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January 21, 2026 18:13
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Solution draft log for https://github.com/suenot/machine-learning-for-trading/pull/416
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| # Solve.mjs Log - 2026-01-21T18:00:26.586Z | |
| [2026-01-21T18:00:26.590Z] [INFO] π Log file: /home/hive/solve-2026-01-21T18-00-26-586Z.log | |
| [2026-01-21T18:00:26.592Z] [INFO] (All output will be logged here) | |
| [2026-01-21T18:00:27.753Z] [INFO] | |
| [2026-01-21T18:00:27.756Z] [INFO] π solve v1.9.0 | |
| [2026-01-21T18:00:27.756Z] [INFO] π§ Raw command executed: | |
| [2026-01-21T18:00:27.757Z] [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/123 --resume 172d23c7-5c62-484f-af89-80e92d0b393e --auto-resume-on-limit-reset --model opus --verbose --fork --attach-logs --working-directory /tmp/gh-issue-solver-1769015705800 | |
| [2026-01-21T18:00:27.760Z] [INFO] | |
| [2026-01-21T18:00:27.801Z] [INFO] | |
| [2026-01-21T18:00:27.807Z] [WARNING] β οΈ SECURITY WARNING: --attach-logs is ENABLED | |
| [2026-01-21T18:00:27.810Z] [INFO] | |
| [2026-01-21T18:00:27.811Z] [INFO] This option will upload the complete solution draft log file to the Pull Request. | |
| [2026-01-21T18:00:27.811Z] [INFO] The log may contain sensitive information such as: | |
| [2026-01-21T18:00:27.814Z] [INFO] β’ API keys, tokens, or secrets | |
| [2026-01-21T18:00:27.814Z] [INFO] β’ File paths and directory structures | |
| [2026-01-21T18:00:27.815Z] [INFO] β’ Command outputs and error messages | |
| [2026-01-21T18:00:27.815Z] [INFO] β’ Internal system information | |
| [2026-01-21T18:00:27.815Z] [INFO] | |
| [2026-01-21T18:00:27.815Z] [INFO] β οΈ DO NOT use this option with public repositories or if the log | |
| [2026-01-21T18:00:27.816Z] [INFO] might contain sensitive data that should not be shared publicly. | |
| [2026-01-21T18:00:27.816Z] [INFO] | |
| [2026-01-21T18:00:27.816Z] [INFO] Continuing in 5 seconds... (Press Ctrl+C to abort) | |
| [2026-01-21T18:00:27.816Z] [INFO] | |
| [2026-01-21T18:00:32.822Z] [INFO] | |
| [2026-01-21T18:00:32.845Z] [INFO] πΎ Disk space check: 9642MB available (2048MB required) β | |
| [2026-01-21T18:00:32.848Z] [INFO] π§ Memory check: 9456MB available, swap: 4095MB (1MB used), total: 13550MB (256MB required) β | |
| [2026-01-21T18:00:32.871Z] [INFO] π Validating Claude CLI connection... | |
| [2026-01-21T18:00:34.467Z] [INFO] π¦ Claude CLI version: 2.1.12 (Claude Code) | |
| [2026-01-21T18:00:44.876Z] [INFO] β Claude CLI connection validated successfully | |
| [2026-01-21T18:00:45.508Z] [INFO] | |
| π Checking GitHub authentication and permissions... | |
| [2026-01-21T18:00:46.011Z] [INFO] β GitHub authentication: OK | |
| [2026-01-21T18:00:46.012Z] [INFO] π Token scopes: gist, read:org, repo, user, workflow | |
| [2026-01-21T18:00:46.014Z] [INFO] β All required permissions: Available | |
| [2026-01-21T18:00:46.015Z] [INFO] π URL validation: | |
| [2026-01-21T18:00:46.015Z] [INFO] Input URL: https://github.com/suenot/machine-learning-for-trading/issues/123 | |
| [2026-01-21T18:00:46.015Z] [INFO] Is Issue URL: true | |
| [2026-01-21T18:00:46.016Z] [INFO] Is PR URL: false | |
| [2026-01-21T18:00:46.017Z] [INFO] β Repository access check: Skipped (fork mode enabled) | |
| [2026-01-21T18:00:46.510Z] [INFO] Repository visibility: public | |
| [2026-01-21T18:00:46.511Z] [INFO] Auto-cleanup default: false (repository is public) | |
| [2026-01-21T18:00:46.512Z] [INFO] π Auto-continue enabled: Checking for existing PRs for issue #123... | |
| [2026-01-21T18:00:47.219Z] [INFO] π Fork mode: Checking for existing branches in konard/suenot-machine-learning-for-trading... | |
| [2026-01-21T18:00:47.870Z] [INFO] π Found 1 existing branch(es) in fork matching pattern 'issue-123-*': | |
| [2026-01-21T18:00:47.876Z] [INFO] β’ issue-123-dd320d406382 | |
| [2026-01-21T18:00:48.405Z] [INFO] π Found 4 existing PR(s) linked to issue #123 | |
| [2026-01-21T18:00:48.406Z] [INFO] PR #417: created 0h ago (OPEN, ready) | |
| [2026-01-21T18:00:48.412Z] [INFO] PR #417: Branch 'issue-122-03236edf6aef' doesn't match expected pattern 'issue-123-*' - skipping | |
| [2026-01-21T18:00:48.412Z] [INFO] PR #416: created 0h ago (OPEN, ready) | |
| [2026-01-21T18:00:50.680Z] [INFO] β Auto-continue: Using PR #416 (CLAUDE.md/.gitkeep missing - work completed, branch: issue-123-dd320d406382) | |
| [2026-01-21T18:00:50.681Z] [INFO] Continue mode activated: Auto-continue (CLAUDE.md/.gitkeep missing) | |
| [2026-01-21T18:00:50.681Z] [INFO] PR Number: 416 | |
| [2026-01-21T18:00:50.681Z] [INFO] PR Branch: issue-123-dd320d406382 | |
| [2026-01-21T18:00:50.682Z] [INFO] Checking if PR is from a fork... | |
| [2026-01-21T18:00:51.280Z] [INFO] PR state: OPEN | |
| [2026-01-21T18:00:51.280Z] [INFO] Merge status: UNSTABLE | |
| [2026-01-21T18:00:51.280Z] [INFO] π΄ Detected fork PR from konard/suenot-machine-learning-for-trading | |
| [2026-01-21T18:00:51.281Z] [INFO] Fork owner: konard | |
| [2026-01-21T18:00:51.281Z] [INFO] Will clone fork repository for continue mode | |
| [2026-01-21T18:00:51.281Z] [INFO] π Issue mode: Working with issue #123 | |
| [2026-01-21T18:00:51.282Z] [INFO] | |
| π Working directory: /tmp/gh-issue-solver-1769015705800 | |
| [2026-01-21T18:00:51.282Z] [INFO] Status: Using existing repository | |
| [2026-01-21T18:00:51.282Z] [INFO] Session: Resuming 172d23c7-5c62-484f-af89-80e92d0b393e | |
| [2026-01-21T18:00:51.284Z] [INFO] | |
| π΄ Fork mode: ENABLED | |
| [2026-01-21T18:00:51.285Z] [INFO] Checking fork status... | |
| [2026-01-21T18:00:51.663Z] [INFO] π Detecting fork conflicts... | |
| [2026-01-21T18:00:52.959Z] [INFO] β No fork conflict: Safe to proceed | |
| [2026-01-21T18:00:53.392Z] [INFO] β Fork exists: konard/suenot-machine-learning-for-trading | |
| [2026-01-21T18:00:53.392Z] [INFO] π Validating fork parent... | |
| [2026-01-21T18:00:53.852Z] [INFO] β Fork parent validated: suenot/machine-learning-for-trading | |
| [2026-01-21T18:00:53.852Z] [INFO] βΉοΈ Skipping clone: Using existing repository in working directory | |
| [2026-01-21T18:00:53.854Z] [INFO] π Setting upstream: suenot/machine-learning-for-trading | |
| [2026-01-21T18:00:53.928Z] [INFO] βΉοΈ Upstream exists: Using existing upstream remote | |
| [2026-01-21T18:00:53.929Z] [INFO] π Fetching upstream... | |
| [2026-01-21T18:00:54.350Z] [INFO] β Upstream fetched: Successfully | |
| [2026-01-21T18:00:54.351Z] [INFO] π Syncing default branch... | |
| [2026-01-21T18:00:55.041Z] [INFO] βΉοΈ Default branch: main | |
| [2026-01-21T18:00:55.045Z] [INFO] π Switching to: main branch | |
| [2026-01-21T18:00:55.283Z] [INFO] β Default branch synced: with upstream/main | |
| [2026-01-21T18:00:55.285Z] [INFO] π Pushing to fork: main branch | |
| [2026-01-21T18:00:55.802Z] [INFO] β Fork updated: Default branch pushed to fork | |
| [2026-01-21T18:00:55.802Z] [INFO] π Returning to: issue-123-dd320d406382 branch | |
| [2026-01-21T18:00:55.888Z] [INFO] β Branch restored: Back on issue-123-dd320d406382 | |
| [2026-01-21T18:00:55.892Z] [INFO] | |
| π Checking PR fork: Determining if branch is in another fork... | |
| [2026-01-21T18:00:56.334Z] [INFO] βΉοΈ PR fork owner: Same as current user, using origin remote | |
| [2026-01-21T18:00:56.573Z] [INFO] | |
| π Default branch: issue-123-dd320d406382 | |
| [2026-01-21T18:00:56.686Z] [INFO] | |
| π Checking out PR branch: issue-123-dd320d406382 | |
| [2026-01-21T18:00:56.687Z] [INFO] π₯ Fetching branches: From remote... | |
| [2026-01-21T18:00:57.481Z] [INFO] π Verifying: Branch checkout... | |
| [2026-01-21T18:00:57.612Z] [INFO] β Branch checked out: issue-123-dd320d406382 | |
| [2026-01-21T18:00:57.614Z] [INFO] β Current branch: issue-123-dd320d406382 | |
| [2026-01-21T18:00:57.615Z] [INFO] Branch operation: Checkout existing PR branch | |
| [2026-01-21T18:00:57.615Z] [INFO] Branch verification: Matches expected | |
| [2026-01-21T18:00:57.618Z] [INFO] | |
| π Continue mode: ACTIVE | |
| [2026-01-21T18:00:57.618Z] [INFO] Using existing PR: #416 | |
| [2026-01-21T18:00:57.618Z] [INFO] PR URL: https://github.com/suenot/machine-learning-for-trading/issues/123 | |
| [2026-01-21T18:00:57.619Z] [INFO] | |
| π Starting work session: 2026-01-21T18:00:57.619Z | |
| [2026-01-21T18:00:58.097Z] [INFO] π Converting PR: Back to draft mode... | |
| [2026-01-21T18:00:59.273Z] [INFO] β PR converted: Now in draft mode | |
| [2026-01-21T18:01:00.225Z] [INFO] π¬ Posted: Work session start comment | |
| [2026-01-21T18:01:00.625Z] [INFO] π€ Current user: konard | |
| [2026-01-21T18:01:00.626Z] [INFO] | |
| π Comment counting conditions: | |
| [2026-01-21T18:01:00.627Z] [INFO] prNumber: 416 | |
| [2026-01-21T18:01:00.627Z] [INFO] branchName: issue-123-dd320d406382 | |
| [2026-01-21T18:01:00.627Z] [INFO] isContinueMode: true | |
| [2026-01-21T18:01:00.627Z] [INFO] Will count comments: true | |
| [2026-01-21T18:01:00.628Z] [INFO] π¬ Counting comments: Checking for new comments since last commit... | |
| [2026-01-21T18:01:00.628Z] [INFO] PR #416 on branch: issue-123-dd320d406382 | |
| [2026-01-21T18:01:00.628Z] [INFO] Owner/Repo: suenot/machine-learning-for-trading | |
| [2026-01-21T18:01:01.473Z] [INFO] π Last commit time (from API): 2026-01-21T17:42:03.000Z | |
| [2026-01-21T18:01:02.998Z] [INFO] π¬ New PR comments: 1 | |
| [2026-01-21T18:01:02.999Z] [INFO] π¬ New PR review comments: 0 | |
| [2026-01-21T18:01:02.999Z] [INFO] π¬ New issue comments: 0 | |
| [2026-01-21T18:01:02.999Z] [INFO] Total new comments: 1 | |
| [2026-01-21T18:01:02.999Z] [INFO] Comment lines to add: Yes | |
| [2026-01-21T18:01:03.000Z] [INFO] PR review comments fetched: 0 | |
| [2026-01-21T18:01:03.000Z] [INFO] PR conversation comments fetched: 4 | |
| [2026-01-21T18:01:03.000Z] [INFO] Total PR comments checked: 4 | |
| [2026-01-21T18:01:06.436Z] [INFO] Feedback info will be added to prompt: | |
| [2026-01-21T18:01:06.437Z] [INFO] - Pull request description was edited after last commit | |
| [2026-01-21T18:01:06.437Z] [INFO] - Merge status is UNSTABLE (non-passing commit status) | |
| [2026-01-21T18:01:06.437Z] [INFO] - New comments on the pull request: 1 | |
| [2026-01-21T18:01:06.437Z] [INFO] π Getting timestamps: From GitHub servers... | |
| [2026-01-21T18:01:06.854Z] [INFO] π Issue updated: 2026-01-18T13:32:36.000Z | |
| [2026-01-21T18:01:07.262Z] [INFO] π¬ Comments: None found | |
| [2026-01-21T18:01:07.769Z] [INFO] π Recent PR: 2026-01-21T17:17:31.000Z | |
| [2026-01-21T18:01:07.772Z] [INFO] | |
| β Reference time: 2026-01-21T17:17:31.000Z | |
| [2026-01-21T18:01:07.773Z] [INFO] | |
| π Checking for uncommitted changes to include as feedback... | |
| [2026-01-21T18:01:07.884Z] [INFO] β No uncommitted changes found | |
| [2026-01-21T18:01:13.353Z] [INFO] π Playwright MCP detected - enabling browser automation hints | |
| [2026-01-21T18:01:13.376Z] [INFO] | |
| π Final prompt structure: | |
| [2026-01-21T18:01:13.382Z] [INFO] Characters: 579 | |
| [2026-01-21T18:01:13.386Z] [INFO] System prompt characters: 11977 | |
| [2026-01-21T18:01:13.390Z] [INFO] Feedback info: Included | |
| [2026-01-21T18:01:13.395Z] [INFO] | |
| π€ Executing Claude: OPUS | |
| [2026-01-21T18:01:13.402Z] [INFO] Model: opus | |
| [2026-01-21T18:01:13.406Z] [INFO] Working directory: /tmp/gh-issue-solver-1769015705800 | |
