/2024/6176898\"},{\"title\":\"A Review of Reinforcement Learning in Financial Applications | Annual Reviews\",\"url\":\"https://www.annualreviews.org/content/journals/10.1146/annurev-statistics-112723-034423?TRACK=RSS\"},{\"title\":\"Deep learning for financial forecasting: A review of recent trends - ScienceDirect\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S1059056025008822\"},{\"title\":\"GitHub - AI4Finance-Foundation/FinRL: FinRL®: Financial Reinforcement Learning. 🔥\",\"url\":\"https://github.com/AI4Finance-Foundation/FinRL\"}]\n\nHere are the search results for task agnostic representation learning, meta learning in finance and trading for 2024-2025:\n\n## Key Research Findings\n\n### Model-Agnostic Meta-Learning (MAML) for Financial NLU\nNatural language understanding (NLU) is challenging for finance due to the lack of annotated data and the specialized language in that domain. Researchers have proposed using pre-trained language models and multi-task learning to learn robust representations. However, aggressive fine-tuning often causes over-fitting and multi-task learning may favor tasks with significantly larger amounts of data. To address these problems, researchers have investigated model-agnostic meta-learning algorithm (MAML) in low-resource financial NLU tasks.(no content)The MAML model for general NLU tasks has been evaluated on financial applications including financial sentiment analysis and stock price prediction. Experiments show the MAML model is able to learn general representations that can be adapted to new tasks with limited samples effectively, and is robust to task-specific scales without over-fitting. This suggests promising applications of meta-learning algorithms in low-resource financial natural language understanding tasks.\n\n### MAML-Based Trading Methods for Fast-Changing Markets (2024)\nA model agnostic meta-learning (MAML)-based trading method involving a meta-learner and a normal learner has been proposed. A trading policy is learned by the meta-learner across multiple task data, which is then fine-tuned by the normal learner through a small amount of data from a new market task before trading in it.(no content)To improve the adaptability of the MAML-based method, an ordered multiple-step updating mechanism was proposed to explore the changing dynamic within a task market. The simulation results demonstrate that the proposed MAML-based trading methods can increase the annualized return rate by approximately 180%, 200%, and 160%, increase the Sharpe ratio by 180%, 90%, and 170%, and decrease the maximum drawdown by 30%, 20%, and 40%, compared to traditional RL approaches in three stock index futures.\n\n### Adaptive Event-Driven Labeling Framework (2025)\nFinancial time-series labeling has been limited by temporal rigidity (fixed horizons regardless of market conditions), scale blindness (single-resolution analysis), and correlation-causation conflation, causing systematic failure during regime shifts. The Adaptive Event-Driven Labeling (AEDL) framework integrates three core innovations: (1) multi-scale temporal analysis capturing hierarchical market patterns across five time resolutions, (2) causal inference using Granger causality and transfer entropy to filter spurious correlations, and (3) model-agnostic meta-learning (MAML) for adaptive parameter optimization. The framework outputs calibrated probability distributions enabling uncertainty-aware trading strategies.(no content)The experimental evaluation utilized a comprehensive dataset spanning 25 years of financial market data from January 1, 2000 to January 1, 2025, capturing multiple market cycles including the dot-com crash (2000–2002), the financial crisis (2007–2009), the European debt crisis (2010–2012), and the COVID-19 pandemic market disruption (2020–2021).\n\n### FinRL Contests 2024-2025\nTwo tasks were designed around LLM-generated signals from news, including FinRL Contest 2024 LLM-Engineered Signals with RLMF and FinRL Contest 2025 FinRL-DeepSeek for Stock Trading.(no content)Participants fine-tuned LLMs to generate signals through reinforcement learning from market feedback. The training dataset was daily OHLCV data and financial news for large-cap stocks. The effectiveness of LLM-generated signals was evaluated using a long-short trading strategy.\n\n### Meta-Learning for Trade Forecasting (2024)\nMeta-learning, or \"learning to learn,\" has emerged as a powerful paradigm for training models to quickly adapt to new tasks with minimal data. In trade forecasting, where data patterns can vary significantly, meta-learning offers a robust solution.\n\n### Reinforcement Learning in Finance - Review (2025)\nThere has been a growing trend of applying reinforcement learning (RL) in financial applications, showing great potential for decision-making tasks in finance. Reviews present comprehensive studies of RL applications in finance and conduct meta-analyses to investigate common themes, identifying challenges including explainability, Markov decision process modeling, and robustness that hinder broader utilization of RL in the financial industry.(no content)Future research directions include benchmarking, contextual RL, multi-agent RL, and model-based RL to address these challenges and further enhance the implementation of RL in finance.\n\n\nREMINDER: You MUST include the sources above in your response to the user using markdown hyperlinks." | |
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| "title": "[2303.02841] Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance", | |
| "url": "https://ar5iv.labs.arxiv.org/html/2303.02841" | |
| }, | |
| { | |
| "title": "(PDF) Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance | Bixing Yan - Academia.edu", | |
| "url": "https://www.academia.edu/108228513/Model_Agnostic_Meta_Learning_for_Natural_Language_Understanding_Tasks_in_Finance" | |
| }, | |
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| "title": "Trading in Fast-Changing Markets with Meta-Reinforcement ...", | |
| "url": "https://cdn.techscience.cn/files/iasc/2024/TSP_IASC-39-2/TSP_IASC_42762/TSP_IASC_42762.pdf" | |
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| "title": "FinRL Contests: Benchmarking Data-driven Financial Reinforcement Learning Agents", | |
| "url": "https://arxiv.org/html/2504.02281v3" | |
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| "title": "Adaptive Event-Driven Labeling: Multi-Scale Causal Framework with Meta-Learning for Financial Time Series | MDPI", | |
| "url": "https://www.mdpi.com/2076-3417/15/24/13204" | |
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| "title": "Reinforcement Learning in Financial Decision Making: A Systematic Review of Performance, Challenges, and Implementation Strategies", | |
| "url": "https://arxiv.org/html/2512.10913v1" | |
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| "title": "Meta‐Learning Enhanced Trade Forecasting: A Neural Framework Leveraging Efficient Multicommodity STL Decomposition - Ma - 2024 - International Journal of Intelligent Systems - Wiley Online Library", | |
| "url": "https://onlinelibrary.wiley.com/doi/10.1155/2024/6176898" | |
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| "title": "A Review of Reinforcement Learning in Financial Applications | Annual Reviews", | |
| "url": "https://www.annualreviews.org/content/journals/10.1146/annurev-statistics-112723-034423?TRACK=RSS" | |
| }, | |
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| "title": "Deep learning for financial forecasting: A review of recent trends - ScienceDirect", | |
| "url": "https://www.sciencedirect.com/science/article/pii/S1059056025008822" | |
| }, | |
| { | |
| "title": "GitHub - AI4Finance-Foundation/FinRL: FinRL®: Financial Reinforcement Learning. 🔥", | |
| "url": "https://github.com/AI4Finance-Foundation/FinRL" | |
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| "Here are the search results for task agnostic representation learning, meta learning in finance and trading for 2024-2025:\n\n## Key Research Findings\n\n### Model-Agnostic Meta-Learning (MAML) for Financial NLU\nNatural language understanding (NLU) is challenging for finance due to the lack of annotated data and the specialized language in that domain. Researchers have proposed using pre-trained language models and multi-task learning to learn robust representations. However, aggressive fine-tuning often causes over-fitting and multi-task learning may favor tasks with significantly larger amounts of data. To address these problems, researchers have investigated model-agnostic meta-learning algorithm (MAML) in low-resource financial NLU tasks.(no content)The MAML model for general NLU tasks has been evaluated on financial applications including financial sentiment analysis and stock price prediction. Experiments show the MAML model is able to learn general representations that can be adapted to new tasks with limited samples effectively, and is robust to task-specific scales without over-fitting. This suggests promising applications of meta-learning algorithms in low-resource financial natural language understanding tasks.\n\n### MAML-Based Trading Methods for Fast-Changing Markets (2024)\nA model agnostic meta-learning (MAML)-based trading method involving a meta-learner and a normal learner has been proposed. A trading policy is learned by the meta-learner across multiple task data, which is then fine-tuned by the normal learner through a small amount of data from a new market task before trading in it.(no content)To improve the adaptability of the MAML-based method, an ordered multiple-step updating mechanism was proposed to explore the changing dynamic within a task market. The simulation results demonstrate that the proposed MAML-based trading methods can increase the annualized return rate by approximately 180%, 200%, and 160%, increase the Sharpe ratio by 180%, 90%, and 170%, and decrease the maximum drawdown by 30%, 20%, and 40%, compared to traditional RL approaches in three stock index futures.\n\n### Adaptive Event-Driven Labeling Framework (2025)\nFinancial time-series labeling has been limited by temporal rigidity (fixed horizons regardless of market conditions), scale blindness (single-resolution analysis), and correlation-causation conflation, causing systematic failure during regime shifts. The Adaptive Event-Driven Labeling (AEDL) framework integrates three core innovations: (1) multi-scale temporal analysis capturing hierarchical market patterns across five time resolutions, (2) causal inference using Granger causality and transfer entropy to filter spurious correlations, and (3) model-agnostic meta-learning (MAML) for adaptive parameter optimization. The framework outputs calibrated probability distributions enabling uncertainty-aware trading strategies.(no content)The experimental evaluation utilized a comprehensive dataset spanning 25 years of financial market data from January 1, 2000 to January 1, 2025, capturing multiple market cycles including the dot-com crash (2000–2002), the financial crisis (2007–2009), the European debt crisis (2010–2012), and the COVID-19 pandemic market disruption (2020–2021).\n\n### FinRL Contests 2024-2025\nTwo tasks were designed around LLM-generated signals from news, including FinRL Contest 2024 LLM-Engineered Signals with RLMF and FinRL Contest 2025 FinRL-DeepSeek for Stock Trading.(no content)Participants fine-tuned LLMs to generate signals through reinforcement learning from market feedback. The training dataset was daily OHLCV data and financial news for large-cap stocks. The effectiveness of LLM-generated signals was evaluated using a long-short trading strategy.\n\n### Meta-Learning for Trade Forecasting (2024)\nMeta-learning, or \"learning to learn,\" has emerged as a powerful paradigm for training models to quickly adapt to new tasks with minimal data. In trade forecasting, where data patterns can vary significantly, meta-learning offers a robust solution.\n\n### Reinforcement Learning in Finance - Review (2025)\nThere has been a growing trend of applying reinforcement learning (RL) in financial applications, showing great potential for decision-making tasks in finance. Reviews present comprehensive studies of RL applications in finance and conduct meta-analyses to investigate common themes, identifying challenges including explainability, Markov decision process modeling, and robustness that hinder broader utilization of RL in the financial industry.(no content)Future research directions include benchmarking, contextual RL, multi-agent RL, and model-based RL to address these challenges and further enhance the implementation of RL in finance." | |
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| "text": "Now I have a good understanding of task-agnostic learning concepts. Let me write the README.md file." | |
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| [2026-01-21T18:53:16.472Z] [INFO] {"type":"assistant","message":{"model":"claude-opus-4-5-20251101","id":"msg_019563dbwzwzCYBvbGZubAiq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01HXwUBqT1EW3YDxGMXgpFj6","name":"Write","input":{"file_path":"/tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/README.md","content":"# Chapter 87: Task-Agnostic Trading\n\n## Overview\n\nTask-Agnostic Trading develops universal models that can perform well across multiple trading tasks without requiring task-specific fine-tuning. Unlike traditional approaches that train separate models for price prediction, volatility forecasting, regime detection, and portfolio optimization, task-agnostic models learn general representations that transfer effectively to any downstream task.\n\n## Table of Contents\n\n1. [Introduction](#introduction)\n2. [Theoretical Foundation](#theoretical-foundation)\n3. [Task-Agnostic Architectures](#task-agnostic-architectures)\n4. [Universal Feature Learning](#universal-feature-learning)\n5. [Multi-Task Training Strategy](#multi-task-training-strategy)\n6. [Application to Trading](#application-to-trading)\n7. [Bybit Integration](#bybit-integration)\n8. [Implementation](#implementation)\n9. [Risk Management](#risk-management)\n10. [Performance Metrics](#performance-metrics)\n11. [References](#references)\n\n---\n\n## Introduction\n\nTraditional machine learning for trading faces a fundamental problem: models are typically task-specific. A model trained for price direction prediction cannot be directly used for volatility forecasting or regime detection. This requires maintaining multiple models, each with its own training pipeline, hyperparameters, and maintenance overhead.\n\n### The Task-Specific Problem\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ Traditional Task-Specific Approach │\n├─────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ SEPARATE MODELS FOR EACH TASK: │\n│ ─────────────────────────────── │\n│ │\n│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │\n│ │ Price Direction │ │ Volatility │ │ Regime │ │\n│ │ Predictor │ │ Forecaster │ │ Detector │ │\n│ ├─────────────────┤ ├─────────────────┤ ├─────────────────┤ │\n│ │ • Own features │ │ • Own features │ │ • Own features │ │\n│ │ • Own training │ │ • Own training │ │ • Own training │ │\n│ │ • Own tuning │ │ • Own tuning │ │ • Own tuning │ │\n│ │ • Own deployment│ │ • Own deployment│ │ • Own deployment│ │\n│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │\n│ │\n│ Problems: │\n│ • 3× development effort │\n│ • 3× maintenance overhead │\n│ • No knowledge sharing between tasks │\n│ • Inconsistent predictions across tasks │\n│ • Each model overfits to its specific task │\n│ │\n└─────────────────────────────────────────────────────────────────────────────┘\n```\n\n### The Task-Agnostic Solution\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ Task-Agnostic Unified Approach │\n├─────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ SINGLE UNIVERSAL MODEL: │\n│ ─────────────────────── │\n│ │\n│ ┌─────────────────────────┐ │\n│ │ Universal Encoder │ │\n│ │ (Shared Backbone) │ │\n│ ├─────────────────────────┤ │\n│ │ • Market representation │ │\n│ │ • Cross-task features │ │\n│ │ • General patterns │ │\n│ └───────────┬─────────────┘ │\n│ │ │\n│ ┌──────────────────┼──────────────────┐ │\n│ │ │ │ │\n│ ▼ ▼ ▼ │\n│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │\n│ │ Direction Head │ │ Volatility Head │ │ Regime Head │ │\n│ │ (tiny MLP) │ │ (tiny MLP) │ │ (tiny MLP) │ │\n│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │\n│ │\n│ Benefits: │\n│ • Single training pipeline │\n│ • Shared knowledge across tasks │\n│ • Consistent market understanding │\n│ • Better generalization │\n│ • Reduced overfitting │\n│ │\n└─────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Key Benefits for Trading\n\n| Aspect | Task-Specific | Task-Agnostic |\n|--------|--------------|---------------|\n| Development effort | N × models | 1 unified model |\n| Training data | Separate per task | Shared across tasks |\n| Knowledge transfer | None | Automatic |\n| Consistency | Independent predictions | Coherent predictions |\n| New task adaptation | Full retraining | Task head only |\n| Maintenance | High | Low |\n| Generalization | Task-specific | Universal |\n\n## Theoretical Foundation\n\n### Universal Representation Learning\n\nThe core principle of task-agnostic learning is to discover representations that capture the fundamental structure of market data, independent of any specific downstream task.\n\n**Definition**: A task-agnostic representation $\\phi: X \\to Z$ maps raw market data $X$ to a latent space $Z$ such that:\n\n$$\\forall \\text{task } T_i: \\exists \\text{ simple predictor } h_i: Z \\to Y_i$$\n\nwhere $h_i$ can solve task $T_i$ using only the shared representation $Z$.\n\n### Mathematical Framework\n\n**Multi-Task Objective**:\n\n$$\\mathcal{L}_{total} = \\sum_{i=1}^{N} \\lambda_i \\mathcal{L}_i(\\phi, h_i)$$\n\nwhere:\n- $\\phi$ is the shared encoder (task-agnostic)\n- $h_i$ is the task-specific head for task $i$\n- $\\lambda_i$ is the task weight\n- $\\mathcal{L}_i$ is the loss for task $i$\n\n**Gradient Balancing**:\n\nTo prevent any single task from dominating training:\n\n$$\\lambda_i = \\frac{\\overline{L} / L_i}{\\sum_j \\overline{L} / L_j}$$\n\nwhere $\\overline{L}$ is the average loss across all tasks.\n\n### Information-Theoretic View\n\nA good task-agnostic representation maximizes mutual information with all tasks:\n\n$$\\max_\\phi \\sum_{i=1}^{N} I(Z; Y_i)$$\n\nsubject to:\n\n$$I(Z; T) = 0$$\n\nwhere $T$ is the task identity. This ensures the representation doesn't encode task-specific biases.\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Information Flow in Task-Agnostic Learning │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Raw Market Data X │\n│ │ │\n│ │ Contains: price, volume, orderbook, news, etc. │\n│ │ │\n│ ▼ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Task-Agnostic Encoder φ │ │\n│ │ ───────────────────────────────────────────────────────────────── │ │\n│ │ │ │\n│ │ Goal: Extract information relevant to ALL trading tasks │ │\n│ │ │ │\n│ │ Keeps: Discards: │ │\n│ │ • Trend structure • Task-specific biases │ │\n│ │ • Volatility patterns • Noise irrelevant to trading │ │\n│ │ • Market microstructure • Spurious correlations │ │\n│ │ • Cross-asset relationships • Overfitting artifacts │ │\n│ │ • Regime characteristics │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ Universal Representation Z │\n│ │ │\n│ ├───── High I(Z; Price Direction) │\n│ ├───── High I(Z; Volatility) │\n│ ├───── High I(Z; Regime) │\n│ └───── Zero I(Z; Task Identity) │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Task-Agnostic Architectures\n\n### Architecture 1: Shared Encoder with Task Heads\n\nThe simplest and most common approach:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Shared Encoder Architecture │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Input: Market Features (OHLCV + Indicators + Orderbook) │\n│ ──────────────────────────────────────────────────── │\n│ │ │\n│ ▼ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Shared Encoder (Frozen after pretraining) │ │\n│ │ ───────────────────────────────────────────────────────────────── │ │\n│ │ │ │\n│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │\n│ │ │ 1D Conv │──▶│ 1D Conv │──▶│ 1D Conv │ │ │\n│ │ │ (local) │ │ (mid-range) │ │ (long-range)│ │ │\n│ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │\n│ │ │ │ │ │ │\n│ │ └────────────────┬┴─────────────────┘ │ │\n│ │ ▼ │ │\n│ │ ┌─────────────────────────┐ │ │\n│ │ │ Multi-Head Attention │ │ │\n│ │ │ (temporal relationships)│ │ │\n│ │ └─────────────────────────┘ │ │\n│ │ │ │ │\n│ │ ▼ │ │\n│ │ ┌─────────────────────────┐ │ │\n│ │ │ Global Average Pool │ │ │\n│ │ │ → 256-dim embedding │ │ │\n│ │ └─────────────────────────┘ │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ [256-dim Universal Representation] │\n│ │ │\n│ ┌─────────────────┼─────────────────┬─────────────────┐ │\n│ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ │\n│ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ │\n│ │ Direction │ │ Volatility│ │ Regime │ │ Return │ │\n│ │ Head │ │ Head │ │ Head │ │ Head │ │\n│ ├───────────┤ ├───────────┤ ├───────────┤ ├───────────┤ │\n│ │ 256→64→3 │ │ 256→64→1 │ │ 256→64→5 │ │ 256→64→1 │ │\n│ │ softmax │ │ ReLU │ │ softmax │ │ linear │ │\n│ └───────────┘ └───────────┘ └───────────┘ └───────────┘ │\n│ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ │\n│ [Up/Down/ [Predicted [Bull/Bear/ [Expected │\n│ Sideways] Volatility] Sideways/ Return] │\n│ HighVol/ │\n│ LowVol] │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Architecture 2: Transformer-Based Universal Encoder\n\nUsing attention mechanisms for richer representations:\n\n```rust\n/// Transformer-based task-agnostic encoder configuration\n#[derive(Debug, Clone)]\npub struct TransformerEncoderConfig {\n /// Input feature dimension\n pub input_dim: usize,\n /// Model dimension (d_model)\n pub model_dim: usize,\n /// Number of attention heads\n pub num_heads: usize,\n /// Number of transformer layers\n pub num_layers: usize,\n /// Feedforward dimension\n pub ff_dim: usize,\n /// Maximum sequence length\n pub max_seq_len: usize,\n /// Dropout rate\n pub dropout: f32,\n /// Output embedding dimension\n pub embedding_dim: usize,\n}\n\nimpl Default for TransformerEncoderConfig {\n fn default() -> Self {\n Self {\n input_dim: 64,\n model_dim: 256,\n num_heads: 8,\n num_layers: 6,\n ff_dim: 1024,\n max_seq_len: 256,\n dropout: 0.1,\n embedding_dim: 256,\n }\n }\n}\n\n/// Task-agnostic transformer encoder\npub struct TransformerEncoder {\n config: TransformerEncoderConfig,\n // Input projection\n input_projection: Linear,\n // Positional encoding\n positional_encoding: PositionalEncoding,\n // Transformer layers\n layers: Vec<TransformerLayer>,\n // Output projection\n output_projection: Linear,\n // Layer normalization\n layer_norm: LayerNorm,\n}\n\nimpl TransformerEncoder {\n /// Forward pass producing task-agnostic embeddings\n pub fn forward(&self, x: &Array3<f32>, mask: Option<&Array2<bool>>) -> Array2<f32> {\n // x shape: (batch, seq_len, input_dim)\n\n // 1. Project input to model dimension\n let projected = self.input_projection.forward(x);\n\n // 2. Add positional encoding\n let encoded = self.positional_encoding.forward(&projected);\n\n // 3. Pass through transformer layers\n let mut hidden = encoded;\n for layer in &self.layers {\n hidden = layer.forward(&hidden, mask);\n }\n\n // 4. Apply layer normalization\n let normalized = self.layer_norm.forward(&hidden);\n\n // 5. Global pooling (mean over sequence)\n let pooled = normalized.mean_axis(Axis(1)).unwrap();\n\n // 6. Project to embedding dimension\n self.output_projection.forward(&pooled)\n }\n}\n```\n\n### Architecture 3: Mixture of Experts (MoE)\n\nDynamic routing for different market conditions:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Mixture of Experts Architecture │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Input: Market Features │\n│ │ │\n│ ▼ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Routing Network │ │\n│ │ ───────────────────────────────────── │ │\n│ │ Analyzes input and determines expert weights │ │\n│ │ │ │\n│ │ Output: [w₁=0.1, w₂=0.6, w₃=0.2, w₄=0.1] │ │\n│ │ (weights sum to 1) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ │ Sparse routing (top-k experts) │\n│ │ │\n│ ┌─────┴─────┬─────────────┬─────────────┬─────────────┐ │\n│ │ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ ▼ │\n│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │\n│ │ E₁ │ │ E₂ │ │ E₃ │ │ E₄ │ │ E₅ │ │\n│ │Trend│ │Vol │ │Mean │ │Tail │ │Event│ │\n│ │Expert│ │Expert│ │Revert│ │Risk │ │Expert│ │\n│ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ │\n│ │ │ │ │ │ │\n│ │ ×w₁ │ ×w₂ │ ×w₃ │ ×w₄ │ ×w₅ │\n│ │ │ │ │ │ │\n│ └─────────┴──────┬──────┴─────────────┴─────────────┘ │\n│ │ │\n│ ▼ │\n│ Weighted Sum of Expert Outputs │\n│ │ │\n│ ▼ │\n│ [Universal Representation Z] │\n│ │ │\n│ ┌─────────┴─────────┐ │\n│ ▼ ▼ │\n│ Task Heads Expert Usage │\n│ Statistics │\n│ (for analysis) │\n│ │\n│ Expert Specializations: │\n│ • E₁ (Trend): Strong directional moves │\n│ • E₂ (Volatility): High uncertainty periods │\n│ • E₃ (Mean Revert): Consolidation patterns │\n│ • E₄ (Tail Risk): Extreme events │\n│ • E₅ (Event): News/earnings driven moves │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Universal Feature Learning\n\n### Self-Supervised Pre-training\n\nTrain the encoder without labels using self-supervised objectives:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Self-Supervised Pre-training Tasks │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. MASKED AUTOENCODING (MAE for Time Series) │\n│ ───────────────────────────────────────────── │\n│ │\n│ Original: [p₁] [p₂] [p₃] [p₄] [p₅] [p₆] [p₇] [p₈] │\n│ Masked: [p₁] [##] [p₃] [##] [##] [p₆] [p₇] [##] (30% masked) │\n│ Task: Reconstruct masked values │\n│ │\n│ This teaches the model to understand temporal patterns! │\n│ │\n│ 2. CONTRASTIVE LEARNING │\n│ ───────────────────────── │\n│ │\n│ Create pairs: │\n│ • Positive: Same asset, close time windows (similar patterns) │\n│ • Negative: Different assets or distant time windows │\n│ │\n│ ┌─────────┐ similar ┌─────────┐ │\n│ │ BTC t=1 │ ◀──────────▶ │ BTC t=2 │ Pull together │\n│ └─────────┘ └─────────┘ │\n│ │ │\n│ │ different │\n│ │ │\n│ ┌─────────┐ │\n│ │ ETH t=1 │ Push apart │\n│ └─────────┘ │\n│ │\n│ 3. TEMPORAL ORDER PREDICTION │\n│ ──────────────────────────── │\n│ │\n│ Given: [Window A] [Window B] │\n│ Task: Did A come before B or after B? │\n│ │\n│ This teaches temporal causality! │\n│ │\n│ 4. FUTURE PREDICTION (Next-Step) │\n│ ───────────────────────────────── │\n│ │\n│ Given: [p₁] [p₂] [p₃] [p₄] [p₅] │\n│ Task: Predict some statistics of [p₆] │\n│ (not exact value, but distribution properties) │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Feature Hierarchy\n\nThe encoder learns features at multiple levels of abstraction:\n\n```rust\n/// Feature hierarchy extracted by task-agnostic encoder\n#[derive(Debug, Clone)]\npub struct FeatureHierarchy {\n /// Low-level features (local patterns)\n pub local_features: LocalFeatures,\n /// Mid-level features (structural patterns)\n pub structural_features: StructuralFeatures,\n /// High-level features (semantic patterns)\n pub semantic_features: SemanticFeatures,\n}\n\n/// Low-level features from early layers\n#[derive(Debug, Clone)]\npub struct LocalFeatures {\n /// Price momentum at various scales\n pub momentum: Vec<f32>,\n /// Local volatility estimates\n pub local_volatility: Vec<f32>,\n /// Volume patterns\n pub volume_patterns: Vec<f32>,\n /// Bid-ask spread dynamics\n pub spread_dynamics: Vec<f32>,\n}\n\n/// Mid-level structural features\n#[derive(Debug, Clone)]\npub struct StructuralFeatures {\n /// Trend strength and direction\n pub trend_structure: Vec<f32>,\n /// Support/resistance levels (learned)\n pub price_levels: Vec<f32>,\n /// Cycle detection\n pub cyclical_patterns: Vec<f32>,\n /// Correlation structure with other assets\n pub correlation_patterns: Vec<f32>,\n}\n\n/// High-level semantic features\n#[derive(Debug, Clone)]\npub struct SemanticFeatures {\n /// Market regime encoding\n pub regime_embedding: Vec<f32>,\n /// Risk state encoding\n pub risk_embedding: Vec<f32>,\n /// Sentiment proxy\n pub sentiment_proxy: Vec<f32>,\n /// Anomaly score\n pub anomaly_score: f32,\n}\n```\n\n## Multi-Task Training Strategy\n\n### Gradient Harmonization\n\nPrevent tasks from conflicting during training:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Multi-Task Gradient Harmonization │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Problem: Task gradients can conflict! │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Example conflicting gradients: │ │\n│ │ │ │\n│ │ Direction task gradient: ▲ (increase this weight) │ │\n│ │ Volatility task gradient: ▼ (decrease this weight) │ │\n│ │ │ │\n│ │ Naive averaging: ▲ + ▼ = → (weights barely move!) │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ Solution: Gradient Harmonization Methods │\n│ │\n│ Method 1: PCGrad (Projecting Conflicting Gradients) │\n│ ──────────────────────────────────────────────── │\n│ If g₁ · g₂ < 0 (conflicting): │\n│ g₁' = g₁ - (g₁·g₂/|g₂|²) × g₂ │\n│ Project away the conflicting component │\n│ │\n│ Method 2: GradNorm (Gradient Normalization) │\n│ ────────────────────────────────────────── │\n│ Dynamically adjust task weights to balance gradient magnitudes │\n│ │\n│ Method 3: Uncertainty Weighting │\n│ ────────────────────────────── │\n│ w_i = 1/(2σ_i²) where σ_i is task-specific uncertainty │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Training Dynamics with Harmonization: │ │\n│ │ │ │\n│ │ Epoch 1-10: All tasks improve steadily │ │\n│ │ Epoch 10-50: Direction task plateaus │ │\n│ │ → Increase direction weight automatically │ │\n│ │ Epoch 50-100: All tasks converge together │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Task Curriculum\n\nTrain tasks in a structured order:\n\n```rust\n/// Task curriculum for training\n#[derive(Debug, Clone)]\npub struct TaskCurriculum {\n /// Training phases\n pub phases: Vec<TrainingPhase>,\n /// Current phase index\n pub current_phase: usize,\n /// Metrics for phase transition\n pub transition_metrics: PhaseTransitionMetrics,\n}\n\n#[derive(Debug, Clone)]\npub struct TrainingPhase {\n /// Phase name\n pub name: String,\n /// Tasks active in this phase\n pub active_tasks: Vec<TaskType>,\n /// Task weights for this phase\n pub task_weights: HashMap<TaskType, f32>,\n /// Minimum epochs in this phase\n pub min_epochs: usize,\n /// Transition condition\n pub transition_condition: TransitionCondition,\n}\n\n/// Trading tasks supported by the model\n#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]\npub enum TaskType {\n /// Predict price direction (up/down/sideways)\n DirectionPrediction,\n /// Forecast future volatility\n VolatilityForecast,\n /// Detect market regime\n RegimeDetection,\n /// Predict expected return\n ReturnPrediction,\n /// Detect anomalies\n AnomalyDetection,\n /// Predict optimal trade timing\n TradeTiming,\n}\n\nimpl TaskCurriculum {\n /// Create standard trading curriculum\n pub fn standard() -> Self {\n Self {\n phases: vec![\n // Phase 1: Self-supervised pre-training\n TrainingPhase {\n name: \"Self-Supervised\".to_string(),\n active_tasks: vec![], // No task heads, just encoder\n task_weights: HashMap::new(),\n min_epochs: 50,\n transition_condition: TransitionCondition::ReconstructionLoss(0.01),\n },\n // Phase 2: Easy tasks (direction, regime)\n TrainingPhase {\n name: \"Easy Tasks\".to_string(),\n active_tasks: vec![\n TaskType::DirectionPrediction,\n TaskType::RegimeDetection,\n ],\n task_weights: [\n (TaskType::DirectionPrediction, 0.5),\n (TaskType::RegimeDetection, 0.5),\n ].into_iter().collect(),\n min_epochs: 30,\n transition_condition: TransitionCondition::Accuracy(0.65),\n },\n // Phase 3: Add regression tasks\n TrainingPhase {\n name: \"Full Multi-Task\".to_string(),\n active_tasks: vec![\n TaskType::DirectionPrediction,\n TaskType::RegimeDetection,\n TaskType::VolatilityForecast,\n TaskType::ReturnPrediction,\n ],\n task_weights: [\n (TaskType::DirectionPrediction, 0.25),\n (TaskType::RegimeDetection, 0.25),\n (TaskType::VolatilityForecast, 0.25),\n (TaskType::ReturnPrediction, 0.25),\n ].into_iter().collect(),\n min_epochs: 50,\n transition_condition: TransitionCondition::Convergence,\n },\n ],\n current_phase: 0,\n transition_metrics: PhaseTransitionMetrics::default(),\n }\n }\n}\n```\n\n## Application to Trading\n\n### Task-Agnostic Trading System\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Task-Agnostic Trading System │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ REAL-TIME DATA INGESTION │\n│ ──────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Bybit WebSocket → Feature Pipeline → Feature Buffer │ │\n│ │ • OHLCV (1m, 5m, 15m, 1h, 4h) │ │\n│ │ • Orderbook (L2 top 20 levels) │ │\n│ │ • Trades (tick-by-tick) │ │\n│ │ • Funding rates │ │\n│ │ • Open interest │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ TASK-AGNOSTIC ENCODER (Pre-trained, Frozen) │\n│ ───────────────────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Market Features → Universal Embedding (256-dim) │ │\n│ │ Latency: < 5ms per inference │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ MULTI-TASK INFERENCE (Parallel Task Heads) │\n│ ────────────────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌───────────┐ │ │\n│ │ │ Direction │ │ Volatility │ │ Regime │ │ Return │ │ │\n│ │ │ 68% Up │ │ 2.3% exp │ │ Bull (73%) │ │ +0.5% │ │ │\n│ │ │ 22% Down │ │ │ │ │ │ expected │ │ │\n│ │ │ 10% Side │ │ │ │ │ │ │ │ │\n│ │ └─────────────┘ └─────────────┘ └─────────────┘ └───────────┘ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ DECISION FUSION (Combine All Task Outputs) │\n│ ────────────────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Consistency Check: │ │\n│ │ • Direction=Up + Regime=Bull + Return=Positive → CONSISTENT ✓ │ │\n│ │ • Direction=Up + Regime=Bear → CONFLICT! → Reduce confidence │ │\n│ │ │ │\n│ │ Risk Assessment: │ │\n│ │ • Volatility forecast determines position sizing │ │\n│ │ • Regime determines stop-loss/take-profit levels │ │\n│ │ │ │\n│ │ Final Signal: │ │\n│ │ • Action: LONG │ │\n│ │ • Confidence: 72% (consistent predictions) │ │\n│ │ • Size: 1.5% of capital (moderate volatility) │ │\n│ │ • Stop: -1.5% (bull regime, tight stop) │ │\n│ │ • Target: +2.0% (expected return + buffer) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ORDER EXECUTION │\n│ ─────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Bybit API → Market/Limit Order → Position Monitoring │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Decision Fusion Logic\n\n```rust\n/// Decision fusion from multiple task outputs\n#[derive(Debug, Clone)]\npub struct TaskOutputs {\n /// Direction prediction\n pub direction: DirectionPrediction,\n /// Volatility forecast\n pub volatility: VolatilityForecast,\n /// Regime detection\n pub regime: RegimeDetection,\n /// Return prediction\n pub expecte | |