| [2026-01-21T18:01:13.407Z] [INFO] Branch: issue-123-dd320d406382 | |
| [2026-01-21T18:01:13.407Z] [INFO] Prompt length: 579 chars | |
| [2026-01-21T18:01:13.407Z] [INFO] System prompt length: 11977 chars | |
| [2026-01-21T18:01:13.410Z] [INFO] Feedback info included: Yes (3 lines) | |
| [2026-01-21T18:01:13.530Z] [INFO] π System resources before execution: | |
| [2026-01-21T18:01:13.531Z] [INFO] Memory: MemFree: 1733444 kB | |
| [2026-01-21T18:01:13.532Z] [INFO] Load: 7.25 2.57 1.38 11/875 1031343 | |
| [2026-01-21T18:01:13.532Z] [INFO] π Resuming from session: 172d23c7-5c62-484f-af89-80e92d0b393e | |
| [2026-01-21T18:01:13.532Z] [INFO] | |
| π Raw command: | |
| [2026-01-21T18:01:13.533Z] [INFO] (cd "/tmp/gh-issue-solver-1769015705800" && claude --resume 172d23c7-5c62-484f-af89-80e92d0b393e --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/123 | |
| Your prepared branch: issue-123-dd320d406382 | |
| Your prepared working directory: /tmp/gh-issue-solver-1769015705800 | |
| Your prepared Pull Request: https://github.com/suenot/machine-learning-for-trading/issues/123 | |
| Your forked repository: konard/suenot-machine-learning-for-trading | |
| Original repository (upstream): suenot/machine-learning-for-trading | |
| New comments on the pull request: 1 | |
| Pull request description was edited after last commit | |
| Merge status is UNSTABLE (non-passing commit status) | |
| Continue. | |
| " --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 β 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-123-dd320d406382 --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 β 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/123. | |
| - 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/416/comments --paginate | |
| 2. PR conversation comments (general discussion): gh api repos/suenot/machine-learning-for-trading/issues/416/comments --paginate | |
| 3. PR reviews (approve/request changes): gh api repos/suenot/machine-learning-for-trading/pulls/416/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/123/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 416 --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 416, 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 416. | |
| Workflow and collaboration. | |
| - When you check branch, verify with git branch --show-current. | |
| - When you push, push only to branch issue-123-dd320d406382. | |
| - When you finish, create a pull request from branch issue-123-dd320d406382. (Note: PR 416 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-123-dd320d406382. | |
| - When you mention result, include pull request url or comment url. | |
| - When you need to create pr, remember pr 416 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 .) | |
| [2026-01-21T18:01:13.537Z] [INFO] | |
| [2026-01-21T18:01:13.538Z] [INFO] π User prompt: | |
| [2026-01-21T18:01:13.538Z] [INFO] ---BEGIN USER PROMPT--- | |
| [2026-01-21T18:01:13.538Z] [INFO] Issue to solve: https://github.com/suenot/machine-learning-for-trading/issues/123 | |
| Your prepared branch: issue-123-dd320d406382 | |
| Your prepared working directory: /tmp/gh-issue-solver-1769015705800 | |
| Your prepared Pull Request: https://github.com/suenot/machine-learning-for-trading/issues/123 | |
| Your forked repository: konard/suenot-machine-learning-for-trading | |
| Original repository (upstream): suenot/machine-learning-for-trading | |
| New comments on the pull request: 1 | |
| Pull request description was edited after last commit | |
| Merge status is UNSTABLE (non-passing commit status) | |
| Continue. | |
| [2026-01-21T18:01:13.539Z] [INFO] ---END USER PROMPT--- | |
| [2026-01-21T18:01:13.539Z] [INFO] | |
| [2026-01-21T18:01:13.539Z] [INFO] π System prompt: | |
| [2026-01-21T18:01:13.540Z] [INFO] ---BEGIN SYSTEM PROMPT--- | |
| [2026-01-21T18:01:13.540Z] [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 β 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-123-dd320d406382 --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 β 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/123. | |
| - 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/416/comments --paginate | |
| 2. PR conversation comments (general discussion): gh api repos/suenot/machine-learning-for-trading/issues/416/comments --paginate | |
| 3. PR reviews (approve/request changes): gh api repos/suenot/machine-learning-for-trading/pulls/416/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/123/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 416 --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 416, 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 416. | |
| Workflow and collaboration. | |
| - When you check branch, verify with git branch --show-current. | |
| - When you push, push only to branch issue-123-dd320d406382. | |
| - When you finish, create a pull request from branch issue-123-dd320d406382. (Note: PR 416 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-123-dd320d406382. | |
| - When you mention result, include pull request url or comment url. | |
| - When you need to create pr, remember pr 416 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. | |
| [2026-01-21T18:01:13.542Z] [INFO] ---END SYSTEM PROMPT--- | |
| [2026-01-21T18:01:13.544Z] [INFO] | |
| [2026-01-21T18:01:13.546Z] [INFO] π CLAUDE_CODE_MAX_OUTPUT_TOKENS: 64000 | |
| [2026-01-21T18:01:13.546Z] [INFO] π Command details: | |
| [2026-01-21T18:01:13.546Z] [INFO] π Working directory: /tmp/gh-issue-solver-1769015705800 | |
| [2026-01-21T18:01:13.547Z] [INFO] πΏ Branch: issue-123-dd320d406382 | |
| [2026-01-21T18:01:13.549Z] [INFO] π€ Model: Claude OPUS | |
| [2026-01-21T18:01:13.549Z] [INFO] π΄ Fork: konard/suenot-machine-learning-for-trading | |
| [2026-01-21T18:01:13.549Z] [INFO] | |
| βΆοΈ Streaming output: | |
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| "KillShell", | |
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| "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", | |
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| "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" | |
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| "mcp_servers": [ | |
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| "name": "playwright", | |
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| "model": "claude-opus-4-5-20251101", | |
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| "apiKeySource": "none", | |
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| "output_style": "default", | |
| "agents": [ | |
| "Bash", | |
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| "skills": [], | |
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| [2026-01-21T18:01:18.561Z] [INFO] π Session ID: 172d23c7-5c62-484f-af89-80e92d0b393e | |
| [2026-01-21T18:01:18.562Z] [INFO] π Log renamed to: /home/hive/172d23c7-5c62-484f-af89-80e92d0b393e.log | |
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| "status": "completed", | |
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| }, | |
| { | |
| "content": "Create README.ru.md (Russian translation)", | |
| "status": "completed", | |
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| "content": "Create Bybit data integration code", | |
| "status": "pending", | |
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| { | |
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| "status": "in_progress", | |
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| }, | |
| { | |
| "content": "Create Bybit data integration code", | |
| "status": "pending", | |
| "activeForm": "Creating Bybit integration" | |
| }, | |
| { | |
| "content": "Create example/demo scripts", | |
| "status": "pending", | |
| "activeForm": "Creating example scripts" | |
| }, | |
| { | |
| "content": "Run tests and validate implementations", | |
| "status": "pending", | |
| "activeForm": "Running tests" | |
| }, | |
| { | |
| "content": "Check PR comments and CI status", | |
| "status": "pending", | |
| "activeForm": "Checking PR feedback" | |
| }, | |
| { | |
| "content": "Commit and push to branch, update PR", | |
| "status": "pending", | |
| "activeForm": "Committing and pushing changes" | |
| } | |
| ] | |
| } | |
| } | |
| [2026-01-21T18:01:28.853Z] [INFO] { | |
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| { | |
| "type": "text", | |
| "text": "Let me check the current state of the strategy directory and then create the missing files." | |
| } | |
| ], | |
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| [2026-01-21T18:01:29.996Z] [INFO] { | |
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| "command": "ls -la /tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/src/strategy/", | |
| "description": "List strategy directory contents" | |
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| [2026-01-21T18:01:30.397Z] [INFO] { | |
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| { | |
| "tool_use_id": "toolu_014fkTJCkjWLdKU7nVLoVMWo", | |
| "type": "tool_result", | |
| "content": "total 12\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:00 .\ndrwxrwxr-x 7 hive hive 4096 Jan 21 19:00 ..\n-rw-rw-r-- 1 hive hive 295 Jan 21 19:00 mod.rs", | |
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| "stdout": "total 12\ndrwxrwxr-x 2 hive hive 4096 Jan 21 19:00 .\ndrwxrwxr-x 7 hive hive 4096 Jan 21 19:00 ..\n-rw-rw-r-- 1 hive hive 295 Jan 21 19:00 mod.rs", | |
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| [2026-01-21T18:01:38.214Z] [INFO] { | |
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| "text": "I need to create the three strategy sub-modules. Let me create them now." | |
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| "file_path": "/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/src/strategy/regime.rs", | |
| "content": "//! Market regime classification for few-shot learning.\n//!\n//! This module provides market regime detection using few-shot learning\n//! to identify market conditions with minimal historical examples.\n\nuse serde::{Deserialize, Serialize};\nuse std::fmt;\n\n/// Market regime types that can be classified.\n#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]\npub enum MarketRegime {\n /// Strong upward price movement with high momentum\n StrongUptrend,\n /// Moderate upward movement\n WeakUptrend,\n /// Range-bound market with no clear direction\n Sideways,\n /// Moderate downward movement\n WeakDowntrend,\n /// Strong downward price movement\n StrongDowntrend,\n /// High volatility with rapid price swings\n Volatile,\n /// Sharp market decline (panic selling)\n Crash,\n /// Recovery from a crash or downtrend\n Recovery,\n}\n\nimpl MarketRegime {\n /// Get all possible regime values.\n pub fn all() -> Vec<MarketRegime> {\n vec![\n MarketRegime::StrongUptrend,\n MarketRegime::WeakUptrend,\n MarketRegime::Sideways,\n MarketRegime::WeakDowntrend,\n MarketRegime::StrongDowntrend,\n MarketRegime::Volatile,\n MarketRegime::Crash,\n MarketRegime::Recovery,\n ]\n }\n\n /// Get the number of regime types.\n pub fn count() -> usize {\n 8\n }\n\n /// Convert regime to class index.\n pub fn to_index(&self) -> usize {\n match self {\n MarketRegime::StrongUptrend => 0,\n MarketRegime::WeakUptrend => 1,\n MarketRegime::Sideways => 2,\n MarketRegime::WeakDowntrend => 3,\n MarketRegime::StrongDowntrend => 4,\n MarketRegime::Volatile => 5,\n MarketRegime::Crash => 6,\n MarketRegime::Recovery => 7,\n }\n }\n\n /// Convert class index to regime.\n pub fn from_index(index: usize) -> Option<MarketRegime> {\n match index {\n 0 => Some(MarketRegime::StrongUptrend),\n 1 => Some(MarketRegime::WeakUptrend),\n 2 => Some(MarketRegime::Sideways),\n 3 => Some(MarketRegime::WeakDowntrend),\n 4 => Some(MarketRegime::StrongDowntrend),\n 5 => Some(MarketRegime::Volatile),\n 6 => Some(MarketRegime::Crash),\n 7 => Some(MarketRegime::Recovery),\n _ => None,\n }\n }\n\n /// Check if the regime is bullish.\n pub fn is_bullish(&self) -> bool {\n matches!(self, MarketRegime::StrongUptrend | MarketRegime::WeakUptrend | MarketRegime::Recovery)\n }\n\n /// Check if the regime is bearish.\n pub fn is_bearish(&self) -> bool {\n matches!(self, MarketRegime::StrongDowntrend | MarketRegime::WeakDowntrend | MarketRegime::Crash)\n }\n\n /// Check if the regime is neutral/sideways.\n pub fn is_neutral(&self) -> bool {\n matches!(self, MarketRegime::Sideways)\n }\n\n /// Check if the regime indicates high volatility.\n pub fn is_volatile(&self) -> bool {\n matches!(self, MarketRegime::Volatile | MarketRegime::Crash)\n }\n\n /// Get recommended position bias for this regime.\n pub fn position_bias(&self) -> f64 {\n match self {\n MarketRegime::StrongUptrend => 1.0,\n MarketRegime::WeakUptrend => 0.5,\n MarketRegime::Sideways => 0.0,\n MarketRegime::WeakDowntrend => -0.5,\n MarketRegime::StrongDowntrend => -1.0,\n MarketRegime::Volatile => 0.0,\n MarketRegime::Crash => -0.8,\n MarketRegime::Recovery => 0.6,\n }\n }\n}\n\nimpl fmt::Display for MarketRegime {\n fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {\n let name = match self {\n MarketRegime::StrongUptrend => \"Strong Uptrend\",\n MarketRegime::WeakUptrend => \"Weak Uptrend\",\n MarketRegime::Sideways => \"Sideways\",\n MarketRegime::WeakDowntrend => \"Weak Downtrend\",\n MarketRegime::StrongDowntrend => \"Strong Downtrend\",\n MarketRegime::Volatile => \"Volatile\",\n MarketRegime::Crash => \"Crash\",\n MarketRegime::Recovery => \"Recovery\",\n };\n write!(f, \"{}\", name)\n }\n}\n\n/// Classifier for detecting market regimes using few-shot learning.\npub struct RegimeClassifier {\n /// Confidence threshold for regime classification\n confidence_threshold: f64,\n /// Minimum samples required for classification\n min_samples: usize,\n /// Historical regime observations for reference\n regime_history: Vec<(MarketRegime, f64)>,\n}\n\nimpl RegimeClassifier {\n /// Create a new regime classifier.\n pub fn new(confidence_threshold: f64) -> Self {\n Self {\n confidence_threshold,\n min_samples: 3,\n regime_history: Vec::new(),\n }\n }\n\n /// Classify market regime based on features and probabilities.\n ///\n /// # Arguments\n /// * `class_probabilities` - Probability distribution over regime classes\n /// * `features` - Market features used for classification\n ///\n /// # Returns\n /// Tuple of (predicted regime, confidence)\n pub fn classify(&self, class_probabilities: &[f64]) -> (MarketRegime, f64) {\n if class_probabilities.len() != MarketRegime::count() {\n return (MarketRegime::Sideways, 0.0);\n }\n\n // Find the class with highest probability\n let (best_idx, &best_prob) = class_probabilities\n .iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .unwrap_or((2, &0.0)); // Default to Sideways\n\n let regime = MarketRegime::from_index(best_idx).unwrap_or(MarketRegime::Sideways);\n\n (regime, best_prob)\n }\n\n /// Classify with uncertainty quantification.\n pub fn classify_with_uncertainty(&self, class_probabilities: &[f64]) -> RegimeClassification {\n let (regime, confidence) = self.classify(class_probabilities);\n\n // Calculate entropy as uncertainty measure\n let entropy = self.calculate_entropy(class_probabilities);\n let max_entropy = (MarketRegime::count() as f64).ln();\n let normalized_uncertainty = entropy / max_entropy;\n\n // Find second best regime\n let mut sorted_probs: Vec<(usize, f64)> = class_probabilities\n .iter()\n .enumerate()\n .map(|(i, &p)| (i, p))\n .collect();\n sorted_probs.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());\n\n let second_best = if sorted_probs.len() > 1 {\n MarketRegime::from_index(sorted_probs[1].0)\n } else {\n None\n };\n\n RegimeClassification {\n regime,\n confidence,\n uncertainty: normalized_uncertainty,\n second_best,\n is_confident: confidence >= self.confidence_threshold,\n }\n }\n\n /// Calculate entropy of probability distribution.\n fn calculate_entropy(&self, probs: &[f64]) -> f64 {\n probs\n .iter()\n .filter(|&&p| p > 1e-10)\n .map(|&p| -p * p.ln())\n .sum()\n }\n\n /// Update regime history with new observation.\n pub fn update_history(&mut self, regime: MarketRegime, confidence: f64) {\n self.regime_history.push((regime, confidence));\n\n // Keep only recent history\n if self.regime_history.len() > 100 {\n self.regime_history.remove(0);\n }\n }\n\n /// Get regime transition probability from history.\n pub fn transition_probability(&self, from: MarketRegime, to: MarketRegime) -> f64 {\n if self.regime_history.len() < 2 {\n return 1.0 / MarketRegime::count() as f64;\n }\n\n let mut transitions = 0;\n let mut from_count = 0;\n\n for i in 0..self.regime_history.len() - 1 {\n if self.regime_history[i].0 == from {\n from_count += 1;\n if self.regime_history[i + 1].0 == to {\n transitions += 1;\n }\n }\n }\n\n if from_count == 0 {\n 1.0 / MarketRegime::count() as f64\n } else {\n transitions as f64 / from_count as f64\n }\n }\n\n /// Check if regime transition is likely based on history.\n pub fn is_transition_likely(&self, from: MarketRegime, to: MarketRegime) -> bool {\n let prob = self.transition_probability(from, to);\n prob > 0.1 // At least 10% historical probability\n }\n\n /// Get the dominant regime from recent history.\n pub fn dominant_regime(&self, lookback: usize) -> Option<MarketRegime> {\n if self.regime_history.is_empty() {\n return None;\n }\n\n let start = self.regime_history.len().saturating_sub(lookback);\n let mut counts = [0usize; 8];\n\n for (regime, _) in &self.regime_history[start..] {\n counts[regime.to_index()] += 1;\n }\n\n counts\n .iter()\n .enumerate()\n .max_by_key(|(_, &c)| c)\n .and_then(|(idx, _)| MarketRegime::from_index(idx))\n }\n}\n\nimpl Default for RegimeClassifier {\n fn default() -> Self {\n Self::new(0.6)\n }\n}\n\n/// Result of regime classification with uncertainty.\n#[derive(Debug, Clone)]\npub struct RegimeClassification {\n /// Predicted market regime\n pub regime: MarketRegime,\n /// Confidence in the prediction (0-1)\n pub confidence: f64,\n /// Uncertainty measure (normalized entropy, 0-1)\n pub uncertainty: f64,\n /// Second most likely regime\n pub second_best: Option<MarketRegime>,\n /// Whether prediction meets confidence threshold\n pub is_confident: bool,\n}\n\nimpl RegimeClassification {\n /// Check if we should act on this classification.\n pub fn should_act(&self) -> bool {\n self.is_confident && self.uncertainty < 0.5\n }\n\n /// Get a description of the classification.\n pub fn description(&self) -> String {\n if self.is_confident {\n format!(\n \"{} (confidence: {:.1}%, uncertainty: {:.1}%)\",\n self.regime,\n self.confidence * 100.0,\n self.uncertainty * 100.0\n )\n } else {\n format!(\n \"Uncertain - possibly {} or {} (confidence: {:.1}%)\",\n self.regime,\n self.second_best.map(|r| r.to_string()).unwrap_or_else(|| \"unknown\".to_string()),\n self.confidence * 100.0\n )\n }\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_regime_index_conversion() {\n for regime in MarketRegime::all() {\n let idx = regime.to_index();\n let recovered = MarketRegime::from_index(idx).unwrap();\n assert_eq!(regime, recovered);\n }\n }\n\n #[test]\n fn test_regime_properties() {\n assert!(MarketRegime::StrongUptrend.is_bullish());\n assert!(MarketRegime::StrongDowntrend.is_bearish());\n assert!(MarketRegime::Sideways.is_neutral());\n assert!(MarketRegime::Volatile.is_volatile());\n }\n\n #[test]\n fn test_classifier() {\n let classifier = RegimeClassifier::new(0.6);\n\n // Strong prediction for uptrend\n let probs = vec![0.8, 0.1, 0.05, 0.02, 0.01, 0.01, 0.005, 0.005];\n let (regime, confidence) = classifier.classify(&probs);\n\n assert_eq!(regime, MarketRegime::StrongUptrend);\n assert!((confidence - 0.8).abs() < 0.001);\n }\n\n #[test]\n fn test_classification_with_uncertainty() {\n let classifier = RegimeClassifier::new(0.6);\n\n // Certain prediction\n let probs = vec![0.9, 0.05, 0.02, 0.01, 0.01, 0.005, 0.0025, 0.0025];\n let result = classifier.classify_with_uncertainty(&probs);\n\n assert!(result.is_confident);\n assert!(result.uncertainty < 0.5);\n }\n}\n" | |
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| [2026-01-21T18:03:04.161Z] [INFO] { | |
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| "file_path": "/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/src/strategy/signal.rs", | |
| "content": "//! Trading signal generation based on few-shot predictions.\n//!\n//! This module converts few-shot learning predictions into actionable\n//! trading signals with confidence-weighted position sizing.\n\nuse crate::strategy::regime::{MarketRegime, RegimeClassification};\nuse serde::{Deserialize, Serialize};\nuse std::time::{Duration, Instant};\n\n/// Type of trading signal.\n#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]\npub enum SignalType {\n /// Open or add to long position\n Long,\n /// Open or add to short position\n Short,\n /// Close existing position\n Close,\n /// Reduce position size\n ReducePosition,\n /// No action recommended\n Hold,\n}\n\nimpl SignalType {\n /// Get the direction multiplier for this signal.\n pub fn direction(&self) -> f64 {\n match self {\n SignalType::Long => 1.0,\n SignalType::Short => -1.0,\n SignalType::Close => 0.0,\n SignalType::ReducePosition => 0.0,\n SignalType::Hold => 0.0,\n }\n }\n\n /// Check if this signal requires action.\n pub fn requires_action(&self) -> bool {\n !matches!(self, SignalType::Hold)\n }\n}\n\n/// A trading signal with associated metadata.\n#[derive(Debug, Clone)]\npub struct TradingSignal {\n /// Type of signal\n pub signal_type: SignalType,\n /// Confidence in the signal (0-1)\n pub confidence: f64,\n /// Recommended position size as fraction of capital (0-1)\n pub position_size: f64,\n /// Take profit level (percentage)\n pub take_profit: Option<f64>,\n /// Stop loss level (percentage)\n pub stop_loss: Option<f64>,\n /// Asset symbol this signal is for\n pub symbol: String,\n /// Market regime that generated this signal\n pub regime: Option<MarketRegime>,\n /// Signal generation timestamp\n pub timestamp: Instant,\n /// Time-to-live for this signal\n pub ttl: Duration,\n /// Additional metadata\n pub metadata: SignalMetadata,\n}\n\nimpl TradingSignal {\n /// Create a new trading signal.\n pub fn new(signal_type: SignalType, confidence: f64, symbol: &str) -> Self {\n Self {\n signal_type,\n confidence: confidence.clamp(0.0, 1.0),\n position_size: confidence.clamp(0.0, 1.0) * 0.5, // Max 50% of capital\n take_profit: None,\n stop_loss: None,\n symbol: symbol.to_string(),\n regime: None,\n timestamp: Instant::now(),\n ttl: Duration::from_secs(300), // 5 minute default TTL\n metadata: SignalMetadata::default(),\n }\n }\n\n /// Create a hold signal.