| [2026-01-21T18:53:16.483Z] [INFO] d_return: ReturnPrediction,\n}\n\n#[derive(Debug, Clone)]\npub struct DirectionPrediction {\n pub up_prob: f32,\n pub down_prob: f32,\n pub sideways_prob: f32,\n}\n\n#[derive(Debug, Clone)]\npub struct VolatilityForecast {\n pub expected_volatility: f32,\n pub volatility_percentile: f32, // Relative to historical\n}\n\n#[derive(Debug, Clone)]\npub struct RegimeDetection {\n pub regime: MarketRegime,\n pub confidence: f32,\n}\n\n#[derive(Debug, Clone, Copy)]\npub enum MarketRegime {\n Bull,\n Bear,\n Sideways,\n HighVolatility,\n LowVolatility,\n}\n\n#[derive(Debug, Clone)]\npub struct ReturnPrediction {\n pub expected_return: f32,\n pub return_std: f32,\n}\n\n/// Fuse multiple task outputs into trading decision\npub struct DecisionFusion {\n /// Consistency weight\n consistency_weight: f32,\n /// Minimum confidence threshold\n min_confidence: f32,\n}\n\nimpl DecisionFusion {\n pub fn fuse(&self, outputs: &TaskOutputs) -> TradingDecision {\n // 1. Determine base direction\n let (direction, dir_confidence) = self.get_direction(&outputs.direction);\n\n // 2. Check consistency with regime\n let regime_agreement = self.check_regime_consistency(\n direction,\n outputs.regime.regime,\n );\n\n // 3. Check consistency with expected return\n let return_agreement = self.check_return_consistency(\n direction,\n outputs.expected_return.expected_return,\n );\n\n // 4. Calculate overall confidence\n let consistency_score = (regime_agreement + return_agreement) / 2.0;\n let final_confidence = dir_confidence *\n (1.0 - self.consistency_weight + self.consistency_weight * consistency_score);\n\n // 5. Determine position size based on volatility\n let position_size = self.calculate_position_size(\n final_confidence,\n outputs.volatility.expected_volatility,\n outputs.volatility.volatility_percentile,\n );\n\n // 6. Set stop-loss and take-profit based on regime and volatility\n let (stop_loss, take_profit) = self.calculate_exits(\n outputs.regime.regime,\n outputs.volatility.expected_volatility,\n outputs.expected_return.expected_return,\n );\n\n TradingDecision {\n action: if final_confidence >= self.min_confidence {\n match direction {\n TradeDirection::Long => TradeAction::Buy,\n TradeDirection::Short => TradeAction::Sell,\n TradeDirection::Neutral => TradeAction::Hold,\n }\n } else {\n TradeAction::Hold\n },\n confidence: final_confidence,\n position_size,\n stop_loss_pct: stop_loss,\n take_profit_pct: take_profit,\n reasoning: self.generate_reasoning(outputs, direction, consistency_score),\n }\n }\n\n fn check_regime_consistency(&self, direction: TradeDirection, regime: MarketRegime) -> f32 {\n match (direction, regime) {\n // High consistency\n (TradeDirection::Long, MarketRegime::Bull) => 1.0,\n (TradeDirection::Short, MarketRegime::Bear) => 1.0,\n (TradeDirection::Neutral, MarketRegime::Sideways) => 1.0,\n // Medium consistency\n (TradeDirection::Long, MarketRegime::LowVolatility) => 0.7,\n (TradeDirection::Short, MarketRegime::HighVolatility) => 0.6,\n // Low consistency (conflicting signals)\n (TradeDirection::Long, MarketRegime::Bear) => 0.2,\n (TradeDirection::Short, MarketRegime::Bull) => 0.2,\n // Neutral\n _ => 0.5,\n }\n }\n\n fn calculate_position_size(\n &self,\n confidence: f32,\n volatility: f32,\n volatility_percentile: f32,\n ) -> f32 {\n let base_size = 0.02; // 2% base position\n\n // Scale down for high volatility\n let vol_multiplier = if volatility_percentile > 0.8 {\n 0.5 // High vol: half size\n } else if volatility_percentile > 0.6 {\n 0.75 // Elevated vol: 3/4 size\n } else {\n 1.0 // Normal vol: full size\n };\n\n // Scale with confidence\n let conf_multiplier = (confidence - self.min_confidence) /\n (1.0 - self.min_confidence);\n\n base_size * vol_multiplier * conf_multiplier\n }\n}\n\n#[derive(Debug, Clone)]\npub struct TradingDecision {\n pub action: TradeAction,\n pub confidence: f32,\n pub position_size: f32,\n pub stop_loss_pct: f32,\n pub take_profit_pct: f32,\n pub reasoning: String,\n}\n\n#[derive(Debug, Clone, Copy)]\npub enum TradeAction {\n Buy,\n Sell,\n Hold,\n}\n```\n\n## Bybit Integration\n\n### Data Collection for Multiple Tasks\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Bybit Data Collection Pipeline │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ DATA SOURCES FOR TASK-AGNOSTIC TRAINING │\n│ ────────────────────────────────────────── │\n│ │\n│ 1. HISTORICAL DATA (Training) │\n│ ───────────────────────────── │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Endpoint: GET /v5/market/kline │ │\n│ │ Symbols: BTCUSDT, ETHUSDT, SOLUSDT, ... (top 50 by volume) │ │\n│ │ Intervals: 1m, 5m, 15m, 1h, 4h, 1d │ │\n│ │ History: 2 years │ │\n│ │ │ │\n│ │ Label Generation for Tasks: │ │\n│ │ ┌─────────────────────────────────────────────────────────────┐ │ │\n│ │ │ Direction: Based on forward return sign │ │ │\n│ │ │ Volatility: Realized volatility over next N periods │ │ │\n│ │ │ Regime: HMM clustering on return/volatility │ │ │\n│ │ │ Return: Actual forward return (continuous) │ │ │\n│ │ └─────────────────────────────────────────────────────────────┘ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 2. REAL-TIME DATA (Inference) │\n│ ───────────────────────────── │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ WebSocket: wss://stream.bybit.com/v5/public/linear │ │\n│ │ │ │\n│ │ Subscriptions: │ │\n│ │ • kline.1.BTCUSDT (1-minute candles) │ │\n│ │ • orderbook.50.BTCUSDT (L2 orderbook) │ │\n│ │ • publicTrade.BTCUSDT (tick trades) │ │\n│ │ • tickers.BTCUSDT (market stats) │ │\n│ │ │ │\n│ │ Additional REST calls: │ │\n│ │ • GET /v5/market/funding/history (funding rates) │ │\n│ │ • GET /v5/market/open-interest (open interest) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 3. FEATURE ENGINEERING │\n│ ────────────────────── │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Raw Data → Feature Pipeline → Normalized Features │ │\n│ │ │ │\n│ │ Feature Groups: │ │\n│ │ • Price features (returns, log-returns, ranges) │ │\n│ │ • Volume features (volume ratios, VWAP deviation) │ │\n│ │ • Volatility features (realized vol, ATR, Parkinson) │ │\n│ │ • Momentum features (ROC, RSI, MACD) │ │\n│ │ • Microstructure (spread, depth imbalance, trade flow) │ │\n│ │ • Crypto-specific (funding, OI change, liquidations) │ │\n│ │ │ │\n│ │ Total features: ~64 per timepoint │ │\n│ │ Sequence length: 96 timepoints (96 hours at 1h resolution) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Bybit Client Implementation\n\n```rust\nuse reqwest::Client;\nuse serde::{Deserialize, Serialize};\nuse tokio::sync::mpsc;\nuse tokio_tungstenite::{connect_async, tungstenite::Message};\nuse futures_util::{SinkExt, StreamExt};\n\n/// Bybit client for task-agnostic trading\npub struct BybitClient {\n http_client: Client,\n api_key: Option<String>,\n api_secret: Option<String>,\n base_url: String,\n ws_url: String,\n}\n\nimpl BybitClient {\n pub fn new(api_key: Option<String>, api_secret: Option<String>) -> Self {\n Self {\n http_client: Client::new(),\n api_key,\n api_secret,\n base_url: \"https://api.bybit.com\".to_string(),\n ws_url: \"wss://stream.bybit.com/v5/public/linear\".to_string(),\n }\n }\n\n /// Fetch historical klines for multiple symbols\n pub async fn fetch_training_data(\n &self,\n symbols: &[&str],\n interval: &str,\n start_time: u64,\n end_time: u64,\n ) -> Result<HashMap<String, Vec<Kline>>, BybitError> {\n let mut all_data = HashMap::new();\n\n for symbol in symbols {\n let klines = self.get_klines(symbol, interval, start_time, end_time).await?;\n all_data.insert(symbol.to_string(), klines);\n }\n\n Ok(all_data)\n }\n\n /// Get klines for a single symbol\n async fn get_klines(\n &self,\n symbol: &str,\n interval: &str,\n start_time: u64,\n end_time: u64,\n ) -> Result<Vec<Kline>, BybitError> {\n let mut all_klines = Vec::new();\n let mut current_start = start_time;\n\n while current_start < end_time {\n let url = format!(\n \"{}/v5/market/kline?category=linear&symbol={}&interval={}&start={}&limit=1000\",\n self.base_url, symbol, interval, current_start\n );\n\n let response: KlineResponse = self.http_client\n .get(&url)\n .send()\n .await?\n .json()\n .await?;\n\n if response.result.list.is_empty() {\n break;\n }\n\n let klines: Vec<Kline> = response.result.list\n .into_iter()\n .map(Kline::from)\n .collect();\n\n current_start = klines.last().map(|k| k.timestamp + 1).unwrap_or(end_time);\n all_klines.extend(klines);\n\n // Rate limiting\n tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;\n }\n\n Ok(all_klines)\n }\n\n /// Start real-time data streaming\n pub async fn start_streaming(\n &self,\n symbols: &[&str],\n tx: mpsc::Sender<MarketUpdate>,\n ) -> Result<(), BybitError> {\n let (ws_stream, _) = connect_async(&self.ws_url).await?;\n let (mut write, mut read) = ws_stream.split();\n\n // Subscribe to channels\n let topics: Vec<String> = symbols.iter()\n .flat_map(|s| vec![\n format!(\"kline.1.{}\", s),\n format!(\"orderbook.50.{}\", s),\n format!(\"publicTrade.{}\", s),\n ])\n .collect();\n\n let subscribe_msg = serde_json::json!({\n \"op\": \"subscribe\",\n \"args\": topics\n });\n\n write.send(Message::Text(subscribe_msg.to_string())).await?;\n\n // Process incoming messages\n while let Some(msg) = read.next().await {\n match msg {\n Ok(Message::Text(text)) => {\n if let Ok(update) = serde_json::from_str::<MarketUpdate>(&text) {\n tx.send(update).await.ok();\n }\n }\n Ok(Message::Ping(data)) => {\n write.send(Message::Pong(data)).await?;\n }\n Err(e) => {\n eprintln!(\"WebSocket error: {:?}\", e);\n break;\n }\n _ => {}\n }\n }\n\n Ok(())\n }\n}\n\n#[derive(Debug, Clone, Deserialize, Serialize)]\npub struct Kline {\n pub timestamp: u64,\n pub open: f64,\n pub high: f64,\n pub low: f64,\n pub close: f64,\n pub volume: f64,\n pub turnover: f64,\n}\n\n#[derive(Debug, Clone, Deserialize)]\npub struct MarketUpdate {\n pub topic: String,\n pub data: serde_json::Value,\n pub ts: u64,\n}\n```\n\n## Implementation\n\n### Module Structure\n\n```\n87_task_agnostic_trading/\n├── Cargo.toml\n├── README.md # Main documentation (English)\n├── README.ru.md # Russian translation\n├── readme.simple.md # Simplified explanation\n├── readme.simple.ru.md # Simplified Russian\n├── src/\n│ ├── lib.rs # Library root\n│ ├── config.rs # Configuration types\n│ ├── encoder/\n│ │ ├── mod.rs # Encoder module\n│ │ ├── transformer.rs # Transformer encoder\n│ │ ├── cnn.rs # CNN encoder\n│ │ └── moe.rs # Mixture of Experts\n│ ├── tasks/\n│ │ ├── mod.rs # Task module\n│ │ ├── direction.rs # Direction prediction\n│ │ ├── volatility.rs # Volatility forecast\n│ │ ├── regime.rs # Regime detection\n│ │ └── returns.rs # Return prediction\n│ ├── training/\n│ │ ├── mod.rs # Training module\n│ │ ├── multitask.rs # Multi-task training\n│ │ ├── curriculum.rs # Task curriculum\n│ │ └── pretraining.rs # Self-supervised pretraining\n│ ├── fusion/\n│ │ ├── mod.rs # Fusion module\n│ │ └── decision.rs # Decision fusion\n│ ├── data/\n│ │ ├── mod.rs # Data module\n│ │ ├── bybit.rs # Bybit API client\n│ │ ├── features.rs # Feature engineering\n│ │ └── dataset.rs # Dataset handling\n│ └── trading/\n│ ├── mod.rs # Trading module\n│ ├── strategy.rs # Trading strategy\n│ └── risk.rs # Risk management\n├── examples/\n│ ├── train_encoder.rs # Train task-agnostic encoder\n│ ├── multi_task_inference.rs # Multi-task inference example\n│ ├── bybit_trading.rs # Live trading with Bybit\n│ └── backtest.rs # Backtesting example\n├── python/\n│ ├── task_agnostic_model.py # PyTorch implementation\n│ ├── train.py # Training script\n│ ├── evaluate.py # Evaluation script\n│ └── requirements.txt # Python dependencies\n└── tests/\n ├── integration.rs # Integration tests\n └── unit_tests.rs # Unit tests\n```\n\n### Core Implementation\n\n```rust\n//! Task-Agnostic Trading Core Module\n\nuse ndarray::{Array1, Array2, Array3, Axis};\nuse std::collections::HashMap;\n\n/// Task-agnostic model configuration\n#[derive(Debug, Clone)]\npub struct TaskAgnosticConfig {\n /// Encoder configuration\n pub encoder_config: EncoderConfig,\n /// Active tasks\n pub tasks: Vec<TaskConfig>,\n /// Multi-task training configuration\n pub training_config: MultiTaskTrainingConfig,\n /// Decision fusion configuration\n pub fusion_config: FusionConfig,\n}\n\nimpl Default for TaskAgnosticConfig {\n fn default() -> Self {\n Self {\n encoder_config: EncoderConfig::default(),\n tasks: vec![\n TaskConfig::direction_prediction(),\n TaskConfig::volatility_forecast(),\n TaskConfig::regime_detection(),\n TaskConfig::return_prediction(),\n ],\n training_config: MultiTaskTrainingConfig::default(),\n fusion_config: FusionConfig::default(),\n }\n }\n}\n\n/// Encoder configuration\n#[derive(Debug, Clone)]\npub struct EncoderConfig {\n /// Input feature dimension\n pub input_dim: usize,\n /// Sequence length\n pub seq_length: usize,\n /// Encoder type\n pub encoder_type: EncoderType,\n /// Hidden dimension\n pub hidden_dim: usize,\n /// Output embedding dimension\n pub embedding_dim: usize,\n /// Dropout rate\n pub dropout: f32,\n}\n\nimpl Default for EncoderConfig {\n fn default() -> Self {\n Self {\n input_dim: 64,\n seq_length: 96,\n encoder_type: EncoderType::Transformer {\n num_layers: 6,\n num_heads: 8,\n ff_dim: 256,\n },\n hidden_dim: 256,\n embedding_dim: 256,\n dropout: 0.1,\n }\n }\n}\n\n/// Encoder architecture types\n#[derive(Debug, Clone)]\npub enum EncoderType {\n /// Transformer-based encoder\n Transformer {\n num_layers: usize,\n num_heads: usize,\n ff_dim: usize,\n },\n /// CNN-based encoder\n CNN {\n num_layers: usize,\n kernel_sizes: Vec<usize>,\n },\n /// Mixture of Experts\n MoE {\n num_experts: usize,\n top_k: usize,\n expert_dim: usize,\n },\n}\n\n/// Task configuration\n#[derive(Debug, Clone)]\npub struct TaskConfig {\n /// Task type\n pub task_type: TaskType,\n /// Task head hidden dimension\n pub head_hidden_dim: usize,\n /// Task weight in training\n pub weight: f32,\n /// Loss function type\n pub loss_type: LossType,\n}\n\nimpl TaskConfig {\n pub fn direction_prediction() -> Self {\n Self {\n task_type: TaskType::DirectionPrediction,\n head_hidden_dim: 64,\n weight: 1.0,\n loss_type: LossType::CrossEntropy,\n }\n }\n\n pub fn volatility_forecast() -> Self {\n Self {\n task_type: TaskType::VolatilityForecast,\n head_hidden_dim: 64,\n weight: 1.0,\n loss_type: LossType::MSE,\n }\n }\n\n pub fn regime_detection() -> Self {\n Self {\n task_type: TaskType::RegimeDetection,\n head_hidden_dim: 64,\n weight: 1.0,\n loss_type: LossType::CrossEntropy,\n }\n }\n\n pub fn return_prediction() -> Self {\n Self {\n task_type: TaskType::ReturnPrediction,\n head_hidden_dim: 64,\n weight: 1.0,\n loss_type: LossType::MSE,\n }\n }\n}\n\n/// Task types\n#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]\npub enum TaskType {\n DirectionPrediction,\n VolatilityForecast,\n RegimeDetection,\n ReturnPrediction,\n AnomalyDetection,\n TradeTiming,\n}\n\n/// Loss function types\n#[derive(Debug, Clone, Copy)]\npub enum LossType {\n CrossEntropy,\n MSE,\n MAE,\n Huber,\n}\n\n/// Task-agnostic trading model\npub struct TaskAgnosticModel {\n /// Configuration\n config: TaskAgnosticConfig,\n /// Shared encoder\n encoder: Box<dyn Encoder>,\n /// Task heads\n task_heads: HashMap<TaskType, Box<dyn TaskHead>>,\n /// Decision fusion\n fusion: DecisionFusion,\n}\n\n/// Encoder trait for different architectures\npub trait Encoder: Send + Sync {\n /// Forward pass producing embeddings\n fn forward(&self, x: &Array3<f32>) -> Array2<f32>;\n\n /// Get embedding dimension\n fn embedding_dim(&self) -> usize;\n}\n\n/// Task head trait for different tasks\npub trait TaskHead: Send + Sync {\n /// Forward pass from embeddings to task output\n fn forward(&self, embeddings: &Array2<f32>) -> TaskOutput;\n\n /// Get task type\n fn task_type(&self) -> TaskType;\n\n /// Compute loss\n fn compute_loss(&self, output: &TaskOutput, target: &TaskTarget) -> f32;\n}\n\n/// Task output enum\n#[derive(Debug, Clone)]\npub enum TaskOutput {\n /// Classification output (logits)\n Classification(Array2<f32>),\n /// Regression output (values)\n Regression(Array2<f32>),\n}\n\n/// Task target enum\n#[derive(Debug, Clone)]\npub enum TaskTarget {\n /// Classification target (class indices)\n Classification(Vec<usize>),\n /// Regression target (values)\n Regression(Array2<f32>),\n}\n\nimpl TaskAgnosticModel {\n /// Create a new task-agnostic model\n pub fn new(config: TaskAgnosticConfig) -> Self {\n let encoder = Self::create_encoder(&config.encoder_config);\n let task_heads = Self::create_task_heads(&config);\n let fusion = DecisionFusion::new(config.fusion_config.clone());\n\n Self {\n config,\n encoder,\n task_heads,\n fusion,\n }\n }\n\n /// Run inference on all tasks\n pub fn forward(&self, features: &Array3<f32>) -> AllTaskOutputs {\n // 1. Get universal embeddings\n let embeddings = self.encoder.forward(features);\n\n // 2. Run each task head\n let mut outputs = HashMap::new();\n for (task_type, head) in &self.task_heads {\n let output = head.forward(&embeddings);\n outputs.insert(*task_type, output);\n }\n\n AllTaskOutputs { outputs, embeddings }\n }\n\n /// Make trading decision from multi-task outputs\n pub fn make_decision(&self, outputs: &AllTaskOutputs) -> TradingDecision {\n // Convert task outputs to structured format\n let structured = self.structure_outputs(outputs);\n\n // Fuse decisions\n self.fusion.fuse(&structured)\n }\n\n fn create_encoder(config: &EncoderConfig) -> Box<dyn Encoder> {\n match &config.encoder_type {\n EncoderType::Transformer { num_layers, num_heads, ff_dim } => {\n Box::new(TransformerEncoder::new(\n config.input_dim,\n config.seq_length,\n config.hidden_dim,\n config.embedding_dim,\n *num_layers,\n *num_heads,\n *ff_dim,\n config.dropout,\n ))\n }\n EncoderType::CNN { num_layers, kernel_sizes } => {\n Box::new(CNNEncoder::new(\n config.input_dim,\n config.seq_length,\n config.hidden_dim,\n config.embedding_dim,\n *num_layers,\n kernel_sizes.clone(),\n config.dropout,\n ))\n }\n EncoderType::MoE { num_experts, top_k, expert_dim } => {\n Box::new(MoEEncoder::new(\n config.input_dim,\n config.seq_length,\n config.hidden_dim,\n config.embedding_dim,\n *num_experts,\n *top_k,\n *expert_dim,\n config.dropout,\n ))\n }\n }\n }\n\n fn create_task_heads(config: &TaskAgnosticConfig) -> HashMap<TaskType, Box<dyn TaskHead>> {\n let mut heads = HashMap::new();\n\n for task_config in &config.tasks {\n let head: Box<dyn TaskHead> = match task_config.task_type {\n TaskType::DirectionPrediction => {\n Box::new(DirectionHead::new(\n config.encoder_config.embedding_dim,\n task_config.head_hidden_dim,\n ))\n }\n TaskType::VolatilityForecast => {\n Box::new(VolatilityHead::new(\n config.encoder_config.embedding_dim,\n task_config.head_hidden_dim,\n ))\n }\n TaskType::RegimeDetection => {\n Box::new(RegimeHead::new(\n config.encoder_config.embedding_dim,\n task_config.head_hidden_dim,\n ))\n }\n TaskType::ReturnPrediction => {\n Box::new(ReturnHead::new(\n config.encoder_config.embedding_dim,\n task_config.head_hidden_dim,\n ))\n }\n _ => continue,\n };\n\n heads.insert(task_config.task_type, head);\n }\n\n heads\n }\n\n fn structure_outputs(&self, outputs: &AllTaskOutputs) -> TaskOutputs {\n // Extract and structure individual task outputs\n // (Implementation details)\n TaskOutputs {\n direction: self.extract_direction(&outputs.outputs[&TaskType::DirectionPrediction]),\n volatility: self.extract_volatility(&outputs.outputs[&TaskType::VolatilityForecast]),\n regime: self.extract_regime(&outputs.outputs[&TaskType::RegimeDetection]),\n expected_return: self.extract_return(&outputs.outputs[&TaskType::ReturnPrediction]),\n }\n }\n}\n\n/// All task outputs from forward pass\n#[derive(Debug, Clone)]\npub struct AllTaskOutputs {\n /// Individual task outputs\n pub outputs: HashMap<TaskType, TaskOutput>,\n /// Universal embeddings (for analysis)\n pub embeddings: Array2<f32>,\n}\n```\n\n## Risk Management\n\n### Multi-Task Risk Assessment\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Multi-Task Risk Assessment │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. TASK AGREEMENT SCORING │\n│ ──────────────────────── │\n│ │\n│ When tasks agree → Higher confidence, larger positions │\n│ When tasks conflict → Lower confidence, smaller positions or no trade │\n│ │\n│ Agreement Matrix: │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ │ Direction │ Volatility │ Regime │ Return │ Score │ │\n│ │──────────────┼───────────┼────────────┼────────┼────────┼─────────│ │\n│ │ Scenario 1 │ UP │ LOW │ BULL │ +0.5% │ 0.95 │ │\n│ │ (All agree) │ │ │ │ │ TRADE │ │\n│ │──────────────┼───────────┼────────────┼────────┼────────┼─────────│ │\n│ │ Scenario 2 │ UP │ HIGH │ BULL │ +0.3% │ 0.70 │ │\n│ │ (Vol warns) │ │ │ │ │ CAUTION│ │\n│ │──────────────┼───────────┼────────────┼────────┼────────┼─────────│ │\n│ │ Scenario 3 │ UP │ HIGH │ BEAR │ -0.2% │ 0.30 │ │\n│ │ (Conflict) │ │ │ │ │ NO TRADE│ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 2. DYNAMIC POSITION SIZING │\n│ ────────────────────────── │\n│ │\n│ Position Size = Base × Confidence × (1 / Volatility_Rank) │\n│ │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Confidence │ Vol Rank │ Multiplier │ Example (2% base) │ │\n│ │───────────────┼───────────┼─────────────┼─────────────────────────│ │\n│ │ 0.90+ │ Low │ 1.5x │ 3.0% position │ │\n│ │ 0.90+ │ High │ 0.75x │ 1.5% position │ │\n│ │ 0.70-0.90 │ Low │ 1.0x │ 2.0% position │ │\n│ │ 0.70-0.90 │ High │ 0.5x │ 1.0% position │ │\n│ │ 0.50-0.70 │ Low │ 0.5x │ 1.0% position │ │\n│ │ 0.50-0.70 │ High │ 0.25x │ 0.5% position │ │\n│ │ <0.50 │ Any │ 0x │ No trade │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 3. REGIME-AWARE STOP-LOSS │\n│ ───────────────────────── │\n│ │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Regime │ Stop-Loss │ Take-Profit │ Rationale │ │\n│ │─────────────────┼────────────┼──────────────┼─────────────────────│ │\n│ │ Bull │ -1.5% │ +3.0% │ Ride the trend │ │\n│ │ Bear │ -1.5% │ +3.0% │ Catch the move │ │\n│ │ Sideways │ -1.0% │ +1.5% │ Tight range play │ │\n│ │ High Volatility│ -2.5% │ +4.0% │ Wide stops │ │\n│ │ Low Volatility │ -1.0% │ +1.5% │ Tight control │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 4. PORTFOLIO-LEVEL CONSTRAINTS │\n│ ────────────────────────────── │\n│ │\n│ • Maximum total exposure: 10% of capital │\n│ • Maximum correlation: Don't trade highly correlated assets together │\n│ • Daily loss limit: -3% → stop trading for the day │\n│ • Weekly drawdown: -5% → reduce position sizes by 50% │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Risk Parameters\n\n```rust\n/// Risk management parameters for task-agnostic trading\n#[derive(Debug, Clone)]\npub struct RiskParameters {\n /// Base position size (fraction of capital)\n pub base_position_size: f32,\n /// Maximum position size multiplier\n pub max_position_multiplier: f32,\n /// Minimum confidence to trade\n pub min_confidence: f32,\n /// Minimum task agreement score\n pub min_agreement_score: f32,\n /// Base stop-loss percentage\n pub base_stop_loss: f32,\n /// Base take-profit percentage\n pub base_take_profit: f32,\n /// Maximum total exposure\n pub max_total_exposure: f32,\n /// Daily loss limit\n pub daily_loss_limit: f32,\n /// Weekly drawdown limit\n pub weekly_drawdown_limit: f32,\n}\n\nimpl Default for RiskParameters {\n fn default() -> Self {\n Self {\n base_position_size: 0.02, // 2% per trade\n max_position_multiplier: 1.5, // Up to 3% max\n min_confidence: 0.50,\n min_agreement_score: 0.60,\n base_stop_loss: 0.015, // 1.5% stop\n base_take_profit: 0.025, // 2.5% take profit\n max_total_exposure: 0.10, // 10% total\n daily_loss_limit: 0.03, // 3% daily limit\n weekly_drawdown_limit: 0.05, // 5% weekly limit\n }\n }\n}\n\nimpl RiskParameters {\n /// Calculate position size based on multi-task outputs\n pub fn calculate_position_size(\n &self,\n confidence: f32,\n agreement_score: f32,\n volatility_percentile: f32,\n current_exposure: f32,\n ) -> f32 {\n // Check minimum thresholds\n if confidence < self.min_confidence || agreement_score < self.min_agreement_score {\n return 0.0;\n }\n\n // Check exposure limit\n let remaining_capacity = self.max_total_exposure - current_exposure;\n if remaining_capacity <= 0.0 {\n return 0.0;\n }\n\n // Base calculation\n let confidence_factor = (confidence - self.min_confidence) /\n (1.0 - self.min_confidence);\n let agreement_factor = (agreement_score - self.min_agreement_score) /\n (1.0 - self.min_agreement_score);\n\n // Volatility adjustment (inverse)\n let vol_factor = if volatility_percentile > 0.8 {\n 0.5\n } else if volatility_percentile > 0.6 {\n 0.75\n } else {\n 1.0\n };\n\n let position_size = self.base_position_size\n * confidence_factor\n * agreement_factor\n * vol_factor\n * self.max_position_multiplier;\n\n // Cap at remaining capacity\n position_size.min(remaining_capacity)\n }\n\n /// Get regime-adjusted stop-loss\n pub fn get_stop_loss(&self, regime: MarketRegime, volatility_percentile: f32) -> f32 {\n let base = match regime {\n MarketRegime::Bull | MarketRegime::Bear => self.base_stop_loss,\n MarketRegime::Sideways | MarketRegime::LowVolatility => self.base_stop_loss * 0.67,\n MarketRegime::HighVolatility => self.base_stop_loss * 1.67,\n };\n\n // Widen for high volatility\n if volatility_percentile > 0.8 {\n base * 1.5\n } else {\n base\n }\n }\n\n /// Get regime-adjusted take-profit\n pub fn get_take_profit(&self, regime: MarketRegime, volatility_percentile: f32) -> f32 {\n let base = match regime {\n MarketRegime::Bull | MarketRegime::Bear => self.base_take_profit * 1.2,\n MarketRegime::Sideways | MarketRegime::LowVolatility => self.base_take_profit * 0.6,\n MarketRegime::HighVolatility => self.base_take_profit * 1.6,\n };\n\n // Widen for high volatility\n if volatility_percentile > 0.8 {\n base * 1.3\n } else {\n base\n }\n }\n}\n```\n\n## Performance Metrics\n\n### Model Evaluation Metrics\n\n| Metric | Description | Target |\n|--------|-------------|--------|\n| **Multi-Task Accuracy** | Average accuracy across all classification tasks | > 65% |\n| **Direction Accuracy** | Price direction prediction accuracy | > 60% |\n| **Regime Accuracy** | Market regime detection accuracy | > 70% |\n| **Volatility RMSE** | Root mean squared error for volatility forecast | < 0.02 |\n| **Return Correlation** | Correlation between predicted and actual returns | > 0.3 |\n| **Task Transfer** | Performance when adding new task | > 90% of from-scratch |\n| **Consistency Score** | How often task predictions agree | > 70% |\n\n### Trading Performance Metrics\n\n| Metric | Description | Target |\n|--------|-------------|--------|\n| **Sharpe Ratio** | Risk-adjusted returns | > 2.0 |\n| **Sortino Ratio** | Downside risk-adjusted returns | > 2.5 |\n| **Max Drawdown** | Largest peak-to-trough decline | < 15% |\n| **Win Rate** | Percentage of profitable trades | > 55% |\n| **Profit Factor** | Gross profit / Gross loss | > 1.5 |\n| **Calmar Ratio** | Annual return / Max drawdown | > 1.0 |\n\n### Latency Budget\n\n```\n┌─────────────────────────────────────────────────┐\n│ Latency Requirements │\n├─────────────────────────────────────────────────┤\n│ Feature Engineering: < 10ms │\n│ Encoder Forward Pass: < 15ms │\n│ All Task Heads: < 5ms │\n│ Decision Fusion: < 2ms │\n├─────────────────────────────────────────────────┤\n│ Total Inference: < 32ms │\n├─────────────────────────────────────────────────┤\n│ Order Placement: < 50ms │\n│ End-to-End: < 100ms │\n└─────────────────────────────────────────────────┘\n```\n\n## References\n\n1. **Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance**\n - URL: https://arxiv.org/abs/2303.02841\n - Year: 2023\n\n2. **Trading in Fast-Changing Markets with Meta-Reinforcement Learning**\n - MAML-based trading method for multiple tasks\n - Year: 2024\n\n3. **Multi-Task Learning in Deep Neural Networks: A Survey**\n - Comprehensive overview of multi-task architectures\n - URL: https://arxiv.org/abs/2009.09796\n\n4. **Universal Language Model Fine-tuning for Text Classification**\n - Transfer learning techniques applicable to time series\n - URL: https://arxiv.org/abs/1801.06146\n\n5. **Gradient Surgery for Multi-Task Learning**\n - PCGrad method for handling gradient conflicts\n - URL: https://arxiv.org/abs/2001.06782\n\n6. **Adaptive Event-Driven Labeling with Meta-Learning for Financial Time Series**\n - Multi-scale temporal analysis with MAML\n - Year: 2025\n\n7. **FinRL: Deep Reinforcement Learning for Quantitative Finance**\n - Comprehensive framework for financial RL\n - URL: https://github.com/AI4Finance-Foundation/FinRL\n\n---\n\n## Next Steps\n\n- [Simple Explanation](readme.simple.md) - Beginner-friendly version\n- [Russian Version](README.ru.md) - Russian translation\n- [Run Examples](examples/) - Working Rust code\n- [Python Implementation](python/) - PyTorch reference implementation\n\n---\n\n*Chapter 87 of Machine Learning for Trading*\n"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":1780,"cache_read_input_tokens":69107,"cache_creation":{"ephemeral_5m_input_tokens":1780,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"},"context_management":null},"parent_tool_use_id":null,"session_id":"8c23df75-76b7-45ba-aa3a-4295879a3b8f","uuid":"1a39c339-c0db-4ce3-aaa4-e72a30d863aa"} | |