\n pub fn hold(symbol: &str) -> Self {\n Self::new(SignalType::Hold, 0.0, symbol)\n }\n\n /// Set take profit level.\n pub fn with_take_profit(mut self, tp_percent: f64) -> Self {\n self.take_profit = Some(tp_percent);\n self\n }\n\n /// Set stop loss level.\n pub fn with_stop_loss(mut self, sl_percent: f64) -> Self {\n self.stop_loss = Some(sl_percent);\n self\n }\n\n /// Set the regime.\n pub fn with_regime(mut self, regime: MarketRegime) -> Self {\n self.regime = Some(regime);\n self\n }\n\n /// Set time-to-live.\n pub fn with_ttl(mut self, ttl: Duration) -> Self {\n self.ttl = ttl;\n self\n }\n\n /// Check if the signal has expired.\n pub fn is_expired(&self) -> bool {\n self.timestamp.elapsed() > self.ttl\n }\n\n /// Check if the signal is strong enough to act on.\n pub fn is_actionable(&self) -> bool {\n self.signal_type.requires_action()\n && self.confidence >= 0.5\n && !self.is_expired()\n }\n\n /// Calculate expected value based on confidence and risk/reward.\n pub fn expected_value(&self) -> f64 {\n let tp = self.take_profit.unwrap_or(2.0);\n let sl = self.stop_loss.unwrap_or(1.0);\n\n // Expected value = (win_prob * win_amount) - (lose_prob * lose_amount)\n let win_prob = self.confidence;\n let lose_prob = 1.0 - self.confidence;\n\n (win_prob * tp) - (lose_prob * sl)\n }\n}\n\n/// Additional metadata for trading signals.\n#[derive(Debug, Clone, Default)]\npub struct SignalMetadata {\n /// Number of support examples used\n pub support_count: usize,\n /// Method used for prediction\n pub method: String,\n /// Feature importance scores\n pub feature_importance: Vec<(String, f64)>,\n /// Raw prediction scores\n pub raw_scores: Vec<f64>,\n}\n\n/// Configuration for signal generation.\n#[derive(Debug, Clone)]\npub struct SignalConfig {\n /// Minimum confidence to generate a signal\n pub min_confidence: f64,\n /// Maximum position size (fraction of capital)\n pub max_position_size: f64,\n /// Default take profit percentage\n pub default_take_profit: f64,\n /// Default stop loss percentage\n pub default_stop_loss: f64,\n /// Enable regime-based adjustments\n pub use_regime_adjustment: bool,\n /// Signal cooldown period\n pub cooldown: Duration,\n}\n\nimpl Default for SignalConfig {\n fn default() -> Self {\n Self {\n min_confidence: 0.6,\n max_position_size: 0.25,\n default_take_profit: 2.0,\n default_stop_loss: 1.0,\n use_regime_adjustment: true,\n cooldown: Duration::from_secs(60),\n }\n }\n}\n\n/// Generator for trading signals based on few-shot predictions.\npub struct SignalGenerator {\n /// Configuration\n config: SignalConfig,\n /// Last signal timestamp per symbol\n last_signals: std::collections::HashMap<String, Instant>,\n}\n\nimpl SignalGenerator {\n /// Create a new signal generator.\n pub fn new(config: SignalConfig) -> Self {\n Self {\n config,\n last_signals: std::collections::HashMap::new(),\n }\n }\n\n /// Generate a trading signal from regime classification.\n pub fn generate(&mut self, symbol: &str, classification: &RegimeClassification) -> TradingSignal {\n // Check cooldown\n if let Some(&last) = self.last_signals.get(symbol) {\n if last.elapsed() < self.config.cooldown {\n return TradingSignal::hold(symbol);\n }\n }\n\n // Check confidence threshold\n if classification.confidence < self.config.min_confidence {\n return TradingSignal::hold(symbol);\n }\n\n // Determine signal type based on regime\n let signal_type = self.regime_to_signal(classification.regime);\n\n if signal_type == SignalType::Hold {\n return TradingSignal::hold(symbol);\n }\n\n // Calculate position size based on confidence\n let base_size = classification.confidence * self.config.max_position_size;\n let position_size = if self.config.use_regime_adjustment {\n self.adjust_for_regime(base_size, classification.regime)\n } else {\n base_size\n };\n\n // Calculate risk parameters\n let (take_profit, stop_loss) = self.calculate_risk_params(classification);\n\n // Update last signal time\n self.last_signals.insert(symbol.to_string(), Instant::now());\n\n let mut signal = TradingSignal::new(signal_type, classification.confidence, symbol);\n signal.position_size = position_size;\n signal.take_profit = Some(take_profit);\n signal.stop_loss = Some(stop_loss);\n signal.regime = Some(classification.regime);\n signal.metadata.method = \"few_shot\".to_string();\n\n signal\n }\n\n /// Generate signal from raw class probabilities.\n pub fn generate_from_probs(\n &mut self,\n symbol: &str,\n class_probs: &[f64],\n class_labels: &[MarketRegime],\n ) -> TradingSignal {\n if class_probs.len() != class_labels.len() || class_probs.is_empty() {\n return TradingSignal::hold(symbol);\n }\n\n // Find best class\n let (best_idx, &best_prob) = class_probs\n .iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .unwrap();\n\n let best_regime = class_labels[best_idx];\n\n // Check confidence\n if best_prob < self.config.min_confidence {\n return TradingSignal::hold(symbol);\n }\n\n let signal_type = self.regime_to_signal(best_regime);\n\n if signal_type == SignalType::Hold {\n return TradingSignal::hold(symbol);\n }\n\n let position_size = best_prob * self.config.max_position_size;\n\n let mut signal = TradingSignal::new(signal_type, best_prob, symbol);\n signal.position_size = position_size;\n signal.take_profit = Some(self.config.default_take_profit);\n signal.stop_loss = Some(self.config.default_stop_loss);\n signal.regime = Some(best_regime);\n signal.metadata.raw_scores = class_probs.to_vec();\n\n signal\n }\n\n /// Convert market regime to signal type.\n fn regime_to_signal(&self, regime: MarketRegime) -> SignalType {\n match regime {\n MarketRegime::StrongUptrend => SignalType::Long,\n MarketRegime::WeakUptrend => SignalType::Long,\n MarketRegime::Recovery => SignalType::Long,\n MarketRegime::StrongDowntrend => SignalType::Short,\n MarketRegime::WeakDowntrend => SignalType::Short,\n MarketRegime::Crash => SignalType::Close, // Close positions during crash\n MarketRegime::Volatile => SignalType::ReducePosition,\n MarketRegime::Sideways => SignalType::Hold,\n }\n }\n\n /// Adjust position size based on regime characteristics.\n fn adjust_for_regime(&self, base_size: f64, regime: MarketRegime) -> f64 {\n let multiplier = match regime {\n MarketRegime::StrongUptrend => 1.0,\n MarketRegime::WeakUptrend => 0.7,\n MarketRegime::Sideways => 0.3,\n MarketRegime::WeakDowntrend => 0.7,\n MarketRegime::StrongDowntrend => 1.0,\n MarketRegime::Volatile => 0.3, // Reduce in volatile markets\n MarketRegime::Crash => 0.2,\n MarketRegime::Recovery => 0.8,\n };\n\n (base_size * multiplier).min(self.config.max_position_size)\n }\n\n /// Calculate take profit and stop loss based on regime.\n fn calculate_risk_params(&self, classification: &RegimeClassification) -> (f64, f64) {\n let base_tp = self.config.default_take_profit;\n let base_sl = self.config.default_stop_loss;\n\n // Adjust based on regime volatility expectations\n let (tp_mult, sl_mult) = match classification.regime {\n MarketRegime::StrongUptrend | MarketRegime::StrongDowntrend => (1.5, 0.8),\n MarketRegime::WeakUptrend | MarketRegime::WeakDowntrend => (1.0, 1.0),\n MarketRegime::Volatile => (2.0, 1.5), // Wider stops in volatile markets\n MarketRegime::Crash => (1.0, 2.0), // Tight TP, wide SL during crash\n MarketRegime::Recovery => (1.5, 1.0),\n MarketRegime::Sideways => (0.8, 0.8), // Tighter in sideways\n };\n\n // Adjust for uncertainty\n let uncertainty_factor = 1.0 + classification.uncertainty * 0.5;\n\n (base_tp * tp_mult, base_sl * sl_mult * uncertainty_factor)\n }\n\n /// Reset cooldown for a symbol.\n pub fn reset_cooldown(&mut self, symbol: &str) {\n self.last_signals.remove(symbol);\n }\n\n /// Clear all cooldowns.\n pub fn clear_cooldowns(&mut self) {\n self.last_signals.clear();\n }\n}\n\nimpl Default for SignalGenerator {\n fn default() -> Self {\n Self::new(SignalConfig::default())\n }\n}\n\n/// Aggregates multiple signals into a final decision.\npub struct SignalAggregator {\n /// Minimum number of signals to aggregate\n min_signals: usize,\n /// Weight for each signal source\n weights: Vec<f64>,\n}\n\nimpl SignalAggregator {\n /// Create a new aggregator.\n pub fn new(min_signals: usize) -> Self {\n Self {\n min_signals,\n weights: Vec::new(),\n }\n }\n\n /// Set weights for signal sources.\n pub fn with_weights(mut self, weights: Vec<f64>) -> Self {\n self.weights = weights;\n self\n }\n\n /// Aggregate multiple signals.\n pub fn aggregate(&self, signals: &[TradingSignal]) -> Option<TradingSignal> {\n if signals.len() < self.min_signals {\n return None;\n }\n\n // Filter to actionable signals\n let actionable: Vec<_> = signals.iter().filter(|s| s.is_actionable()).collect();\n\n if actionable.is_empty() {\n return signals.first().map(|s| TradingSignal::hold(&s.symbol));\n }\n\n // Weighted vote for signal type\n let mut long_score = 0.0;\n let mut short_score = 0.0;\n let mut close_score = 0.0;\n\n for (i, signal) in actionable.iter().enumerate() {\n let weight = self.weights.get(i).copied().unwrap_or(1.0);\n let score = signal.confidence * weight;\n\n match signal.signal_type {\n SignalType::Long => long_score += score,\n SignalType::Short => short_score += score,\n SignalType::Close | SignalType::ReducePosition => close_score += score,\n SignalType::Hold => {}\n }\n }\n\n let total = long_score + short_score + close_score;\n if total < 0.01 {\n return signals.first().map(|s| TradingSignal::hold(&s.symbol));\n }\n\n // Determine winning signal type\n let (signal_type, confidence) = if long_score > short_score && long_score > close_score {\n (SignalType::Long, long_score / total)\n } else if short_score > long_score && short_score > close_score {\n (SignalType::Short, short_score / total)\n } else {\n (SignalType::Close, close_score / total)\n };\n\n let symbol = &actionable[0].symbol;\n let mut result = TradingSignal::new(signal_type, confidence, symbol);\n\n // Average position size from agreeing signals\n let agreeing: Vec<_> = actionable\n .iter()\n .filter(|s| s.signal_type == signal_type)\n .collect();\n\n if !agreeing.is_empty() {\n result.position_size = agreeing.iter().map(|s| s.position_size).sum::<f64>() / agreeing.len() as f64;\n result.take_profit = agreeing.iter().filter_map(|s| s.take_profit).next();\n result.stop_loss = agreeing.iter().filter_map(|s| s.stop_loss).next();\n result.regime = agreeing.iter().filter_map(|s| s.regime).next();\n }\n\n Some(result)\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_signal_creation() {\n let signal = TradingSignal::new(SignalType::Long, 0.8, \"BTCUSDT\");\n\n assert_eq!(signal.signal_type, SignalType::Long);\n assert!((signal.confidence - 0.8).abs() < 0.001);\n assert!(signal.is_actionable());\n }\n\n #[test]\n fn test_signal_expected_value() {\n let signal = TradingSignal::new(SignalType::Long, 0.7, \"BTCUSDT\")\n .with_take_profit(3.0)\n .with_stop_loss(1.0);\n\n // EV = 0.7 * 3.0 - 0.3 * 1.0 = 2.1 - 0.3 = 1.8\n let ev = signal.expected_value();\n assert!((ev - 1.8).abs() < 0.001);\n }\n\n #[test]\n fn test_signal_generator() {\n let mut generator = SignalGenerator::default();\n\n let classification = RegimeClassification {\n regime: MarketRegime::StrongUptrend,\n confidence: 0.85,\n uncertainty: 0.1,\n second_best: Some(MarketRegime::WeakUptrend),\n is_confident: true,\n };\n\n let signal = generator.generate(\"BTCUSDT\", &classification);\n\n assert_eq!(signal.signal_type, SignalType::Long);\n assert!(signal.confidence > 0.8);\n }\n\n #[test]\n fn test_hold_on_low_confidence() {\n let mut generator = SignalGenerator::default();\n\n let classification = RegimeClassification {\n regime: MarketRegime::StrongUptrend,\n confidence: 0.3, // Below threshold\n uncertainty: 0.5,\n second_best: None,\n is_confident: false,\n };\n\n let signal = generator.generate(\"BTCUSDT\", &classification);\n\n assert_eq!(signal.signal_type, SignalType::Hold);\n }\n}\n" | |
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| "content": "//! Risk management for few-shot trading strategies.\n//!\n//! This module provides risk management utilities that integrate with\n//! few-shot learning predictions to manage position sizing and exposure.\n\nuse crate::strategy::regime::MarketRegime;\nuse crate::strategy::signal::{SignalType, TradingSignal};\nuse serde::{Deserialize, Serialize};\nuse std::collections::HashMap;\n\n/// Configuration for risk management.\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct RiskConfig {\n /// Maximum position size as fraction of portfolio (0-1)\n pub max_position_size: f64,\n /// Maximum total exposure across all positions (0-1)\n pub max_total_exposure: f64,\n /// Maximum loss per trade as fraction of portfolio\n pub max_loss_per_trade: f64,\n /// Daily loss limit as fraction of portfolio\n pub daily_loss_limit: f64,\n /// Minimum confidence required to open position\n pub min_confidence: f64,\n /// Enable regime-based risk adjustment\n pub use_regime_adjustment: bool,\n /// Maximum correlation between positions\n pub max_correlation: f64,\n /// Minimum time between trades (seconds)\n pub min_trade_interval: u64,\n}\n\nimpl Default for RiskConfig {\n fn default() -> Self {\n Self {\n max_position_size: 0.1, // 10% max per position\n max_total_exposure: 0.5, // 50% max total exposure\n max_loss_per_trade: 0.02, // 2% max loss per trade\n daily_loss_limit: 0.05, // 5% daily loss limit\n min_confidence: 0.6, // 60% min confidence\n use_regime_adjustment: true,\n max_correlation: 0.7,\n min_trade_interval: 60,\n }\n }\n}\n\nimpl RiskConfig {\n /// Create a conservative risk configuration.\n pub fn conservative() -> Self {\n Self {\n max_position_size: 0.05,\n max_total_exposure: 0.25,\n max_loss_per_trade: 0.01,\n daily_loss_limit: 0.02,\n min_confidence: 0.75,\n use_regime_adjustment: true,\n max_correlation: 0.5,\n min_trade_interval: 300,\n }\n }\n\n /// Create an aggressive risk configuration.\n pub fn aggressive() -> Self {\n Self {\n max_position_size: 0.2,\n max_total_exposure: 0.8,\n max_loss_per_trade: 0.05,\n daily_loss_limit: 0.1,\n min_confidence: 0.5,\n use_regime_adjustment: true,\n max_correlation: 0.8,\n min_trade_interval: 30,\n }\n }\n}\n\n/// Current position in an asset.\n#[derive(Debug, Clone)]\npub struct Position {\n /// Asset symbol\n pub symbol: String,\n /// Position size (negative for short)\n pub size: f64,\n /// Entry price\n pub entry_price: f64,\n /// Current price\n pub current_price: f64,\n /// Unrealized PnL\n pub unrealized_pnl: f64,\n /// Stop loss price\n pub stop_loss: Option<f64>,\n /// Take profit price\n pub take_profit: Option<f64>,\n /// Timestamp of position open\n pub opened_at: u64,\n}\n\nimpl Position {\n /// Calculate unrealized PnL percentage.\n pub fn pnl_percent(&self) -> f64 {\n if self.entry_price == 0.0 {\n return 0.0;\n }\n (self.current_price - self.entry_price) / self.entry_price * self.size.signum() * 100.0\n }\n\n /// Check if stop loss is hit.\n pub fn is_stop_hit(&self) -> bool {\n if let Some(sl) = self.stop_loss {\n if self.size > 0.0 {\n self.current_price <= sl\n } else {\n self.current_price >= sl\n }\n } else {\n false\n }\n }\n\n /// Check if take profit is hit.\n pub fn is_tp_hit(&self) -> bool {\n if let Some(tp) = self.take_profit {\n if self.size > 0.0 {\n self.current_price >= tp\n } else {\n self.current_price <= tp\n }\n } else {\n false\n }\n }\n}\n\n/// Risk manager for few-shot trading strategies.\npub struct RiskManager {\n /// Risk configuration\n config: RiskConfig,\n /// Current positions\n positions: HashMap<String, Position>,\n /// Daily realized PnL\n daily_pnl: f64,\n /// Portfolio value\n portfolio_value: f64,\n /// Trade timestamps per symbol\n last_trades: HashMap<String, u64>,\n}\n\nimpl RiskManager {\n /// Create a new risk manager.\n pub fn new(config: RiskConfig, portfolio_value: f64) -> Self {\n Self {\n config,\n positions: HashMap::new(),\n daily_pnl: 0.0,\n portfolio_value,\n last_trades: HashMap::new(),\n }\n }\n\n /// Check if a signal passes risk checks.\n pub fn check_signal(&self, signal: &TradingSignal, current_time: u64) -> RiskCheckResult {\n let mut result = RiskCheckResult::new();\n\n // Check confidence threshold\n if signal.confidence < self.config.min_confidence {\n result.add_rejection(\"Confidence below threshold\");\n return result;\n }\n\n // Check if signal requires action\n if !signal.signal_type.requires_action() {\n result.approved = true;\n return result;\n }\n\n // Check trade interval\n if let Some(&last_trade) = self.last_trades.get(&signal.symbol) {\n if current_time.saturating_sub(last_trade) < self.config.min_trade_interval {\n result.add_rejection(\"Trade interval too short\");\n return result;\n }\n }\n\n // Check daily loss limit\n if self.daily_pnl < -self.config.daily_loss_limit * self.portfolio_value {\n result.add_rejection(\"Daily loss limit reached\");\n return result;\n }\n\n // Check position-specific risks\n match signal.signal_type {\n SignalType::Long | SignalType::Short => {\n // Check if we already have opposite position\n if let Some(pos) = self.positions.get(&signal.symbol) {\n let is_opposite = (signal.signal_type == SignalType::Long && pos.size < 0.0)\n || (signal.signal_type == SignalType::Short && pos.size > 0.0);\n if is_opposite {\n result.add_warning(\"Reversing existing position\");\n }\n }\n\n // Check total exposure\n let current_exposure = self.total_exposure();\n if current_exposure >= self.config.max_total_exposure {\n result.add_rejection(\"Maximum total exposure reached\");\n return result;\n }\n\n // Calculate allowed position size\n let max_new_position = (self.config.max_total_exposure - current_exposure)\n .min(self.config.max_position_size);\n\n result.max_position_size = max_new_position;\n result.approved = true;\n }\n SignalType::Close | SignalType::ReducePosition => {\n // Always allow closing/reducing positions\n result.approved = true;\n }\n SignalType::Hold => {\n result.approved = true;\n }\n }\n\n result\n }\n\n /// Apply risk adjustments to a trading signal.\n pub fn adjust_signal(&self, signal: &mut TradingSignal, regime: Option<MarketRegime>) {\n // Apply regime-based adjustments\n if self.config.use_regime_adjustment {\n if let Some(r) = regime {\n let regime_factor = self.regime_risk_factor(r);\n signal.position_size *= regime_factor;\n }\n }\n\n // Cap position size\n signal.position_size = signal.position_size.min(self.config.max_position_size);\n\n // Ensure proper stop loss based on max loss per trade\n if signal.stop_loss.is_none() || signal.stop_loss.unwrap() > self.config.max_loss_per_trade * 100.0 {\n signal.stop_loss = Some(self.config.max_loss_per_trade * 100.0);\n }\n }\n\n /// Calculate risk factor based on market regime.\n fn regime_risk_factor(&self, regime: MarketRegime) -> f64 {\n match regime {\n MarketRegime::StrongUptrend | MarketRegime::StrongDowntrend => 1.0,\n MarketRegime::WeakUptrend | MarketRegime::WeakDowntrend => 0.8,\n MarketRegime::Sideways => 0.5,\n MarketRegime::Volatile => 0.4,\n MarketRegime::Crash => 0.2,\n MarketRegime::Recovery => 0.7,\n }\n }\n\n /// Calculate Kelly criterion position size.\n pub fn kelly_position_size(&self, win_rate: f64, win_loss_ratio: f64) -> f64 {\n // Kelly formula: f* = (bp - q) / b\n // where b = win/loss ratio, p = win probability, q = loss probability\n let b = win_loss_ratio;\n let p = win_rate;\n let q = 1.0 - p;\n\n let kelly = (b * p - q) / b;\n\n // Use fractional Kelly (25%) for safety\n let fractional_kelly = kelly * 0.25;\n\n // Clamp to max position size\n fractional_kelly.max(0.0).min(self.config.max_position_size)\n }\n\n /// Calculate total exposure as fraction of portfolio.\n pub fn total_exposure(&self) -> f64 {\n if self.portfolio_value == 0.0 {\n return 0.0;\n }\n\n self.positions\n .values()\n .map(|p| p.size.abs() * p.current_price)\n .sum::<f64>()\n / self.portfolio_value\n }\n\n /// Get current drawdown.\n pub fn current_drawdown(&self) -> f64 {\n // Simplified: just use daily PnL as proxy\n if self.portfolio_value == 0.0 {\n return 0.0;\n }\n (-self.daily_pnl / self.portfolio_value).max(0.0)\n }\n\n /// Update position with new price.\n pub fn update_position(&mut self, symbol: &str, current_price: f64) {\n if let Some(pos) = self.positions.get_mut(symbol) {\n pos.current_price = current_price;\n let price_change = current_price - pos.entry_price;\n pos.unrealized_pnl = price_change * pos.size;\n }\n }\n\n /// Open a new position.\n pub fn open_position(&mut self, symbol: &str, size: f64, entry_price: f64, stop_loss: Option<f64>, take_profit: Option<f64>, timestamp: u64) {\n let position = Position {\n symbol: symbol.to_string(),\n size,\n entry_price,\n current_price: entry_price,\n unrealized_pnl: 0.0,\n stop_loss,\n take_profit,\n opened_at: timestamp,\n };\n\n self.positions.insert(symbol.to_string(), position);\n self.last_trades.insert(symbol.to_string(), timestamp);\n }\n\n /// Close a position.\n pub fn close_position(&mut self, symbol: &str) -> Option<f64> {\n if let Some(pos) = self.positions.remove(symbol) {\n let realized_pnl = pos.unrealized_pnl;\n self.daily_pnl += realized_pnl;\n Some(realized_pnl)\n } else {\n None\n }\n }\n\n /// Get position for a symbol.\n pub fn get_position(&self, symbol: &str) -> Option<&Position> {\n self.positions.get(symbol)\n }\n\n /// Get all positions.\n pub fn all_positions(&self) -> &HashMap<String, Position> {\n &self.positions\n }\n\n /// Reset daily PnL (call at start of new day).\n pub fn reset_daily(&mut self) {\n self.daily_pnl = 0.0;\n }\n\n /// Update portfolio value.\n pub fn set_portfolio_value(&mut self, value: f64) {\n self.portfolio_value = value;\n }\n\n /// Calculate value at risk (simplified historical VaR).\n pub fn value_at_risk(&self, confidence_level: f64) -> f64 {\n let total_exposure = self.total_exposure();\n\n // Simplified VaR: assume 2% daily volatility for crypto\n let daily_volatility = 0.02;\n\n // Z-score for confidence level (e.g., 1.645 for 95%, 2.326 for 99%)\n let z_score = match confidence_level {\n c if c >= 0.99 => 2.326,\n c if c >= 0.95 => 1.645,\n c if c >= 0.90 => 1.282,\n _ => 1.0,\n };\n\n total_exposure * self.portfolio_value * daily_volatility * z_score\n }\n\n /// Get a summary of current risk metrics.\n pub fn risk_summary(&self) -> RiskSummary {\n RiskSummary {\n total_exposure: self.total_exposure(),\n daily_pnl: self.daily_pnl,\n daily_pnl_percent: if self.portfolio_value > 0.0 {\n self.daily_pnl / self.portfolio_value * 100.0\n } else {\n 0.0\n },\n position_count: self.positions.len(),\n value_at_risk_95: self.value_at_risk(0.95),\n available_exposure: self.config.max_total_exposure - self.total_exposure(),\n daily_limit_remaining: self.config.daily_loss_limit + (self.daily_pnl / self.portfolio_value),\n }\n }\n}\n\n/// Result of a risk check.