| [2026-01-21T18:53:16.624Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01HXwUBqT1EW3YDxGMXgpFj6","type":"tool_result","content":"File created successfully at: /tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/README.md"}]},"parent_tool_use_id":null,"session_id":"8c23df75-76b7-45ba-aa3a-4295879a3b8f","uuid":"7763a42b-134e-4b24-aa69-e6729d310cbd","tool_use_result":{"type":"create","filePath":"/tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/README.md","content":"# Chapter 87: Task-Agnostic Trading\n\n## Overview\n\nTask-Agnostic Trading develops universal models that can perform well across multiple trading tasks without requiring task-specific fine-tuning. Unlike traditional approaches that train separate models for price prediction, volatility forecasting, regime detection, and portfolio optimization, task-agnostic models learn general representations that transfer effectively to any downstream task.\n\n## Table of Contents\n\n1. [Introduction](#introduction)\n2. [Theoretical Foundation](#theoretical-foundation)\n3. [Task-Agnostic Architectures](#task-agnostic-architectures)\n4. [Universal Feature Learning](#universal-feature-learning)\n5. [Multi-Task Training Strategy](#multi-task-training-strategy)\n6. [Application to Trading](#application-to-trading)\n7. [Bybit Integration](#bybit-integration)\n8. [Implementation](#implementation)\n9. [Risk Management](#risk-management)\n10. [Performance Metrics](#performance-metrics)\n11. [References](#references)\n\n---\n\n## Introduction\n\nTraditional machine learning for trading faces a fundamental problem: models are typically task-specific. A model trained for price direction prediction cannot be directly used for volatility forecasting or regime detection. This requires maintaining multiple models, each with its own training pipeline, hyperparameters, and maintenance overhead.\n\n### The Task-Specific Problem\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ Traditional Task-Specific Approach │\n├─────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ SEPARATE MODELS FOR EACH TASK: │\n│ ─────────────────────────────── │\n│ │\n│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │\n│ │ Price Direction │ │ Volatility │ │ Regime │ │\n│ │ Predictor │ │ Forecaster │ │ Detector │ │\n│ ├─────────────────┤ ├─────────────────┤ ├─────────────────┤ │\n│ │ • Own features │ │ • Own features │ │ • Own features │ │\n│ │ • Own training │ │ • Own training │ │ • Own training │ │\n│ │ • Own tuning │ │ • Own tuning │ │ • Own tuning │ │\n│ │ • Own deployment│ │ • Own deployment│ │ • Own deployment│ │\n│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │\n│ │\n│ Problems: │\n│ • 3× development effort │\n│ • 3× maintenance overhead │\n│ • No knowledge sharing between tasks │\n│ • Inconsistent predictions across tasks │\n│ • Each model overfits to its specific task │\n│ │\n└─────────────────────────────────────────────────────────────────────────────┘\n```\n\n### The Task-Agnostic Solution\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ Task-Agnostic Unified Approach │\n├─────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ SINGLE UNIVERSAL MODEL: │\n│ ─────────────────────── │\n│ │\n│ ┌─────────────────────────┐ │\n│ │ Universal Encoder │ │\n│ │ (Shared Backbone) │ │\n│ ├─────────────────────────┤ │\n│ │ • Market representation │ │\n│ │ • Cross-task features │ │\n│ │ • General patterns │ │\n│ └───────────┬─────────────┘ │\n│ │ │\n│ ┌──────────────────┼──────────────────┐ │\n│ │ │ │ │\n│ ▼ ▼ ▼ │\n│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │\n│ │ Direction Head │ │ Volatility Head │ │ Regime Head │ │\n│ │ (tiny MLP) │ │ (tiny MLP) │ │ (tiny MLP) │ │\n│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │\n│ │\n│ Benefits: │\n│ • Single training pipeline │\n│ • Shared knowledge across tasks │\n│ • Consistent market understanding │\n│ • Better generalization │\n│ • Reduced overfitting │\n│ │\n└─────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Key Benefits for Trading\n\n| Aspect | Task-Specific | Task-Agnostic |\n|--------|--------------|---------------|\n| Development effort | N × models | 1 unified model |\n| Training data | Separate per task | Shared across tasks |\n| Knowledge transfer | None | Automatic |\n| Consistency | Independent predictions | Coherent predictions |\n| New task adaptation | Full retraining | Task head only |\n| Maintenance | High | Low |\n| Generalization | Task-specific | Universal |\n\n## Theoretical Foundation\n\n### Universal Representation Learning\n\nThe core principle of task-agnostic learning is to discover representations that capture the fundamental structure of market data, independent of any specific downstream task.\n\n**Definition**: A task-agnostic representation $\\phi: X \\to Z$ maps raw market data $X$ to a latent space $Z$ such that:\n\n$$\\forall \\text{task } T_i: \\exists \\text{ simple predictor } h_i: Z \\to Y_i$$\n\nwhere $h_i$ can solve task $T_i$ using only the shared representation $Z$.\n\n### Mathematical Framework\n\n**Multi-Task Objective**:\n\n$$\\mathcal{L}_{total} = \\sum_{i=1}^{N} \\lambda_i \\mathcal{L}_i(\\phi, h_i)$$\n\nwhere:\n- $\\phi$ is the shared encoder (task-agnostic)\n- $h_i$ is the task-specific head for task $i$\n- $\\lambda_i$ is the task weight\n- $\\mathcal{L}_i$ is the loss for task $i$\n\n**Gradient Balancing**:\n\nTo prevent any single task from dominating training:\n\n$$\\lambda_i = \\frac{\\overline{L} / L_i}{\\sum_j \\overline{L} / L_j}$$\n\nwhere $\\overline{L}$ is the average loss across all tasks.\n\n### Information-Theoretic View\n\nA good task-agnostic representation maximizes mutual information with all tasks:\n\n$$\\max_\\phi \\sum_{i=1}^{N} I(Z; Y_i)$$\n\nsubject to:\n\n$$I(Z; T) = 0$$\n\nwhere $T$ is the task identity. This ensures the representation doesn't encode task-specific biases.\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Information Flow in Task-Agnostic Learning │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Raw Market Data X │\n│ │ │\n│ │ Contains: price, volume, orderbook, news, etc. │\n│ │ │\n│ ▼ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Task-Agnostic Encoder φ │ │\n│ │ ───────────────────────────────────────────────────────────────── │ │\n│ │ │ │\n│ │ Goal: Extract information relevant to ALL trading tasks │ │\n│ │ │ │\n│ │ Keeps: Discards: │ │\n│ │ • Trend structure • Task-specific biases │ │\n│ │ • Volatility patterns • Noise irrelevant to trading │ │\n│ │ • Market microstructure • Spurious correlations │ │\n│ │ • Cross-asset relationships • Overfitting artifacts │ │\n│ │ • Regime characteristics │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ Universal Representation Z │\n│ │ │\n│ ├───── High I(Z; Price Direction) │\n│ ├───── High I(Z; Volatility) │\n│ ├───── High I(Z; Regime) │\n│ └───── Zero I(Z; Task Identity) │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Task-Agnostic Architectures\n\n### Architecture 1: Shared Encoder with Task Heads\n\nThe simplest and most common approach:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Shared Encoder Architecture │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Input: Market Features (OHLCV + Indicators + Orderbook) │\n│ ──────────────────────────────────────────────────── │\n│ │ │\n│ ▼ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Shared Encoder (Frozen after pretraining) │ │\n│ │ ───────────────────────────────────────────────────────────────── │ │\n│ │ │ │\n│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │\n│ │ │ 1D Conv │──▶│ 1D Conv │──▶│ 1D Conv │ │ │\n│ │ │ (local) │ │ (mid-range) │ │ (long-range)│ │ │\n│ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │\n│ │ │ │ │ │ │\n│ │ └────────────────┬┴─────────────────┘ │ │\n│ │ ▼ │ │\n│ │ ┌─────────────────────────┐ │ │\n│ │ │ Multi-Head Attention │ │ │\n│ │ │ (temporal relationships)│ │ │\n│ │ └─────────────────────────┘ │ │\n│ │ │ │ │\n│ │ ▼ │ │\n│ │ ┌─────────────────────────┐ │ │\n│ │ │ Global Average Pool │ │ │\n│ │ │ → 256-dim embedding │ │ │\n│ │ └─────────────────────────┘ │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ [256-dim Universal Representation] │\n│ │ │\n│ ┌─────────────────┼─────────────────┬─────────────────┐ │\n│ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ │\n│ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ │\n│ │ Direction │ │ Volatility│ │ Regime │ │ Return │ │\n│ │ Head │ │ Head │ │ Head │ │ Head │ │\n│ ├───────────┤ ├───────────┤ ├───────────┤ ├───────────┤ │\n│ │ 256→64→3 │ │ 256→64→1 │ │ 256→64→5 │ │ 256→64→1 │ │\n│ │ softmax │ │ ReLU │ │ softmax │ │ linear │ │\n│ └───────────┘ └───────────┘ └───────────┘ └───────────┘ │\n│ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ │\n│ [Up/Down/ [Predicted [Bull/Bear/ [Expected │\n│ Sideways] Volatility] Sideways/ Return] │\n│ HighVol/ │\n│ LowVol] │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Architecture 2: Transformer-Based Universal Encoder\n\nUsing attention mechanisms for richer representations:\n\n```rust\n/// Transformer-based task-agnostic encoder configuration\n#[derive(Debug, Clone)]\npub struct TransformerEncoderConfig {\n /// Input feature dimension\n pub input_dim: usize,\n /// Model dimension (d_model)\n pub model_dim: usize,\n /// Number of attention heads\n pub num_heads: usize,\n /// Number of transformer layers\n pub num_layers: usize,\n /// Feedforward dimension\n pub ff_dim: usize,\n /// Maximum sequence length\n pub max_seq_len: usize,\n /// Dropout rate\n pub dropout: f32,\n /// Output embedding dimension\n pub embedding_dim: usize,\n}\n\nimpl Default for TransformerEncoderConfig {\n fn default() -> Self {\n Self {\n input_dim: 64,\n model_dim: 256,\n num_heads: 8,\n num_layers: 6,\n ff_dim: 1024,\n max_seq_len: 256,\n dropout: 0.1,\n embedding_dim: 256,\n }\n }\n}\n\n/// Task-agnostic transformer encoder\npub struct TransformerEncoder {\n config: TransformerEncoderConfig,\n // Input projection\n input_projection: Linear,\n // Positional encoding\n positional_encoding: PositionalEncoding,\n // Transformer layers\n layers: Vec<TransformerLayer>,\n // Output projection\n output_projection: Linear,\n // Layer normalization\n layer_norm: LayerNorm,\n}\n\nimpl TransformerEncoder {\n /// Forward pass producing task-agnostic embeddings\n pub fn forward(&self, x: &Array3<f32>, mask: Option<&Array2<bool>>) -> Array2<f32> {\n // x shape: (batch, seq_len, input_dim)\n\n // 1. Project input to model dimension\n let projected = self.input_projection.forward(x);\n\n // 2. Add positional encoding\n let encoded = self.positional_encoding.forward(&projected);\n\n // 3. Pass through transformer layers\n let mut hidden = encoded;\n for layer in &self.layers {\n hidden = layer.forward(&hidden, mask);\n }\n\n // 4. Apply layer normalization\n let normalized = self.layer_norm.forward(&hidden);\n\n // 5. Global pooling (mean over sequence)\n let pooled = normalized.mean_axis(Axis(1)).unwrap();\n\n // 6. Project to embedding dimension\n self.output_projection.forward(&pooled)\n }\n}\n```\n\n### Architecture 3: Mixture of Experts (MoE)\n\nDynamic routing for different market conditions:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Mixture of Experts Architecture │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Input: Market Features │\n│ │ │\n│ ▼ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Routing Network │ │\n│ │ ───────────────────────────────────── │ │\n│ │ Analyzes input and determines expert weights │ │\n│ │ │ │\n│ │ Output: [w₁=0.1, w₂=0.6, w₃=0.2, w₄=0.1] │ │\n│ │ (weights sum to 1) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ │ Sparse routing (top-k experts) │\n│ │ │\n│ ┌─────┴─────┬─────────────┬─────────────┬─────────────┐ │\n│ │ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ ▼ │\n│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │\n│ │ E₁ │ │ E₂ │ │ E₃ │ │ E₄ │ │ E₅ │ │\n│ │Trend│ │Vol │ │Mean │ │Tail │ │Event│ │\n│ │Expert│ │Expert│ │Revert│ │Risk │ │Expert│ │\n│ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ │\n│ │ │ │ │ │ │\n│ │ ×w₁ │ ×w₂ │ ×w₃ │ ×w₄ │ ×w₅ │\n│ │ │ │ │ │ │\n│ └─────────┴──────┬──────┴─────────────┴─────────────┘ │\n│ │ │\n│ ▼ │\n│ Weighted Sum of Expert Outputs │\n│ │ │\n│ ▼ │\n│ [Universal Representation Z] │\n│ │ │\n│ ┌─────────┴─────────┐ │\n│ ▼ ▼ │\n│ Task Heads Expert Usage │\n│ Statistics │\n│ (for analysis) │\n│ │\n│ Expert Specializations: │\n│ • E₁ (Trend): Strong directional moves │\n│ • E₂ (Volatility): High uncertainty periods │\n│ • E₃ (Mean Revert): Consolidation patterns │\n│ • E₄ (Tail Risk): Extreme events │\n│ • E₅ (Event): News/earnings driven moves │\n│ | |
| [2026-01-21T18:53:16.627Z] [INFO] │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Universal Feature Learning\n\n### Self-Supervised Pre-training\n\nTrain the encoder without labels using self-supervised objectives:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Self-Supervised Pre-training Tasks │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. MASKED AUTOENCODING (MAE for Time Series) │\n│ ───────────────────────────────────────────── │\n│ │\n│ Original: [p₁] [p₂] [p₃] [p₄] [p₅] [p₆] [p₇] [p₈] │\n│ Masked: [p₁] [##] [p₃] [##] [##] [p₆] [p₇] [##] (30% masked) │\n│ Task: Reconstruct masked values │\n│ │\n│ This teaches the model to understand temporal patterns! │\n│ │\n│ 2. CONTRASTIVE LEARNING │\n│ ───────────────────────── │\n│ │\n│ Create pairs: │\n│ • Positive: Same asset, close time windows (similar patterns) │\n│ • Negative: Different assets or distant time windows │\n│ │\n│ ┌─────────┐ similar ┌─────────┐ │\n│ │ BTC t=1 │ ◀──────────▶ │ BTC t=2 │ Pull together │\n│ └─────────┘ └─────────┘ │\n│ │ │\n│ │ different │\n│ │ │\n│ ┌─────────┐ │\n│ │ ETH t=1 │ Push apart │\n│ └─────────┘ │\n│ │\n│ 3. TEMPORAL ORDER PREDICTION │\n│ ──────────────────────────── │\n│ │\n│ Given: [Window A] [Window B] │\n│ Task: Did A come before B or after B? │\n│ │\n│ This teaches temporal causality! │\n│ │\n│ 4. FUTURE PREDICTION (Next-Step) │\n│ ───────────────────────────────── │\n│ │\n│ Given: [p₁] [p₂] [p₃] [p₄] [p₅] │\n│ Task: Predict some statistics of [p₆] │\n│ (not exact value, but distribution properties) │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Feature Hierarchy\n\nThe encoder learns features at multiple levels of abstraction:\n\n```rust\n/// Feature hierarchy extracted by task-agnostic encoder\n#[derive(Debug, Clone)]\npub struct FeatureHierarchy {\n /// Low-level features (local patterns)\n pub local_features: LocalFeatures,\n /// Mid-level features (structural patterns)\n pub structural_features: StructuralFeatures,\n /// High-level features (semantic patterns)\n pub semantic_features: SemanticFeatures,\n}\n\n/// Low-level features from early layers\n#[derive(Debug, Clone)]\npub struct LocalFeatures {\n /// Price momentum at various scales\n pub momentum: Vec<f32>,\n /// Local volatility estimates\n pub local_volatility: Vec<f32>,\n /// Volume patterns\n pub volume_patterns: Vec<f32>,\n /// Bid-ask spread dynamics\n pub spread_dynamics: Vec<f32>,\n}\n\n/// Mid-level structural features\n#[derive(Debug, Clone)]\npub struct StructuralFeatures {\n /// Trend strength and direction\n pub trend_structure: Vec<f32>,\n /// Support/resistance levels (learned)\n pub price_levels: Vec<f32>,\n /// Cycle detection\n pub cyclical_patterns: Vec<f32>,\n /// Correlation structure with other assets\n pub correlation_patterns: Vec<f32>,\n}\n\n/// High-level semantic features\n#[derive(Debug, Clone)]\npub struct SemanticFeatures {\n /// Market regime encoding\n pub regime_embedding: Vec<f32>,\n /// Risk state encoding\n pub risk_embedding: Vec<f32>,\n /// Sentiment proxy\n pub sentiment_proxy: Vec<f32>,\n /// Anomaly score\n pub anomaly_score: f32,\n}\n```\n\n## Multi-Task Training Strategy\n\n### Gradient Harmonization\n\nPrevent tasks from conflicting during training:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Multi-Task Gradient Harmonization │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Problem: Task gradients can conflict! │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Example conflicting gradients: │ │\n│ │ │ │\n│ │ Direction task gradient: ▲ (increase this weight) │ │\n│ │ Volatility task gradient: ▼ (decrease this weight) │ │\n│ │ │ │\n│ │ Naive averaging: ▲ + ▼ = → (weights barely move!) │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ Solution: Gradient Harmonization Methods │\n│ │\n│ Method 1: PCGrad (Projecting Conflicting Gradients) │\n│ ──────────────────────────────────────────────── │\n│ If g₁ · g₂ < 0 (conflicting): │\n│ g₁' = g₁ - (g₁·g₂/|g₂|²) × g₂ │\n│ Project away the conflicting component │\n│ │\n│ Method 2: GradNorm (Gradient Normalization) │\n│ ────────────────────────────────────────── │\n│ Dynamically adjust task weights to balance gradient magnitudes │\n│ │\n│ Method 3: Uncertainty Weighting │\n│ ────────────────────────────── │\n│ w_i = 1/(2σ_i²) where σ_i is task-specific uncertainty │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Training Dynamics with Harmonization: │ │\n│ │ │ │\n│ │ Epoch 1-10: All tasks improve steadily │ │\n│ │ Epoch 10-50: Direction task plateaus │ │\n│ │ → Increase direction weight automatically │ │\n│ │ Epoch 50-100: All tasks converge together │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Task Curriculum\n\nTrain tasks in a structured order:\n\n```rust\n/// Task curriculum for training\n#[derive(Debug, Clone)]\npub struct TaskCurriculum {\n /// Training phases\n pub phases: Vec<TrainingPhase>,\n /// Current phase index\n pub current_phase: usize,\n /// Metrics for phase transition\n pub transition_metrics: PhaseTransitionMetrics,\n}\n\n#[derive(Debug, Clone)]\npub struct TrainingPhase {\n /// Phase name\n pub name: String,\n /// Tasks active in this phase\n pub active_tasks: Vec<TaskType>,\n /// Task weights for this phase\n pub task_weights: HashMap<TaskType, f32>,\n /// Minimum epochs in this phase\n pub min_epochs: usize,\n /// Transition condition\n pub transition_condition: TransitionCondition,\n}\n\n/// Trading tasks supported by the model\n#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]\npub enum TaskType {\n /// Predict price direction (up/down/sideways)\n DirectionPrediction,\n /// Forecast future volatility\n VolatilityForecast,\n /// Detect market regime\n RegimeDetection,\n /// Predict expected return\n ReturnPrediction,\n /// Detect anomalies\n AnomalyDetection,\n /// Predict optimal trade timing\n TradeTiming,\n}\n\nimpl TaskCurriculum {\n /// Create standard trading curriculum\n pub fn standard() -> Self {\n Self {\n phases: vec![\n // Phase 1: Self-supervised pre-training\n TrainingPhase {\n name: \"Self-Supervised\".to_string(),\n active_tasks: vec![], // No task heads, just encoder\n task_weights: HashMap::new(),\n min_epochs: 50,\n transition_condition: TransitionCondition::ReconstructionLoss(0.01),\n },\n // Phase 2: Easy tasks (direction, regime)\n TrainingPhase {\n name: \"Easy Tasks\".to_string(),\n active_tasks: vec![\n TaskType::DirectionPrediction,\n TaskType::RegimeDetection,\n ],\n task_weights: [\n (TaskType::DirectionPrediction, 0.5),\n (TaskType::RegimeDetection, 0.5),\n ].into_iter().collect(),\n min_epochs: 30,\n transition_condition: TransitionCondition::Accuracy(0.65),\n },\n // Phase 3: Add regression tasks\n TrainingPhase {\n name: \"Full Multi-Task\".to_string(),\n active_tasks: vec![\n TaskType::DirectionPrediction,\n TaskType::RegimeDetection,\n TaskType::VolatilityForecast,\n TaskType::ReturnPrediction,\n ],\n task_weights: [\n (TaskType::DirectionPrediction, 0.25),\n (TaskType::RegimeDetection, 0.25),\n (TaskType::VolatilityForecast, 0.25),\n (TaskType::ReturnPrediction, 0.25),\n ].into_iter().collect(),\n min_epochs: 50,\n transition_condition: TransitionCondition::Convergence,\n },\n ],\n current_phase: 0,\n transition_metrics: PhaseTransitionMetrics::default(),\n }\n }\n}\n```\n\n## Application to Trading\n\n### Task-Agnostic Trading System\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Task-Agnostic Trading System │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ REAL-TIME DATA INGESTION │\n│ ──────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Bybit WebSocket → Feature Pipeline → Feature Buffer │ │\n│ │ • OHLCV (1m, 5m, 15m, 1h, 4h) │ │\n│ │ • Orderbook (L2 top 20 levels) │ │\n│ │ • Trades (tick-by-tick) │ │\n│ │ • Funding rates │ │\n│ │ • Open interest │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ TASK-AGNOSTIC ENCODER (Pre-trained, Frozen) │\n│ ───────────────────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Market Features → Universal Embedding (256-dim) │ │\n│ │ Latency: < 5ms per inference │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ MULTI-TASK INFERENCE (Parallel Task Heads) │\n│ ────────────────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌───────────┐ │ │\n│ │ │ Direction │ │ Volatility │ │ Regime │ │ Return │ │ │\n│ │ │ 68% Up │ │ 2.3% exp │ │ Bull (73%) │ │ +0.5% │ │ │\n│ │ │ 22% Down │ │ │ │ │ │ expected │ │ │\n│ │ │ 10% Side │ │ │ │ │ │ │ │ │\n│ │ └─────────────┘ └─────────────┘ └─────────────┘ └───────────┘ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ DECISION FUSION (Combine All Task Outputs) │\n│ ────────────────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Consistency Check: │ │\n│ │ • Direction=Up + Regime=Bull + Return=Positive → CONSISTENT ✓ │ │\n│ │ • Direction=Up + Regime=Bear → CONFLICT! → Reduce confidence │ │\n│ │ │ │\n│ │ Risk Assessment: │ │\n│ │ • Volatility forecast determines position sizing │ │\n│ │ • Regime determines stop-loss/take-profit levels │ │\n│ │ │ │\n│ │ Final Signal: │ │\n│ │ • Action: LONG │ │\n│ │ • Confidence: 72% (consistent predictions) │ │\n│ │ • Size: 1.5% of capital (moderate volatility) │ │\n│ │ • Stop: -1.5% (bull regime, tight stop) │ │\n│ │ • Target: +2.0% (expected return + buffer) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ORDER EXECUTION │\n│ ─────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Bybit API → Market/Limit Order → Position Monitoring │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Decision Fusion Logic\n\n```rust\n/// Decision fusion from multiple task outputs\n#[derive(Debug, Clone)]\npub struct TaskOutputs {\n /// Direction prediction\n pub direction: DirectionPrediction,\n /// Volatility forecast\n pub volatility: VolatilityForecast,\n /// Regime detection\n pub regime: RegimeDetection,\n /// Return prediction\n pub expected_return: ReturnPrediction,\n}\n\n#[derive(Debug, Clone)]\npub struct DirectionPrediction {\n pub up_prob: f32,\n pub down_prob: f32,\n pub sideways_prob: f32,\n}\n\n#[derive(Debug, Clone)]\npub struct VolatilityForecast {\n pub expected_volatility: f32,\n pub volatility_percentile: f32, // Relative to historical\n}\n\n#[derive(Debug, Clone)]\npub struct RegimeDetection {\n pub regime: MarketRegime,\n pub confidence: f32,\n}\n\n#[derive(Debug, Clone, Copy)]\npub enum MarketRegime {\n Bull,\n Bear,\n Sideways,\n HighVolatility,\n LowVolatility,\n}\n\n#[derive(Debug, Clone)]\npub struct ReturnPrediction {\n pub expected_return: f32,\n pub return_std: f32,\n}\n\n/// Fuse multiple task outputs into trading decision\npub struct DecisionFusion {\n /// Consistency weight\n consistency_weight: f32,\n /// Minimum confidence threshold\n min_confidence: f32,\n}\n\nimpl DecisionFusion {\n pub fn fuse(&self, outputs: &TaskOutputs) -> TradingDecision {\n // 1. Determine base direction\n let (direction, dir_confidence) = self.get_direction(&outputs.direction);\n\n // 2. Check consistency with regime\n let regime_agreement = self.check_regime_consistency(\n direction,\n outputs.regime.regime,\n );\n\n // 3. Check consistency with expected return\n let return_agreement = self.check_return_consistency(\n direction,\n outputs.expected_return.expected_return,\n );\n\n // 4. Calculate overall confidence\n let consistency_score = (regime_agreement + return_agreement) / 2.0;\n let final_confidence = dir_confidence *\n (1.0 - self.consistency_weight + self.consistency_weight * consistency_score);\n\n // 5. Determine position size based on volatility\n let position_size = self.calculate_position_size(\n final_confidence,\n outputs.volatility.expected_volatility,\n outputs.volatility.volatility_percentile,\n );\n\n // 6. Set stop-loss and take-profit based on regime and volatility\n let (stop_loss, take_profit) = self.calculate_exits(\n outputs.regime.regime,\n outputs.volatility.expected_volatility,\n outputs.expected_return.expected_return,\n );\n\n TradingDecision {\n action: if final_confidence >= self.min_confidence {\n match direction {\n TradeDirection::Long => TradeAction::Buy,\n TradeDirection::Short => TradeAction::Sell,\n TradeDirection::Neutral => TradeAction::Hold,\n }\n } else {\n TradeAction::Hold\n },\n confidence: final_confidence,\n position_size,\n stop_loss_pct: stop_loss,\n take_profit_pct: take_profit,\n reasoning: self.generate_reasoning(outputs, direction, consistency_score),\n }\n }\n\n fn check_regime_consistency(&self, direction: TradeDirection, regime: MarketRegime) -> f32 {\n match (direction, regime) {\n // High consistency\n (TradeDirection::Long, MarketRegime::Bull) => 1.0,\n (TradeDirection::Short, MarketRegime::Bear) => 1.0,\n (TradeDirection::Neutral, MarketRegime::Sideways) => 1.0,\n // Medium consistency\n (TradeDirection::Long, MarketRegime::LowVolatility) => 0.7,\n (TradeDirection::Short, MarketRegime::HighVolatility) => 0.6,\n // Low consistency (conflicting signals)\n (TradeDirection::Long, MarketRegime::Bear) => 0.2,\n (TradeDirection::Short, MarketRegime::Bull) => 0.2,\n // Neutral\n _ => 0.5,\n }\n }\n\n fn calculate_position_size(\n &self,\n confidence: f32,\n volatility: f32,\n volatility_percentile: f32,\n ) -> f32 {\n let base_size = 0.02; // 2% base position\n\n // Scale down for high volatility\n let vol_multiplier = if volatility_percentile > 0.8 {\n 0.5 // High vol: half size\n } else if volatility_percentile > 0.6 {\n 0.75 // Elevated vol: 3/4 size\n } else {\n 1.0 // Normal vol: full size\n };\n\n // Scale with confidence\n let conf_multiplier = (confidence - self.min_confidence) /\n (1.0 - self.min_confidence);\n\n base_size * vol_multiplier * conf_multiplier\n }\n}\n\n#[derive(Debug, Clone)]\npub struct TradingDecision {\n pub action: TradeAction,\n pub confidence: f32,\n pub position_size: f32,\n pub stop_loss_pct: f32,\n pub take_profit_pct: f32,\n pub reasoning: String,\n}\n\n#[derive(Debug, Clone, Copy)]\npub enum TradeAction {\n Buy,\n Sell,\n Hold,\n}\n```\n\n## Bybit Integration\n\n### Data Collection for Multiple Tasks\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Bybit Data Collection Pipeline │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ DATA SOURCES FOR TASK-AGNOSTIC TRAINING │\n│ ────────────────────────────────────────── │\n│ │\n│ 1. HISTORICAL DATA (Training) │\n│ ───────────────────────────── │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Endpoint: GET /v5/market/kline │ │\n│ │ Symbols: BTCUSDT, ETHUSDT, SOLUSDT, ... (top 50 by volume) │ │\n│ │ Intervals: 1m, 5m, 15m, 1h, 4h, 1d │ │\n│ │ History: 2 years │ │\n│ │ │ │\n│ │ Label Generation for Tasks: │ │\n│ │ ┌─────────────────────────────────────────────────────────────┐ │ │\n│ │ │ Direction: Based on forward return sign │ │ │\n│ │ │ Volatility: Realized volatility over next N periods │ │ │\n│ │ │ Regime: HMM clustering on return/volatility │ │ │\n│ │ │ Return: Actual forward return (continuous) │ │ │\n│ │ └─────────────────────────────────────────────────────────────┘ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 2. REAL-TIME DATA (Inference) │\n│ ───────────────────────────── │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ WebSocket: wss://stream.bybit.com/v5/public/linear │ │\n│ │ │ │\n│ │ Subscriptions: │ │\n│ │ • kline.1.BTCUSDT (1-minute candles) │ │\n│ │ • orderbook.50.BTCUSDT (L2 orderbook) │ │\n│ │ • publicTrade.BTCUSDT (tick trades) │ │\n│ │ • tickers.BTCUSDT (market stats) │ │\n│ │ │ │\n│ │ Additional REST calls: │ │\n│ │ • GET /v5/market/funding/history (funding rates) │ │\n│ │ • GET /v5/market/open-interest (open interest) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 3. FEATURE ENGINEERING │\n│ ────────────────────── │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Raw Data → Feature Pipeline → Normalized Features │ │\n│ │ │ │\n│ │ Feature Groups: │ │\n│ │ • Price features (returns, log-returns, ranges) │ │\n│ │ • Volume features (volume ratios, VWAP deviation) │ │\n│ │ • Volatility features (realized vol, ATR, Parkinson) │ │\n│ │ • Momentum features (ROC, RSI, MACD) │ │\n│ │ • Microstructure (spread, depth imbalance, trade flow) │ │\n│ │ • Crypto-specific (funding, OI change, liquidations) │ │\n│ │ │ │\n│ │ Total features: ~64 per timepoint │ │\n│ │ Sequence length: 96 timepoints (96 hours at 1h resolution) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Bybit Client Implementation\n\n```rust\nuse reqwest::Client;\nuse serde::{Deserialize, Serialize};\nuse tokio::sync::mpsc;\nuse tokio_tungstenite::{connect_async, tungstenite::Message};\nuse futures_util::{SinkExt, StreamExt};\n\n/// Bybit client for task-agnostic trading\npub struct BybitClient {\n http_client: Client,\n api_key: Option<String>,\n api_secret: Option<String>,\n base_url: String,\n ws_url: String,\n}\n\nimpl BybitClient {\n pub fn new(api_key: Option<String>, api_secret: Option<String>) -> Self {\n Self {\n http_client: Client::new(),\n api_key,\n api_secret,\n base_url: \"https://api.bybit.com\".to_string(),\n ws_url: \"wss://stream.bybit.com/v5/public/linear\".to_string(),\n }\n }\n\n /// Fetch historical klines for multiple symbols\n pub async fn fetch_training_data(\n &self,\n symbols: &[&str],\n interval: &str,\n start_time: u64,\n end_time: u64,\n ) -> Result<HashMap<String, Vec<Kline>>, BybitError> {\n let mut all_data = HashMap::new();\n\n for symbol in symbols {\n let klines = self.get_klines(symbol, interval, start_time, end_time).await?;\n all_data.insert(symbol.to_string(), klines);\n }\n\n Ok(all_data)\n }\n\n /// Get klines for a single symbol\n async fn get_klines(\n &self,\n symbol: &str,\n interval: &str,\n start_time: u64,\n end_time: u64,\n ) -> Result<Vec<Kline>, BybitError> {\n let mut all_klines = Vec::new();\n let mut current_start = start_time;\n\n while current_start < end_time {\n let url = format!(\n \"{}/v5/market/kline?category=linear&symbol={}&interval={}&start={}&limit=1000\",\n self.base_url, symbol, interval, current_start\n );\n\n let response: KlineResponse = self.http_client\n .get(&url)\n .send()\n .await?\n .json()\n .await?;\n\n if response.result.list.is_empty() {\n break;\n }\n\n let klines: Vec<Kline> = response.result.list\n .into_iter()\n .map(Kline::from)\n .collect();\n\n current_start = klines.last().map(|k| k.timestamp + 1).unwrap_or(end_time);\n all_klines.extend(klines);\n\n // Rate limiting\n tokio::time::sleep(tokio::time::Duration::from_millis(100)).await;\n }\n\n Ok(all_klines)\n }\n\n /// Start real-time data streaming\n pub async fn start_streaming(\n &self,\n symbols: &[&str],\n tx: mpsc::Sender<MarketUpdate>,\n ) -> Result<(), BybitError> {\n let (ws_stream, _) = connect_async(&self.ws_url).await?;\n let (mut write, mut read) = ws_stream.split();\n\n // Subscribe to channels\n let topics: Vec<String> = symbols.iter()\n .flat_map(|s| vec![\n format!(\"kline.1.{}\", s),\n format!(\"orderbook.50.{}\", s),\n format!(\"publicTrade.{}\", s),\n ])\n .collect();\n\n let subscribe_msg = serde_json::json!({\n \"op\": \"subscribe\",\n \"args\": topics\n });\n\n write.send(Message::Text(subscribe_msg.to_string())).await?;\n\n // Process incoming messages\n while let Some(msg) = read.next().await {\n match msg {\n Ok(Message::Text(text)) => {\n if let Ok(update) = serde_json::from_str::<MarketUpdate>(&text) {\n tx.send(update).await.ok();\n }\n }\n Ok(Message::Ping(data)) => {\n write.send(Message::Pong(data)).await?;\n }\n Err(e) => {\n eprintln!(\"WebSocket error: {:?