\n#[derive(Debug, Clone)]\npub struct RiskCheckResult {\n /// Whether the signal is approved\n pub approved: bool,\n /// Maximum allowed position size\n pub max_position_size: f64,\n /// Rejection reasons\n pub rejections: Vec<String>,\n /// Warnings (signal approved but with caveats)\n pub warnings: Vec<String>,\n}\n\nimpl RiskCheckResult {\n fn new() -> Self {\n Self {\n approved: false,\n max_position_size: 0.0,\n rejections: Vec::new(),\n warnings: Vec::new(),\n }\n }\n\n fn add_rejection(&mut self, reason: &str) {\n self.approved = false;\n self.rejections.push(reason.to_string());\n }\n\n fn add_warning(&mut self, warning: &str) {\n self.warnings.push(warning.to_string());\n }\n\n /// Check if there are any warnings.\n pub fn has_warnings(&self) -> bool {\n !self.warnings.is_empty()\n }\n}\n\n/// Summary of current risk metrics.\n#[derive(Debug, Clone)]\npub struct RiskSummary {\n /// Total exposure as fraction of portfolio\n pub total_exposure: f64,\n /// Daily realized + unrealized PnL\n pub daily_pnl: f64,\n /// Daily PnL as percentage\n pub daily_pnl_percent: f64,\n /// Number of open positions\n pub position_count: usize,\n /// 95% Value at Risk\n pub value_at_risk_95: f64,\n /// Remaining exposure capacity\n pub available_exposure: f64,\n /// Remaining daily loss limit\n pub daily_limit_remaining: f64,\n}\n\nimpl std::fmt::Display for RiskSummary {\n fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {\n write!(\n f,\n \"Risk Summary:\\n\\\n - Exposure: {:.1}%\\n\\\n - Daily PnL: {:.2}%\\n\\\n - Positions: {}\\n\\\n - VaR (95%): {:.2}\\n\\\n - Available: {:.1}%\",\n self.total_exposure * 100.0,\n self.daily_pnl_percent,\n self.position_count,\n self.value_at_risk_95,\n self.available_exposure * 100.0\n )\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_risk_config() {\n let config = RiskConfig::default();\n assert!((config.max_position_size - 0.1).abs() < 0.001);\n\n let conservative = RiskConfig::conservative();\n assert!(conservative.max_position_size < config.max_position_size);\n\n let aggressive = RiskConfig::aggressive();\n assert!(aggressive.max_position_size > config.max_position_size);\n }\n\n #[test]\n fn test_position_pnl() {\n let pos = Position {\n symbol: \"BTCUSDT\".to_string(),\n size: 1.0,\n entry_price: 50000.0,\n current_price: 51000.0,\n unrealized_pnl: 1000.0,\n stop_loss: Some(49000.0),\n take_profit: Some(55000.0),\n opened_at: 0,\n };\n\n let pnl = pos.pnl_percent();\n assert!((pnl - 2.0).abs() < 0.01); // 2% gain\n }\n\n #[test]\n fn test_kelly_criterion() {\n let config = RiskConfig::default();\n let manager = RiskManager::new(config, 100000.0);\n\n // 60% win rate, 2:1 win/loss ratio\n let kelly = manager.kelly_position_size(0.6, 2.0);\n\n // Kelly = (2 * 0.6 - 0.4) / 2 = 0.4\n // Fractional = 0.4 * 0.25 = 0.1\n assert!(kelly > 0.0);\n assert!(kelly <= 0.1); // Should be capped at max position size\n }\n\n #[test]\n fn test_risk_check() {\n let config = RiskConfig::default();\n let manager = RiskManager::new(config, 100000.0);\n\n let signal = TradingSignal::new(SignalType::Long, 0.8, \"BTCUSDT\");\n let result = manager.check_signal(&signal, 0);\n\n assert!(result.approved);\n }\n\n #[test]\n fn test_reject_low_confidence() {\n let config = RiskConfig::default();\n let manager = RiskManager::new(config, 100000.0);\n\n let signal = TradingSignal::new(SignalType::Long, 0.3, \"BTCUSDT\"); // Low confidence\n let result = manager.check_signal(&signal, 0);\n\n assert!(!result.approved);\n assert!(!result.rejections.is_empty());\n }\n}\n" | |
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| "content": "//! Market regime classification for few-shot learning.\n//!\n//! This module provides market regime detection using few-shot learning\n//! to identify market conditions with minimal historical examples.\n\nuse serde::{Deserialize, Serialize};\nuse std::fmt;\n\n/// Market regime types that can be classified.\n#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]\npub enum MarketRegime {\n /// Strong upward price movement with high momentum\n StrongUptrend,\n /// Moderate upward movement\n WeakUptrend,\n /// Range-bound market with no clear direction\n Sideways,\n /// Moderate downward movement\n WeakDowntrend,\n /// Strong downward price movement\n StrongDowntrend,\n /// High volatility with rapid price swings\n Volatile,\n /// Sharp market decline (panic selling)\n Crash,\n /// Recovery from a crash or downtrend\n Recovery,\n}\n\nimpl MarketRegime {\n /// Get all possible regime values.\n pub fn all() -> Vec<MarketRegime> {\n vec![\n MarketRegime::StrongUptrend,\n MarketRegime::WeakUptrend,\n MarketRegime::Sideways,\n MarketRegime::WeakDowntrend,\n MarketRegime::StrongDowntrend,\n MarketRegime::Volatile,\n MarketRegime::Crash,\n MarketRegime::Recovery,\n ]\n }\n\n /// Get the number of regime types.\n pub fn count() -> usize {\n 8\n }\n\n /// Convert regime to class index.\n pub fn to_index(&self) -> usize {\n match self {\n MarketRegime::StrongUptrend => 0,\n MarketRegime::WeakUptrend => 1,\n MarketRegime::Sideways => 2,\n MarketRegime::WeakDowntrend => 3,\n MarketRegime::StrongDowntrend => 4,\n MarketRegime::Volatile => 5,\n MarketRegime::Crash => 6,\n MarketRegime::Recovery => 7,\n }\n }\n\n /// Convert class index to regime.\n pub fn from_index(index: usize) -> Option<MarketRegime> {\n match index {\n 0 => Some(MarketRegime::StrongUptrend),\n 1 => Some(MarketRegime::WeakUptrend),\n 2 => Some(MarketRegime::Sideways),\n 3 => Some(MarketRegime::WeakDowntrend),\n 4 => Some(MarketRegime::StrongDowntrend),\n 5 => Some(MarketRegime::Volatile),\n 6 => Some(MarketRegime::Crash),\n 7 => Some(MarketRegime::Recovery),\n _ => None,\n }\n }\n\n /// Check if the regime is bullish.\n pub fn is_bullish(&self) -> bool {\n matches!(self, MarketRegime::StrongUptrend | MarketRegime::WeakUptrend | MarketRegime::Recovery)\n }\n\n /// Check if the regime is bearish.\n pub fn is_bearish(&self) -> bool {\n matches!(self, MarketRegime::StrongDowntrend | MarketRegime::WeakDowntrend | MarketRegime::Crash)\n }\n\n /// Check if the regime is neutral/sideways.\n pub fn is_neutral(&self) -> bool {\n matches!(self, MarketRegime::Sideways)\n }\n\n /// Check if the regime indicates high volatility.\n pub fn is_volatile(&self) -> bool {\n matches!(self, MarketRegime::Volatile | MarketRegime::Crash)\n }\n\n /// Get recommended position bias for this regime.\n pub fn position_bias(&self) -> f64 {\n match self {\n MarketRegime::StrongUptrend => 1.0,\n MarketRegime::WeakUptrend => 0.5,\n MarketRegime::Sideways => 0.0,\n MarketRegime::WeakDowntrend => -0.5,\n MarketRegime::StrongDowntrend => -1.0,\n MarketRegime::Volatile => 0.0,\n MarketRegime::Crash => -0.8,\n MarketRegime::Recovery => 0.6,\n }\n }\n}\n\nimpl fmt::Display for MarketRegime {\n fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {\n let name = match self {\n MarketRegime::StrongUptrend => \"Strong Uptrend\",\n MarketRegime::WeakUptrend => \"Weak Uptrend\",\n MarketRegime::Sideways => \"Sideways\",\n MarketRegime::WeakDowntrend => \"Weak Downtrend\",\n MarketRegime::StrongDowntrend => \"Strong Downtrend\",\n MarketRegime::Volatile => \"Volatile\",\n MarketRegime::Crash => \"Crash\",\n MarketRegime::Recovery => \"Recovery\",\n };\n write!(f, \"{}\", name)\n }\n}\n\n/// Classifier for detecting market regimes using few-shot learning.\npub struct RegimeClassifier {\n /// Confidence threshold for regime classification\n confidence_threshold: f64,\n /// Minimum samples required for classification\n min_samples: usize,\n /// Historical regime observations for reference\n regime_history: Vec<(MarketRegime, f64)>,\n}\n\nimpl RegimeClassifier {\n /// Create a new regime classifier.\n pub fn new(confidence_threshold: f64) -> Self {\n Self {\n confidence_threshold,\n min_samples: 3,\n regime_history: Vec::new(),\n }\n }\n\n /// Classify market regime based on features and probabilities.\n ///\n /// # Arguments\n /// * `class_probabilities` - Probability distribution over regime classes\n /// * `features` - Market features used for classification\n ///\n /// # Returns\n /// Tuple of (predicted regime, confidence)\n pub fn classify(&self, class_probabilities: &[f64]) -> (MarketRegime, f64) {\n if class_probabilities.len() != MarketRegime::count() {\n return (MarketRegime::Sideways, 0.0);\n }\n\n // Find the class with highest probability\n let (best_idx, &best_prob) = class_probabilities\n .iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .unwrap_or((2, &0.0)); // Default to Sideways\n\n let regime = MarketRegime::from_index(best_idx).unwrap_or(MarketRegime::Sideways);\n\n (regime, best_prob)\n }\n\n /// Classify with uncertainty quantification.\n pub fn classify_with_uncertainty(&self, class_probabilities: &[f64]) -> RegimeClassification {\n let (regime, confidence) = self.classify(class_probabilities);\n\n // Calculate entropy as uncertainty measure\n let entropy = self.calculate_entropy(class_probabilities);\n let max_entropy = (MarketRegime::count() as f64).ln();\n let normalized_uncertainty = entropy / max_entropy;\n\n // Find second best regime\n let mut sorted_probs: Vec<(usize, f64)> = class_probabilities\n .iter()\n .enumerate()\n .map(|(i, &p)| (i, p))\n .collect();\n sorted_probs.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());\n\n let second_best = if sorted_probs.len() > 1 {\n MarketRegime::from_index(sorted_probs[1].0)\n } else {\n None\n };\n\n RegimeClassification {\n regime,\n confidence,\n uncertainty: normalized_uncertainty,\n second_best,\n is_confident: confidence >= self.confidence_threshold,\n }\n }\n\n /// Calculate entropy of probability distribution.\n fn calculate_entropy(&self, probs: &[f64]) -> f64 {\n probs\n .iter()\n .filter(|&&p| p > 1e-10)\n .map(|&p| -p * p.ln())\n .sum()\n }\n\n /// Update regime history with new observation.\n pub fn update_history(&mut self, regime: MarketRegime, confidence: f64) {\n self.regime_history.push((regime, confidence));\n\n // Keep only recent history\n if self.regime_history.len() > 100 {\n self.regime_history.remove(0);\n }\n }\n\n /// Get regime transition probability from history.\n pub fn transition_probability(&self, from: MarketRegime, to: MarketRegime) -> f64 {\n if self.regime_history.len() < 2 {\n return 1.0 / MarketRegime::count() as f64;\n }\n\n let mut transitions = 0;\n let mut from_count = 0;\n\n for i in 0..self.regime_history.len() - 1 {\n if self.regime_history[i].0 == from {\n from_count += 1;\n if self.regime_history[i + 1].0 == to {\n transitions += 1;\n }\n }\n }\n\n if from_count == 0 {\n 1.0 / MarketRegime::count() as f64\n } else {\n transitions as f64 / from_count as f64\n }\n }\n\n /// Check if regime transition is likely based on history.\n pub fn is_transition_likely(&self, from: MarketRegime, to: MarketRegime) -> bool {\n let prob = self.transition_probability(from, to);\n prob > 0.1 // At least 10% historical probability\n }\n\n /// Get the dominant regime from recent history.\n pub fn dominant_regime(&self, lookback: usize) -> Option<MarketRegime> {\n if self.regime_history.is_empty() {\n return None;\n }\n\n let start = self.regime_history.len().saturating_sub(lookback);\n let mut counts = [0usize; 8];\n\n for (regime, _) in &self.regime_history[start..] {\n counts[regime.to_index()] += 1;\n }\n\n counts\n .iter()\n .enumerate()\n .max_by_key(|(_, &c)| c)\n .and_then(|(idx, _)| MarketRegime::from_index(idx))\n }\n}\n\nimpl Default for RegimeClassifier {\n fn default() -> Self {\n Self::new(0.6)\n }\n}\n\n/// Result of regime classification with uncertainty.\n#[derive(Debug, Clone)]\npub struct RegimeClassification {\n /// Predicted market regime\n pub regime: MarketRegime,\n /// Confidence in the prediction (0-1)\n pub confidence: f64,\n /// Uncertainty measure (normalized entropy, 0-1)\n pub uncertainty: f64,\n /// Second most likely regime\n pub second_best: Option<MarketRegime>,\n /// Whether prediction meets confidence threshold\n pub is_confident: bool,\n}\n\nimpl RegimeClassification {\n /// Check if we should act on this classification.\n pub fn should_act(&self) -> bool {\n self.is_confident && self.uncertainty < 0.5\n }\n\n /// Get a description of the classification.\n pub fn description(&self) -> String {\n if self.is_confident {\n format!(\n \"{} (confidence: {:.1}%, uncertainty: {:.1}%)\",\n self.regime,\n self.confidence * 100.0,\n self.uncertainty * 100.0\n )\n } else {\n format!(\n \"Uncertain - possibly {} or {} (confidence: {:.1}%)\",\n self.regime,\n self.second_best.map(|r| r.to_string()).unwrap_or_else(|| \"unknown\".to_string()),\n self.confidence * 100.0\n )\n }\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_regime_index_conversion() {\n for regime in MarketRegime::all() {\n let idx = regime.to_index();\n let recovered = MarketRegime::from_index(idx).unwrap();\n assert_eq!(regime, recovered);\n }\n }\n\n #[test]\n fn test_regime_properties() {\n assert!(MarketRegime::StrongUptrend.is_bullish());\n assert!(MarketRegime::StrongDowntrend.is_bearish());\n assert!(MarketRegime::Sideways.is_neutral());\n assert!(MarketRegime::Volatile.is_volatile());\n }\n\n #[test]\n fn test_classifier() {\n let classifier = RegimeClassifier::new(0.6);\n\n // Strong prediction for uptrend\n let probs = vec![0.8, 0.1, 0.05, 0.02, 0.01, 0.01, 0.005, 0.005];\n let (regime, confidence) = classifier.classify(&probs);\n\n assert_eq!(regime, MarketRegime::StrongUptrend);\n assert!((confidence - 0.8).abs() < 0.001);\n }\n\n #[test]\n fn test_classification_with_uncertainty() {\n let classifier = RegimeClassifier::new(0.6);\n\n // Certain prediction\n let probs = vec![0.9, 0.05, 0.02, 0.01, 0.01, 0.005, 0.0025, 0.0025];\n let result = classifier.classify_with_uncertainty(&probs);\n\n assert!(result.is_confident);\n assert!(result.uncertainty < 0.5);\n }\n}\n", | |
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| "content": "//! Trading signal generation based on few-shot predictions.\n//!\n//! This module converts few-shot learning predictions into actionable\n//! trading signals with confidence-weighted position sizing.\n\nuse crate::strategy::regime::{MarketRegime, RegimeClassification};\nuse serde::{Deserialize, Serialize};\nuse std::time::{Duration, Instant};\n\n/// Type of trading signal.\n#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]\npub enum SignalType {\n /// Open or add to long position\n Long,\n /// Open or add to short position\n Short,\n /// Close existing position\n Close,\n /// Reduce position size\n ReducePosition,\n /// No action recommended\n Hold,\n}\n\nimpl SignalType {\n /// Get the direction multiplier for this signal.\n pub fn direction(&self) -> f64 {\n match self {\n SignalType::Long => 1.0,\n SignalType::Short => -1.0,\n SignalType::Close => 0.0,\n SignalType::ReducePosition => 0.0,\n SignalType::Hold => 0.0,\n }\n }\n\n /// Check if this signal requires action.\n pub fn requires_action(&self) -> bool {\n !matches!(self, SignalType::Hold)\n }\n}\n\n/// A trading signal with associated metadata.\n#[derive(Debug, Clone)]\npub struct TradingSignal {\n /// Type of signal\n pub signal_type: SignalType,\n /// Confidence in the signal (0-1)\n pub confidence: f64,\n /// Recommended position size as fraction of capital (0-1)\n pub position_size: f64,\n /// Take profit level (percentage)\n pub take_profit: Option<f64>,\n /// Stop loss level (percentage)\n pub stop_loss: Option<f64>,\n /// Asset symbol this signal is for\n pub symbol: String,\n /// Market regime that generated this signal\n pub regime: Option<MarketRegime>,\n /// Signal generation timestamp\n pub timestamp: Instant,\n /// Time-to-live for this signal\n pub ttl: Duration,\n /// Additional metadata\n pub metadata: SignalMetadata,\n}\n\nimpl TradingSignal {\n /// Create a new trading signal.\n pub fn new(signal_type: SignalType, confidence: f64, symbol: &str) -> Self {\n Self {\n signal_type,\n confidence: confidence.clamp(0.0, 1.0),\n position_size: confidence.clamp(0.0, 1.0) * 0.5, // Max 50% of capital\n take_profit: None,\n stop_loss: None,\n symbol: symbol.to_string(),\n regime: None,\n timestamp: Instant::now(),\n ttl: Duration::from_secs(300), // 5 minute default TTL\n metadata: SignalMetadata::default(),\n }\n }\n\n /// Create a hold signal.\n pub fn hold(symbol: &str) -> Self {\n Self::new(SignalType::Hold, 0.0, symbol)\n }\n\n /// Set take profit level.\n pub fn with_take_profit(mut self, tp_percent: f64) -> Self {\n self.take_profit = Some(tp_percent);\n self\n }\n\n /// Set stop loss level.\n pub fn with_stop_loss(mut self, sl_percent: f64) -> Self {\n self.stop_loss = Some(sl_percent);\n self\n }\n\n /// Set the regime.\n pub fn with_regime(mut self, regime: MarketRegime) -> Self {\n self.regime = Some(regime);\n self\n }\n\n /// Set time-to-live.\n pub fn with_ttl(mut self, ttl: Duration) -> Self {\n self.ttl = ttl;\n self\n }\n\n /// Check if the signal has expired.\n pub fn is_expired(&self) -> bool {\n self.timestamp.elapsed() > self.ttl\n }\n\n /// Check if the signal is strong enough to act on.\n pub fn is_actionable(&self) -> bool {\n self.signal_type.requires_action()\n && self.confidence >= 0.5\n && !self.is_expired()\n }\n\n /// Calculate expected value based on confidence and risk/reward.\n pub fn expected_value(&self) -> f64 {\n let tp = self.take_profit.unwrap_or(2.0);\n let sl = self.stop_loss.unwrap_or(1.0);\n\n // Expected value = (win_prob * win_amount) - (lose_prob * lose_amount)\n let win_prob = self.confidence;\n let lose_prob = 1.0 - self.confidence;\n\n (win_prob * tp) - (lose_prob * sl)\n }\n}\n\n/// Additional metadata for trading signals.\n#[derive(Debug, Clone, Default)]\npub struct SignalMetadata {\n /// Number of support examples used\n pub support_count: usize,\n /// Method used for prediction\n pub method: String,\n /// Feature importance scores\n pub feature_importance: Vec<(String, f64)>,\n /// Raw prediction scores\n pub raw_scores: Vec<f64>,\n}\n\n/// Configuration for signal generation.\n#[derive(Debug, Clone)]\npub struct SignalConfig {\n /// Minimum confidence to generate a signal\n pub min_confidence: f64,\n /// Maximum position size (fraction of capital)\n pub max_position_size: f64,\n /// Default take profit percentage\n pub default_take_profit: f64,\n /// Default stop loss percentage\n pub default_stop_loss: f64,\n /// Enable regime-based adjustments\n pub use_regime_adjustment: bool,\n /// Signal cooldown period\n pub cooldown: Duration,\n}\n\nimpl Default for SignalConfig {\n fn default() -> Self {\n Self {\n min_confidence: 0.6,\n max_position_size: 0.25,\n default_take_profit: 2.0,\n default_stop_loss: 1.0,\n use_regime_adjustment: true,\n cooldown: Duration::from_secs(60),\n }\n }\n}\n\n/// Generator for trading signals based on few-shot predictions.\npub struct SignalGenerator {\n /// Configuration\n config: SignalConfig,\n /// Last signal timestamp per symbol\n last_signals: std::collections::HashMap<String, Instant>,\n}\n\nimpl SignalGenerator {\n /// Create a new signal generator.\n pub fn new(config: SignalConfig) -> Self {\n Self {\n config,\n last_signals: std::collections::HashMap::new(),\n }\n }\n\n /// Generate a trading signal from regime classification.\n pub fn generate(&mut self, symbol: &str, classification: &RegimeClassification) -> TradingSignal {\n // Check cooldown\n if let Some(&last) = self.last_signals.get(symbol) {\n if last.elapsed() < self.config.cooldown {\n return TradingSignal::hold(symbol);\n }\n }\n\n // Check confidence threshold\n if classification.confidence < self.config.min_confidence {\n return TradingSignal::hold(symbol);\n }\n\n // Determine signal type based on regime\n let signal_type = self.regime_to_signal(classification.regime);\n\n if signal_type == SignalType::Hold {\n return TradingSignal::hold(symbol);\n }\n\n // Calculate position size based on confidence\n let base_size = classification.confidence * self.config.max_position_size;\n let position_size = if self.config.use_regime_adjustment {\n self.adjust_for_regime(base_size, classification.regime)\n } else {\n base_size\n };\n\n // Calculate risk parameters\n let (take_profit, stop_loss) = self.calculate_risk_params(classification);\n\n // Update last signal time\n self.last_signals.insert(symbol.to_string(), Instant::now());\n\n let mut signal = TradingSignal::new(signal_type, classification.confidence, symbol);\n signal.position_size = position_size;\n signal.take_profit = Some(take_profit);\n signal.stop_loss = Some(stop_loss);\n signal.regime = Some(classification.regime);\n signal.metadata.method = \"few_shot\".to_string();\n\n signal\n }\n\n /// Generate signal from raw class probabilities.\n pub fn generate_from_probs(\n &mut self,\n symbol: &str,\n class_probs: &[f64],\n class_labels: &[MarketRegime],\n ) -> TradingSignal {\n if class_probs.len() != class_labels.len() || class_probs.is_empty() {\n return TradingSignal::hold(symbol);\n }\n\n // Find best class\n let (best_idx, &best_prob) = class_probs\n .iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .unwrap();\n\n let best_regime = class_labels[best_idx];\n\n // Check confidence\n if best_prob < self.config.min_confidence {\n return TradingSignal::hold(symbol);\n }\n\n let signal_type = self.regime_to_signal(best_regime);\n\n if signal_type == SignalType::Hold {\n return TradingSignal::hold(symbol);\n }\n\n let position_size = best_prob * self.config.max_position_size;\n\n let mut signal = TradingSignal::new(signal_type, best_prob, symbol);\n signal.position_size = position_size;\n signal.take_profit = Some(self.config.default_take_profit);\n signal.stop_loss = Some(self.config.default_stop_loss);\n signal.regime = Some(best_regime);\n signal.metadata.raw_scores = class_probs.to_vec();\n\n signal\n }\n\n /// Convert market regime to signal type.\n fn regime_to_signal(&self, regime: MarketRegime) -> SignalType {\n match regime {\n MarketRegime::StrongUptrend => SignalType::Long,\n MarketRegime::WeakUptrend => SignalType::Long,\n MarketRegime::Recovery => SignalType::Long,\n MarketRegime::StrongDowntrend => SignalType::Short,\n MarketRegime::WeakDowntrend => SignalType::Short,\n MarketRegime::Crash => SignalType::Close, // Close positions during crash\n MarketRegime::Volatile => SignalType::ReducePosition,\n MarketRegime::Sideways => SignalType::Hold,\n }\n }\n\n /// Adjust position size based on regime characteristics.