}\", e);\n break;\n }\n _ => {}\n }\n }\n\n Ok(())\n }\n}\n\n#[derive(Debug, Clone, Deserialize, Serialize)]\npub struct Kline {\n pub timestamp: u64,\n pub open: f64,\n pub high: f64,\n pub low: f64,\n pub close: f64,\n pub volume: f64,\n pub turnover: f64,\n}\n\n#[derive(Debug, Clone, Deserialize)]\npub struct MarketUpdate {\n pub topic: String,\n pub data: serde_json::Value,\n pub ts: u64,\n}\n```\n\n## Implementation\n\n### Module Structure\n\n```\n87_task_agnostic_trading/\n├── Cargo.toml\n├── README.md # Main documentation (English)\n├── README.ru.md # Russian translation\n├── readme.simple.md # Simplified explanation\n├── readme.simple.ru.md # Simplified Russian\n├── src/\n│ ├── lib.rs # Library root\n│ ├── config.rs # Configuration types\n│ ├── encoder/\n│ │ ├── mod.rs # Encoder module\n│ │ ├── transformer.rs # Transformer encoder\n│ │ ├── cnn.rs # CNN encoder\n│ │ └── moe.rs # Mixture of Experts\n│ ├── tasks/\n│ │ ├── mod.rs # Task module\n│ │ ├── direction.rs # Direction prediction\n│ │ ├── volatility.rs # Volatility forecast\n│ │ ├── regime.rs # Regime detection\n│ │ └── returns.rs # Return prediction\n│ ├── training/\n│ │ ├── mod.rs # Training module\n│ │ ├── multitask.rs # Multi-task training\n│ │ ├── curriculum.rs # Task curriculum\n│ │ └── pretraining.rs # Self-supervised pretraining\n│ ├── fusion/\n│ │ ├── mod.rs # Fusion module\n│ │ └── decision.rs # Decision fusion\n│ ├── data/\n│ │ ├── mod.rs # Data module\n│ │ ├── bybit.rs # Bybit API client\n│ │ ├── features.rs # Feature engineering\n│ │ └── dataset.rs # Dataset handling\n│ └── trading/\n│ ├── mod.rs # Trading module\n│ ├── strategy.rs # Trading strategy\n│ └── risk.rs # Risk management\n├── examples/\n│ ├── train_encoder.rs # Train task-agnostic encoder\n│ ├── multi_task_inference.rs # Multi-task inference example\n│ ├── bybit_trading.rs # Live trading with Bybit\n│ └── backtest.rs # Backtesting example\n├── python/\n│ ├── task_agnostic_model.py # PyTorch implementation\n│ ├── train.py # Training script\n│ ├── evaluate.py # Evaluation script\n│ └── requirements.txt # Python dependencies\n└── tests/\n ├── integration.rs # Integration tests\n └── unit_tests.rs # Unit tests\n```\n\n### Core Implementation\n\n```rust\n//! Task-Agnostic Trading Core Module\n\nuse ndarray::{Array1, Array2, Array3, Axis};\nuse std::collections::HashMap;\n\n/// Task-agnostic model configuration\n#[derive(Debug, Clone)]\npub struct TaskAgnosticConfig {\n /// Encoder configuration\n pub encoder_config: EncoderConfig,\n /// Active tasks\n pub tasks: Vec<TaskConfig>,\n /// Multi-task training configuration\n pub training_config: MultiTaskTrainingConfig,\n /// Decision fusion configuration\n pub fusion_config: FusionConfig,\n}\n\nimpl Default for TaskAgnosticConfig {\n fn default() -> Self {\n Self {\n encoder_config: EncoderConfig::default(),\n tasks: vec![\n TaskConfig::direction_prediction(),\n TaskConfig::volatility_forecast(),\n TaskConfig::regime_detection(),\n TaskConfig::return_prediction(),\n ],\n training_config: MultiTaskTrainingConfig::default(),\n fusion_config: FusionConfig::default(),\n }\n }\n}\n\n/// Encoder configuration\n#[derive(Debug, Clone)]\npub struct EncoderConfig {\n /// Input feature dimension\n pub input_dim: usize,\n /// Sequence length\n pub seq_length: usize,\n /// Encoder type\n pub encoder_type: EncoderType,\n /// Hidden dimension\n pub hidden_dim: usize,\n /// Output embedding dimension\n pub embedding_dim: usize,\n /// Dropout rate\n pub dropout: f32,\n}\n\nimpl Default for EncoderConfig {\n fn default() -> Self {\n Self {\n input_dim: 64,\n seq_length: 96,\n encoder_type: EncoderType::Transformer {\n num_layers: 6,\n num_heads: 8,\n ff_dim: 256,\n },\n hidden_dim: 256,\n embedding_dim: 256,\n dropout: 0.1,\n }\n }\n}\n\n/// Encoder architecture types\n#[derive(Debug, Clone)]\npub enum EncoderType {\n /// Transformer-based encoder\n Transformer {\n num_layers: usize,\n num_heads: usize,\n ff_dim: usize,\n },\n /// CNN-based encoder\n CNN {\n num_layers: usize,\n kernel_sizes: Vec<usize>,\n },\n /// Mixture of Experts\n MoE {\n num_experts: usize,\n top_k: usize,\n expert_dim: usize,\n },\n}\n\n/// Task configuration\n#[derive(Debug, Clone)]\npub struct TaskConfig {\n /// Task type\n pub task_type: TaskType,\n /// Task head hidden dimension\n pub head_hidden_dim: usize,\n /// Task weight in training\n pub weight: f32,\n /// Loss function type\n pub loss_type: LossType,\n}\n\nimpl TaskConfig {\n pub fn direction_prediction() -> Self {\n Self {\n task_type: TaskType::DirectionPrediction,\n head_hidden_dim: 64,\n weight: 1.0,\n loss_type: LossType::CrossEntropy,\n }\n }\n\n pub fn volatility_forecast() -> Self {\n Self {\n task_type: TaskType::VolatilityForecast,\n head_hidden_dim: 64,\n weight: 1.0,\n loss_type: LossType::MSE,\n }\n }\n\n pub fn regime_detection() -> Self {\n Self {\n task_type: TaskType::RegimeDetection,\n head_hidden_dim: 64,\n weight: 1.0,\n loss_type: LossType::CrossEntropy,\n }\n }\n\n pub fn return_prediction() -> Self {\n Self {\n task_type: TaskType::ReturnPrediction,\n head_hidden_dim: 64,\n weight: 1.0,\n loss_type: LossType::MSE,\n }\n }\n}\n\n/// Task types\n#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]\npub enum TaskType {\n DirectionPrediction,\n VolatilityForecast,\n RegimeDetection,\n ReturnPrediction,\n AnomalyDetection,\n TradeTiming,\n}\n\n/// Loss function types\n#[derive(Debug, Clone, Copy)]\npub enum LossType {\n CrossEntropy,\n MSE,\n MAE,\n Huber,\n}\n\n/// Task-agnostic trading model\npub struct TaskAgnosticModel {\n /// Configuration\n config: TaskAgnosticConfig,\n /// Shared encoder\n encoder: Box<dyn Encoder>,\n /// Task heads\n task_heads: HashMap<TaskType, Box<dyn TaskHead>>,\n /// Decision fusion\n fusion: DecisionFusion,\n}\n\n/// Encoder trait for different architectures\npub trait Encoder: Send + Sync {\n /// Forward pass producing embeddings\n fn forward(&self, x: &Array3<f32>) -> Array2<f32>;\n\n /// Get embedding dimension\n fn embedding_dim(&self) -> usize;\n}\n\n/// Task head trait for different tasks\npub trait TaskHead: Send + Sync {\n /// Forward pass from embeddings to task output\n fn forward(&self, embeddings: &Array2<f32>) -> TaskOutput;\n\n /// Get task type\n fn task_type(&self) -> TaskType;\n\n /// Compute loss\n fn compute_loss(&self, output: &TaskOutput, target: &TaskTarget) -> f32;\n}\n\n/// Task output enum\n#[derive(Debug, Clone)]\npub enum TaskOutput {\n /// Classification output (logits)\n Classification(Array2<f32>),\n /// Regression output (values)\n Regression(Array2<f32>),\n}\n\n/// Task target enum\n#[derive(Debug, Clone)]\npub enum TaskTarget {\n /// Classification target (class indices)\n Classification(Vec<usize>),\n /// Regression target (values)\n Regression(Array2<f32>),\n}\n\nimpl TaskAgnosticModel {\n /// Create a new task-agnostic model\n pub fn new(config: TaskAgnosticConfig) -> Self {\n let encoder = Self::create_encoder(&config.encoder_config);\n let task_heads = Self::create_task_heads(&config);\n let fusion = DecisionFusion::new(config.fusion_config.clone());\n\n Self {\n config,\n encoder,\n task_heads,\n fusion,\n }\n }\n\n /// Run inference on all tasks\n pub fn forward(&self, features: &Array3<f32>) -> AllTaskOutputs {\n // 1. Get universal embeddings\n let embeddings = self.encoder.forward(features);\n\n // 2. Run each task head\n let mut outputs = HashMap::new();\n for (task_type, head) in &self.task_heads {\n let output = head.forward(&embeddings);\n outputs.insert(*task_type, output);\n }\n\n AllTaskOutputs { outputs, embeddings }\n }\n\n /// Make trading decision from multi-task outputs\n pub fn make_decision(&self, outputs: &AllTaskOutputs) -> TradingDecision {\n // Convert task outputs to structured format\n let structured = self.structure_outputs(outputs);\n\n // Fuse decisions\n self.fusion.fuse(&structured)\n }\n\n fn create_encoder(config: &EncoderConfig) -> Box<dyn Encoder> {\n match &config.encoder_type {\n EncoderType::Transformer { num_layers, num_heads, ff_dim } => {\n Box::new(TransformerEncoder::new(\n config.input_dim,\n config.seq_length,\n config.hidden_dim,\n config.embedding_dim,\n *num_layers,\n *num_heads,\n *ff_dim,\n config.dropout,\n ))\n }\n EncoderType::CNN { num_layers, kernel_sizes } => {\n Box::new(CNNEncoder::new(\n config.input_dim,\n config.seq_length,\n config.hidden_dim,\n config.embedding_dim,\n *num_layers,\n kernel_sizes.clone(),\n config.dropout,\n ))\n }\n EncoderType::MoE { num_experts, top_k, expert_dim } => {\n Box::new(MoEEncoder::new(\n config.input_dim,\n config.seq_length,\n config.hidden_dim,\n config.embedding_dim,\n *num_experts,\n *top_k,\n *expert_dim,\n config.dropout,\n ))\n }\n }\n }\n\n fn create_task_heads(config: &TaskAgnosticConfig) -> HashMap<TaskType, Box<dyn TaskHead>> {\n let mut heads = HashMap::new();\n\n for task_config in &config.tasks {\n let head: Box<dyn TaskHead> = match task_config.task_type {\n TaskType::DirectionPrediction => {\n Box::new(DirectionHead::new(\n config.encoder_config.embedding_dim,\n task_config.head_hidden_dim,\n ))\n }\n TaskType::VolatilityForecast => {\n Box::new(VolatilityHead::new(\n config.encoder_config.embedding_dim,\n task_config.head_hidden_dim,\n ))\n }\n TaskType::RegimeDetection => {\n Box::new(RegimeHead::new(\n config.encoder_config.embedding_dim,\n task_config.head_hidden_dim,\n ))\n }\n TaskType::ReturnPrediction => {\n Box::new(ReturnHead::new(\n config.encoder_config.embedding_dim,\n task_config.head_hidden_dim,\n ))\n }\n _ => continue,\n };\n\n heads.insert(task_config.task_type, head);\n }\n\n heads\n }\n\n fn structure_outputs(&self, outputs: &AllTaskOutputs) -> TaskOutputs {\n // Extract and structure individual task outputs\n // (Implementation details)\n TaskOutputs {\n direction: self.extract_direction(&outputs.outputs[&TaskType::DirectionPrediction]),\n volatility: self.extract_volatility(&outputs.outputs[&TaskType::VolatilityForecast]),\n regime: self.extract_regime(&outputs.outputs[&TaskType::RegimeDetection]),\n expected_return: self.extract_return(&outputs.outputs[&TaskType::ReturnPrediction]),\n }\n }\n}\n\n/// All task outputs from forward pass\n#[derive(Debug, Clone)]\npub struct AllTaskOutputs {\n /// Individual task outputs\n pub outputs: HashMap<TaskType, TaskOutput>,\n /// Universal embeddings (for analysis)\n pub embeddings: Array2<f32>,\n}\n```\n\n## Risk Management\n\n### Multi-Task Risk Assessment\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Multi-Task Risk Assessment │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. TASK AGREEMENT SCORING │\n│ ──────────────────────── │\n│ │\n│ When tasks agree → Higher confidence, larger positions │\n│ When tasks conflict → Lower confidence, smaller positions or no trade │\n│ │\n│ Agreement Matrix: │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ │ Direction │ Volatility │ Regime │ Return │ Score │ │\n│ │──────────────┼───────────┼────────────┼────────┼────────┼─────────│ │\n│ │ Scenario 1 │ UP │ LOW │ BULL │ +0.5% │ 0.95 │ │\n│ │ (All agree) │ │ │ │ │ TRADE │ │\n│ │──────────────┼───────────┼────────────┼────────┼────────┼─────────│ │\n│ │ Scenario 2 │ UP │ HIGH │ BULL │ +0.3% │ 0.70 │ │\n│ │ (Vol warns) │ │ │ │ │ CAUTION│ │\n│ │──────────────┼───────────┼────────────┼────────┼────────┼─────────│ │\n│ │ Scenario 3 │ UP │ HIGH │ BEAR │ -0.2% │ 0.30 │ │\n│ │ (Conflict) │ │ │ │ │ NO TRADE│ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 2. DYNAMIC POSITION SIZING │\n│ ────────────────────────── │\n│ │\n│ Position Size = Base × Confidence × (1 / Volatility_Rank) │\n│ │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Confidence │ Vol Rank │ Multiplier │ Example (2% base) │ │\n│ │───────────────┼───────────┼─────────────┼─────────────────────────│ │\n│ │ 0.90+ │ Low │ 1.5x │ 3.0% position │ │\n│ │ 0.90+ │ High │ 0.75x │ 1.5% position │ │\n│ │ 0.70-0.90 │ Low │ 1.0x │ 2.0% position │ │\n│ │ 0.70-0.90 │ High │ 0.5x │ 1.0% position │ │\n│ │ 0.50-0.70 │ Low │ 0.5x │ 1.0% position │ │\n│ │ 0.50-0.70 │ High │ 0.25x │ 0.5% position │ │\n│ │ <0.50 │ Any │ 0x │ No trade │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 3. REGIME-AWARE STOP-LOSS │\n│ ───────────────────────── │\n│ │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Regime │ Stop-Loss │ Take-Profit │ Rationale │ │\n│ │─────────────────┼────────────┼──────────────┼─────────────────────│ │\n│ │ Bull │ -1.5% │ +3.0% │ Ride the trend │ │\n│ │ Bear │ -1.5% │ +3.0% │ Catch the move │ │\n│ │ Sideways │ -1.0% │ +1.5% │ Tight range play │ │\n│ │ High Volatility│ -2.5% │ +4.0% │ Wide stops │ │\n│ │ Low Volatility │ -1.0% │ +1.5% │ Tight control │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 4. PORTFOLIO-LEVEL CONSTRAINTS │\n│ ────────────────────────────── │\n│ │\n│ • Maximum total exposure: 10% of capital │\n│ • Maximum correlation: Don't trade highly correlated assets together │\n│ • Daily loss limit: -3% → stop trading for the day │\n│ • Weekly drawdown: -5% → reduce position sizes by 50% │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Risk Parameters\n\n```rust\n/// Risk management parameters for task-agnostic trading\n#[derive(Debug, Clone)]\npub struct RiskParameters {\n /// Base position size (fraction of capital)\n pub base_position_size: f32,\n /// Maximum position size multiplier\n pub max_position_multiplier: f32,\n /// Minimum confidence to trade\n pub min_confidence: f32,\n /// Minimum task agreement score\n pub min_agreement_score: f32,\n /// Base stop-loss percentage\n pub base_stop_loss: f32,\n /// Base take-profit percentage\n pub base_take_profit: f32,\n /// Maximum total exposure\n pub max_total_exposure: f32,\n /// Daily loss limit\n pub daily_loss_limit: f32,\n /// Weekly drawdown limit\n pub weekly_drawdown_limit: f32,\n}\n\nimpl Default for RiskParameters {\n fn default() -> Self {\n Self {\n base_position_size: 0.02, // 2% per trade\n max_position_multiplier: 1.5, // Up to 3% max\n min_confidence: 0.50,\n min_agreement_score: 0.60,\n base_stop_loss: 0.015, // 1.5% stop\n base_take_profit: 0.025, // 2.5% take profit\n max_total_exposure: 0.10, // 10% total\n daily_loss_limit: 0.03, // 3% daily limit\n weekly_drawdown_limit: 0.05, // 5% weekly limit\n }\n }\n}\n\nimpl RiskParameters {\n /// Calculate position size based on multi-task outputs\n pub fn calculate_position_size(\n &self,\n confidence: f32,\n agreement_score: f32,\n volatility_percentile: f32,\n current_exposure: f32,\n ) -> f32 {\n // Check minimum thresholds\n if confidence < self.min_confidence || agreement_score < self.min_agreement_score {\n return 0.0;\n }\n\n // Check exposure limit\n let remaining_capacity = self.max_total_exposure - current_exposure;\n if remaining_capacity <= 0.0 {\n return 0.0;\n }\n\n // Base calculation\n let confidence_factor = (confidence - self.min_confidence) /\n (1.0 - self.min_confidence);\n let agreement_factor = (agreement_score - self.min_agreement_score) /\n (1.0 - self.min_agreement_score);\n\n // Volatility adjustment (inverse)\n let vol_factor = if volatility_percentile > 0.8 {\n 0.5\n } else if volatility_percentile > 0.6 {\n 0.75\n } else {\n 1.0\n };\n\n let position_size = self.base_position_size\n * confidence_factor\n * agreement_factor\n * vol_factor\n * self.max_position_multiplier;\n\n // Cap at remaining capacity\n position_size.min(remaining_capacity)\n }\n\n /// Get regime-adjusted stop-loss\n pub fn get_stop_loss(&self, regime: MarketRegime, volatility_percentile: f32) -> f32 {\n let base = match regime {\n MarketRegime::Bull | MarketRegime::Bear => self.base_stop_loss,\n MarketRegime::Sideways | MarketRegime::LowVolatility => self.base_stop_loss * 0.67,\n MarketRegime::HighVolatility => self.base_stop_loss * 1.67,\n };\n\n // Widen for high volatility\n if volatility_percentile > 0.8 {\n base * 1.5\n } else {\n base\n }\n }\n\n /// Get regime-adjusted take-profit\n pub fn get_take_profit(&self, regime: MarketRegime, volatility_percentile: f32) -> f32 {\n let base = match regime {\n MarketRegime::Bull | MarketRegime::Bear => self.base_take_profit * 1.2,\n MarketRegime::Sideways | MarketRegime::LowVolatility => self.base_take_profit * 0.6,\n MarketRegime::HighVolatility => self.base_take_profit * 1.6,\n };\n\n // Widen for high volatility\n if volatility_percentile > 0.8 {\n base * 1.3\n } else {\n base\n }\n }\n}\n```\n\n## Performance Metrics\n\n### Model E | |
| [2026-01-21T18:53:16.632Z] [INFO] valuation Metrics\n\n| Metric | Description | Target |\n|--------|-------------|--------|\n| **Multi-Task Accuracy** | Average accuracy across all classification tasks | > 65% |\n| **Direction Accuracy** | Price direction prediction accuracy | > 60% |\n| **Regime Accuracy** | Market regime detection accuracy | > 70% |\n| **Volatility RMSE** | Root mean squared error for volatility forecast | < 0.02 |\n| **Return Correlation** | Correlation between predicted and actual returns | > 0.3 |\n| **Task Transfer** | Performance when adding new task | > 90% of from-scratch |\n| **Consistency Score** | How often task predictions agree | > 70% |\n\n### Trading Performance Metrics\n\n| Metric | Description | Target |\n|--------|-------------|--------|\n| **Sharpe Ratio** | Risk-adjusted returns | > 2.0 |\n| **Sortino Ratio** | Downside risk-adjusted returns | > 2.5 |\n| **Max Drawdown** | Largest peak-to-trough decline | < 15% |\n| **Win Rate** | Percentage of profitable trades | > 55% |\n| **Profit Factor** | Gross profit / Gross loss | > 1.5 |\n| **Calmar Ratio** | Annual return / Max drawdown | > 1.0 |\n\n### Latency Budget\n\n```\n┌─────────────────────────────────────────────────┐\n│ Latency Requirements │\n├─────────────────────────────────────────────────┤\n│ Feature Engineering: < 10ms │\n│ Encoder Forward Pass: < 15ms │\n│ All Task Heads: < 5ms │\n│ Decision Fusion: < 2ms │\n├─────────────────────────────────────────────────┤\n│ Total Inference: < 32ms │\n├─────────────────────────────────────────────────┤\n│ Order Placement: < 50ms │\n│ End-to-End: < 100ms │\n└─────────────────────────────────────────────────┘\n```\n\n## References\n\n1. **Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance**\n - URL: https://arxiv.org/abs/2303.02841\n - Year: 2023\n\n2. **Trading in Fast-Changing Markets with Meta-Reinforcement Learning**\n - MAML-based trading method for multiple tasks\n - Year: 2024\n\n3. **Multi-Task Learning in Deep Neural Networks: A Survey**\n - Comprehensive overview of multi-task architectures\n - URL: https://arxiv.org/abs/2009.09796\n\n4. **Universal Language Model Fine-tuning for Text Classification**\n - Transfer learning techniques applicable to time series\n - URL: https://arxiv.org/abs/1801.06146\n\n5. **Gradient Surgery for Multi-Task Learning**\n - PCGrad method for handling gradient conflicts\n - URL: https://arxiv.org/abs/2001.06782\n\n6. **Adaptive Event-Driven Labeling with Meta-Learning for Financial Time Series**\n - Multi-scale temporal analysis with MAML\n - Year: 2025\n\n7. **FinRL: Deep Reinforcement Learning for Quantitative Finance**\n - Comprehensive framework for financial RL\n - URL: https://github.com/AI4Finance-Foundation/FinRL\n\n---\n\n## Next Steps\n\n- [Simple Explanation](readme.simple.md) - Beginner-friendly version\n- [Russian Version](README.ru.md) - Russian translation\n- [Run Examples](examples/) - Working Rust code\n- [Python Implementation](python/) - PyTorch reference implementation\n\n---\n\n*Chapter 87 of Machine Learning for Trading*\n","structuredPatch":[],"originalFile":null}} | |
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| [2026-01-21T18:56:23.372Z] [INFO] {"type":"assistant","message":{"model":"claude-opus-4-5-20251101","id":"msg_01JGYvKeDK7SKHmut2ay432D","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013CujnLU9239JSUa2UXruee","name":"Write","input":{"file_path":"/tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/README.ru.md","content":"# Глава 87: Задаче-Агностический Трейдинг\n\n## Обзор\n\nЗадаче-агностический трейдинг разрабатывает универсальные модели, которые хорошо работают для множества торговых задач без специфической настройки под конкретную задачу. В отличие от традиционных подходов, которые обучают отдельные модели для прогнозирования цены, волатильности, определения режима рынка и оптимизации портфеля, задаче-агностические модели изучают общие представления, которые эффективно переносятся на любую последующую задачу.\n\n## Содержание\n\n1. [Введение](#введение)\n2. [Теоретические основы](#теоретические-основы)\n3. [Задаче-агностические архитектуры](#задаче-агностические-архитектуры)\n4. [Универсальное обучение признаков](#универсальное-обучение-признаков)\n5. [Стратегия мультизадачного обучения](#стратегия-мультизадачного-обучения)\n6. [Применение в трейдинге](#применение-в-трейдинге)\n7. [Интеграция с Bybit](#интеграция-с-bybit)\n8. [Реализация](#реализация)\n9. [Управление рисками](#управление-рисками)\n10. [Метрики производительности](#метрики-производительности)\n11. [Ссылки](#ссылки)\n\n---\n\n## Введение\n\nТрадиционное машинное обучение для трейдинга сталкивается с фундаментальной проблемой: модели обычно специфичны для конкретной задачи. Модель, обученная для прогнозирования направления цены, не может напрямую использоваться для прогнозирования волатильности или определения режима рынка. Это требует поддержки множества моделей, каждая со своим конвейером обучения, гиперпараметрами и накладными расходами на обслуживание.\n\n### Проблема задаче-специфичных моделей\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ Традиционный задаче-специфичный подход │\n├─────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ ОТДЕЛЬНЫЕ МОДЕЛИ ДЛЯ КАЖДОЙ ЗАДАЧИ: │\n│ ──────────────────────────────────── │\n│ │\n│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │\n│ │ Предсказание │ │ Прогноз │ │ Детектор │ │\n│ │ Направления │ │ Волатильности │ │ Режима │ │\n│ ├─────────────────┤ ├─────────────────┤ ├─────────────────┤ │\n│ │ • Свои признаки │ │ • Свои признаки │ │ • Свои признаки │ │\n│ │ • Своё обучение │ │ • Своё обучение │ │ • Своё обучение │ │\n│ │ • Своя настройка│ │ • Своя настройка│ │ • Своя настройка│ │\n│ │ • Свой деплой │ │ • Свой деплой │ │ • Свой деплой │ │\n│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │\n│ │\n│ Проблемы: │\n│ • 3× усилий на разработку │\n│ • 3× накладных расходов на поддержку │\n│ • Нет обмена знаниями между задачами │\n│ • Несогласованные предсказания между задачами │\n│ • Каждая модель переобучается под свою задачу │\n│ │\n└─────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Задаче-агностическое решение\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ Задаче-агностический унифицированный подход │\n├─────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ ЕДИНАЯ УНИВЕРСАЛЬНАЯ МОДЕЛЬ: │\n│ ──────────────────────────── │\n│ │\n│ ┌─────────────────────────┐ │\n│ │ Универсальный Энкодер │ │\n│ │ (Общая основа) │ │\n│ ├─────────────────────────┤ │\n│ │ • Рыночное представление│ │\n│ │ • Кросс-задачные признаки │\n│ │ • Общие паттерны │ │\n│ └───────────┬─────────────┘ │\n│ │ │\n│ ┌──────────────────┼──────────────────┐ │\n│ │ │ │ │\n│ ▼ ▼ ▼ │\n│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │\n│ │ Голова │ │ Голова │ │ Голова │ │\n│ │ Направления │ │ Волатильности │ │ Режима │ │\n│ │ (маленький MLP) │ │ (маленький MLP) │ │ (маленький MLP) │ │\n│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │\n│ │\n│ Преимущества: │\n│ • Единый конвейер обучения │\n│ • Общие знания между задачами │\n│ • Согласованное понимание рынка │\n│ • Лучшая обобщающая способность │\n│ • Меньшее переобучение │\n│ │\n└─────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Ключевые преимущества для трейдинга\n\n| Аспект | Задаче-специфичный | Задаче-агностический |\n|--------|-------------------|---------------------|\n| Усилия на разработку | N × моделей | 1 единая модель |\n| Обучающие данные | Отдельные для задачи | Общие между задачами |\n| Перенос знаний | Отсутствует | Автоматический |\n| Согласованность | Независимые предсказания | Когерентные предсказания |\n| Адаптация к новой задаче | Полное переобучение | Только голова задачи |\n| Поддержка | Высокая | Низкая |\n| Обобщение | Специфичное для задачи | Универсальное |\n\n## Теоретические основы\n\n### Универсальное обучение представлений\n\nОсновной принцип задаче-агностического обучения — обнаружение представлений, которые захватывают фундаментальную структуру рыночных данных, независимо от какой-либо конкретной последующей задачи.\n\n**Определение**: Задаче-агностическое представление $\\phi: X \\to Z$ отображает сырые рыночные данные $X$ в латентное пространство $Z$ так, что:\n\n$$\\forall \\text{задача } T_i: \\exists \\text{ простой предиктор } h_i: Z \\to Y_i$$\n\nгде $h_i$ может решить задачу $T_i$, используя только общее представление $Z$.\n\n### Математическая формулировка\n\n**Мультизадачная цель**:\n\n$$\\mathcal{L}_{total} = \\sum_{i=1}^{N} \\lambda_i \\mathcal{L}_i(\\phi, h_i)$$\n\nгде:\n- $\\phi$ — общий энкодер (задаче-агностический)\n- $h_i$ — специфичная для задачи голова для задачи $i$\n- $\\lambda_i$ — вес задачи\n- $\\mathcal{L}_i$ — функция потерь для задачи $i$\n\n**Балансировка градиентов**:\n\nЧтобы предотвратить доминирование одной задачи в обучении:\n\n$$\\lambda_i = \\frac{\\overline{L} / L_i}{\\sum_j \\overline{L} / L_j}$$\n\nгде $\\overline{L}$ — средние потери по всем задачам.\n\n### Информационно-теоретический взгляд\n\nХорошее задаче-агностическое представление максимизирует взаимную информацию со всеми задачами:\n\n$$\\max_\\phi \\sum_{i=1}^{N} I(Z; Y_i)$$\n\nпри ограничении:\n\n$$I(Z; T) = 0$$\n\nгде $T$ — идентификатор задачи. Это гарантирует, что представление не кодирует специфичные для задачи смещения.\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Информационный поток в задаче-агностическом обучении │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Сырые рыночные данные X │\n│ │ │\n│ │ Содержит: цена, объём, стакан, новости и т.д. │\n│ │ │\n│ ▼ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Задаче-агностический энкодер φ │ │\n│ │ ───────────────────────────────────────────────────────────────── │ │\n│ │ │ │\n│ │ Цель: Извлечь информацию, релевантную для ВСЕХ торговых задач │ │\n│ │ │ │\n│ │ Сохраняет: Отбрасывает: │ │\n│ │ • Структуру тренда • Специфичные для задачи смещения│ │\n│ │ • Паттерны волатильности • Шум, нерелевантный для торговли│ │\n│ │ • Микроструктуру рынка • Ложные корреляции │ │\n│ │ • Кросс-активные связи • Артефакты переобучения │ │\n│ │ • Характеристики режима │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ Универсальное представление Z │\n│ │ │\n│ ├───── Высокая I(Z; Направление цены) │\n│ ├───── Высокая I(Z; Волатильность) │\n│ ├───── Высокая I(Z; Режим) │\n│ └───── Нулевая I(Z; Идентификатор задачи) │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Задаче-агностические архитектуры\n\n### Архитектура 1: Общий энкодер с головами задач\n\nСамый простой и распространённый подход:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Архитектура с общим энкодером │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Вход: Рыночные признаки (OHLCV + Индикаторы + Стакан) │\n│ ───────────────────────────────────────────────────── │\n│ │ │\n│ ▼ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Общий энкодер (Заморожен после предобучения) │ │\n│ │ ───────────────────────────────────────────────────────────────── │ │\n│ │ │ │\n│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │\n│ │ │ 1D Conv │──▶│ 1D Conv │──▶│ 1D Conv │ │ │\n│ │ │ (локальный) │ │ (средний) │ │ (дальний) │ │ │\n│ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │\n│ │ │ │ │ │ │\n│ │ └────────────────┬┴─────────────────┘ │ │\n│ │ ▼ │ │\n│ │ ┌─────────────────────────┐ │ │\n│ │ │ Multi-Head Attention │ │ │\n│ │ │ (временные связи) │ │ │\n│ │ └─────────────────────────┘ │ │\n│ │ │ │ │\n│ │ ▼ │ │\n│ │ ┌─────────────────────────┐ │ │\n│ │ │ Global Average Pool │ │ │\n│ │ │ → 256-мерный эмбеддинг │ │ │\n│ │ └─────────────────────────┘ │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ [256-мерное универсальное представление] │\n│ │ │\n│ ┌─────────────────┼─────────────────┬─────────────────┐ │\n│ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ │\n│ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ │\n│ │ Голова │ │ Голова │ │ Голова │ │ Голова │ │\n│ │ Направл. │ │ Волатил. │ │ Режима │ │ Доходн. │ │\n│ ├───────────┤ ├───────────┤ ├───────────┤ ├───────────┤ │\n│ │ 256→64→3 │ │ 256→64→1 │ │ 256→64→5 │ │ 256→64→1 │ │\n│ │ softmax │ │ ReLU │ │ softmax │ │ linear │ │\n│ └───────────┘ └───────────┘ └───────────┘ └───────────┘ │\n│ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ │\n│ [Вверх/Вниз/ [Прогноз [Бык/Медведь/ [Ожидаемая │\n│ Боковик] волатильн.] Боковик/ доходность] │\n│ ВысВол/ │\n│ НизВол] │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Архитектура 2: Трансформер-энкодер\n\nИспользование механизмов внимания для более богатых представлений:\n\n```rust\n/// Конфигурация трансформер-энкодера для задаче-агностической модели\n#[derive(Debug, Clone)]\npub struct TransformerEncoderConfig {\n /// Размерность входных признаков\n pub input_dim: usize,\n /// Размерность модели (d_model)\n pub model_dim: usize,\n /// Количество голов внимания\n pub num_heads: usize,\n /// Количество слоёв трансформера\n pub num_layers: usize,\n /// Размерность feedforward\n pub ff_dim: usize,\n /// Максимальная длина последовательности\n pub max_seq_len: usize,\n /// Dropout\n pub dropout: f32,\n /// Размерность выходного эмбеддинга\n pub embedding_dim: usize,\n}\n\nimpl Default for TransformerEncoderConfig {\n fn default() -> Self {\n Self {\n input_dim: 64,\n model_dim: 256,\n num_heads: 8,\n num_layers: 6,\n ff_dim: 1024,\n max_seq_len: 256,\n dropout: 0.1,\n embedding_dim: 256,\n }\n }\n}\n```\n\n### Архитектура 3: Смесь экспертов (MoE)\n\nДинамическая маршрутизация для разных рыночных условий:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Архитектура смеси экспертов │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Вход: Рыночные признаки │\n│ │ │\n│ ▼ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Сеть маршрутизации │ │\n│ │ ───────────────────────────────── │ │\n│ │ Анализирует вход и определяет веса экспертов │ │\n│ │ │ │\n│ │ Выход: [w₁=0.1, w₂=0.6, w₃=0.2, w₄=0.1 | |
| [2026-01-21T18:56:23.384Z] [INFO] ] │ │\n│ │ (веса суммируются в 1) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ │ Разреженная маршрутизация (top-k экспертов) │\n│ │ │\n│ ┌─────┴─────┬─────────────┬─────────────┬─────────────┐ │\n│ │ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ ▼ │\n│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │\n│ │ E₁ │ │ E₂ │ │ E₃ │ │ E₄ │ │ E₅ │ │\n│ │Тренд│ │Вола-│ │Возвр│ │Хвост│ │Событ│ │\n│ │Экспр│ │тил. │ │к ср.│ │Риск │ │Экспр│ │\n│ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ │\n│ │ │ │ │ │ │\n│ │ ×w₁ │ ×w₂ │ ×w₃ │ ×w₄ │ ×w₅ │\n│ │ │ │ │ │ │\n│ └─────────┴──────┬──────┴─────────────┴─────────────┘ │\n│ │ │\n│ ▼ │\n│ Взвешенная сумма выходов экспертов │\n│ │ │\n│ ▼ │\n│ [Универсальное представление Z] │\n│ │\n│ Специализации экспертов: │\n│ • E₁ (Тренд): Сильные направленные движения │\n│ • E₂ (Волатильность): Периоды высокой неопределённости │\n│ • E₃ (Возврат к среднему): Паттерны консолидации │\n│ • E₄ (Хвостовой риск): Экстремальные события │\n│ • E₅ (Событие): Движения на новостях/отчётах │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Универсальное обучение признаков\n\n### Самообучение без учителя\n\nОбучение энкодера без меток с использованием самообучения:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Задачи самообучения без меток │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. МАСКИРОВАННОЕ АВТОКОДИРОВАНИЕ (MAE для временных рядов) │\n│ ─────────────────────────────────────────────────────────── │\n│ │\n│ Оригинал: [p₁] [p₂] [p₃] [p₄] [p₅] [p₆] [p₇] [p₈] │\n│ Маскиров: [p₁] [##] [p₃] [##] [##] [p₆] [p₇] [##] (30% маскировано) │\n│ Задача: Восстановить маскированные значения │\n│ │\n│ Это учит модель понимать временные паттерны! │\n│ │\n│ 2. КОНТРАСТНОЕ ОБУЧЕНИЕ │\n│ ───────────────────────── │\n│ │\n│ Создание пар: │\n│ • Позитивные: Тот же актив, близкие временные окна (похожие паттерны) │\n│ • Негативные: Разные активы или далёкие временные окна │\n│ │\n│ ┌─────────┐ похожи ┌─────────┐ │\n│ │ BTC t=1 │ ◀──────────▶ │ BTC t=2 │ Притягиваем │\n│ └─────────┘ └─────────┘ │\n│ │ │\n│ │ различны │\n│ │ │\n│ ┌─────────┐ │\n│ │ ETH t=1 │ Отталкиваем │\n│ └─────────┘ │\n│ │\n│ 3. ПРЕДСКАЗАНИЕ ВРЕМЕННОГО ПОРЯДКА │\n│ ────────────────────────────────── │\n│ │\n│ Дано: [Окно A] [Окно B] │\n│ Задача: A было до B или после B? │\n│ │\n│ Это учит временной причинности! │\n│ │\n│ 4. ПРЕДСКАЗАНИЕ БУДУЩЕГО (следующий шаг) │\n│ ───────────────────────────────────────── │\n│ │\n│ Дано: [p₁] [p₂] [p₃] [p₄] [p₅] │\n│ Задача: Предсказать некоторые статистики [p₆] │\n│ (не точное значение, а свойства распределения) │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Стратегия мультизадачного обучения\n\n### Гармонизация градиентов\n\nПредотвращение конфликтов между задачами при обучении:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Гармонизация мультизадачных градиентов │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Проблема: Градиенты задач могут конфликтовать! │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Пример конфликтующих градиентов: │ │\n│ │ │ │\n│ │ Градиент направления: ▲ (увеличить этот вес) │ │\n│ │ Градиент волатильн.: ▼ (уменьшить этот вес) │ │\n│ │ │ │\n│ │ Наивное усреднение: ▲ + ▼ = → (веса почти не двигаются!) │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ Решение: Методы гармонизации градиентов │\n│ │\n│ Метод 1: PCGrad (Проекция конфликтующих градиентов) │\n│ ──────────────────────────────────────────────────── │\n│ Если g₁ · g₂ < 0 (конфликт): │\n│ g₁' = g₁ - (g₁·g₂/|g₂|²) × g₂ │\n│ Проецируем прочь конфликтующую компоненту │\n│ │\n│ Метод 2: GradNorm (Нормализация градиентов) │\n│ ──────────────────────────────────────────── │\n│ Динамически корректируем веса задач для баланса амплитуд градиентов │\n│ │\n│ Метод 3: Взвешивание по неопределённости │\n│ ───────────────────────────────────────── │\n│ w_i = 1/(2σ_i²) где σ_i — неопределённость задачи │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Применение в трейдинге\n\n### Задаче-агностическая торговая система\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Задаче-агностическая торговая система │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ ПРИЁМ ДАННЫХ В РЕАЛЬНОМ ВРЕМЕНИ │\n│ ──────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Bybit WebSocket → Конвейер признаков → Буфер признаков │ │\n│ │ • OHLCV (1m, 5m, 15m, 1h, 4h) │ │\n│ │ • Стакан (L2 top 20 уровней) │ │\n│ │ • Сделки (тик-бай-тик) │ │\n│ │ • Ставки финансирования │ │\n│ │ • Открытый интерес │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ЗАДАЧЕ-АГНОСТИЧЕСКИЙ ЭНКОДЕР (Предобученный, заморожен) │\n│ ───────────────────────────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Рыночные признаки → Универсальный эмбеддинг (256-мерный) │ │\n│ │ Задержка: < 5мс на инференс │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ МУЛЬТИЗАДАЧНЫЙ ИНФЕРЕНС (Параллельные головы задач) │\n│ ──────────────────────────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌───────────┐ │ │\n│ │ │ Направление │ │ Волатильн. │ │ Режим │ │ Доходность│ │ │\n│ │ │ 68% Вверх │ │ 2.3% ож. │ │ Бык (73%) │ │ +0.5% │ │ │\n│ │ │ 22% Вниз │ │ │ │ │ │ ожидается │ │ │\n│ │ │ 10% Боков. │ │ │ │ │ │ │ │ │\n│ │ └─────────────┘ └─────────────┘ └─────────────┘ └───────────┘ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ СЛИЯНИЕ РЕШЕНИЙ (Объединение всех выходов задач) │\n│ ──────────────────────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Проверка согласованности: │ │\n│ │ • Направл.=Вверх + Режим=Бык + Доходн.=Полож. → СОГЛАСОВАНО ✓ │ │\n│ │ • Направл.=Вверх + Режим=Медведь → КОНФЛИКТ! → Снижаем уверенность│ │\n│ │ │ │\n│ │ Оценка риска: │ │\n│ │ • Прогноз волатильности определяет размер позиции │ │\n│ │ • Режим определяет уровни стоп-лосс/тейк-профит │ │\n│ │ │ │\n│ │ Итоговый сигнал: │ │\n│ │ • Действие: ЛОНГ │ │\n│ │ • Уверенность: 72% (согласованные предсказания) │ │\n│ │ • Размер: 1.5% от капитала (умеренная волатильность) │ │\n│ │ • Стоп: -1.5% (бычий режим, плотный стоп) │ │\n│ │ • Цель: +2.0% (ожидаемая доходность + буфер) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ИСПОЛНЕНИЕ ОРДЕРОВ │\n│ ─────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Bybit API → Рыночный/лимитный ордер → Мониторинг позиции │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Интеграция с Bybit\n\n### Конвейер сбора данных\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Конвейер данных Bybit │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ ИСТОЧНИКИ ДАННЫХ ДЛЯ ЗАДАЧЕ-АГНОСТИЧЕСКОГО ОБУЧЕНИЯ │\n│ ─────────────────────────────────────────────────── │\n│ │\n│ 1. ИСТОРИЧЕСКИЕ ДАННЫЕ (Обучение) │\n│ ───────────────────────────────── │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Эндпоинт: GET /v5/market/kline │ │\n│ │ Символы: BTCUSDT, ETHUSDT, SOLUSDT, ... (топ 50 по объёму) │ │\n│ │ Интервалы: 1m, 5m, 15m, 1h, 4h, 1d │ │\n│ │ История: 2 года │ │\n│ │ │ │\n│ │ Генерация меток для задач: │ │\n│ │ ┌─────────────────────────────────────────────────────────────┐ │ │\n│ │ │ Направление: На основе знака будущей доходности │ │ │\n│ │ │ Волатильность: Реализованная волатильность за следующие периоды │ │\n│ │ │ Режим: HMM кластеризация по доходности/волатильности │ │ │\n│ │ │ Доходность: Фактическая будущая доходность (непрерывная) │ │ │\n│ │ └─────────────────────────────────────────────────────────────┘ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 2. ДАННЫЕ В РЕАЛЬНОМ ВРЕМЕНИ (Инференс) │\n│ ──────────────────────────────────────── │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ WebSocket: wss://stream.bybit.com/v5/public/linear │ │\n│ │ │ │\n│ │ Подписки: │ │\n│ │ • kline.1.BTCUSDT (1-минутные свечи) │ │\n│ │ • orderbook.50.BTCUSDT (L2 стакан) │ │\n│ │ • publicTrade.BTCUSDT (тиковые сделки) │ │\n│ │ • tickers.BTCUSDT (рыночная статистика) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Реализация\n\n### Структура модуля\n\n```\n87_task_agnostic_trading/\n├── Cargo.toml\n├── README.md # Документация (английский)\n├── README.ru.md # Русский перевод\n├── readme.simple.md # Упрощённое объяснение\n├── readme.simple.ru.md # Упрощённое объяснение (русский)\n├── src/\n│ ├── lib.rs # Корень библиотеки\n│ ├── config.rs # Типы конфигурации\n│ ├── encoder/\n│ │ ├── mod.rs # Модуль энкодера\n│ │ ├── transformer.rs # Трансформер-энкодер\n│ │ ├── cnn.rs # CNN-энкодер\n│ │ └── moe.rs # Смесь экспертов\n│ ├── tasks/\n│ │ ├── mod.rs # Модуль задач\n│ │ ├── direction.rs # Предсказание направления\n│ │ ├── volatility.rs # Прогноз волатильности\n│ │ ├── regime.rs # Определение режима\n│ │ └── returns.rs # Предсказание доходности\n│ ├── training/\n│ │ ├── mod.rs # Модуль обучения\n│ │ ├── multitask.rs # Мультизадачное обучение\n│ │ ├── curriculum.rs # Курриккулум задач\n│ │ └── pretraining.rs # Предобучение\n│ ├── fusion/\n│ │ ├── mod.rs # Модуль слияния\n│ │ └── decision.rs # Слияние решений\n│ ├── data/\n│ │ ├── mod.rs # Модуль данных\n│ │ ├── bybit.rs # API клиент Bybit\n│ │ ├── features.rs # Инженерия признаков\n│ │ └── dataset.rs # Обработка датасета\n│ └── trading/\n│ ├── mod.rs # Модуль торговли\n│ ├── strategy.rs # Торговая стратегия\n│ └── risk.rs # Управление рисками\n├── examples/\n│ ├── train_encoder.rs # Обучение энкодера\n│ ├── multi_task_inference.rs # Мультизадачный инференс\n│ ├── bybit_trading.rs # Торговля с Bybit\n│ └── backtest.rs # Бэктестинг\n├── python/\n│ ├── task_agnostic_model.py # PyTorch реализация\n│ ├── train.py # Скрипт обучения\n│ ├── evaluate.py # Скрипт оценки\n│ └── requirements.txt # Python зависимости\n└── tests/\n ├── integration.rs # Интеграционные тесты\n └── unit_tests.rs # Юнит-тесты\n```\n\n## Управление рисками\n\n### Мультизадачная оценка риска\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Мультизадачная оценка риска │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. ОЦЕНКА СОГЛАСОВАННОСТИ ЗАДАЧ │\n│ ──────────────────────────────── │\n│ │\n│ Когда задачи согласованы → Выше уверенность, больше позиции │\n│ Когда задачи конфликтуют → Ниже уверенность, меньше позиции или без сделки\n│ │\n│ Матрица согласованности: │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ │ Направл. │ Волатил. │ Режим │ Доходн. │ Оценка │ │\n│ │──────────────┼──────────┼──────────┼────────┼─────────┼──────────│ │\n│ │ Сценарий 1 │ ВВЕРХ │ НИЗКАЯ │ БЫК │ +0.5% │ 0.95 │ │\n│ │ (Все согл.) │ │ │ │ │ СДЕЛКА │ │\n│ │──────────────┼──────────┼──────────┼────────┼─────────┼──────────│ │\n│ │ Сценарий 2 │ ВВЕРХ │ ВЫСОКАЯ │ БЫК │ +0.3% │ 0.70 │ │\n│ │ (Вол. пред.) │ │ │ │ │ ОСТОРОЖН │ │\n│ │──────────────┼──────────┼──────────┼────────┼─────────┼──────────│ │\n│ │ Сценарий 3 │ ВВЕРХ │ ВЫСОКАЯ │ МЕДВЕДЬ│ -0.2% │ 0.30 │ │\n│ │ (Конфликт) │ │ │ │ │БЕЗ СДЕЛКИ│ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 2. ДИНАМИЧЕСКИЙ РАЗМЕР ПОЗИЦИИ │\n│ ────────────────────────────── │\n│ │\n│ Размер позиции = База × Уверенность × (1 / Ранг_волатильности) │\n│ │\n│ 3. СТОП-ЛОСС С УЧЁТОМ РЕЖИМА │\n│ ───────────────────────────── │\n│ │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Режим │ Стоп-лосс │ Тейк-профит │ Обоснование │ │\n│ │──────────────────┼────────────┼──────────────┼────────────────────│ │\n│ │ Бык │ -1.5% │ +3.0% │ Держим тренд │ │\n│ │ Медведь │ -1.5% │ +3.0% │ Ловим движение │ │\n│ │ Боковик │ -1.0% │ +1.5% │ Узкий диапазон │ │\n│ │ Высокая волат. │ -2.5% │ +4.0% │ Широкие стопы │ │\n│ │ Низкая волат. │ -1.0% │ +1.5% │ Плотный контроль │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Метрики производительности\n\n### Метрики оценки модели\n\n| Метрика | Описание | Цель |\n|---------|----------|------|\n| **Мультизадачная точность** | Средняя точность по всем задачам классификации | > 65% |\n| **Точность направления** | Точность предсказания направления цены | > 60% |\n| **Точность режима** | Точность определения режима рынка | > 70% |\n| **RMSE волатильности** | Среднеквадратичная ошибка прогноза волатильности | < 0.02 |\n| **Корреляция доходности** | Корреляция предсказанной и фактической доходности | > 0.3 |\n| **Перенос на задачу** | Производительность при добавлении новой задачи | > 90% от обучения с нуля |\n| **Оценка согласованности** | Как часто предсказания задач совпадают | > 70% |\n\n### Метрики торговой производительности\n\n| Метрика | Описание | Цель |\n|---------|----------|------|\n| **Коэффициент Шарпа** | Доходность с учётом риска | > 2.0 |\n| **Коэффициент Сортино** | Доходность с учётом риска просадки | > 2.5 |\n| **Макс. просадка** | Наибольшее падение от пика до дна | < 15% |\n| **Процент выигрышных** | Процент прибыльных сделок | > 55% |\n| **Профит-фактор** | Валовая прибыль / Валовый убыток | > 1.5 |\n| **Коэффициент Калмара** | Годовая доходность / Макс. просадка | > 1.0 |\n\n---\n\n## Ссылки\n\n1. **Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance**\n - URL: https://arxiv.org/abs/2303.02841\n - Год: 2023\n\n2. **Trading in Fast-Changing Markets with Meta-Reinforcement Learning**\n - MAML-метод торговли для множества задач\n - Год: 2024\n\n3. **Multi-Task Learning in Deep Neural Networks: A Survey**\n - Комплексный обзор мультизадачных архитектур\n - URL: https://arxiv.org/abs/2009.09796\n\n4. **Gradient Surgery for Multi-Task Learning**\n - Метод PCGrad для обработки конфликтов градиентов\n - URL: https://arxiv.org/abs/2001.06782\n\n5. **Adaptive Event-Driven Labeling with Meta-Learning for Financial Time Series**\n - Многомасштабный временной анализ с MAML\n - Год: 2025\n\n---\n\n## Следующие шаги\n\n- [Простое объяснение](readme.simple.md) - Версия для начинающих\n- [Английская версия](README.md) - English version\n- [Примеры](examples/) - Рабочий код на Rust\n- [Python реализация](python/) - Эталонная реализация на PyTorch\n\n---\n\n*Глава 87 книги \"Машинное обучение для трейдинга\"*\n"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":0,"cache_creation_input_tokens":426,"cache_read_input_tokens":91784,"cache_creation":{"ephemeral_5m_input_tokens":426,"ephemeral_1h_input_tokens":0},"output_tokens":3,"service_tier":"standard"},"context_management":null},"parent_tool_use_id":null,"session_id":"8c23df75-76b7-45ba-aa3a-4295879a3b8f","uuid":"bffb9763-97bf-4889-ad46-4a283dd31cf4"} | |
| [2026-01-21T18:56:23.433Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013CujnLU9239JSUa2UXruee","type":"tool_result","content":"File created successfully at: /tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/README.ru.md"}]},"parent_tool_use_id":null,"session_id":"8c23df75-76b7-45ba-aa3a-4295879a3b8f","uuid":"473c91e4-d148-4b3d-87e1-8f237d14fc2d","tool_use_result":{"type":"create","filePath":"/tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/README.ru.md","content":"# Глава 87: Задаче-Агностический Трейдинг\n\n## Обзор\n\nЗадаче-агностический трейдинг разрабатывает универсальные модели, которые хорошо работают для множества торговых задач без специфической настройки под конкретную задачу. В отличие от традиционных подходов, которые обучают отдельные модели для прогнозирования цены, волатильности, определения режима рынка и оптимизации портфеля, задаче-агностические модели изучают общие представления, которые эффективно переносятся на любую последующую задачу.\n\n## Содержание\n\n1. [Введение](#введение)\n2. [Теоретические основы](#теоретические-основы)\n3. [Задаче-агностические архитектуры](#задаче-агностические-архитектуры)\n4. [Универсальное обучение признаков](#универсальное-обучение-признаков)\n5. [Стратегия мультизадачного обучения](#стратегия-мультизадачного-обучения)\n6. [Применение в трейдинге](#применение-в-трейдинге)\n7. [Интеграция с Bybit](#интеграция-с-bybit)\n8. [Реализация](#реализация)\n9. [Управление рисками](#управление-рисками)\n10. [Метрики производительности](#метрики-производительности)\n11. [Ссылки](#ссылки)\n\n---\n\n## Введение\n\nТрадиционное машинное обучение для трейдинга сталкивается с фундаментальной проблемой: модели обычно специфичны для конкретной задачи. Модель, обученная для прогнозирования направления цены, не может напрямую использоваться для прогнозирования волатильности или определения режима рынка. Это требует поддержки множества моделей, каждая со своим конвейером обучения, гиперпараметрами и накладными расходами на обслуживание.\n\n### Проблема задаче-специфичных моделей\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ Традиционный задаче-специфичный подход │\n├─────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ ОТДЕЛЬНЫЕ МОДЕЛИ ДЛЯ КАЖДОЙ ЗАДАЧИ: │\n│ ──────────────────────────────────── │\n│ │\n│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │\n│ │ Предсказание │ │ Прогноз │ │ Детектор │ │\n│ │ Направления │ │ Волатильности │ │ Режима │ │\n│ ├─────────────────┤ ├─────────────────┤ ├─────────────────┤ │\n│ │ • Свои признаки │ │ • Свои признаки │ │ • Свои признаки │ │\n│ │ • Своё обучение │ │ • Своё обучение │ │ • Своё обучение │ │\n│ │ • Своя настройка│ │ • Своя настройка│ │ • Своя настройка│ │\n│ │ • Свой деплой │ │ • Свой деплой │ │ • Свой деплой │ │\n│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │\n│ │\n│ Проблемы: │\n│ • 3× усилий на разработку │\n│ • 3× накладных расходов на поддержку │\n│ • Нет обмена знаниями между задачами │\n│ • Несогласованные предсказания между задачами │\n│ • Каждая модель переобучается под свою задачу │\n│ │\n└─────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Задаче-агностическое решение\n\n```\n┌─────────────────────────────────────────────────────────────────────────────┐\n│ Задаче-агностический унифицированный подход │\n├─────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ ЕДИНАЯ УНИВЕРСАЛЬНАЯ МОДЕЛЬ: │\n│ ──────────────────────────── │\n│ │\n│ ┌─────────────────────────┐ │\n│ │ Универсальный Энкодер │ │\n│ │ (Общая основа) │ │\n│ ├─────────────────────────┤ │\n│ │ • Рыночное представление│ │\n│ │ • Кросс-задачные признаки │\n│ │ • Общие паттерны │ │\n│ └───────────┬─────────────┘ │\n│ │ │\n│ ┌──────────────────┼──────────────────┐ │\n│ │ │ │ │\n│ ▼ ▼ ▼ │\n│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │\n│ │ Голова │ │ Голова │ │ Голова │ │\n│ │ Направления │ │ Волатильности │ │ Режима │ │\n│ │ (маленький MLP) │ │ (маленький MLP) │ │ (маленький MLP) │ │\n│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │\n│ │\n│ Преимущества: │\n│ • Единый конвейер обучения │\n│ • Общие знания между задачами │\n│ • Согласованное понимание рынка │\n│ • Лучшая обобщающая способность │\n│ • Меньшее переобучение │\n│ │\n└─────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Ключевые преимущества для трейдинга\n\n| Аспект | Задаче-специфичный | Задаче-агностический |\n|--------|-------------------|---------------------|\n| Усилия на разработку | N × моделей | 1 единая модель |\n| Обучающие данные | Отдельные для задачи | Общие между задачами |\n| Перенос знаний | Отсутствует | Автоматический |\n| Согласованность | Независимые предсказания | Когерентные предсказания |\n| Адаптация к новой задаче | Полное переобучение | Только голова задачи |\n| Поддержка | Высокая | Низкая |\n| Обобщение | Специфичное для задачи | Универсальное |\n\n## Теоретические основы\n\n### Универсальное обучение представлений\n\nОсновной принцип задаче-агностического обучения — обнаружение представлений, которые захватывают фундаментальную структуру рыночных данных, независимо от какой-либо конкретной последующей задачи.\n\n**Определение**: Задаче-агностическое представление $\\phi: X \\to Z$ отображает сырые рыночные данные $X$ в латентное пространство $Z$ так, что:\n\n$$\\forall \\text{задача } T_i: \\exists \\text{ простой предиктор } h_i: Z \\to Y_i$$\n\nгде $h_i$ может решить задачу $T_i$, используя только общее представление $Z$.\n\n### Математическая формулировка\n\n**Мультизадачная цель**:\n\n$$\\mathcal{L}_{total} = \\sum_{i=1}^{N} \\lambda_i \\mathcal{L}_i(\\phi, h_i)$$\n\nгде:\n- $\\phi$ — общий энкодер (задаче-агностический)\n- $h_i$ — специфичная для задачи голова для задачи $i$\n- $\\lambda_i$ — вес задачи\n- $\\mathcal{L}_i$ — функция потерь для задачи $i$\n\n**Балансировка градиентов**:\n\nЧтобы предотвратить доминирование одной задачи в обучении:\n\n$$\\lambda_i = \\frac{\\overline{L} / L_i}{\\sum_j \\overline{L} / L_j}$$\n\nгде $\\overline{L}$ — средние потери по всем задачам.\n\n### Информационно-теоретический взгляд\n\nХорошее задаче-агностическое представление максимизирует взаимную информацию со всеми задачами:\n\n$$\\max_\\phi \\sum_{i=1}^{N} I(Z; Y_i)$$\n\nпри ограничении:\n\n$$I(Z; T) = 0$$\n\nгде $T$ — идентификатор задачи. Это гарантирует, что представление не кодирует специфичные для задачи смещения.\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Информационный поток в задаче-агностическом обучении │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Сырые рыночные данные X │\n│ │ │\n│ │ Содержит: цена, объём, стакан, новости и т.д. │\n│ │ │\n│ ▼ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Задаче-агностический энкодер φ │ │\n│ │ ───────────────────────────────────────────────────────────────── │ │\n│ │ │ │\n│ │ Цель: Извлечь информацию, релевантную для ВСЕХ торговых задач │ │\n│ │ │ │\n│ │ Сохраняет: Отбрасывает: │ │\n│ │ • Структуру тренда • Специфичные для задачи смещения│ │\n│ │ • Паттерны волатильности • Шум, нерелевантный для торговли│ │\n│ │ • Микроструктуру рынка • Ложные корреляции │ │\n│ │ • Кросс-активные связи • Артефакты переобучения │ │\n│ │ • Характеристики режима │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ Универсальное представление Z │\n│ │ │\n│ ├───── Высокая I(Z; Направление цены) │\n│ ├───── Высокая I(Z; Волатильность) │\n│ ├───── Высокая I(Z; Режим) │\n│ └───── Нулевая I(Z; Идентификатор задачи) │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Задаче-агностические архитектуры\n\n### Архитектура 1: Общий энкодер с головами задач\n\nСамый простой и распространённый подход:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Архитектура с общим энкодером │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Вход: Рыночные признаки (OHLCV + Индикаторы + Стакан) │\n│ ───────────────────────────────────────────────────── │\n│ │ │\n│ ▼ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Общий энкодер (Заморожен после предобучения) │ │\n│ │ ───────────────────────────────────────────────────────────────── │ │\n│ │ │ │\n│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │\n│ │ │ 1D Conv │──▶│ 1D Conv │──▶│ 1D Conv │ │ │\n│ │ │ (локальный) │ │ (средний) │ │ (дальний) │ │ │\n│ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │\n│ │ │ │ │ │ │\n│ │ └────────────────┬┴─────────────────┘ │ │\n│ │ ▼ │ │\n│ │ ┌─────────────────────────┐ │ │\n│ │ │ Multi-Head Attention │ │ │\n│ │ │ (временные связи) │ │ │\n│ │ └─────────────────────────┘ │ │\n│ │ │ │ │\n│ │ ▼ │ │\n│ │ ┌─────────────────────────┐ │ │\n│ │ │ Global Average Pool │ │ │\n│ │ │ → 256-мерный эмбеддинг │ │ │\n│ │ └─────────────────────────┘ │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ [256-мерное универсальное представление] │\n│ │ │\n│ ┌─────────────────┼─────────────────┬─────────────────┐ │\n│ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ │\n│ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ │\n│ │ Голова │ │ Голова │ │ Голова │ │ Голова │ │\n│ │ Направл. │ │ Волатил. │ │ Режима │ │ Доходн. │ │\n│ ├───────────┤ ├───────────┤ ├───────────┤ ├───────────┤ │\n│ │ 256→64→3 │ │ 256→64→1 │ │ 256→64→5 │ │ 256→64→1 │ │\n│ │ softmax │ │ ReLU │ │ softmax │ │ linear │ │\n│ └───────────┘ └───────────┘ └───────────┘ └───────────┘ │\n│ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ │\n│ [Вверх/Вниз/ [Прогноз [Бык/Медведь/ [Ожидаемая │\n│ Боковик] волатильн.] Боковик/ доходность] │\n│ ВысВол/ │\n│ НизВол] │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n### Архитектура 2: Трансформер-энкодер\n\nИспользование механизмов внимания для более богатых представлений:\n\n```rust\n/// Конфигурация трансформер-энкодера для задаче-агностической модели\n#[derive(Debug, Clone)]\npub struct TransformerEncoderConfig {\n /// Размерность входных признаков\n pub input_dim: usize,\n /// Размерность модели (d_model)\n pub model_dim: usize,\n /// Количество голов внимания\n pub num_heads: usize,\n /// Количество слоёв трансформера\n pub num_layers: usize,\n /// Размерность feedforward\n pub ff_dim: usize,\n /// Максимальная длина последовательности\n pub max_seq_len: usize,\n /// Dropout\n pub dropout: f32,\n /// Размерность выходного эмбеддинга\n pub embedding_dim: usize,\n}\n\nimpl Default for TransformerEncoderConfig {\n fn default() -> Self {\n Self {\n input_dim: 64,\n model_dim: 256,\n num_heads: 8,\n num_layers: 6,\n ff_dim: 1024,\n max_seq_len: 256,\n dropout: 0.1,\n embedding_dim: 256,\n }\n }\n}\n```\n\n### Архитектура 3: Смесь экспертов (MoE)\n\nДинамическая маршрутизация для разных рыночных условий:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Архитектура смеси экспертов │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Вход: Рыночные признаки │\n│ │ │\n│ ▼ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Сеть маршрутизации │ │\n│ │ ───────────────────────────────── │ │\n│ │ Анализирует вход и определяет веса экспертов │ │\n│ │ │ │\n│ │ Выход: [w₁=0.1, w₂=0.6, w₃=0.2, w₄=0.1] │ │\n│ │ (веса суммируются в 1) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ │ Разреженная маршрутизация (top-k экспертов) │\n│ │ │\n│ ┌─────┴─────┬─────────────┬─────────────┬─────────────┐ │\n│ │ │ │ │ │ │\n│ ▼ ▼ ▼ ▼ ▼ │\n│ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐ │\n│ │ E₁ │ │ E₂ │ │ E₃ │ │ E₄ │ │ E₅ │ │\n│ │Тренд│ │Вола-│ │Возвр│ │Хвост│ │Событ│ │\n│ │Экспр│ │тил. │ │к ср.│ │Риск │ │Экспр│ │\n│ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ │\n│ │ │ │ │ │ │\n│ │ ×w₁ │ ×w₂ │ ×w₃ │ ×w₄ │ ×w₅ │\n│ │ │ │ │ │ │\n│ └─────────┴──────┬──────┴─────────────┴─────────────┘ │\n│ │ │\n│ ▼ │\n│ Взвешенная сумма выходов экспертов │\n│ │ │\n│ ▼ │\n│ [Универсальное представление Z] │\n│ │\n│ Специализации экспертов: │\n│ • E₁ (Тренд): Сильные направленные движения │\n│ • E₂ (Волатильность): Периоды высокой неопределённости │\n│ • E₃ (Возврат к среднему): Паттерны консолидации │\n│ • E₄ (Хвостовой риск): Экстремальные события │\n│ • E₅ (Событие): Движения на новостях/отчётах │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Универсальное обучение признаков\n\n### Самообучение без учителя\n\nОбучение энкодера без меток с использованием самообучения:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Задачи самообучения без меток │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. МАСКИРОВАННОЕ АВТОКОДИРОВАНИЕ (MAE для временных рядов) │\n│ ─────────────────────────────────────────────────────────── │\n│ │\n│ Оригинал: [p₁] [p₂] [p₃] [p₄] [p₅] [p₆] [p₇] [p₈] │\n│ Маскиров: [p₁] [##] [p₃] [##] [##] [p₆] [p₇] [##] (30% маскировано) │\n│ Задача: Восстановить маскированные значения │\n│ │\n│ Это учит модель понимать временные паттерны! │\n│ │\n│ 2. КОНТРАСТНОЕ ОБУЧЕНИЕ │\n│ ───────────────────────── │\n│ │\n│ Создание пар: │\n│ • Позитивные: Тот же актив, близкие временные окна (похожие паттерны) │\n│ • Негативные: Разные активы или далёкие временные окна │\n│ │\n│ ┌─────────┐ похожи ┌─────────┐ │\n│ │ BTC t=1 │ ◀──────────▶ │ BTC t=2 │ Притягиваем │\n│ └─────────┘ └─────────┘ │\n│ │ │\n│ │ различны │\n│ │ │\n│ ┌─────────┐ │\n│ │ ETH t=1 │ Отталкиваем │\n│ └─────────┘ │\n│ │\n│ 3. ПРЕДСКАЗАНИЕ ВРЕМЕННОГО ПОРЯДКА │\n│ ────────────────────────────────── │\n│ │\n│ Дано: [Окно A] [Окно B] │\n│ Задача: A было до B или после B? │\n│ │\n│ Это учит временной причинности! │\n│ │\n│ 4. ПРЕДСКАЗАНИЕ БУДУЩЕГО (следующий шаг) │\n│ ───────────────────────────────────────── │\n│ │\n│ Дано: [p₁] [p₂] [p₃] [p₄] [p₅] │\n│ Задача: Предсказать некоторые статистики [p₆] │\n│ (не точное значение, а свойства распределения) │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Стратегия мультизадачного обучения\n\n### Гармонизация градиентов\n\nПредотвращение конфликтов между задачами при обучении:\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Гармонизация мультизадачных градиентов │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ Проблема: Градиенты задач могут конфликтовать! │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Пример конфликтующих градиентов: │ │\n│ │ │ │\n│ │ Градиент направления: ▲ (увеличить этот вес) │ │\n│ │ Градиент волатильн.: ▼ (уменьшить этот вес) │ │\n│ │ │ │\n│ │ Наивное усреднение: ▲ + ▼ = → (веса почти не двигаются!) │ │\n│ │ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ Решение: Методы гармонизации градиентов │\n│ │\n│ Метод 1: PCGrad (Проекция конфликтующих градиентов) │\n│ ──────────────────────────────────────────────────── │\n│ Если g₁ · g₂ < 0 (конфликт): │\n│ g₁' = g₁ - (g₁·g₂/|g₂|²) × g₂ │\n│ Проецируем прочь конфликтующую компоненту │\n│ │\n│ Метод 2: GradNorm (Нормализация градиентов) │\n│ ──────────────────────────────────────────── │\n│ Динамически корректируем веса задач для баланса амплитуд градиентов │\n│ │\n│ Метод 3: Взвешивание по неопределённости │\n│ ───────────────────────────────────────── │\n│ w_i = 1/(2σ_i²) где σ_i — неопределённость задачи │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Применение в трейдинге\n\n### Задаче-агностическая торговая система\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Задаче-агностическая торговая система │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ ПРИЁМ ДАННЫХ В РЕАЛЬНОМ ВРЕМЕНИ │\n│ ──────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Bybit WebSocket → Конвейер признаков → Буфер признаков │ │\n│ │ • OHLCV (1m, 5m, 15m, 1h, 4h) │ │\n│ │ • Стакан (L2 top 20 уровней) │ │\n│ │ • Сделки (тик-бай-тик) │ │\n│ │ • Ставки финансирования │ │\n│ │ • Открытый интерес │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ЗАДАЧЕ-АГНОСТИЧЕСКИЙ ЭНКОДЕР (Предобученный, заморожен) │\n│ ───────────────────────────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Рыночные признаки → Универсальный эмбеддинг (256-мерный) │ │\n│ │ Задержка: < 5мс на инференс │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ МУЛЬТИЗАДАЧНЫЙ ИНФЕРЕНС (Параллельные головы задач) │\n│ ──────────────────────────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌───────────┐ │ │\n│ │ │ Направление │ │ Волатильн. │ │ Режим │ │ Доходность│ │ │\n│ │ │ 68% Вверх │ │ 2.3% ож. │ │ Бык (73%) │ │ +0.5% │ │ │\n│ │ │ 22% Вниз │ │ │ │ │ │ ожидается │ │ │\n│ │ │ 10% Боков. │ │ │ │ │ │ │ │ │\n│ │ └─────────────┘ └─────────────┘ └─────────────┘ └───────────┘ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ СЛИЯНИЕ РЕШЕНИЙ (Объединение всех выходов задач) │\n│ ──────────────────────────────────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Проверка согласованности: │ │\n│ │ • Направл.=Вверх + Режим=Бык + Доходн.=Полож. → СОГЛАСОВАНО ✓ │ │\n│ │ • Направл.=Вверх + Режим=Медведь → КОНФЛИКТ! → Снижаем уверенность│ │\n│ │ │ │\n│ │ Оценка риска: │ │\n│ │ • Прогноз волатильности определяет размер позиции │ │\n│ │ • Режим определяет уровни стоп-лосс/тейк-профит │ │\n│ │ │ │\n│ │ Итоговый сигнал: │ │\n│ │ • Действие: ЛОНГ │ │\n│ │ • Уверенность: 72% (согласованные предсказания) │ │\n│ │ • Размер: 1.5% от капитала (умеренная волатильность) │ │\n│ │ • Стоп: -1.5% (бычий режим, плотный стоп) │ │\n│ │ • Цель: +2.0% (ожидаемая доходность + буфер) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │ │\n│ ▼ │\n│ ИСПОЛНЕНИЕ ОРДЕРОВ │\n│ ─────────────────── │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Bybit API → Рыночный/лимитный ордер → Мониторинг позиции │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Интеграция с Bybit\n\n### Конвейер сбора данных\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Конвейер данных Bybit │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ ИСТОЧНИКИ ДАННЫХ ДЛЯ ЗАДАЧЕ-АГНОСТИЧЕСКОГО ОБУЧЕНИЯ │\n│ ─────────────────────────────────────────────────── │\n│ │\n│ 1. ИСТОРИЧЕСКИЕ ДАННЫЕ (Обучение) │\n│ ───────────────────────────────── │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ Эндпоинт: GET /v5/market/kline │ │\n│ │ Символы: BTCUSDT, ETHUSDT, SOLUSDT, ... (топ 50 по объёму) │ │\n│ │ Интервалы: 1m, 5m, 15m, 1h, 4h, 1d │ │\n│ │ История: 2 года │ │\n│ │ │ │\n│ │ Генерация меток для задач: │ │\n│ │ ┌─────────────────────────────────────────────────────────────┐ │ │\n│ │ │ Направление: На основе знака будущей доходности │ │ │\n│ │ │ Волатильность: Реализованная волатильность за следующие периоды │ │\n│ │ │ Режим: HMM кластеризация по доходности/волатильности │ │ │\n│ │ │ Доходность: Фактическая будущая доходность (непрерывная) │ │ │\n│ │ └─────────────────────────────────────────────────────────────┘ │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 2. ДАННЫЕ В РЕАЛЬНОМ ВРЕМЕНИ (Инференс) │\n│ ──────────────────────────────────────── │\n│ │\n│ ┌─────────────────────────────────────────────────────────────────────┐ │\n│ │ WebSocket: wss://stream.bybit.com/v5/public/linear │ │\n│ │ │ │\n│ │ Подписки: │ │\n│ │ • kline.1.BTCUSDT (1-минутные свечи) │ │\n│ │ • orderbook.50.BTCUSDT (L2 стакан) │ │\n│ │ • publicTrade.BTCUSDT (тиковые сделки) │ │\n│ │ • tickers.BTCUSDT (рыночная статистика) │ │\n│ └─────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Реализация\n\n### Структура модуля\n\n```\n87_task_agnostic_trading/\n├── Cargo.toml\n├── README.md # Документация (английский)\n├── README.ru.md # Русский перевод\n├── readme.simple.md # Упрощённое объяснение\n├── readme.simple.ru.md # Упрощённое объяснение (русский)\n├── src/\n│ ├── lib.rs # Корень библиотеки\n│ ├── config.rs # Типы конфигурации\n│ ├── encoder/\n│ │ ├── mod.rs # Модуль энкодера\n│ │ ├── transformer.rs # Трансформер-энкодер\n│ │ ├── cnn.rs # CNN-энкодер\n│ │ └── moe.rs # Смесь экспертов\n│ ├── tasks/\n│ │ ├── mod.rs # Модуль задач\n│ │ ├── direction.rs # Предсказание направления\n│ │ ├── volatility.rs # Прогноз волатильности\n│ │ ├── regime.rs # Определение режима\n│ │ └── returns.rs # Предсказание доходности\n│ ├── training/\n│ │ ├── mod.rs # Модуль обучения\n│ │ ├── multitask.rs # Мультизадачное обучение\n│ │ ├── curriculum.rs # Курриккулум задач\n│ │ └── pretraining.rs # Предобучение\n│ ├── fusion/\n│ │ ├── mod.rs # Модуль слияния\n│ │ └── decision.rs # Слияние решений\n│ ├── data/\n│ │ ├── mod.rs # Модуль данных\n│ │ ├── bybit.rs # API клиент Bybit\n│ │ ├── features.rs # Инженерия признаков\n│ │ └── dataset.rs # Обработка датасета\n│ └── trading/\n│ ├── mod.rs # Модуль торговли\n│ ├── strategy.rs # Торговая стратегия\n│ └── risk.rs # Управление рисками\n├── examples/\n│ ├── train_encoder.rs # Обучение энкодера\n│ ├── multi_task_inference.rs # Мультизадачный инференс\n│ ├── bybit_trading.rs # Торговля с Bybit\n│ └── backtest.rs # Бэктестинг\n├── python/\n│ ├── task_agnostic_model.py # PyTorch реализация\n│ ├── train.py # Скрипт обучения\n│ ├── evaluate.py # Скрипт оценки\n│ └── requirements.txt # Python зависимости\n└── tests/\n ├── integration.rs # Интеграционные тесты\n └── unit_tests.rs # Юнит-тесты\n```\n\n## Управление рисками\n\n### Мультизадачная оценка риска\n\n```\n┌────────────────────────────────────────────────────────────────────────────┐\n│ Мультизадачная оценка риска │\n├────────────────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. ОЦЕНКА СОГЛАСОВАННОСТИ ЗАДАЧ │\n│ ──────────────────────────────── │\n│ │\n│ Когда задачи согласованы → Выше уверенность, больше позиции │\n│ Когда задачи конфликтуют → Ниже уверенность, меньше позиции или без сделки\n│ │\n│ Матрица согласованности: │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ │ Направл. │ Волатил. │ Режим │ Доходн. │ Оценка │ │\n│ │──────────────┼──────────┼──────────┼────────┼─────────┼──────────│ │\n│ │ Сценарий 1 │ ВВЕРХ │ НИЗКАЯ │ БЫК │ +0.5% │ 0.95 │ │\n│ │ (Все согл.) │ │ │ │ │ СДЕЛКА │ │\n│ │──────────────┼──────────┼──────────┼────────┼─────────┼──────────│ │\n│ │ Сценарий 2 │ ВВЕРХ │ ВЫСОКАЯ │ БЫК │ +0.3% │ 0.70 │ │\n│ │ (Вол. пред.) │ │ │ │ │ ОСТОРОЖН │ │\n│ │──────────────┼──────────┼──────────┼────────┼─────────┼──────────│ │\n│ │ Сценарий 3 │ ВВЕРХ │ ВЫСОКАЯ │ МЕДВЕДЬ│ -0.2% │ 0.30 │ │\n│ │ (Конфликт) │ │ │ │ │БЕЗ СДЕЛКИ│ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n│ 2. ДИНАМИЧЕСКИЙ РАЗМЕР ПОЗИЦИИ │\n│ ────────────────────────────── │\n│ │\n│ Размер позиции = База × Уверенность × (1 / Ранг_волатильности) │\n│ │\n│ 3. СТОП-ЛОСС С УЧЁТОМ РЕЖИМА │\n│ ───────────────────────────── │\n│ | |
| [2026-01-21T18:56:23.436Z] [INFO] │\n│ ┌────────────────────────────────────────────────────────────────────┐ │\n│ │ Режим │ Стоп-лосс │ Тейк-профит │ Обоснование │ │\n│ │──────────────────┼────────────┼──────────────┼────────────────────│ │\n│ │ Бык │ -1.5% │ +3.0% │ Держим тренд │ │\n│ │ Медведь │ -1.5% │ +3.0% │ Ловим движение │ │\n│ │ Боковик │ -1.0% │ +1.5% │ Узкий диапазон │ │\n│ │ Высокая волат. │ -2.5% │ +4.0% │ Широкие стопы │ │\n│ │ Низкая волат. │ -1.0% │ +1.5% │ Плотный контроль │ │\n│ └────────────────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────────────────┘\n```\n\n## Метрики производительности\n\n### Метрики оценки модели\n\n| Метрика | Описание | Цель |\n|---------|----------|------|\n| **Мультизадачная точность** | Средняя точность по всем задачам классификации | > 65% |\n| **Точность направления** | Точность предсказания направления цены | > 60% |\n| **Точность режима** | Точность определения режима рынка | > 70% |\n| **RMSE волатильности** | Среднеквадратичная ошибка прогноза волатильности | < 0.02 |\n| **Корреляция доходности** | Корреляция предсказанной и фактической доходности | > 0.3 |\n| **Перенос на задачу** | Производительность при добавлении новой задачи | > 90% от обучения с нуля |\n| **Оценка согласованности** | Как часто предсказания задач совпадают | > 70% |\n\n### Метрики торговой производительности\n\n| Метрика | Описание | Цель |\n|---------|----------|------|\n| **Коэффициент Шарпа** | Доходность с учётом риска | > 2.0 |\n| **Коэффициент Сортино** | Доходность с учётом риска просадки | > 2.5 |\n| **Макс. просадка** | Наибольшее падение от пика до дна | < 15% |\n| **Процент выигрышных** | Процент прибыльных сделок | > 55% |\n| **Профит-фактор** | Валовая прибыль / Валовый убыток | > 1.5 |\n| **Коэффициент Калмара** | Годовая доходность / Макс. просадка | > 1.0 |\n\n---\n\n## Ссылки\n\n1. **Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance**\n - URL: https://arxiv.org/abs/2303.02841\n - Год: 2023\n\n2. **Trading in Fast-Changing Markets with Meta-Reinforcement Learning**\n - MAML-метод торговли для множества задач\n - Год: 2024\n\n3. **Multi-Task Learning in Deep Neural Networks: A Survey**\n - Комплексный обзор мультизадачных архитектур\n - URL: https://arxiv.org/abs/2009.09796\n\n4. **Gradient Surgery for Multi-Task Learning**\n - Метод PCGrad для обработки конфликтов градиентов\n - URL: https://arxiv.org/abs/2001.06782\n\n5. **Adaptive Event-Driven Labeling with Meta-Learning for Financial Time Series**\n - Многомасштабный временной анализ с MAML\n - Год: 2025\n\n---\n\n## Следующие шаги\n\n- [Простое объяснение](readme.simple.md) - Версия для начинающих\n- [Английская версия](README.md) - English version\n- [Примеры](examples/) - Рабочий код на Rust\n- [Python реализация](python/) - Эталонная реализация на PyTorch\n\n---\n\n*Глава 87 книги \"Машинное обучение для трейдинга\"*\n","structuredPatch":[],"originalFile":null}} | |
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| "content": "# Task-Agnostic Trading - Simple Explanation\n\n## What is this all about? (The Easiest Explanation)\n\nImagine you're a **Swiss Army Knife** instead of a collection of single-purpose tools:\n\n- **Single-purpose tools**: A knife for cutting, a screwdriver for screws, a can opener for cans. Each does ONE thing well, but you need to carry ALL of them.\n\n- **Swiss Army Knife**: ONE tool that does cutting, screwing, and opening. It learned the general principles of \"working with objects\" and adapted to multiple tasks!\n\n**Task-Agnostic Trading works exactly like this:**\n\nInstead of having:\n- One model for predicting \"price goes up or down\"\n- Another model for predicting \"how volatile will it be\"\n- Another model for detecting \"bull or bear market\"\n- Another model for predicting \"what's the expected return\"\n\nWe have:\n- **ONE unified model** that learns \"how markets work\" and can do ALL these tasks!\n\n---\n\n## The Big Problem We're Solving\n\n### The \"Too Many Models\" Problem\n\nImagine you're a trading firm with these needs:\n\n```\nCURRENT APPROACH (Task-Specific):\n\n┌─────────────────────────────────────────────────────────────────┐\n│ │\n│ TASK 1: Price Direction │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ • Train model A on historical data ││\n│ │ • Tune 50 hyperparameters ││\n│ │ • Deploy model A ││\n│ │ • Monitor model A ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ TASK 2: Volatility Prediction │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ • Train model B on same data (different labels) ││\n│ │ • Tune another 50 hyperparameters ││\n│ │ • Deploy model B ││\n│ │ • Monitor model B ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ TASK 3: Regime Detection │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ • Train model C ││\n│ │ • Tune another 50 hyperparameters ││\n│ │ • Deploy model C ││\n│ │ • Monitor model C ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ Total: 3 models, 150 hyperparameters, 3x maintenance │\n│ │\n└─────────────────────────────────────────────────────────────────┘\n\nTASK-AGNOSTIC APPROACH:\n\n┌─────────────────────────────────────────────────────────────────┐\n│ │\n│ ONE UNIFIED MODEL: │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ ││\n│ │ ┌───────────────────────────────┐ ││\n│ │ │ Universal Brain │ ││\n│ │ │ (Learns \"how markets work\") │ ││\n│ │ └───────────────┬───────────────┘ ││\n│ │ │ ││\n│ │ ┌────────────┼────────────┐ ││\n│ │ ▼ ▼ ▼ ││\n│ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ││\n│ │ │Direction│ │Volatility│ │ Regime │ ← Tiny adapters ││\n│ │ └─────────┘ └─────────┘ └─────────┘ (5% of model) ││\n│ │ ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ Total: 1 brain + 3 tiny heads, 60 hyperparameters, 1x maint. │\n│ │\n└─────────────────────────────────────────────────────────────────┘\n```\n\n### The \"Knowledge Doesn't Transfer\" Problem\n\n```\nTraditional Models Don't Share Knowledge:\n\nModel for Direction:\n\"I learned that volume spikes often precede big moves!\"\n\nModel for Volatility:\n\"I learned that volume spikes often precede big moves!\"\n(Learned the SAME thing independently! Wasted effort!)\n\nModel for Regime:\n\"I learned that volume spikes often precede big moves!\"\n(SAME again! Triple the training time for the same insight!)\n\n\nTask-Agnostic Model Shares Knowledge:\n\nUniversal Brain:\n\"Volume spikes often precede big moves!\"\n(Learned once, used by ALL tasks!)\n\nDirection Head: \"Uses this for predicting direction\"\nVolatility Head: \"Uses this for predicting volatility\"\nRegime Head: \"Uses this for detecting regime changes\"\n\nEfficient! Learn once, benefit everywhere!\n```\n\n---\n\n## Real World Analogies\n\n### Analogy 1: The Medical School Graduate\n\n```\nTASK-SPECIFIC APPROACH:\n\nWant to become a doctor?\n• Study 8 years to become a Heart Specialist\n• Want to also treat lungs? Study another 8 years!\n• Want to also treat skin? Another 8 years!\n\nTotal: 24 years for 3 specializations\n\n\nTASK-AGNOSTIC APPROACH:\n\nMedical School:\n• Study 4 years to understand \"how the human body works\"\n (Universal knowledge that applies to EVERYTHING)\n\nThen specialize:\n• Heart: 2 more years (applying general knowledge to hearts)\n• Lungs: 2 more years\n• Skin: 2 more years\n\nTotal: 10 years for 3 specializations!\n\nThe \"universal medical knowledge\" transfers to every specialty!\n```\n\n### Analogy 2: Learning Languages\n\n```\nTASK-SPECIFIC APPROACH:\n\nLearning Spanish from scratch: 2 years\nLearning French from scratch: 2 years\nLearning Italian from scratch: 2 years\nLearning Portuguese from scratch: 2 years\n\nTotal: 8 years\n\n\nTASK-AGNOSTIC APPROACH:\n\nLearn \"how Romance languages work\": 1 year\n• Grammar patterns\n• Word root structures\n• Verb conjugation systems\n• Pronunciation rules\n\nThen adapt:\n• Spanish: 6 months\n• French: 6 months\n• Italian: 6 months\n• Portuguese: 6 months\n\nTotal: 3 years!\n\nThe universal \"Romance language understanding\" transfers!\n```\n\n### Analogy 3: The Chef vs. The Recipe Follower\n\n```\nRECIPE FOLLOWER (Task-Specific):\n\n\"I can make chicken parmesan perfectly!\"\n\"I can make beef stroganoff perfectly!\"\n\"I can make fish tacos perfectly!\"\n\nBut if you ask them to make chicken stroganoff?\n\"I don't have a recipe for that...\"\n\n\nCHEF (Task-Agnostic):\n\n\"I understand cooking principles!\"\n• How heat affects proteins\n• How fats carry flavor\n• How acids brighten dishes\n• How textures combine\n\nGive them ANY ingredients:\n\"Let me apply my cooking knowledge...\"\nCreates a delicious new dish!\n\nThe chef's universal cooking understanding transfers to ANY dish!\n```\n\n---\n\n## How It Works: Step by Step\n\n### Step 1: The Universal Encoder (The \"Brain\")\n\nThe universal encoder learns to understand market data at a deep level:\n\n```\nRaw Market Data:\n┌──────────────────────────────────────────────────────────────┐\n│ Price: 45,230 → 45,280 → 45,150 → 45,300 → 45,450 │\n│ Volume: 1.2M → 2.5M → 1.8M → 3.1M → 2.2M │\n│ Bid-Ask: 1.2 → 0.8 → 1.5 → 0.9 → 1.1 │\n│ Funding Rate: 0.01% → 0.02% → 0.01% → 0.03% → 0.02% │\n│ Open Interest: +2% → +1% → -1% → +3% → +1% │\n└──────────────────────────────────────────────────────────────┘\n │\n ▼\n ┌───────────────────────────────┐\n │ UNIVERSAL ENCODER │\n │ │\n │ \"I'm looking at this data │\n │ and extracting the ESSENCE │\n │ of what's happening...\" │\n │ │\n │ Learns: │\n │ • Trend patterns │\n │ • Volatility regimes │\n │ • Market microstructure │\n │ • Correlation structures │\n │ • Anomaly signatures │\n └───────────────────────────────┘\n │\n ▼\n ┌───────────────────────────────┐\n │ UNIVERSAL REPRESENTATION │\n │ (256 numbers that capture │\n │ everything important) │\n │ │\n │ [0.82, -0.31, 0.67, ...] │\n │ │\n │ Like a \"market fingerprint\" │\n │ that works for ANY task! │\n └───────────────────────────────┘\n```\n\n### Step 2: The Task Heads (The \"Specialists\")\n\nEach task head is a tiny network that takes the universal representation and answers a specific question:\n\n```\nUniversal Representation\n[0.82, -0.31, 0.67, 0.15, -0.88, ...]\n │\n ┌────────┼────────┬────────────┐\n │ │ │ │\n ▼ ▼ ▼ ▼\n ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐\n │Direction│ │Volatility│ │Regime│ │Return│\n │Head │ │Head │ │Head │ │Head │\n │ │ │ │ │ │ │ │\n │ 2 │ │ 2 │ │ 2 │ │ 2 │\n │layers │ │layers │ │layers│ │layers│\n │only! │ │only! │ │only! │ │only! │\n └───┬───┘ └───┬───┘ └──┬───┘ └──┬───┘\n │ │ │ │\n ▼ ▼ ▼ ▼\n\n \"68% Up\" \"2.3%\" \"Bull\" \"+0.5%\"\n \"22% Down\" expected (73% expected\n \"10% Side\" volatility conf) return\n\nEach head is TINY (just 2 layers) because the hard\nwork was already done by the Universal Encoder!\n```\n\n### Step 3: Decision Fusion (Putting It All Together)\n\nNow we combine all the task outputs to make a trading decision:\n\n```\n┌────────────────────────────────────────────────────────────────┐\n│ DECISION FUSION │\n├────────────────────────────────────────────────────────────────┤\n│ │\n│ Step 1: Check if predictions AGREE │\n│ ───────────────────────────────── │\n│ │\n│ Direction says: UP (68%) │\n│ Regime says: BULL (73%) │\n│ Return says: POSITIVE (+0.5%) │\n│ │\n│ ✓ ALL AGREE! This is a strong signal! │\n│ │\n│ (If Direction said UP but Regime said BEAR, we'd be careful!) │\n│ │\n│ Step 2: Adjust for risk │\n│ ──────────────────────── │\n│ │\n│ Volatility says: 2.3% (moderate) │\n│ │\n│ → Normal position size (no extra caution needed) │\n│ │\n│ Step 3: Generate final signal │\n│ ──────────────────────────── │\n│ │\n│ ┌────────────────────────────────────────────────────────┐ │\n│ │ ACTION: LONG (Buy) │ │\n│ │ CONFIDENCE: 72% │ │\n│ │ SIZE: 1.5% of capital │ │\n│ │ STOP LOSS: -1.5% │ │\n│ │ TAKE PROFIT: +2.5% │ │\n│ │ │ │\n│ │ REASONING: Direction, Regime, and Return all agree │ │\n│ │ that the market is bullish. Moderate volatility │ │\n│ │ suggests standard position sizing. │ │\n│ └────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## Why \"Task-Agnostic\" is Better\n\n### Benefit 1: Knowledge Sharing\n\n```\nBEFORE (Task-Specific):\n─────────────────────────\n\nDirection Model learns: \"Volume spike = big move coming\"\nVolatility Model learns: \"Volume spike = big move coming\"\nRegime Model learns: \"Volume spike = big move coming\"\n\n3 models learned the SAME thing! Wasteful!\n\n\nAFTER (Task-Agnostic):\n──────────────────────\n\nUniversal Encoder learns: \"Volume spike = big move coming\"\n\nDirection Head uses it ──┐\nVolatility Head uses it ─┼── All benefit from ONE learning!\nRegime Head uses it ─────┘\n\nEfficient! Learn once, use everywhere!\n```\n\n### Benefit 2: Consistent Predictions\n\n```\nBEFORE (Task-Specific):\n─────────────────────────\n\nDirection Model: \"Price will go UP!\"\nRegime Model: \"We're in a BEAR market!\"\n\nWait... those contradict each other!\nEach model sees the world differently.\n\n\nAFTER (Task-Agnostic):\n──────────────────────\n\nUniversal Encoder: \"Here's my understanding of the market...\"\n\nDirection Head: \"Based on this, price goes UP\"\nRegime Head: \"Based on this, we're in a BULL market\"\n\nThey AGREE because they see the SAME thing!\nConsistent and coherent predictions!\n```\n\n### Benefit 3: Easier Maintenance\n\n```\nBEFORE (Task-Specific):\n─────────────────────────\n\nNew data available?\n→ Retrain Model A\n→ Retrain Model B\n→ Retrain Model C\n→ Redeploy all 3\n→ Monitor all 3\n\nBug in preprocessing?\n→ Fix in Model A\n→ Fix in Model B\n→ Fix in Model C\n→ Hope you didn't miss any!\n\n\nAFTER (Task-Agnostic):\n──────────────────────\n\nNew data available?\n→ Retrain ONE encoder\n→ Task heads are fine (they're tiny)\n→ Deploy one update\n\nBug in preprocessing?\n→ Fix in ONE place\n→ All tasks benefit automatically\n\nMuch simpler!\n```\n\n### Benefit 4: Adding New Tasks is Easy\n\n```\nBEFORE (Task-Specific):\n─────────────────────────\n\nWant to add \"Anomaly Detection\"?\n→ Design new model architecture\n→ Collect training data\n→ Train from scratch (weeks!)\n→ Tune hyperparameters\n→ Deploy and monitor\n\nBasically starting over!\n\n\nAFTER (Task-Agnostic):\n──────────────────────\n\nWant to add \"Anomaly Detection\"?\n→ Add tiny new head (2 layers)\n→ Train just the head (hours!)\n→ Uses existing encoder\n→ Done!\n\nThe encoder already knows how markets work!\nJust teach it one more \"output format\"!\n```\n\n---\n\n## Training the Task-Agnostic Model\n\n### Phase 1: Pre-training (Learning \"How Markets Work\")\n\n```\nSELF-SUPERVISED PRE-TRAINING:\n────────────────────────────────\n\nNo labels needed! The model learns by solving puzzles:\n\nPuzzle 1: MASKED PREDICTION\n─────────────────────────────\n\nGiven: [Price Day 1] [Price Day 2] [???] [Price Day 4] [Price Day 5]\n\nTask: What was Price Day 3?\n\nThe model learns to understand temporal patterns!\n\n\nPuzzle 2: CONTRASTIVE LEARNING\n──────────────────────────────\n\n\"These two windows are from the same bull market\"\n\"These two windows are from different market conditions\"\n\nThe model learns to recognize similar patterns!\n\n\nPuzzle 3: TEMPORAL ORDER\n────────────────────────\n\nGiven: Window A and Window B\n\nTask: Did A happen before B, or after?\n\nThe model learns cause and effect!\n\n\nAfter pre-training:\n\"I understand how markets work, even though\n I wasn't told ANY specific trading rules!\"\n```\n\n### Phase 2: Multi-Task Training (Learning All Tasks Together)\n\n```\nMULTI-TASK TRAINING:\n────────────────────\n\nNow we train all tasks SIMULTANEOUSLY:\n\nStep 1: Feed data through encoder\n [Market Data] → [Universal Representation]\n\nStep 2: Each task head makes predictions\n [Universal Rep] → Direction: \"Up\"\n [Universal Rep] → Volatility: \"2.3%\"\n [Universal Rep] → Regime: \"Bull\"\n [Universal Rep] → Return: \"+0.5%\"\n\nStep 3: Compare to actual outcomes\n Direction was actually: Up ✓\n Volatility was actually: 2.1% (close!)\n Regime was actually: Bull ✓\n Return was actually: +0.4% (close!)\n\nStep 4: Update ALL parts together\n → Encoder learns what's useful for ALL tasks\n → Each head learns its specific output format\n\nThe magic: The encoder gets better for EVERY task\n because it's trained on ALL of them!\n```\n\n### The Gradient Conflict Problem (and Solution)\n\n```\nPROBLEM: Tasks can conflict!\n─────────────────────────────\n\nDirection task says: \"Make this weight bigger!\"\nVolatility task says: \"Make this weight smaller!\"\n\nIf we just average: Weight barely changes!\n\n\nSOLUTION: Gradient Harmonization\n─────────────────────────────────\n\nWhen tasks conflict, we adjust:\n\nMethod 1: Project conflicting gradients\n \"Remove the conflicting part, keep the rest\"\n\nMethod 2: Balance task weights\n \"Give more importance to the task that's struggling\"\n\nMethod 3: Uncertainty weighting\n \"Trust tasks more when they're confident\"\n\nResult: All tasks improve together without fighting!\n```\n\n---\n\n## Real Trading Example\n\n### Scenario: Trading BTC on Bybit\n\n```\nREAL-TIME TRADING PIPELINE:\n─────────────────────────────\n\nTime: 14:32:05 UTC\n\nStep 1: DATA ARRIVES\n┌────────────────────────────────────────────────────────┐\n│ Bybit WebSocket delivers: │\n│ • Last 96 hourly candles (OHLCV) │\n│ • Current orderbook (top 20 levels) │\n│ • Recent trades (last 100) │\n│ • Funding rate: 0.012% │\n│ • Open interest change: +1.3% │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 10ms)\n\nStep 2: FEATURE ENGINEERING\n┌────────────────────────────────────────────────────────┐\n│ Calculate: │\n│ • Returns at multiple horizons │\n│ • Volatility estimates │\n│ • Momentum indicators │\n│ • Orderbook imbalance │\n│ • Volume patterns │\n│ │\n│ Result: 64-dimensional feature vector for each of │\n│ 96 time steps = [96 x 64] input matrix │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 5ms)\n\nStep 3: UNIVERSAL ENCODER\n┌────────────────────────────────────────────────────────┐\n│ Input: [96 x 64] market features │\n│ │\n│ Processing: │\n│ • 1D convolutions extract local patterns │\n│ • Attention captures long-range dependencies │\n│ • Global pooling creates summary │\n│ │\n│ Output: 256-dimensional universal representation │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 15ms)\n\nStep 4: ALL TASK HEADS (in parallel!)\n┌────────────────────────────────────────────────────────┐\n│ │\n│ Direction Head: Up: 68% Down: 22% Side: 10% │\n│ │\n│ Volatility Head: Expected: 2.3% │\n│ │\n│ Regime Head: Bull: 73% Bear: 15% Side: 12% │\n│ │\n│ Return Head: Expected: +0.48% │\n│ │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 5ms)\n\nStep 5: DECISION FUSION\n┌────────────────────────────────────────────────────────┐\n│ │\n│ Consistency Check: │\n│ • Direction (Up) ↔ Regime (Bull) ↔ Return (+): ✓ │\n│ • All signals point the same way! │\n│ • Consistency Score: 0.91 (very high!) │\n│ │\n│ Risk Assessment: │\n│ • Volatility (2.3%) is moderate │\n│ • No need to reduce position size │\n│ │\n│ Final Decision: │\n│ ┌────────────────────────────────────────────────┐ │\n│ │ ACTION: BUY (Long) │ │\n│ │ SIZE: 1.5% of capital │ │\n│ │ CONFIDENCE: 72% │ │\n│ │ STOP LOSS: $44,925 (-1.5%) │ │\n│ │ TAKE PROFIT: $46,395 (+2.5%) │ │\n│ └────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 2ms)\n\nStep 6: ORDER EXECUTION\n┌────────────────────────────────────────────────────────┐\n│ Bybit API Call: │\n│ │\n│ POST /v5/order/create │\n│ { │\n│ \"symbol\": \"BTCUSDT\", │\n│ \"side\": \"Buy\", │\n│ \"orderType\": \"Market\", │\n│ \"qty\": \"0.015\", // 1.5% of capital │\n│ \"stopLoss\": \"44925\", │\n│ \"takeProfit\": \"46395\" │\n│ } │\n│ │\n│ Response: Order filled at $45,650 │\n└────────────────────────────────────────────────────────┘\n\nTotal time: < 37ms from data arrival to order placed!\n```\n\n---\n\n## When to Use Task-Agnostic Trading\n\n### Good Use Cases\n\n```\n✓ Multiple related trading tasks\n \"I need direction, volatility, and regime predictions\"\n\n✓ Limited training data\n \"I only have 1 year of data for this new asset\"\n\n✓ Consistency matters\n \"I need all my signals to make sense together\"\n\n✓ Easy maintenance\n \"I don't want to maintain 10 different models\"\n\n✓ Fast iteration\n \"I want to add new tasks quickly\"\n```\n\n### Maybe Not Ideal For\n\n```\n✗ Single very specific task\n \"I only care about predicting exact prices\"\n (A specialized model might be better)\n\n✗ Completely unrelated tasks\n \"I want to predict weather AND stock prices\"\n (Too different to share knowledge)\n\n✗ When interpretability is critical\n \"I need to explain exactly why each prediction was made\"\n (Task-specific might be more interpretable)\n```\n\n---\n\n## Key Takeaways\n\n```\n┌────────────────────────────────────────────────────────────────┐\n│ REMEMBER THESE: │\n├────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. ONE brain, MANY skills │\n│ Like a Swiss Army Knife, not a toolbox │\n│ │\n│ 2. Learn once, benefit everywhere │\n│ Knowledge transfers between all tasks │\n│ │\n│ 3. Consistent predictions │\n│ All tasks see the same \"market understanding\" │\n│ │\n│ 4. Easy to extend │\n│ Adding new tasks is just adding tiny heads │\n│ │\n│ 5. Robust decisions │\n│ When multiple tasks agree, we're confident │\n│ When they disagree, we're cautious │\n│ │\n└────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## Next Steps\n\n- [Full Technical Documentation](README.md) - Dive deeper into the math and code\n- [Russian Version](readme.simple.ru.md) - Russian translation\n- [Python Code](python/) - Try the implementation yourself\n- [Examples](examples/) - Run real trading examples\n\n---\n\n*Chapter 87 of Machine Learning for Trading*\n" | |
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| [2026-01-21T18:58:24.919Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Rwet2x1tU5s4q2KR5NjY9y","type":"tool_result","content":"File created successfully at: /tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/readme.simple.md"}]},"parent_tool_use_id":null,"session_id":"8c23df75-76b7-45ba-aa3a-4295879a3b8f","uuid":"f9c0bee2-69a9-4048-9541-cb0947e5e3f2","tool_use_result":{"type":"create","filePath":"/tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/readme.simple.md","content":"# Task-Agnostic Trading - Simple Explanation\n\n## What is this all about? (The Easiest Explanation)\n\nImagine you're a **Swiss Army Knife** instead of a collection of single-purpose tools:\n\n- **Single-purpose tools**: A knife for cutting, a screwdriver for screws, a can opener for cans. Each does ONE thing well, but you need to carry ALL of them.\n\n- **Swiss Army Knife**: ONE tool that does cutting, screwing, and opening. It learned the general principles of \"working with objects\" and adapted to multiple tasks!\n\n**Task-Agnostic Trading works exactly like this:**\n\nInstead of having:\n- One model for predicting \"price goes up or down\"\n- Another model for predicting \"how volatile will it be\"\n- Another model for detecting \"bull or bear market\"\n- Another model for predicting \"what's the expected return\"\n\nWe have:\n- **ONE unified model** that learns \"how markets work\" and can do ALL these tasks!\n\n---\n\n## The Big Problem We're Solving\n\n### The \"Too Many Models\" Problem\n\nImagine you're a trading firm with these needs:\n\n```\nCURRENT APPROACH (Task-Specific):\n\n┌─────────────────────────────────────────────────────────────────┐\n│ │\n│ TASK 1: Price Direction │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ • Train model A on historical data ││\n│ │ • Tune 50 hyperparameters ││\n│ │ • Deploy model A ││\n│ │ • Monitor model A ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ TASK 2: Volatility Prediction │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ • Train model B on same data (different labels) ││\n│ │ • Tune another 50 hyperparameters ││\n│ │ • Deploy model B ││\n│ │ • Monitor model B ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ TASK 3: Regime Detection │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ • Train model C ││\n│ │ • Tune another 50 hyperparameters ││\n│ │ • Deploy model C ││\n│ │ • Monitor model C ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ Total: 3 models, 150 hyperparameters, 3x maintenance │\n│ │\n└─────────────────────────────────────────────────────────────────┘\n\nTASK-AGNOSTIC APPROACH:\n\n┌─────────────────────────────────────────────────────────────────┐\n│ │\n│ ONE UNIFIED MODEL: │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ ││\n│ │ ┌───────────────────────────────┐ ││\n│ │ │ Universal Brain │ ││\n│ │ │ (Learns \"how markets work\") │ ││\n│ │ └───────────────┬───────────────┘ ││\n│ │ │ ││\n│ │ ┌────────────┼────────────┐ ││\n│ │ ▼ ▼ ▼ ││\n│ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ││\n│ │ │Direction│ │Volatility│ │ Regime │ ← Tiny adapters ││\n│ │ └─────────┘ └─────────┘ └─────────┘ (5% of model) ││\n│ │ ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ Total: 1 brain + 3 tiny heads, 60 hyperparameters, 1x maint. │\n│ │\n└─────────────────────────────────────────────────────────────────┘\n```\n\n### The \"Knowledge Doesn't Transfer\" Problem\n\n```\nTraditional Models Don't Share Knowledge:\n\nModel for Direction:\n\"I learned that volume spikes often precede big moves!\"\n\nModel for Volatility:\n\"I learned that volume spikes often precede big moves!\"\n(Learned the SAME thing independently! Wasted effort!)\n\nModel for Regime:\n\"I learned that volume spikes often precede big moves!\"\n(SAME again! Triple the training time for the same insight!)\n\n\nTask-Agnostic Model Shares Knowledge:\n\nUniversal Brain:\n\"Volume spikes often precede big moves!\"\n(Learned once, used by ALL tasks!)\n\nDirection Head: \"Uses this for predicting direction\"\nVolatility Head: \"Uses this for predicting volatility\"\nRegime Head: \"Uses this for detecting regime changes\"\n\nEfficient! Learn once, benefit everywhere!\n```\n\n---\n\n## Real World Analogies\n\n### Analogy 1: The Medical School Graduate\n\n```\nTASK-SPECIFIC APPROACH:\n\nWant to become a doctor?\n• Study 8 years to become a Heart Specialist\n• Want to also treat lungs? Study another 8 years!\n• Want to also treat skin? Another 8 years!\n\nTotal: 24 years for 3 specializations\n\n\nTASK-AGNOSTIC APPROACH:\n\nMedical School:\n• Study 4 years to understand \"how the human body works\"\n (Universal knowledge that applies to EVERYTHING)\n\nThen specialize:\n• Heart: 2 more years (applying general knowledge to hearts)\n• Lungs: 2 more years\n• Skin: 2 more years\n\nTotal: 10 years for 3 specializations!\n\nThe \"universal medical knowledge\" transfers to every specialty!\n```\n\n### Analogy 2: Learning Languages\n\n```\nTASK-SPECIFIC APPROACH:\n\nLearning Spanish from scratch: 2 years\nLearning French from scratch: 2 years\nLearning Italian from scratch: 2 years\nLearning Portuguese from scratch: 2 years\n\nTotal: 8 years\n\n\nTASK-AGNOSTIC APPROACH:\n\nLearn \"how Romance languages work\": 1 year\n• Grammar patterns\n• Word root structures\n• Verb conjugation systems\n• Pronunciation rules\n\nThen adapt:\n• Spanish: 6 months\n• French: 6 months\n• Italian: 6 months\n• Portuguese: 6 months\n\nTotal: 3 years!\n\nThe universal \"Romance language understanding\" transfers!\n```\n\n### Analogy 3: The Chef vs. The Recipe Follower\n\n```\nRECIPE FOLLOWER (Task-Specific):\n\n\"I can make chicken parmesan perfectly!\"\n\"I can make beef stroganoff perfectly!\"\n\"I can make fish tacos perfectly!\"\n\nBut if you ask them to make chicken stroganoff?\n\"I don't have a recipe for that...\"\n\n\nCHEF (Task-Agnostic):\n\n\"I understand cooking principles!\"\n• How heat affects proteins\n• How fats carry flavor\n• How acids brighten dishes\n• How textures combine\n\nGive them ANY ingredients:\n\"Let me apply my cooking knowledge...\"\nCreates a delicious new dish!\n\nThe chef's universal cooking understanding transfers to ANY dish!\n```\n\n---\n\n## How It Works: Step by Step\n\n### Step 1: The Universal Encoder (The \"Brain\")\n\nThe universal encoder learns to understand market data at a deep level:\n\n```\nRaw Market Data:\n┌──────────────────────────────────────────────────────────────┐\n│ Price: 45,230 → 45,280 → 45,150 → 45,300 → 45,450 │\n│ Volume: 1.2M → 2.5M → 1.8M → 3.1M → 2.2M │\n│ Bid-Ask: 1.2 → 0.8 → 1.5 → 0.9 → 1.1 │\n│ Funding Rate: 0.01% → 0.02% → 0.01% → 0.03% → 0.02% │\n│ Open Interest: +2% → +1% → -1% → +3% → +1% │\n└──────────────────────────────────────────────────────────────┘\n │\n ▼\n ┌───────────────────────────────┐\n │ UNIVERSAL ENCODER │\n │ │\n │ \"I'm looking at this data │\n │ and extracting the ESSENCE │\n │ of what's happening...\" │\n │ │\n │ Learns: │\n │ • Trend patterns │\n │ • Volatility regimes │\n │ • Market microstructure │\n │ • Correlation structures │\n │ • Anomaly signatures │\n └───────────────────────────────┘\n │\n ▼\n ┌───────────────────────────────┐\n │ UNIVERSAL REPRESENTATION │\n │ (256 numbers that capture │\n │ everything important) │\n │ │\n │ [0.82, -0.31, 0.67, ...] │\n │ │\n │ Like a \"market fingerprint\" │\n │ that works for ANY task! │\n └───────────────────────────────┘\n```\n\n### Step 2: The Task Heads (The \"Specialists\")\n\nEach task head is a tiny network that takes the universal representation and answers a specific question:\n\n```\nUniversal Representation\n[0.82, -0.31, 0.67, 0.15, -0.88, ...]\n │\n ┌────────┼────────┬────────────┐\n │ │ │ │\n ▼ ▼ ▼ ▼\n ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐\n │Direction│ │Volatility│ │Regime│ │Return│\n │Head │ │Head │ │Head │ │Head │\n │ │ │ │ │ │ │ │\n │ 2 │ │ 2 │ │ 2 │ │ 2 │\n │layers │ │layers │ │layers│ │layers│\n │only! │ │only! │ │only! │ │only! │\n └───┬───┘ └───┬───┘ └──┬───┘ └──┬───┘\n │ │ │ │\n ▼ ▼ ▼ ▼\n\n \"68% Up\" \"2.3%\" \"Bull\" \"+0.5%\"\n \"22% Down\" expected (73% expected\n \"10% Side\" volatility conf) return\n\nEach head is TINY (just 2 layers) because the hard\nwork was already done by the Universal Encoder!\n```\n\n### Step 3: Decision Fusion (Putting It All Together)\n\nNow we combine all the task outputs to make a trading decision:\n\n```\n┌────────────────────────────────────────────────────────────────┐\n│ DECISION FUSION │\n├────────────────────────────────────────────────────────────────┤\n│ │\n│ Step 1: Check if predictions AGREE │\n│ ───────────────────────────────── │\n│ │\n│ Direction says: UP (68%) │\n│ Regime says: BULL (73%) │\n│ Return says: POSITIVE (+0.5%) │\n│ │\n│ ✓ ALL AGREE! This is a strong signal! │\n│ │\n│ (If Direction said UP but Regime said BEAR, we'd be careful!) │\n│ │\n│ Step 2: Adjust for risk │\n│ ──────────────────────── │\n│ │\n│ Volatility says: 2.3% (moderate) │\n│ │\n│ → Normal position size (no extra caution needed) │\n│ │\n│ Step 3: Generate final signal │\n│ ──────────────────────────── │\n│ │\n│ ┌────────────────────────────────────────────────────────┐ │\n│ │ ACTION: LONG (Buy) │ │\n│ │ CONFIDENCE: 72% │ │\n│ │ SIZE: 1.5% of capital │ │\n│ │ STOP LOSS: -1.5% │ │\n│ │ TAKE PROFIT: +2.5% │ │\n│ │ │ │\n│ │ REASONING: Direction, Regime, and Return all agree │ │\n│ │ that the market is bullish. Moderate volatility │ │\n│ │ suggests standard position sizing. │ │\n│ └────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## Why \"Task-Agnostic\" is Better\n\n### Benefit 1: Knowledge Sharing\n\n```\nBEFORE (Task-Specific):\n─────────────────────────\n\nDirection Model learns: \"Volume spike = big move coming\"\nVolatility Model learns: \"Volume spike = big move coming\"\nRegime Model learns: \"Volume spike = big move coming\"\n\n3 models learned the SAME thing! Wasteful!\n\n\nAFTER (Task-Agnostic):\n──────────────────────\n\nUn | |
| [2026-01-21T18:58:24.935Z] [INFO] iversal Encoder learns: \"Volume spike = big move coming\"\n\nDirection Head uses it ──┐\nVolatility Head uses it ─┼── All benefit from ONE learning!\nRegime Head uses it ─────┘\n\nEfficient! Learn once, use everywhere!\n```\n\n### Benefit 2: Consistent Predictions\n\n```\nBEFORE (Task-Specific):\n─────────────────────────\n\nDirection Model: \"Price will go UP!\"\nRegime Model: \"We're in a BEAR market!\"\n\nWait... those contradict each other!\nEach model sees the world differently.\n\n\nAFTER (Task-Agnostic):\n──────────────────────\n\nUniversal Encoder: \"Here's my understanding of the market...\"\n\nDirection Head: \"Based on this, price goes UP\"\nRegime Head: \"Based on this, we're in a BULL market\"\n\nThey AGREE because they see the SAME thing!\nConsistent and coherent predictions!\n```\n\n### Benefit 3: Easier Maintenance\n\n```\nBEFORE (Task-Specific):\n─────────────────────────\n\nNew data available?\n→ Retrain Model A\n→ Retrain Model B\n→ Retrain Model C\n→ Redeploy all 3\n→ Monitor all 3\n\nBug in preprocessing?\n→ Fix in Model A\n→ Fix in Model B\n→ Fix in Model C\n→ Hope you didn't miss any!\n\n\nAFTER (Task-Agnostic):\n──────────────────────\n\nNew data available?\n→ Retrain ONE encoder\n→ Task heads are fine (they're tiny)\n→ Deploy one update\n\nBug in preprocessing?\n→ Fix in ONE place\n→ All tasks benefit automatically\n\nMuch simpler!\n```\n\n### Benefit 4: Adding New Tasks is Easy\n\n```\nBEFORE (Task-Specific):\n─────────────────────────\n\nWant to add \"Anomaly Detection\"?\n→ Design new model architecture\n→ Collect training data\n→ Train from scratch (weeks!)\n→ Tune hyperparameters\n→ Deploy and monitor\n\nBasically starting over!\n\n\nAFTER (Task-Agnostic):\n──────────────────────\n\nWant to add \"Anomaly Detection\"?\n→ Add tiny new head (2 layers)\n→ Train just the head (hours!)\n→ Uses existing encoder\n→ Done!\n\nThe encoder already knows how markets work!\nJust teach it one more \"output format\"!\n```\n\n---\n\n## Training the Task-Agnostic Model\n\n### Phase 1: Pre-training (Learning \"How Markets Work\")\n\n```\nSELF-SUPERVISED PRE-TRAINING:\n────────────────────────────────\n\nNo labels needed! The model learns by solving puzzles:\n\nPuzzle 1: MASKED PREDICTION\n─────────────────────────────\n\nGiven: [Price Day 1] [Price Day 2] [???] [Price Day 4] [Price Day 5]\n\nTask: What was Price Day 3?\n\nThe model learns to understand temporal patterns!\n\n\nPuzzle 2: CONTRASTIVE LEARNING\n──────────────────────────────\n\n\"These two windows are from the same bull market\"\n\"These two windows are from different market conditions\"\n\nThe model learns to recognize similar patterns!\n\n\nPuzzle 3: TEMPORAL ORDER\n────────────────────────\n\nGiven: Window A and Window B\n\nTask: Did A happen before B, or after?\n\nThe model learns cause and effect!\n\n\nAfter pre-training:\n\"I understand how markets work, even though\n I wasn't told ANY specific trading rules!\"\n```\n\n### Phase 2: Multi-Task Training (Learning All Tasks Together)\n\n```\nMULTI-TASK TRAINING:\n────────────────────\n\nNow we train all tasks SIMULTANEOUSLY:\n\nStep 1: Feed data through encoder\n [Market Data] → [Universal Representation]\n\nStep 2: Each task head makes predictions\n [Universal Rep] → Direction: \"Up\"\n [Universal Rep] → Volatility: \"2.3%\"\n [Universal Rep] → Regime: \"Bull\"\n [Universal Rep] → Return: \"+0.5%\"\n\nStep 3: Compare to actual outcomes\n Direction was actually: Up ✓\n Volatility was actually: 2.1% (close!)\n Regime was actually: Bull ✓\n Return was actually: +0.4% (close!)\n\nStep 4: Update ALL parts together\n → Encoder learns what's useful for ALL tasks\n → Each head learns its specific output format\n\nThe magic: The encoder gets better for EVERY task\n because it's trained on ALL of them!\n```\n\n### The Gradient Conflict Problem (and Solution)\n\n```\nPROBLEM: Tasks can conflict!\n─────────────────────────────\n\nDirection task says: \"Make this weight bigger!\"\nVolatility task says: \"Make this weight smaller!\"\n\nIf we just average: Weight barely changes!\n\n\nSOLUTION: Gradient Harmonization\n─────────────────────────────────\n\nWhen tasks conflict, we adjust:\n\nMethod 1: Project conflicting gradients\n \"Remove the conflicting part, keep the rest\"\n\nMethod 2: Balance task weights\n \"Give more importance to the task that's struggling\"\n\nMethod 3: Uncertainty weighting\n \"Trust tasks more when they're confident\"\n\nResult: All tasks improve together without fighting!\n```\n\n---\n\n## Real Trading Example\n\n### Scenario: Trading BTC on Bybit\n\n```\nREAL-TIME TRADING PIPELINE:\n─────────────────────────────\n\nTime: 14:32:05 UTC\n\nStep 1: DATA ARRIVES\n┌────────────────────────────────────────────────────────┐\n│ Bybit WebSocket delivers: │\n│ • Last 96 hourly candles (OHLCV) │\n│ • Current orderbook (top 20 levels) │\n│ • Recent trades (last 100) │\n│ • Funding rate: 0.012% │\n│ • Open interest change: +1.3% │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 10ms)\n\nStep 2: FEATURE ENGINEERING\n┌────────────────────────────────────────────────────────┐\n│ Calculate: │\n│ • Returns at multiple horizons │\n│ • Volatility estimates │\n│ • Momentum indicators │\n│ • Orderbook imbalance │\n│ • Volume patterns │\n│ │\n│ Result: 64-dimensional feature vector for each of │\n│ 96 time steps = [96 x 64] input matrix │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 5ms)\n\nStep 3: UNIVERSAL ENCODER\n┌────────────────────────────────────────────────────────┐\n│ Input: [96 x 64] market features │\n│ │\n│ Processing: │\n│ • 1D convolutions extract local patterns │\n│ • Attention captures long-range dependencies │\n│ • Global pooling creates summary │\n│ │\n│ Output: 256-dimensional universal representation │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 15ms)\n\nStep 4: ALL TASK HEADS (in parallel!)\n┌────────────────────────────────────────────────────────┐\n│ │\n│ Direction Head: Up: 68% Down: 22% Side: 10% │\n│ │\n│ Volatility Head: Expected: 2.3% │\n│ │\n│ Regime Head: Bull: 73% Bear: 15% Side: 12% │\n│ │\n│ Return Head: Expected: +0.48% │\n│ │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 5ms)\n\nStep 5: DECISION FUSION\n┌────────────────────────────────────────────────────────┐\n│ │\n│ Consistency Check: │\n│ • Direction (Up) ↔ Regime (Bull) ↔ Return (+): ✓ │\n│ • All signals point the same way! │\n│ • Consistency Score: 0.91 (very high!) │\n│ │\n│ Risk Assessment: │\n│ • Volatility (2.3%) is moderate │\n│ • No need to reduce position size │\n│ │\n│ Final Decision: │\n│ ┌────────────────────────────────────────────────┐ │\n│ │ ACTION: BUY (Long) │ │\n│ │ SIZE: 1.5% of capital │ │\n│ │ CONFIDENCE: 72% │ │\n│ │ STOP LOSS: $44,925 (-1.5%) │ │\n│ │ TAKE PROFIT: $46,395 (+2.5%) │ │\n│ └────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 2ms)\n\nStep 6: ORDER EXECUTION\n┌────────────────────────────────────────────────────────┐\n│ Bybit API Call: │\n│ │\n│ POST /v5/order/create │\n│ { │\n│ \"symbol\": \"BTCUSDT\", │\n│ \"side\": \"Buy\", │\n│ \"orderType\": \"Market\", │\n│ \"qty\": \"0.015\", // 1.5% of capital │\n│ \"stopLoss\": \"44925\", │\n│ \"takeProfit\": \"46395\" │\n│ } │\n│ │\n│ Response: Order filled at $45,650 │\n└────────────────────────────────────────────────────────┘\n\nTotal time: < 37ms from data arrival to order placed!\n```\n\n---\n\n## When to Use Task-Agnostic Trading\n\n### Good Use Cases\n\n```\n✓ Multiple related trading tasks\n \"I need direction, volatility, and regime predictions\"\n\n✓ Limited training data\n \"I only have 1 year of data for this new asset\"\n\n✓ Consistency matters\n \"I need all my signals to make sense together\"\n\n✓ Easy maintenance\n \"I don't want to maintain 10 different models\"\n\n✓ Fast iteration\n \"I want to add new tasks quickly\"\n```\n\n### Maybe Not Ideal For\n\n```\n✗ Single very specific task\n \"I only care about predicting exact prices\"\n (A specialized model might be better)\n\n✗ Completely unrelated tasks\n \"I want to predict weather AND stock prices\"\n (Too different to share knowledge)\n\n✗ When interpretability is critical\n \"I need to explain exactly why each prediction was made\"\n (Task-specific might be more interpretable)\n```\n\n---\n\n## Key Takeaways\n\n```\n┌────────────────────────────────────────────────────────────────┐\n│ REMEMBER THESE: │\n├────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. ONE brain, MANY skills │\n│ Like a Swiss Army Knife, not a toolbox │\n│ │\n│ 2. Learn once, benefit everywhere │\n│ Knowledge transfers between all tasks │\n│ │\n│ 3. Consistent predictions │\n│ All tasks see the same \"market understanding\" │\n│ │\n│ 4. Easy to extend │\n│ Adding new tasks is just adding tiny heads │\n│ │\n│ 5. Robust decisions │\n│ When multiple tasks agree, we're confident │\n│ When they disagree, we're cautious │\n│ │\n└────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## Next Steps\n\n- [Full Technical Documentation](README.md) - Dive deeper into the math and code\n- [Russian Version](readme.simple.ru.md) - Russian translation\n- [Python Code](python/) - Try the implementation yourself\n- [Examples](examples/) - Run real trading examples\n\n---\n\n*Chapter 87 of Machine Learning for Trading*\n","structuredPatch":[],"originalFile":null}} | |
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| "content": "# Задаче-Агностический Трейдинг - Простое Объяснение\n\n## О чём вообще идёт речь? (Самое простое объяснение)\n\nПредставьте, что вы **швейцарский армейский нож**, а не набор отдельных инструментов:\n\n- **Отдельные инструменты**: Нож для резки, отвёртка для винтов, открывалка для консервов. Каждый делает ОДНУ вещь хорошо, но вам нужно носить ВСЕ их.\n\n- **Швейцарский армейский нож**: ОДИН инструмент, который режет, закручивает и открывает. Он изучил общие принципы \"работы с предметами\" и адаптировался к множеству задач!\n\n**Задаче-агностический трейдинг работает точно так же:**\n\nВместо того чтобы иметь:\n- Одну модель для предсказания \"цена пойдёт вверх или вниз\"\n- Другую модель для предсказания \"какой будет волатильность\"\n- Ещё одну модель для определения \"бычий или медвежий рынок\"\n- И ещё одну для предсказания \"какая ожидаемая доходность\"\n\nУ нас есть:\n- **ОДНА единая модель**, которая изучает \"как работают рынки\" и может делать ВСЕ эти задачи!\n\n---\n\n## Большая проблема, которую мы решаем\n\n### Проблема \"слишком много моделей\"\n\nПредставьте, что вы торговая фирма с такими потребностями:\n\n```\nТЕКУЩИЙ ПОДХОД (Специфичный для задачи):\n\n┌─────────────────────────────────────────────────────────────────┐\n│ │\n│ ЗАДАЧА 1: Направление цены │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ • Обучить модель A на исторических данных ││\n│ │ • Настроить 50 гиперпараметров ││\n│ │ • Развернуть модель A ││\n│ │ • Мониторить модель A ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ ЗАДАЧА 2: Предсказание волатильности │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ • Обучить модель B на тех же данных (другие метки) ││\n│ │ • Настроить ещё 50 гиперпараметров ││\n│ │ • Развернуть модель B ││\n│ │ • Мониторить модель B ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ ЗАДАЧА 3: Определение режима │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ • Обучить модель C ││\n│ │ • Настроить ещё 50 гиперпараметров ││\n│ │ • Развернуть модель C ││\n│ │ • Мониторить модель C ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ Итого: 3 модели, 150 гиперпараметров, 3x поддержки │\n│ │\n└─────────────────────────────────────────────────────────────────┘\n\nЗАДАЧЕ-АГНОСТИЧЕСКИЙ ПОДХОД:\n\n┌─────────────────────────────────────────────────────────────────┐\n│ │\n│ ОДНА ЕДИНАЯ МОДЕЛЬ: │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ ││\n│ │ ┌───────────────────────────────┐ ││\n│ │ │ Универсальный мозг │ ││\n│ │ │ (Изучает \"как работают │ ││\n│ │ │ рынки\") │ ││\n│ │ └───────────────┬───────────────┘ ││\n│ │ │ ││\n│ │ ┌────────────┼────────────┐ ││\n│ │ ▼ ▼ ▼ ││\n│ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ││\n│ │ │Направл. │ │Волатил. │ │ Режим │ ← Крошечные ││\n│ │ └─────────┘ └─────────┘ └─────────┘ адаптеры ││\n│ │ (5% модели) ││\n│ │ ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ Итого: 1 мозг + 3 крошечные головы, 60 гиперпараметров, 1x под.│\n│ │\n└─────────────────────────────────────────────────────────────────┘\n```\n\n### Проблема \"знания не переносятся\"\n\n```\nТрадиционные модели НЕ делятся знаниями:\n\nМодель направления:\n\"Я узнал, что всплески объёма часто предшествуют большим движениям!\"\n\nМодель волатильности:\n\"Я узнал, что всплески объёма часто предшествуют большим движениям!\"\n(Выучили ОДНО И ТО ЖЕ независимо! Потеря времени!)\n\nМодель режима:\n\"Я узнал, что всплески объёма часто предшествуют большим движениям!\"\n(ОПЯТЬ то же самое! Тройное время обучения для одного инсайта!)\n\n\nЗадаче-агностическая модель ДЕЛИТСЯ знаниями:\n\nУниверсальный мозг:\n\"Всплески объёма часто предшествуют большим движениям!\"\n(Выучил один раз, используют ВСЕ задачи!)\n\nГолова направления: \"Использую это для предсказания направления\"\nГолова волатильности: \"Использую это для предсказания волатильности\"\nГолова режима: \"Использую это для определения смены режима\"\n\nЭффективно! Выучил раз — пользуйся везде!\n```\n\n---\n\n## Аналогии из реальной жизни\n\n### Аналогия 1: Выпускник медицинского института\n\n```\nЗАДАЧЕ-СПЕЦИФИЧНЫЙ ПОДХОД:\n\nХотите стать врачом?\n• Учиться 8 лет, чтобы стать кардиологом\n• Хотите также лечить лёгкие? Ещё 8 лет!\n• Хотите также лечить кожу? Ещё 8 лет!\n\nИтого: 24 года на 3 специализации\n\n\nЗАДАЧЕ-АГНОСТИЧЕСКИЙ ПОДХОД:\n\nМедицинский институт:\n• Учиться 4 года, чтобы понять \"как работает человеческое тело\"\n (Универсальные знания, применимые ко ВСЕМУ)\n\nЗатем специализация:\n• Сердце: ещё 2 года (применение общих знаний к сердцу)\n• Лёгкие: ещё 2 года\n• Кожа: ещё 2 года\n\nИтого: 10 лет на 3 специализации!\n\n\"Универсальные медицинские знания\" переносятся на каждую специальность!\n```\n\n### Аналогия 2: Изучение языков\n\n```\nЗАДАЧЕ-СПЕЦИФИЧНЫЙ ПОДХОД:\n\nИзучение испанского с нуля: 2 года\nИзучение французского с нуля: 2 года\nИзучение итальянского с нуля: 2 года\nИзучение португальского с нуля: 2 года\n\nИтого: 8 лет\n\n\nЗАДАЧЕ-АГНОСТИЧЕСКИЙ ПОДХОД:\n\nИзучить \"как работают романские языки\": 1 год\n• Грамматические паттерны\n• Корневые структуры слов\n• Системы спряжения глаголов\n• Правила произношения\n\nЗатем адаптировать:\n• Испанский: 6 месяцев\n• Французский: 6 месяцев\n• Итальянский: 6 месяцев\n• Португальский: 6 месяцев\n\nИтого: 3 года!\n\nУниверсальное \"понимание романских языков\" переносится!\n```\n\n### Аналогия 3: Шеф-повар vs. Следователь рецептов\n\n```\nСЛЕДОВАТЕЛЬ РЕЦЕПТОВ (Задаче-специфичный):\n\n\"Я могу идеально приготовить курицу пармезан!\"\n\"Я могу идеально приготовить бефстроганов!\"\n\"Я могу идеально приготовить тако с рыбой!\"\n\nНо если попросить приготовить куриный строганов?\n\"У меня нет рецепта для этого...\"\n\n\nШЕФ-ПОВАР (Задаче-агностический):\n\n\"Я понимаю принципы кулинарии!\"\n• Как тепло влияет на белки\n• Как жиры переносят вкус\n• Как кислоты освежают блюда\n• Как текстуры сочетаются\n\nДайте ему ЛЮБЫЕ ингредиенты:\n\"Позвольте применить мои знания о готовке...\"\nСоздаёт вкусное новое блюдо!\n\nУниверсальное понимание шефа переносится на ЛЮБОЕ блюдо!\n```\n\n---\n\n## Как это работает: Шаг за шагом\n\n### Шаг 1: Универсальный энкодер (\"Мозг\")\n\nУниверсальный энкодер учится понимать рыночные данные на глубоком уровне:\n\n```\nСырые рыночные данные:\n┌──────────────────────────────────────────────────────────────┐\n│ Цена: 45,230 → 45,280 → 45,150 → 45,300 → 45,450 │\n│ Объём: 1.2M → 2.5M → 1.8M → 3.1M → 2.2M │\n│ Спред: 1.2 → 0.8 → 1.5 → 0.9 → 1.1 │\n│ Фандинг: 0.01% → 0.02% → 0.01% → 0.03% → 0.02% │\n│ Открытый интерес: +2% → +1% → -1% → +3% → +1% │\n└──────────────────────────────────────────────────────────────┘\n │\n ▼\n ┌───────────────────────────────┐\n │ УНИВЕРСАЛЬНЫЙ ЭНКОДЕР │\n │ │\n │ \"Я смотрю на эти данные │\n │ и извлекаю СУТЬ того, │\n │ что происходит...\" │\n │ │\n │ Изучает: │\n │ • Трендовые паттерны │\n │ • Режимы волатильности │\n │ • Микроструктуру рынка │\n │ • Корреляционные структуры │\n │ • Сигнатуры аномалий │\n └───────────────────────────────┘\n │\n ▼\n ┌───────────────────────────────┐\n │ УНИВЕРСАЛЬНОЕ ПРЕДСТАВЛЕНИЕ │\n │ (256 чисел, которые │\n │ захватывают всё важное) │\n │ │\n │ [0.82, -0.31, 0.67, ...] │\n │ │\n │ Как \"отпечаток рынка\", │\n │ работающий для ЛЮБОЙ задачи! │\n └───────────────────────────────┘\n```\n\n### Шаг 2: Головы задач (\"Специалисты\")\n\nКаждая голова задачи — крошечная сеть, которая берёт универсальное представление и отвечает на конкретный вопрос:\n\n```\nУниверсальное представление\n[0.82, -0.31, 0.67, 0.15, -0.88, ...]\n │\n ┌────────┼────────┬────────────┐\n │ │ │ │\n ▼ ▼ ▼ ▼\n ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐\n │Направ-│ │Волати-│ │Режим │ │Доход- │\n │ление │ │льность│ │ │ │ность │\n │ │ │ │ │ │ │ │\n │ 2 │ │ 2 │ │ 2 │ │ 2 │\n │ слоя │ │ слоя │ │ слоя │ │ слоя │\n │только!│ │только!│ │только!│ │только!│\n └───┬───┘ └───┬───┘ └──┬───┘ └──┬───┘\n │ │ │ │\n ▼ ▼ ▼ ▼\n\n \"68% Вверх\" \"2.3%\" \"Бык\" \"+0.5%\"\n \"22% Вниз\" ожидаем. (73% ожидаем.\n \"10% Боков\" волат. увер.) доходн.\n\nКаждая голова КРОШЕЧНАЯ (всего 2 слоя), потому что\nтяжёлую работу уже сделал Универсальный Энкодер!\n```\n\n### Шаг 3: Слияние решений (Собираем всё вместе)\n\nТеперь мы объединяем все выходы задач, чтобы принять торговое решение:\n\n```\n┌────────────────────────────────────────────────────────────────┐\n│ СЛИЯНИЕ РЕШЕНИЙ │\n├────────────────────────────────────────────────────────────────┤\n│ │\n│ Шаг 1: Проверить, СОГЛАСОВАНЫ ли предсказания │\n│ ───────────────────────────────────────────── │\n│ │\n│ Направление говорит: ВВЕРХ (68%) │\n│ Режим говорит: БЫК (73%) │\n│ Доходность говорит: ПОЛОЖИТЕЛЬНАЯ (+0.5%) │\n│ │\n│ ✓ ВСЕ СОГЛАСОВАНЫ! Это сильный сигнал! │\n│ │\n│ (Если бы Направление сказало ВВЕРХ, а Режим — МЕДВЕДЬ, │\n│ мы были бы осторожны!) │\n│ │\n│ Шаг 2: Скорректировать на риск │\n│ ───────────────────────────── │\n│ │\n│ Волатильность говорит: 2.3% (умеренная) │\n│ │\n│ → Нормальный размер позиции (дополнительная осторожность │\n│ не нужна) │\n│ │\n│ Шаг 3: Сгенерировать итоговый сигнал │\n│ ──────────────────────────────────── │\n│ │\n│ ┌────────────────────────────────────────────────────────┐ │\n│ │ ДЕЙСТВИЕ: ЛОНГ (Покупка) │ │\n│ │ УВЕРЕННОСТЬ: 72% │ │\n│ │ РАЗМЕР: 1.5% от капитала │ │\n│ │ СТОП-ЛОСС: -1.5% │ │\n│ │ ТЕЙК-ПРОФИТ: +2.5% │ │\n│ │ │ │\n│ │ ОБОСНОВАНИЕ: Направление, Режим и Доходность │ │\n│ │ все согласны, что рынок бычий. Умеренная │ │\n│ │ волатильность предполагает стандартный размер. │ │\n│ └────────────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## Почему \"задаче-агностический\" лучше\n\n### Преимущество 1: Обмен знаниями\n\n```\nДО (Задаче-специфичный):\n─────────────────────────\n\nМодель направления выучила: \"Всплеск объёма = скоро большое движение\"\nМодель волатильности выучила: \"Всплеск объёма = скоро большое движение\"\nМодель режима выучила: \"Всплеск объёма = скоро большое движение\"\n\n3 модели выучили ОДНО И ТО ЖЕ! Расточительно!\n\n\nПОСЛЕ (Задаче-агностический):\n──────────────────────────────\n\nУниверсальный энкодер выучил: \"Всплеск объёма = скоро большое движение\"\n\nГолова направления использует ──┐\nГолова волатильности использует ┼── Все получают выгоду от ОДНОГО обучения!\nГолова режима использует ───────┘\n\nЭффективно! Выучил раз — пользуйся везде!\n```\n\n### Преимущество 2: Согласованные предсказания\n\n```\nДО (Задаче-специфичный):\n─────────────────────────\n\nМодель направления: \"Цена пойдёт ВВЕРХ!\"\nМодель режима: \"Мы в МЕДВЕЖЬЕМ рынке!\"\n\nПогодите... это же противоречит друг другу!\nКаждая модель видит мир по-разному.\n\n\nПОСЛЕ (Задаче-агностический):\n──────────────────────────────\n\nУниверсальный энкодер: \"Вот моё понимание рынка...\"\n\nГолова направления: \"Исходя из этого, цена пойдёт ВВЕРХ\"\nГолова режима: \"Исходя из этого, мы в БЫЧЬЕМ рынке\"\n\nОни СОГЛАСОВАНЫ, потому что видят ОДНО И ТО ЖЕ!\nСогласованные и когерентные предсказания!\n```\n\n### Преимущество 3: Проще поддержка\n\n```\nДО (Задаче-специфичный):\n─────────────────────────\n\nПоявились новые данные?\n→ Переобучить модель A\n→ Переобучить модель B\n→ Переобучить модель C\n→ Передеплоить все 3\n→ Мониторить все 3\n\nБаг в предобработке?\n→ Исправить в модели A\n→ Исправить в модели B\n→ Исправить в модели C\n→ Надеяться, что ничего не пропустили!\n\n\nПОСЛЕ (Задаче-агностический):\n──────────────────────────────\n\nПоявились новые данные?\n→ Переобучить ОДИН энкодер\n→ Головы задач в порядке (они крошечные)\n→ Задеплоить одно обновление\n\nБаг в предобработке?\n→ Исправить в ОДНОМ месте\n→ Все задачи автоматически получают выгоду\n\nНамного проще!\n```\n\n### Преимущество 4: Добавить новые задачи легко\n\n```\nДО (Задаче-специфичный):\n─────────────────────────\n\nХотите добавить \"Детекцию аномалий\"?\n→ Спроектировать новую архитектуру\n→ Собрать обучающие данные\n→ Обучить с нуля (недели!)\n→ Настроить гиперпараметры\n→ Задеплоить и мониторить\n\nПо сути, начинаем заново!\n\n\nПОСЛЕ (Задаче-агностический):\n──────────────────────────────\n\nХотите добавить \"Детекцию аномалий\"?\n→ Добавить крошечную новую голову (2 слоя)\n→ Обучить только голову (часы!)\n→ Использует существующий энкодер\n→ Готово!\n\nЭнкодер уже знает, как работают рынки!\nПросто научите его ещё одному \"формату вывода\"!\n```\n\n---\n\n## Обучение задаче-агностической модели\n\n### Фаза 1: Предобучение (Изучение \"как работают рынки\")\n\n```\nСАМООБУЧЕНИЕ БЕЗ УЧИТЕЛЯ:\n────────────────────────────\n\nМетки не нужны! Модель учится, решая загадки:\n\nЗагадка 1: МАСКИРОВАННОЕ ПРЕДСКАЗАНИЕ\n─────────────────────────────────────\n\nДано: [Цена День 1] [Цена День 2] [???] [Цена День 4] [Цена День 5]\n\nЗадача: Какой была Цена День 3?\n\nМодель учится понимать временные паттерны!\n\n\nЗагадка 2: КОНТРАСТНОЕ ОБУЧЕНИЕ\n───────────────────────────────\n\n\"Эти два окна из одного бычьего рынка\"\n\"Эти два окна из разных рыночных условий\"\n\nМодель учится распознавать похожие паттерны!\n\n\nЗагадка 3: ВРЕМЕННОЙ ПОРЯДОК\n────────────────────────────\n\nДано: Окно A и Окно B\n\nЗадача: A было до B или после?\n\nМодель учится причине и следствию!\n\n\nПосле предобучения:\n\"Я понимаю, как работают рынки, хотя мне\n НЕ сказали НИКАКИХ конкретных торговых правил!\"\n```\n\n### Фаза 2: Мультизадачное обучение (Изучение всех задач вместе)\n\n```\nМУЛЬТИЗАДАЧНОЕ ОБУЧЕНИЕ:\n────────────────────────\n\nТеперь обучаем все задачи ОДНОВРЕМЕННО:\n\nШаг 1: Пропустить данные через энкодер\n [Рыночные данные] → [Универсальное представление]\n\nШаг 2: Каждая голова делает предсказания\n [Универс. предст.] → Направление: \"Вверх\"\n [Универс. предст.] → Волатильность: \"2.3%\"\n [Универс. предст.] → Режим: \"Бык\"\n [Универс. предст.] → Доходность: \"+0.5%\"\n\nШаг 3: Сравнить с реальными исходами\n Направление было на самом деле: Вверх ✓\n Волатильность была на самом деле: 2.1% (близко!)\n Режим был на самом деле: Бык ✓\n Доходность была на самом деле: +0.4% (близко!)\n\nШаг 4: Обновить ВСЕ части вместе\n → Энкодер учится, что полезно для ВСЕХ задач\n → Каждая голова учится своему формату вывода\n\nМагия: Энкодер становится лучше для КАЖДОЙ задачи,\n потому что обучается на ВСЕХ них!\n```\n\n---\n\n## Реальный пример торговли\n\n### Сценарий: Торговля BTC на Bybit\n\n```\nКОНВЕЙЕР ТОРГОВЛИ В РЕАЛЬНОМ ВРЕМЕНИ:\n─────────────────────────────────────\n\nВремя: 14:32:05 UTC\n\nШаг 1: ДАННЫЕ ПРИХОДЯТ\n┌────────────────────────────────────────────────────────┐\n│ Bybit WebSocket доставляет: │\n│ • Последние 96 часовых свечей (OHLCV) │\n│ • Текущий стакан (top 20 уровней) │\n│ • Недавние сделки (последние 100) │\n│ • Ставка финансирования: 0.012% │\n│ • Изменение открытого интереса: +1.3% │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 10мс)\n\nШаг 2: ИНЖЕНЕРИЯ ПРИЗНАКОВ\n┌────────────────────────────────────────────────────────┐\n│ Рассчитать: │\n│ • Доходности на разных горизонтах │\n│ • Оценки волатильности │\n│ • Индикаторы моментума │\n│ • Дисбаланс стакана │\n│ • Паттерны объёма │\n│ │\n│ Результат: 64-мерный вектор признаков для каждого из │\n│ 96 временных шагов = матрица [96 x 64] │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 5мс)\n\nШаг 3: УНИВЕРСАЛЬНЫЙ ЭНКОДЕР\n┌────────────────────────────────────────────────────────┐\n│ Вход: [96 x 64] рыночные признаки │\n│ │\n│ Обработка: │\n│ • 1D свёртки извлекают локальные паттерны │\n│ • Внимание захватывает дальние зависимости │\n│ • Глобальный пулинг создаёт сводку │\n│ │\n│ Выход: 256-мерное универсальное представление │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 15мс)\n\nШаг 4: ВСЕ ГОЛОВЫ ЗАДАЧ (параллельно!)\n┌────────────────────────────────────────────────────────┐\n│ │\n│ Голова направления: Вверх: 68% Вниз: 22% Бок: 10% │\n│ │\n│ Голова волатильности: Ожидаемая: 2.3% │\n│ │\n│ Голова режима: Бык: 73% Медв: 15% Бок: 12% │\n│ │\n│ Голова доходности: Ожидаемая: +0.48% │\n│ │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 5мс)\n\nШаг 5: СЛИЯНИЕ РЕШЕНИЙ\n┌────────────────────────────────────────────────────────┐\n│ │\n│ Проверка согласованности: │\n│ • Направление (Вверх) ↔ Режим (Бык) ↔ Доходн (+): ✓ │\n│ • Все сигналы указывают в одну сторону! │\n│ • Оценка согласованности: 0.91 (очень высокая!) │\n│ │\n│ Оценка риска: │\n│ • Волатильность (2.3%) умеренная │\n│ • Уменьшать позицию не нужно │\n│ │\n│ Итоговое решение: │\n│ ┌────────────────────────────────────────────────┐ │\n│ │ ДЕЙСТВИЕ: ПОКУПКА (Лонг) │ │\n│ │ РАЗМЕР: 1.5% от капитала │ │\n│ │ УВЕРЕННОСТЬ: 72% │ │\n│ │ СТОП-ЛОСС: $44,925 (-1.5%) │ │\n│ │ ТЕЙК-ПРОФИТ: $46,395 (+2.5%) │ │\n│ └────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 2мс)\n\nШаг 6: ИСПОЛНЕНИЕ ОРДЕРА\n┌────────────────────────────────────────────────────────┐\n│ Вызов Bybit API: │\n│ │\n│ POST /v5/order/create │\n│ { │\n│ \"symbol\": \"BTCUSDT\", │\n│ \"side\": \"Buy\", │\n│ \"orderType\": \"Market\", │\n│ \"qty\": \"0.015\", // 1.5% от капитала │\n│ \"stopLoss\": \"44925\", │\n│ \"takeProfit\": \"46395\" │\n│ } │\n│ │\n│ Ответ: Ордер исполнен по $45,650 │\n└────────────────────────────────────────────────────────┘\n\nОбщее время: < 37мс от прихода данных до размещения ордера!\n```\n\n---\n\n## Когда использовать задаче-агностический трейдинг\n\n### Хорошие случаи использования\n\n```\n✓ Несколько связанных торговых задач\n \"Мне нужны предсказания направления, волатильности и режима\"\n\n✓ Ограниченные обучающие данные\n \"У меня только 1 год данных для этого нового актива\"\n\n✓ Важна согласованность\n \"Мне нужно, чтобы все мои сигналы имели смысл вместе\"\n\n✓ Простая поддержка\n \"Я не хочу поддерживать 10 разных моделей\"\n\n✓ Быстрые итерации\n \"Я хочу быстро добавлять новые задачи\"\n```\n\n### Возможно, не идеально для\n\n```\n✗ Единственная очень специфичная задача\n \"Меня интересует только предсказание точных цен\"\n (Специализированная модель может быть лучше)\n\n✗ Совершенно не связанные задачи\n \"Я хочу предсказывать погоду И цены акций\"\n (Слишком разные, чтобы делиться знаниями)\n\n✗ Когда критична интерпретируемость\n \"Мне нужно объяснить, почему каждое предсказание было сделано\"\n (Задаче-специфичные могут быть более интерпретируемыми)\n```\n\n---\n\n## Ключевые выводы\n\n```\n┌────────────────────────────────────────────────────────────────┐\n│ ЗАПОМНИТЕ ЭТО: │\n├────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. ОДИН мозг, МНОГО навыков │\n│ Как швейцарский нож, а не ящик с инструментами │\n│ │\n│ 2. Выучил раз — пользуйся везде │\n│ Знания переносятся между всеми задачами │\n│ │\n│ 3. Согласованные предсказания │\n│ Все задачи видят одно и то же \"понимание рынка\" │\n│ │\n│ 4. Легко расширять │\n│ Добавление новых задач — это просто добавление крошечных │\n│ голов │\n│ │\n│ 5. Надёжные решения │\n│ Когда несколько задач согласны — мы уверены │\n│ Когда они не согласны — мы осторожны │\n│ │\n└────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## Следующие шаги\n\n- [Полная техническая документация](README.md) - Погрузитесь глубже в математику и код\n- [Английская версия](readme.simple.md) - English version\n- [Python код](python/) - Попробуйте реализацию сами\n- [Примеры](examples/) - Запустите реальные торговые примеры\n\n---\n\n*Глава 87 книги \"Машинное обучение для трейдинга\"*\n" | |
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| [2026-01-21T19:00:57.110Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Jr7b8FYTiZLDC6aCywkMmQ","type":"tool_result","content":"File created successfully at: /tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/readme.simple.ru.md"}]},"parent_tool_use_id":null,"session_id":"8c23df75-76b7-45ba-aa3a-4295879a3b8f","uuid":"233485e2-502c-4e27-850d-d776c1101a3d","tool_use_result":{"type":"create","filePath":"/tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/readme.simple.ru.md","content":"# Задаче-Агностический Трейдинг - Простое Объяснение\n\n## О чём вообще идёт речь? (Самое простое объяснение)\n\nПредставьте, что вы **швейцарский армейский нож**, а не набор отдельных инструментов:\n\n- **Отдельные инструменты**: Нож для резки, отвёртка для винтов, открывалка для консервов. Каждый делает ОДНУ вещь хорошо, но вам нужно носить ВСЕ их.\n\n- **Швейцарский армейский нож**: ОДИН инструмент, который режет, закручивает и открывает. Он изучил общие принципы \"работы с предметами\" и адаптировался к множеству задач!\n\n**Задаче-агностический трейдинг работает точно так же:**\n\nВместо того чтобы иметь:\n- Одну модель для предсказания \"цена пойдёт вверх или вниз\"\n- Другую модель для предсказания \"какой будет волатильность\"\n- Ещё одну модель для определения \"бычий или медвежий рынок\"\n- И ещё одну для предсказания \"какая ожидаемая доходность\"\n\nУ нас есть:\n- **ОДНА единая модель**, которая изучает \"как работают рынки\" и может делать ВСЕ эти задачи!\n\n---\n\n## Большая проблема, которую мы решаем\n\n### Проблема \"слишком много моделей\"\n\nПредставьте, что вы торговая фирма с такими потребностями:\n\n```\nТЕКУЩИЙ ПОДХОД (Специфичный для задачи):\n\n┌─────────────────────────────────────────────────────────────────┐\n│ │\n│ ЗАДАЧА 1: Направление цены │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ • Обучить модель A на исторических данных ││\n│ │ • Настроить 50 гиперпараметров ││\n│ │ • Развернуть модель A ││\n│ │ • Мониторить модель A ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ ЗАДАЧА 2: Предсказание волатильности │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ • Обучить модель B на тех же данных (другие метки) ││\n│ │ • Настроить ещё 50 гиперпараметров ││\n│ │ • Развернуть модель B ││\n│ │ • Мониторить модель B ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ ЗАДАЧА 3: Определение режима │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ • Обучить модель C ││\n│ │ • Настроить ещё 50 гиперпараметров ││\n│ │ • Развернуть модель C ││\n│ │ • Мониторить модель C ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ Итого: 3 модели, 150 гиперпараметров, 3x поддержки │\n│ │\n└─────────────────────────────────────────────────────────────────┘\n\nЗАДАЧЕ-АГНОСТИЧЕСКИЙ ПОДХОД:\n\n┌─────────────────────────────────────────────────────────────────┐\n│ │\n│ ОДНА ЕДИНАЯ МОДЕЛЬ: │\n│ ┌─────────────────────────────────────────────────────────────┐│\n│ │ ││\n│ │ ┌───────────────────────────────┐ ││\n│ │ │ Универсальный мозг │ ││\n│ │ │ (Изучает \"как работают │ ││\n│ │ │ рынки\") │ ││\n│ │ └───────────────┬───────────────┘ ││\n│ │ │ ││\n│ │ ┌────────────┼────────────┐ ││\n│ │ ▼ ▼ ▼ ││\n│ │ ┌─────────┐ ┌─────────┐ ┌─────────┐ ││\n│ │ │Направл. │ │Волатил. │ │ Режим │ ← Крошечные ││\n│ │ └─────────┘ └─────────┘ └─────────┘ адаптеры ││\n│ │ (5% модели) ││\n│ │ ││\n│ └─────────────────────────────────────────────────────────────┘│\n│ │\n│ Итого: 1 мозг + 3 крошечные головы, 60 гиперпараметров, 1x под.│\n│ │\n└─────────────────────────────────────────────────────────────────┘\n```\n\n### Проблема \"знания не переносятся\"\n\n```\nТрадиционные модели НЕ делятся знаниями:\n\nМодель направления:\n\"Я узнал, что всплески объёма часто предшествуют большим движениям!\"\n\nМодель волатильности:\n\"Я узнал, что всплески объёма часто предшествуют большим движениям!\"\n(Выучили ОДНО И ТО ЖЕ независимо! Потеря времени!)\n\nМодель режима:\n\"Я узнал, что всплески объёма часто предшествуют большим движениям!\"\n(ОПЯТЬ то же самое! Тройное время обучения для одного инсайта!)\n\n\nЗадаче-агностическая модель ДЕЛИТСЯ знаниями:\n\nУниверсальный мозг:\n\"Всплески объёма часто предшествуют большим движениям!\"\n(Выучил один раз, используют ВСЕ задачи!)\n\nГолова направления: \"Использую это для предсказания направления\"\nГолова волатильности: \"Использую это для предсказания волатильности\"\nГолова режима: \"Использую это для определения смены режима\"\n\nЭффективно! Выучил раз — пользуйся везде!\n```\n\n---\n\n## Аналогии из реальной жизни\n\n### Аналогия 1: Выпускник медицинского института\n\n```\nЗАДАЧЕ-СПЕЦИФИЧНЫЙ ПОДХОД:\n\nХотите стать врачом?\n• Учиться 8 лет, чтобы стать кардиологом\n• Хотите также лечить лёгкие? Ещё 8 лет!\n• Хотите также лечить кожу? Ещё 8 лет!\n\nИтого: 24 года на 3 специализации\n\n\nЗАДАЧЕ-АГНОСТИЧЕСКИЙ ПОДХОД:\n\nМедицинский институт:\n• Учиться 4 года, чтобы понять \"как работает человеческое тело\"\n (Универсальные знания, применимые ко ВСЕМУ)\n\nЗатем специализация:\n• Сердце: ещё 2 года (применение общих знаний к сердцу)\n• Лёгкие: ещё 2 года\n• Кожа: ещё 2 года\n\nИтого: 10 лет на 3 специализации!\n\n\"Универсальные медицинские знания\" переносятся на каждую специальность!\n```\n\n### Аналогия 2: Изучение языков\n\n```\nЗАДАЧЕ-СПЕЦИФИЧНЫЙ ПОДХОД:\n\nИзучение испанского с нуля: 2 года\nИзучение французского с нуля: 2 года\nИзучение итальянского с нуля: 2 года\nИзучение португальского с нуля: 2 года\n\nИтого: 8 лет\n\n\nЗАДАЧЕ-АГНОСТИЧЕСКИЙ ПОДХОД:\n\nИзучить \"как работают романские языки\": 1 год\n• Грамматические паттерны\n• Корневые структуры слов\n• Системы спряжения глаголов\n• Правила произношения\n\nЗатем адаптировать:\n• Испанский: 6 месяцев\n• Французский: 6 месяцев\n• Итальянский: 6 месяцев\n• Португальский: 6 месяцев\n\nИтого: 3 года!\n\nУниверсальное \"понимание романских языков\" переносится!\n```\n\n### Аналогия 3: Шеф-повар vs. Следователь рецептов\n\n```\nСЛЕДОВАТЕЛЬ РЕЦЕПТОВ (Задаче-специфичный):\n\n\"Я могу идеально приготовить курицу пармезан!\"\n\"Я могу идеально приготовить бефстроганов!\"\n\"Я могу идеально приготовить тако с рыбой!\"\n\nНо если попросить приготовить куриный строганов?\n\"У меня нет рецепта для этого...\"\n\n\nШЕФ-ПОВАР (Задаче-агностический):\n\n\"Я понимаю принципы кулинарии!\"\n• Как тепло влияет на белки\n• Как жиры переносят вкус\n• Как кислоты освежают блюда\n• Как текстуры сочетаются\n\nДайте ему ЛЮБЫЕ ингредиенты:\n\"Позвольте применить мои знания о готовке...\"\nСоздаёт вкусное новое блюдо!\n\nУниверсальное понимание шефа переносится на ЛЮБОЕ блюдо!\n```\n\n---\n\n## Как это работает: Шаг за шагом\n\n### Шаг 1: Универсальный энкодер (\"Мозг\")\n\nУниверсальный энкодер учится понимать рыночные данные на глубоком уровне:\n\n```\nСырые рыночные данные:\n┌──────────────────────────────────────────────────────────────┐\n│ Цена: 45,230 → 45,280 → 45,150 → 45,300 → 45,450 │\n│ Объём: 1.2M → 2.5M → 1.8M → 3.1M → 2.2M │\n│ Спред: 1.2 → 0.8 → 1.5 → 0.9 → 1.1 │\n│ Фандинг: 0.01% → 0.02% → 0.01% → 0.03% → 0.02% │\n│ Открытый интерес: +2% → +1% → -1% → +3% → +1% │\n└──────────────────────────────────────────────────────────────┘\n │\n ▼\n ┌───────────────────────────────┐\n │ УНИВЕРСАЛЬНЫЙ ЭНКОДЕР │\n │ │\n │ \"Я смотрю на эти данные │\n │ и извлекаю СУТЬ того, │\n │ что происходит...\" │\n │ │\n │ Изучает: │\n │ • Трендовые паттерны │\n │ • Режимы волатильности │\n │ • Микроструктуру рынка │\n │ • Корреляционные структуры │\n │ • Сигнатуры аномалий │\n └───────────────────────────────┘\n │\n ▼\n ┌───────────────────────────────┐\n │ УНИВЕРСАЛЬНОЕ ПРЕДСТАВЛЕНИЕ │\n │ (256 чисел, которые │\n │ захватывают всё важное) │\n │ │\n │ [0.82, -0.31, 0.67, ...] │\n │ │\n │ Как \"отпечаток рынка\", │\n │ работающий для ЛЮБОЙ задачи! │\n └───────────────────────────────┘\n```\n\n### Шаг 2: Головы задач (\"Специалисты\")\n\nКаждая голова задачи — крошечная сеть, которая берёт универсальное представление и отвечает на конкретный вопрос:\n\n```\nУниверсальное представление\n[0.82, -0.31, 0.67, 0.15, -0.88, ...]\n │\n ┌────────┼────────┬────────────┐\n │ │ │ │\n ▼ ▼ ▼ ▼\n ┌───────┐ ┌───────┐ ┌───────┐ ┌───────┐\n │Направ-│ │Волати-│ │Режим │ │Доход- │\n │ление │ │льность│ │ │ │ность │\n │ │ │ │ │ │ │ │\n │ 2 │ │ 2 │ │ 2 │ │ 2 │\n │ слоя │ │ слоя │ │ слоя │ │ слоя │\n │только!│ │только!│ │только!│ │только!│\n └───┬───┘ └───┬───┘ └──┬───┘ └──┬───┘\n │ │ │ │\n ▼ ▼ ▼ ▼\n\n \"68% Вверх\" \"2.3%\" \"Бык\" \"+0.5%\"\n \"22% Вниз\" ожидаем. (73% ожидаем.\n \"10% Боков\" волат. увер.) доходн.\n\nКаждая голова КРОШЕЧНАЯ (всего 2 слоя), потому что\nтяжёлую работу уже сделал Универсальный Энкодер!\n```\n\n### Шаг 3: Слияние решений (Собираем всё вместе)\n\nТеперь мы объединяем все выходы задач, чтобы принять торговое решение:\n\n```\n┌────────────────────────────────────────────────────────────────┐\n│ СЛИЯНИЕ РЕШЕНИЙ │\n├────────────────────────────────────────────────────────────────┤\n│ │\n│ Шаг 1: Проверить, СОГЛАСОВАНЫ ли предсказания │\n│ ───────────────────────────────────────────── │\n│ │\n│ Направление говорит: ВВЕРХ (68%) │\n│ Режим говорит: БЫК (73%) │\n│ Доходность говорит: ПОЛОЖИТЕЛЬНАЯ (+0.5%) │\n│ │\n│ ✓ ВСЕ СОГЛАСОВАНЫ! Это сильный сигнал! │\n│ │\n│ (Если бы Направление сказало ВВЕРХ, а Режим — МЕДВЕДЬ, │\n│ мы были бы осторожны!) │\n│ │\n│ Шаг 2: Скорректировать на риск │\n│ ───────────────────────────── │\n│ │\n│ Волатильность говорит: 2.3% (умеренная) │\n│ │\n│ → Нормальный размер позиции (дополнительная осторожность │\n│ не нужна) │\n│ │\n│ Шаг 3: Сгенерировать итоговый сигнал │\n│ ──────────────────────────────────── │\n│ │\n│ ┌────────────────────────────────────────────────────────┐ │\n│ │ ДЕЙСТВИЕ: ЛОНГ (Покупка) │ │\n│ │ УВЕРЕННОСТЬ: 72% │ │\n│ │ РАЗМЕР: 1.5% от капитала │ │\n│ │ СТОП-ЛОСС: -1.5% │ │\n│ │ ТЕЙК-ПРОФИТ: +2.5% │ │\n│ │ │ │\n│ │ ОБОСНОВАНИЕ: Направление, Режим и Доходность │ │\n│ │ все согласны, что рынок бычий. Умеренная │ │\n│ │ волатильность предполагает стандартный размер. │ │\n│ └────────────────────────────────────────────────────────┘ │\n│ | |
| [2026-01-21T19:00:57.125Z] [INFO] │\n└────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## Почему \"задаче-агностический\" лучше\n\n### Преимущество 1: Обмен знаниями\n\n```\nДО (Задаче-специфичный):\n─────────────────────────\n\nМодель направления выучила: \"Всплеск объёма = скоро большое движение\"\nМодель волатильности выучила: \"Всплеск объёма = скоро большое движение\"\nМодель режима выучила: \"Всплеск объёма = скоро большое движение\"\n\n3 модели выучили ОДНО И ТО ЖЕ! Расточительно!\n\n\nПОСЛЕ (Задаче-агностический):\n──────────────────────────────\n\nУниверсальный энкодер выучил: \"Всплеск объёма = скоро большое движение\"\n\nГолова направления использует ──┐\nГолова волатильности использует ┼── Все получают выгоду от ОДНОГО обучения!\nГолова режима использует ───────┘\n\nЭффективно! Выучил раз — пользуйся везде!\n```\n\n### Преимущество 2: Согласованные предсказания\n\n```\nДО (Задаче-специфичный):\n─────────────────────────\n\nМодель направления: \"Цена пойдёт ВВЕРХ!\"\nМодель режима: \"Мы в МЕДВЕЖЬЕМ рынке!\"\n\nПогодите... это же противоречит друг другу!\nКаждая модель видит мир по-разному.\n\n\nПОСЛЕ (Задаче-агностический):\n──────────────────────────────\n\nУниверсальный энкодер: \"Вот моё понимание рынка...\"\n\nГолова направления: \"Исходя из этого, цена пойдёт ВВЕРХ\"\nГолова режима: \"Исходя из этого, мы в БЫЧЬЕМ рынке\"\n\nОни СОГЛАСОВАНЫ, потому что видят ОДНО И ТО ЖЕ!\nСогласованные и когерентные предсказания!\n```\n\n### Преимущество 3: Проще поддержка\n\n```\nДО (Задаче-специфичный):\n─────────────────────────\n\nПоявились новые данные?\n→ Переобучить модель A\n→ Переобучить модель B\n→ Переобучить модель C\n→ Передеплоить все 3\n→ Мониторить все 3\n\nБаг в предобработке?\n→ Исправить в модели A\n→ Исправить в модели B\n→ Исправить в модели C\n→ Надеяться, что ничего не пропустили!\n\n\nПОСЛЕ (Задаче-агностический):\n──────────────────────────────\n\nПоявились новые данные?\n→ Переобучить ОДИН энкодер\n→ Головы задач в порядке (они крошечные)\n→ Задеплоить одно обновление\n\nБаг в предобработке?\n→ Исправить в ОДНОМ месте\n→ Все задачи автоматически получают выгоду\n\nНамного проще!\n```\n\n### Преимущество 4: Добавить новые задачи легко\n\n```\nДО (Задаче-специфичный):\n─────────────────────────\n\nХотите добавить \"Детекцию аномалий\"?\n→ Спроектировать новую архитектуру\n→ Собрать обучающие данные\n→ Обучить с нуля (недели!)\n→ Настроить гиперпараметры\n→ Задеплоить и мониторить\n\nПо сути, начинаем заново!\n\n\nПОСЛЕ (Задаче-агностический):\n──────────────────────────────\n\nХотите добавить \"Детекцию аномалий\"?\n→ Добавить крошечную новую голову (2 слоя)\n→ Обучить только голову (часы!)\n→ Использует существующий энкодер\n→ Готово!\n\nЭнкодер уже знает, как работают рынки!\nПросто научите его ещё одному \"формату вывода\"!\n```\n\n---\n\n## Обучение задаче-агностической модели\n\n### Фаза 1: Предобучение (Изучение \"как работают рынки\")\n\n```\nСАМООБУЧЕНИЕ БЕЗ УЧИТЕЛЯ:\n────────────────────────────\n\nМетки не нужны! Модель учится, решая загадки:\n\nЗагадка 1: МАСКИРОВАННОЕ ПРЕДСКАЗАНИЕ\n─────────────────────────────────────\n\nДано: [Цена День 1] [Цена День 2] [???] [Цена День 4] [Цена День 5]\n\nЗадача: Какой была Цена День 3?\n\nМодель учится понимать временные паттерны!\n\n\nЗагадка 2: КОНТРАСТНОЕ ОБУЧЕНИЕ\n───────────────────────────────\n\n\"Эти два окна из одного бычьего рынка\"\n\"Эти два окна из разных рыночных условий\"\n\nМодель учится распознавать похожие паттерны!\n\n\nЗагадка 3: ВРЕМЕННОЙ ПОРЯДОК\n────────────────────────────\n\nДано: Окно A и Окно B\n\nЗадача: A было до B или после?\n\nМодель учится причине и следствию!\n\n\nПосле предобучения:\n\"Я понимаю, как работают рынки, хотя мне\n НЕ сказали НИКАКИХ конкретных торговых правил!\"\n```\n\n### Фаза 2: Мультизадачное обучение (Изучение всех задач вместе)\n\n```\nМУЛЬТИЗАДАЧНОЕ ОБУЧЕНИЕ:\n────────────────────────\n\nТеперь обучаем все задачи ОДНОВРЕМЕННО:\n\nШаг 1: Пропустить данные через энкодер\n [Рыночные данные] → [Универсальное представление]\n\nШаг 2: Каждая голова делает предсказания\n [Универс. предст.] → Направление: \"Вверх\"\n [Универс. предст.] → Волатильность: \"2.3%\"\n [Универс. предст.] → Режим: \"Бык\"\n [Универс. предст.] → Доходность: \"+0.5%\"\n\nШаг 3: Сравнить с реальными исходами\n Направление было на самом деле: Вверх ✓\n Волатильность была на самом деле: 2.1% (близко!)\n Режим был на самом деле: Бык ✓\n Доходность была на самом деле: +0.4% (близко!)\n\nШаг 4: Обновить ВСЕ части вместе\n → Энкодер учится, что полезно для ВСЕХ задач\n → Каждая голова учится своему формату вывода\n\nМагия: Энкодер становится лучше для КАЖДОЙ задачи,\n потому что обучается на ВСЕХ них!\n```\n\n---\n\n## Реальный пример торговли\n\n### Сценарий: Торговля BTC на Bybit\n\n```\nКОНВЕЙЕР ТОРГОВЛИ В РЕАЛЬНОМ ВРЕМЕНИ:\n─────────────────────────────────────\n\nВремя: 14:32:05 UTC\n\nШаг 1: ДАННЫЕ ПРИХОДЯТ\n┌────────────────────────────────────────────────────────┐\n│ Bybit WebSocket доставляет: │\n│ • Последние 96 часовых свечей (OHLCV) │\n│ • Текущий стакан (top 20 уровней) │\n│ • Недавние сделки (последние 100) │\n│ • Ставка финансирования: 0.012% │\n│ • Изменение открытого интереса: +1.3% │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 10мс)\n\nШаг 2: ИНЖЕНЕРИЯ ПРИЗНАКОВ\n┌────────────────────────────────────────────────────────┐\n│ Рассчитать: │\n│ • Доходности на разных горизонтах │\n│ • Оценки волатильности │\n│ • Индикаторы моментума │\n│ • Дисбаланс стакана │\n│ • Паттерны объёма │\n│ │\n│ Результат: 64-мерный вектор признаков для каждого из │\n│ 96 временных шагов = матрица [96 x 64] │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 5мс)\n\nШаг 3: УНИВЕРСАЛЬНЫЙ ЭНКОДЕР\n┌────────────────────────────────────────────────────────┐\n│ Вход: [96 x 64] рыночные признаки │\n│ │\n│ Обработка: │\n│ • 1D свёртки извлекают локальные паттерны │\n│ • Внимание захватывает дальние зависимости │\n│ • Глобальный пулинг создаёт сводку │\n│ │\n│ Выход: 256-мерное универсальное представление │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 15мс)\n\nШаг 4: ВСЕ ГОЛОВЫ ЗАДАЧ (параллельно!)\n┌────────────────────────────────────────────────────────┐\n│ │\n│ Голова направления: Вверх: 68% Вниз: 22% Бок: 10% │\n│ │\n│ Голова волатильности: Ожидаемая: 2.3% │\n│ │\n│ Голова режима: Бык: 73% Медв: 15% Бок: 12% │\n│ │\n│ Голова доходности: Ожидаемая: +0.48% │\n│ │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 5мс)\n\nШаг 5: СЛИЯНИЕ РЕШЕНИЙ\n┌────────────────────────────────────────────────────────┐\n│ │\n│ Проверка согласованности: │\n│ • Направление (Вверх) ↔ Режим (Бык) ↔ Доходн (+): ✓ │\n│ • Все сигналы указывают в одну сторону! │\n│ • Оценка согласованности: 0.91 (очень высокая!) │\n│ │\n│ Оценка риска: │\n│ • Волатильность (2.3%) умеренная │\n│ • Уменьшать позицию не нужно │\n│ │\n│ Итоговое решение: │\n│ ┌────────────────────────────────────────────────┐ │\n│ │ ДЕЙСТВИЕ: ПОКУПКА (Лонг) │ │\n│ │ РАЗМЕР: 1.5% от капитала │ │\n│ │ УВЕРЕННОСТЬ: 72% │ │\n│ │ СТОП-ЛОСС: $44,925 (-1.5%) │ │\n│ │ ТЕЙК-ПРОФИТ: $46,395 (+2.5%) │ │\n│ └────────────────────────────────────────────────┘ │\n│ │\n└────────────────────────────────────────────────────────┘\n │\n ▼ (< 2мс)\n\nШаг 6: ИСПОЛНЕНИЕ ОРДЕРА\n┌────────────────────────────────────────────────────────┐\n│ Вызов Bybit API: │\n│ │\n│ POST /v5/order/create │\n│ { │\n│ \"symbol\": \"BTCUSDT\", │\n│ \"side\": \"Buy\", │\n│ \"orderType\": \"Market\", │\n│ \"qty\": \"0.015\", // 1.5% от капитала │\n│ \"stopLoss\": \"44925\", │\n│ \"takeProfit\": \"46395\" │\n│ } │\n│ │\n│ Ответ: Ордер исполнен по $45,650 │\n└────────────────────────────────────────────────────────┘\n\nОбщее время: < 37мс от прихода данных до размещения ордера!\n```\n\n---\n\n## Когда использовать задаче-агностический трейдинг\n\n### Хорошие случаи использования\n\n```\n✓ Несколько связанных торговых задач\n \"Мне нужны предсказания направления, волатильности и режима\"\n\n✓ Ограниченные обучающие данные\n \"У меня только 1 год данных для этого нового актива\"\n\n✓ Важна согласованность\n \"Мне нужно, чтобы все мои сигналы имели смысл вместе\"\n\n✓ Простая поддержка\n \"Я не хочу поддерживать 10 разных моделей\"\n\n✓ Быстрые итерации\n \"Я хочу быстро добавлять новые задачи\"\n```\n\n### Возможно, не идеально для\n\n```\n✗ Единственная очень специфичная задача\n \"Меня интересует только предсказание точных цен\"\n (Специализированная модель может быть лучше)\n\n✗ Совершенно не связанные задачи\n \"Я хочу предсказывать погоду И цены акций\"\n (Слишком разные, чтобы делиться знаниями)\n\n✗ Когда критична интерпретируемость\n \"Мне нужно объяснить, почему каждое предсказание было сделано\"\n (Задаче-специфичные могут быть более интерпретируемыми)\n```\n\n---\n\n## Ключевые выводы\n\n```\n┌────────────────────────────────────────────────────────────────┐\n│ ЗАПОМНИТЕ ЭТО: │\n├────────────────────────────────────────────────────────────────┤\n│ │\n│ 1. ОДИН мозг, МНОГО навыков │\n│ Как швейцарский нож, а не ящик с инструментами │\n│ │\n│ 2. Выучил раз — пользуйся везде │\n│ Знания переносятся между всеми задачами │\n│ │\n│ 3. Согласованные предсказания │\n│ Все задачи видят одно и то же \"понимание рынка\" │\n│ │\n│ 4. Легко расширять │\n│ Добавление новых задач — это просто добавление крошечных │\n│ голов │\n│ │\n│ 5. Надёжные решения │\n│ Когда несколько задач согласны — мы уверены │\n│ Когда они не согласны — мы осторожны │\n│ │\n└────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## Следующие шаги\n\n- [Полная техническая документация](README.md) - Погрузитесь глубже в математику и код\n- [Английская версия](readme.simple.md) - English version\n- [Python код](python/) - Попробуйте реализацию сами\n- [Примеры](examples/) - Запустите реальные торговые примеры\n\n---\n\n*Глава 87 книги \"Машинное обучение для трейдинга\"*\n","structuredPatch":[],"originalFile":null}} | |
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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 User Request**: The user asked to solve GitHub issue #124 for the repository `suenot/machine-learning-for-trading`. The issue is about writing Chapter 87: Task-Agnostic Trading. Key details provided:\n - Issue URL: https://github.com/suenot/machine-learning-for-trading/issues/124\n - Branch: issue-124-ba7373c4f0fd\n - Working directory: /tmp/gh-issue-solver-1769021101508\n - PR #418 (draft) already created\n - Forked repo: konard/suenot-machine-learning-for-trading\n\n2. **Issue Requirements** (from viewing the GitHub issue):\n - Create README.md (English)\n - Create README.ru.md (Russian)\n - Create readme.simple.md (simplified explanation - \"for a schoolchild\")\n - Create readme.simple.ru.md (simplified Russian)\n - Include working code examples in both Rust and Python\n - Use both stock market and Bybit cryptocurrency data\n - Code must be modular\n - All files must stay within the chapter's folder (87_task_agnostic_trading)\n\n3. **Research Phase**:\n - Examined the existing chapter 87 folder which only had README.specify.md\n - Examined reference chapters (85, 86) to understand structure and style\n - Read README.specify.md to understand the technical specification\n - Searched for task-agnostic learning research papers and concepts\n - Found relevant papers on MAML-based trading, meta-learning in finance\n\n4. **Implementation Progress**:\n - Created README.md (comprehensive English documentation)\n - Created README.ru.md (Russian translation)\n - Created readme.simple.md (simplified explanation with analogies)\n - Created readme.simple.ru.md (simplified Russian)\n - Todo list shows Rust implementation is next (currently in_progress)\n\n5. **Files Created**:\n - /tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/README.md\n - /tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/README.ru.md\n - /tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/readme.simple.md\n - /tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/readme.simple.ru.md\n\n6. **Pending Tasks** (from todo list):\n - Create Rust implementation with Cargo.toml (in_progress)\n - Create Python implementation with PyTorch\n - Create working examples using Bybit data\n - Test code and verify functionality\n - Commit and push changes to the branch\n\n7. **Technical Concepts**:\n - Task-agnostic representation learning\n - Multi-task learning (shared encoder with task-specific heads)\n - Transformer encoder architectures\n - Mixture of Experts (MoE)\n - Self-supervised pre-training\n - Gradient harmonization (PCGrad, GradNorm)\n - Decision fusion for trading\n - Bybit API integration\n\nSummary:\n1. Primary Request and Intent:\n The user requested solving GitHub issue #124 for the machine-learning-for-trading repository. The issue requires writing Chapter 87: Task-Agnostic Trading. The chapter must include:\n - README.md (English main content)\n - README.ru.md (Russian translation)\n - readme.simple.md (simplified \"for a schoolchild\" explanation with life analogies)\n - readme.simple.ru.md (simplified Russian)\n - Working code examples in Rust AND Python\n - Use of both stock market and Bybit cryptocurrency exchange data\n - Modular code structure\n - All content must remain within the chapter folder (87_task_agnostic_trading)\n\n2. Key Technical Concepts:\n - Task-agnostic representation learning (universal models for multiple trading tasks)\n - Multi-task learning with shared encoder architecture\n - Task-specific heads (Direction, Volatility, Regime, Return prediction)\n - Transformer-based encoders with attention mechanisms\n - Mixture of Experts (MoE) architecture\n - Self-supervised pre-training (masked autoencoding, contrastive learning)\n - Gradient harmonization (PCGrad, GradNorm, uncertainty weighting)\n - Decision fusion combining multiple task outputs\n - Bybit WebSocket and REST API integration\n - Risk management with task agreement scoring\n\n3. Files and Code Sections:\n - `/tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/README.specify.md` (read)\n - Contains technical specification for the chapter\n - References paper: https://arxiv.org/abs/1907.12157\n - Defines module structure, tech stack (PyTorch, Rust ndarray/polars)\n \n - `/tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/README.md` (created)\n - Comprehensive English documentation (~1400 lines)\n - Includes theoretical foundation, architectures, Bybit integration, Rust code examples\n - Key architecture: shared encoder with task heads pattern\n \n - `/tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/README.ru.md` (created)\n - Full Russian translation of main README\n \n - `/tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/readme.simple.md` (created)\n - Simplified explanation using analogies (Swiss Army Knife, Medical School Graduate, Chef)\n - Step-by-step explanations with ASCII diagrams\n \n - `/tmp/gh-issue-solver-1769021101508/87_task_agnostic_trading/readme.simple.ru.md` (created)\n - Russian translation of simplified explanation\n\n - Reference files examined:\n - `/tmp/gh-issue-solver-1769021101508/86_few_shot_market_prediction/README.md`\n - `/tmp/gh-issue-solver-1769021101508/86_few_shot_market_prediction/readme.simple.md`\n - Chapter 86 structure: Cargo.toml, src/, python/, examples/\n\n4. Errors and fixes:\n - No errors encountered during implementation\n - The arxiv link in README.specify.md (1907.12157) was for wrong paper; resolved by searching for actual task-agnostic/meta-learning finance papers\n\n5. Problem Solving:\n - Researched task-agnostic learning concepts since the specified arxiv paper was unrelated\n - Found relevant papers: MAML for financial NLU (2303.02841), Trading in Fast-Changing Markets with Meta-RL (2024)\n - Used chapter 86 as reference for structure and style\n - Created comprehensive documentation with ASCII diagrams, code examples, and trading scenarios\n\n6. All user messages:\n - \"Issue to solve: https://github.com/suenot/machine-learning-for-trading/issues/124\nYour prepared branch: issue-124-ba7373c4f0fd\nYour prepared working directory: /tmp/gh-issue-solver-1769021101508\nYour prepared Pull Request: https://github.com/suenot/machine-learning-for-trading/pull/418\nYour forked repository: konard/suenot-machine-learning-for-trading\nOriginal repository (upstream): suenot/machine-learning-for-trading\n\nProceed.\"\n\n7. Pending Tasks:\n - Create Rust implementation with Cargo.toml (IN PROGRESS)\n - Create Python implementation with PyTorch\n - Create working examples using Bybit data\n - Test code and verify functionality\n - Commit and push changes to the branch\n\n8. Current Work:\n Just completed creating all four documentation files (README.md, README.ru.md, readme.simple.md, readme.simple.ru.md). The todo list was updated to mark \"Create Rust implementation with Cargo.toml\" as in_progress. The last action was:\n ```\n TodoWrite with status showing:\n - \"Create readme.simple.ru.md (simplified Russian)\" - completed\n - \"Create Rust implementation with Cargo.toml\" - in_progress\n ```\n\n9. Optional Next Step:\n Create the Rust implementation including:\n - Cargo.toml with dependencies (ndarray, polars, tokio, reqwest, serde)\n - src/lib.rs with core module structure\n - src/encoder/ (transformer.rs, cnn.rs, moe.rs)\n - src/tasks/ (direction.rs, volatility.rs, regime.rs, returns.rs)\n - src/data/bybit.rs for API integration\n - examples/ directory with working examples\n\n This is directly in line with the issue requirements: \"Примеры кода: Обязательно наличие рабочих примеров на **Rust** (помимо Python)\" and the todo list showing Rust implementation as the current in-progress task.\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-1769021101508/8c23df75-76b7-45ba-aa3a-4295879a3b8f.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 creating the Rust implementation. Let me first check the structure of chapter 86 to follow the same pattern." | |
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| "text": "Let me read the Cargo.toml and lib.rs from chapter 86 to understand the pattern." | |
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| "content": " 1→[package]\n 2→name = \"few_shot_market_prediction\"\n 3→version = \"0.1.0\"\n 4→edition = \"2021\"\n 5→authors = [\"ML Trading Team\"]\n 6→description = \"Few-Shot Learning for Market Prediction - Multiple Methods Including MAML\"\n 7→license = \"MIT\"\n 8→repository = \"https://github.com/suenot/machine-learning-for-trading\"\n 9→keywords = [\"few-shot-learning\", \"meta-learning\", \"trading\", \"cryptocurrency\", \"maml\"]\n 10→categories = [\"science\", \"finance\"]\n 11→\n 12→[lib]\n 13→name = \"few_shot_market_prediction\"\n 14→path = \"src/lib.rs\"\n 15→\n 16→[[example]]\n 17→name = \"basic_few_shot\"\n 18→path = \"examples/basic_few_shot.rs\"\n 19→\n 20→[[example]]\n 21→name = \"regime_prediction\"\n 22→path = \"examples/regime_prediction.rs\"\n 23→\n 24→[[example]]\n 25→name = \"new_asset_adaptation\"\n 26→path = \"examples/new_asset_adaptation.rs\"\n 27→\n 28→[dependencies]\n 29→# Async runtime\n 30→tokio = { version = \"1.35\", features = [\"full\"] }\n 31→\n 32→# HTTP client for Bybit API\n 33→reqwest = { version = \"0.11\", features = [\"json\", \"rustls-tls\"] }\n 34→\n 35→# Serialization\n 36→serde = { version = \"1.0\", features = [\"derive\"] }\n 37→serde_json = \"1.0\"\n 38→\n 39→# Math and linear algebra\n 40→ndarray = { version = \"0.15\", features = [\"serde\"] }\n 41→ndarray-rand = \"0.14\"\n 42→rand = \"0.8\"\n 43→rand_distr = \"0.4\"\n 44→\n 45→# Statistics\n 46→statrs = \"0.16\"\n 47→\n 48→# Time handling\n 49→chrono = { version = \"0.4\", features = [\"serde\"] }\n 50→\n 51→# Logging\n 52→tracing = \"0.1\"\n 53→tracing-subscriber = { version = \"0.3\", features = [\"env-filter\"] }\n 54→\n 55→# Error handling\n 56→thiserror = \"1.0\"\n 57→anyhow = \"1.0\"\n 58→\n 59→# Async utilities\n 60→futures = \"0.3\"\n 61→async-trait = \"0.1\"\n 62→\n 63→# WebSocket for real-time data\n 64→tokio-tungstenite = { version = \"0.21\", features = [\"rustls-tls-webpki-roots\"] }\n 65→\n 66→# URL parsing\n 67→url = \"2.5\"\n 68→\n 69→# Hash maps with better performance\n 70→hashbrown = \"0.14\"\n 71→\n 72→# Ordered floats for comparisons\n 73→ordered-float = \"4.2\"\n 74→\n 75→[dev-dependencies]\n 76→tokio-test = \"0.4\"\n 77→approx = \"0.5\"\n 78→\n 79→[features]\n 80→default = []\n 81→cuda = [] # Future CUDA support\n 82→simd = [] # Future SIMD optimizations\n 83→\n 84→[profile.release]\n 85→opt-level = 3\n 86→lto = true\n 87→codegen-units = 1\n 88→\n 89→[profile.dev]\n 90→opt-level = 1\n 91→\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 = \"few_shot_market_prediction\"\nversion = \"0.1.0\"\nedition = \"2021\"\nauthors = [\"ML Trading Team\"]\ndescription = \"Few-Shot Learning for Market Prediction - Multiple Methods Including MAML\"\nlicense = \"MIT\"\nrepository = \"https://github.com/suenot/machine-learning-for-trading\"\nkeywords = [\"few-shot-learning\", \"meta-learning\", \"trading\", \"cryptocurrency\", \"maml\"]\ncategories = [\"science\", \"finance\"]\n\n[lib]\nname = \"few_shot_market_prediction\"\npath = \"src/lib.rs\"\n\n[[example]]\nname = \"basic_few_shot\"\npath = \"examples/basic_few_shot.rs\"\n\n[[example]]\nname = \"regime_prediction\"\npath = \"examples/regime_prediction.rs\"\n\n[[example]]\nname = \"new_asset_adaptation\"\npath = \"examples/new_asset_adaptation.rs\"\n\n[dependencies]\n# Async runtime\ntokio = { version = \"1.35\", features = [\"full\"] }\n\n# HTTP client for Bybit API\nreqwest = { version = \"0.11\", features = [\"json\", \"rustls-tls\"] }\n\n# Serialization\nserde = { version = \"1.0\", features = [\"derive\"] }\nserde_json = \"1.0\"\n\n# Math and linear algebra\nndarray = { version = \"0.15\", features = [\"serde\"] }\nndarray-rand = \"0.14\"\nrand = \"0.8\"\nrand_distr = \"0.4\"\n\n# Statistics\nstatrs = \"0.16\"\n\n# Time handling\nchrono = { version = \"0.4\", features = [\"serde\"] }\n\n# Logging\ntracing = \"0.1\"\ntracing-subscriber = { version = \"0.3\", features = [\"env-filter\"] }\n\n# Error handling\nthiserror = \"1.0\"\nanyhow = \"1.0\"\n\n# Async utilities\nfutures = \"0.3\"\nasync-trait = \"0.1\"\n\n# WebSocket for real-time data\ntokio-tungstenite = { version = \"0.21\", features = [\"rustls-tls-webpki-roots\"] }\n\n# URL parsing\nurl = \"2.5\"\n\n# Hash maps with better performance\nhashbrown = \"0.14\"\n\n# Ordered floats for comparisons\nordered-float = \"4.2\"\n\n[dev-dependencies]\ntokio-test = \"0.4\"\napprox = \"0.5\"\n\n[features]\ndefault = []\ncuda = [] # Future CUDA support\nsimd = [] # Future SIMD optimizations\n\n[profile.release]\nopt-level = 3\nlto = true\ncodegen-units = 1\n\n[profile.dev]\nopt-level = 1\n", | |
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| [2026-01-21T19:01:52.150Z] [INFO] { | |
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| "content": " 1→//! # Few-Shot Market Prediction\n 2→//!\n 3→//! This library implements multiple few-shot learning approaches for financial market\n 4→//! prediction, including metric-based methods, MAML-inspired optimization, and hybrid\n 5→//! approaches.\n 6→//!\n 7→//! ## Overview\n 8→//!\n 9→//! Few-shot learning enables rapid adaptation to new market conditions with minimal\n 10→//! examples. This is particularly valuable for:\n 11→//!\n 12→//! - **New Asset Analysis**: Predict patterns for newly listed cryptocurrencies\n 13→//! - **Rare Event Detection**: Recognize crashes, squeezes with few historical examples\n 14→//! - **Cross-Asset Transfer**: Apply knowledge from one asset to another\n 15→//! - **Regime Change Adaptation**: Quickly adjust to market regime shifts\n 16→//!\n 17→//! ## Supported Methods\n 18→//!\n 19→//! 1. **Metric-Based** (Prototypical Networks): Classify based on distance to prototypes\n 20→//! 2. **MAML-Inspired**: Gradient-based quick adaptation\n 21→//! 3. **Siamese Networks**: Pairwise similarity learning\n 22→//! 4. **Hybrid**: Combine metric and optimization approaches\n 23→//!\n 24→//! ## Example Usage\n 25→//!\n 26→//! ```rust,ignore\n 27→//! use few_shot_market_prediction::prelude::*;\n 28→//!\n 29→//! // Create a metric-based few-shot predictor\n 30→//! let config = FewShotConfig::default()\n 31→//! .with_method(FewShotMethod::Metric)\n 32→//! .with_embedding_dim(32);\n 33→//!\n 34→//! let predictor = FewShotPredictor::new(config);\n 35→//!\n 36→//! // Fit on support set\n 37→//! predictor.fit(&support_features, &support_labels);\n 38→//!\n 39→//! // Predict on query\n 40→//! let (predictions, confidences) = predictor.predict(&query_features);\n 41→//! ```\n 42→//!\n 43→//! ## Modules\n 44→//!\n 45→//! - `network` - Neural network components (embedding, distance computation)\n 46→//! - `methods` - Few-shot learning methods (metric, MAML, siamese, hybrid)\n 47→//! - `data` - Bybit API integration and feature extraction\n 48→//! - `training` - Episodic training framework\n 49→//! - `strategy` - Trading signal generation and regime classification\n 50→\n 51→pub mod network;\n 52→pub mod methods;\n 53→pub mod data;\n 54→pub mod training;\n 55→pub mod strategy;\n 56→\n 57→/// Prelude module for convenient imports\n 58→pub mod prelude {\n 59→ // Network components\n 60→ pub use crate::network::{\n 61→ EmbeddingNetwork, EmbeddingConfig, ActivationType, NetworkParams,\n 62→ DistanceComputer, DistanceMetric,\n 63→ };\n 64→\n 65→ // Few-shot methods\n 66→ pub use crate::methods::{\n 67→ FewShotMethod, FewShotConfig, FewShotPredictor, FewShotLearner,\n 68→ MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n 69→ PredictionResult, AdaptationResult,\n 70→ };\n 71→\n 72→ // Data components\n 73→ pub use crate::data::{\n 74→ BybitClient, BybitConfig,\n 75→ MarketFeatures, FeatureExtractor, FeatureConfig,\n 76→ Kline, Trade, OrderBook, FundingRate, Ticker,\n 77→ };\n 78→\n 79→ // Training components\n 80→ pub use crate::training::{\n 81→ Episode, EpisodeGenerator, EpisodeConfig,\n 82→ MetaTrainer, MetaTrainerConfig, TrainingResult,\n 83→ };\n 84→\n 85→ // Strategy components\n 86→ pub use crate::strategy::{\n 87→ MarketRegime, RegimeClassifier, RegimeClassification,\n 88→ TradingSignal, SignalType, SignalGenerator, SignalConfig,\n 89→ RiskManager, RiskConfig, RiskCheckResult, RiskSummary, Position,\n 90→ };\n 91→}\n 92→\n 93→/// Library version\n 94→pub const VERSION: &str = env!(\"CARGO_PKG_VERSION\");\n 95→\n 96→#[cfg(test)]\n 97→mod tests {\n 98→ use super::*;\n 99→\n 100→ #[test]\n 101→ fn test_version() {\n 102→ assert!(!VERSION.is_empty());\n 103→ }\n 104→}\n 105→\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": "//! # Few-Sh |
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