\n fn adjust_for_regime(&self, base_size: f64, regime: MarketRegime) -> f64 {\n let multiplier = match regime {\n MarketRegime::StrongUptrend => 1.0,\n MarketRegime::WeakUptrend => 0.7,\n MarketRegime::Sideways => 0.3,\n MarketRegime::WeakDowntrend => 0.7,\n MarketRegime::StrongDowntrend => 1.0,\n MarketRegime::Volatile => 0.3, // Reduce in volatile markets\n MarketRegime::Crash => 0.2,\n MarketRegime::Recovery => 0.8,\n };\n\n (base_size * multiplier).min(self.config.max_position_size)\n }\n\n /// Calculate take profit and stop loss based on regime.\n fn calculate_risk_params(&self, classification: &RegimeClassification) -> (f64, f64) {\n let base_tp = self.config.default_take_profit;\n let base_sl = self.config.default_stop_loss;\n\n // Adjust based on regime volatility expectations\n let (tp_mult, sl_mult) = match classification.regime {\n MarketRegime::StrongUptrend | MarketRegime::StrongDowntrend => (1.5, 0.8),\n MarketRegime::WeakUptrend | MarketRegime::WeakDowntrend => (1.0, 1.0),\n MarketRegime::Volatile => (2.0, 1.5), // Wider stops in volatile markets\n MarketRegime::Crash => (1.0, 2.0), // Tight TP, wide SL during crash\n MarketRegime::Recovery => (1.5, 1.0),\n MarketRegime::Sideways => (0.8, 0.8), // Tighter in sideways\n };\n\n // Adjust for uncertainty\n let uncertainty_factor = 1.0 + classification.uncertainty * 0.5;\n\n (base_tp * tp_mult, base_sl * sl_mult * uncertainty_factor)\n }\n\n /// Reset cooldown for a symbol.\n pub fn reset_cooldown(&mut self, symbol: &str) {\n self.last_signals.remove(symbol);\n }\n\n /// Clear all cooldowns.\n pub fn clear_cooldowns(&mut self) {\n self.last_signals.clear();\n }\n}\n\nimpl Default for SignalGenerator {\n fn default() -> Self {\n Self::new(SignalConfig::default())\n }\n}\n\n/// Aggregates multiple signals into a final decision.\npub struct SignalAggregator {\n /// Minimum number of signals to aggregate\n min_signals: usize,\n /// Weight for each signal source\n weights: Vec<f64>,\n}\n\nimpl SignalAggregator {\n /// Create a new aggregator.\n pub fn new(min_signals: usize) -> Self {\n Self {\n min_signals,\n weights: Vec::new(),\n }\n }\n\n /// Set weights for signal sources.\n pub fn with_weights(mut self, weights: Vec<f64>) -> Self {\n self.weights = weights;\n self\n }\n\n /// Aggregate multiple signals.\n pub fn aggregate(&self, signals: &[TradingSignal]) -> Option<TradingSignal> {\n if signals.len() < self.min_signals {\n return None;\n }\n\n // Filter to actionable signals\n let actionable: Vec<_> = signals.iter().filter(|s| s.is_actionable()).collect();\n\n if actionable.is_empty() {\n return signals.first().map(|s| TradingSignal::hold(&s.symbol));\n }\n\n // Weighted vote for signal type\n let mut long_score = 0.0;\n let mut short_score = 0.0;\n let mut close_score = 0.0;\n\n for (i, signal) in actionable.iter().enumerate() {\n let weight = self.weights.get(i).copied().unwrap_or(1.0);\n let score = signal.confidence * weight;\n\n match signal.signal_type {\n SignalType::Long => long_score += score,\n SignalType::Short => short_score += score,\n SignalType::Close | SignalType::ReducePosition => close_score += score,\n SignalType::Hold => {}\n }\n }\n\n let total = long_score + short_score + close_score;\n if total < 0.01 {\n return signals.first().map(|s| TradingSignal::hold(&s.symbol));\n }\n\n // Determine winning signal type\n let (signal_type, confidence) = if long_score > short_score && long_score > close_score {\n (SignalType::Long, long_score / total)\n } else if short_score > long_score && short_score > close_score {\n (SignalType::Short, short_score / total)\n } else {\n (SignalType::Close, close_score / total)\n };\n\n let symbol = &actionable[0].symbol;\n let mut result = TradingSignal::new(signal_type, confidence, symbol);\n\n // Average position size from agreeing signals\n let agreeing: Vec<_> = actionable\n .iter()\n .filter(|s| s.signal_type == signal_type)\n .collect();\n\n if !agreeing.is_empty() {\n result.position_size = agreeing.iter().map(|s| s.position_size).sum::<f64>() / agreeing.len() as f64;\n result.take_profit = agreeing.iter().filter_map(|s| s.take_profit).next();\n result.stop_loss = agreeing.iter().filter_map(|s| s.stop_loss).next();\n result.regime = agreeing.iter().filter_map(|s| s.regime).next();\n }\n\n Some(result)\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_signal_creation() {\n let signal = TradingSignal::new(SignalType::Long, 0.8, \"BTCUSDT\");\n\n assert_eq!(signal.signal_type, SignalType::Long);\n assert!((signal.confidence - 0.8).abs() < 0.001);\n assert!(signal.is_actionable());\n }\n\n #[test]\n fn test_signal_expected_value() {\n let signal = TradingSignal::new(SignalType::Long, 0.7, \"BTCUSDT\")\n .with_take_profit(3.0)\n .with_stop_loss(1.0);\n\n // EV = 0.7 * 3.0 - 0.3 * 1.0 = 2.1 - 0.3 = 1.8\n let ev = signal.expected_value();\n assert!((ev - 1.8).abs() < 0.001);\n }\n\n #[test]\n fn test_signal_generator() {\n let mut generator = SignalGenerator::default();\n\n let classification = RegimeClassification {\n regime: MarketRegime::StrongUptrend,\n confidence: 0.85,\n uncertainty: 0.1,\n second_best: Some(MarketRegime::WeakUptrend),\n is_confident: true,\n };\n\n let signal = generator.generate(\"BTCUSDT\", &classification);\n\n assert_eq!(signal.signal_type, SignalType::Long);\n assert!(signal.confidence > 0.8);\n }\n\n #[test]\n fn test_hold_on_low_confidence() {\n let mut generator = SignalGenerator::default();\n\n let classification = RegimeClassification {\n regime: MarketRegime::StrongUptrend,\n confidence: 0.3, // Below threshold\n uncertainty: 0.5,\n second_best: None,\n is_confident: false,\n };\n\n let signal = generator.generate(\"BTCUSDT\", &classification);\n\n assert_eq!(signal.signal_type, SignalType::Hold);\n }\n}\n", | |
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| "content": "//! Risk management for few-shot trading strategies.\n//!\n//! This module provides risk management utilities that integrate with\n//! few-shot learning predictions to manage position sizing and exposure.\n\nuse crate::strategy::regime::MarketRegime;\nuse crate::strategy::signal::{SignalType, TradingSignal};\nuse serde::{Deserialize, Serialize};\nuse std::collections::HashMap;\n\n/// Configuration for risk management.\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct RiskConfig {\n /// Maximum position size as fraction of portfolio (0-1)\n pub max_position_size: f64,\n /// Maximum total exposure across all positions (0-1)\n pub max_total_exposure: f64,\n /// Maximum loss per trade as fraction of portfolio\n pub max_loss_per_trade: f64,\n /// Daily loss limit as fraction of portfolio\n pub daily_loss_limit: f64,\n /// Minimum confidence required to open position\n pub min_confidence: f64,\n /// Enable regime-based risk adjustment\n pub use_regime_adjustment: bool,\n /// Maximum correlation between positions\n pub max_correlation: f64,\n /// Minimum time between trades (seconds)\n pub min_trade_interval: u64,\n}\n\nimpl Default for RiskConfig {\n fn default() -> Self {\n Self {\n max_position_size: 0.1, // 10% max per position\n max_total_exposure: 0.5, // 50% max total exposure\n max_loss_per_trade: 0.02, // 2% max loss per trade\n daily_loss_limit: 0.05, // 5% daily loss limit\n min_confidence: 0.6, // 60% min confidence\n use_regime_adjustment: true,\n max_correlation: 0.7,\n min_trade_interval: 60,\n }\n }\n}\n\nimpl RiskConfig {\n /// Create a conservative risk configuration.\n pub fn conservative() -> Self {\n Self {\n max_position_size: 0.05,\n max_total_exposure: 0.25,\n max_loss_per_trade: 0.01,\n daily_loss_limit: 0.02,\n min_confidence: 0.75,\n use_regime_adjustment: true,\n max_correlation: 0.5,\n min_trade_interval: 300,\n }\n }\n\n /// Create an aggressive risk configuration.\n pub fn aggressive() -> Self {\n Self {\n max_position_size: 0.2,\n max_total_exposure: 0.8,\n max_loss_per_trade: 0.05,\n daily_loss_limit: 0.1,\n min_confidence: 0.5,\n use_regime_adjustment: true,\n max_correlation: 0.8,\n min_trade_interval: 30,\n }\n }\n}\n\n/// Current position in an asset.\n#[derive(Debug, Clone)]\npub struct Position {\n /// Asset symbol\n pub symbol: String,\n /// Position size (negative for short)\n pub size: f64,\n /// Entry price\n pub entry_price: f64,\n /// Current price\n pub current_price: f64,\n /// Unrealized PnL\n pub unrealized_pnl: f64,\n /// Stop loss price\n pub stop_loss: Option<f64>,\n /// Take profit price\n pub take_profit: Option<f64>,\n /// Timestamp of position open\n pub opened_at: u64,\n}\n\nimpl Position {\n /// Calculate unrealized PnL percentage.\n pub fn pnl_percent(&self) -> f64 {\n if self.entry_price == 0.0 {\n return 0.0;\n }\n (self.current_price - self.entry_price) / self.entry_price * self.size.signum() * 100.0\n }\n\n /// Check if stop loss is hit.\n pub fn is_stop_hit(&self) -> bool {\n if let Some(sl) = self.stop_loss {\n if self.size > 0.0 {\n self.current_price <= sl\n } else {\n self.current_price >= sl\n }\n } else {\n false\n }\n }\n\n /// Check if take profit is hit.\n pub fn is_tp_hit(&self) -> bool {\n if let Some(tp) = self.take_profit {\n if self.size > 0.0 {\n self.current_price >= tp\n } else {\n self.current_price <= tp\n }\n } else {\n false\n }\n }\n}\n\n/// Risk manager for few-shot trading strategies.\npub struct RiskManager {\n /// Risk configuration\n config: RiskConfig,\n /// Current positions\n positions: HashMap<String, Position>,\n /// Daily realized PnL\n daily_pnl: f64,\n /// Portfolio value\n portfolio_value: f64,\n /// Trade timestamps per symbol\n last_trades: HashMap<String, u64>,\n}\n\nimpl RiskManager {\n /// Create a new risk manager.\n pub fn new(config: RiskConfig, portfolio_value: f64) -> Self {\n Self {\n config,\n positions: HashMap::new(),\n daily_pnl: 0.0,\n portfolio_value,\n last_trades: HashMap::new(),\n }\n }\n\n /// Check if a signal passes risk checks.\n pub fn check_signal(&self, signal: &TradingSignal, current_time: u64) -> RiskCheckResult {\n let mut result = RiskCheckResult::new();\n\n // Check confidence threshold\n if signal.confidence < self.config.min_confidence {\n result.add_rejection(\"Confidence below threshold\");\n return result;\n }\n\n // Check if signal requires action\n if !signal.signal_type.requires_action() {\n result.approved = true;\n return result;\n }\n\n // Check trade interval\n if let Some(&last_trade) = self.last_trades.get(&signal.symbol) {\n if current_time.saturating_sub(last_trade) < self.config.min_trade_interval {\n result.add_rejection(\"Trade interval too short\");\n return result;\n }\n }\n\n // Check daily loss limit\n if self.daily_pnl < -self.config.daily_loss_limit * self.portfolio_value {\n result.add_rejection(\"Daily loss limit reached\");\n return result;\n }\n\n // Check position-specific risks\n match signal.signal_type {\n SignalType::Long | SignalType::Short => {\n // Check if we already have opposite position\n if let Some(pos) = self.positions.get(&signal.symbol) {\n let is_opposite = (signal.signal_type == SignalType::Long && pos.size < 0.0)\n || (signal.signal_type == SignalType::Short && pos.size > 0.0);\n if is_opposite {\n result.add_warning(\"Reversing existing position\");\n }\n }\n\n // Check total exposure\n let current_exposure = self.total_exposure();\n if current_exposure >= self.config.max_total_exposure {\n result.add_rejection(\"Maximum total exposure reached\");\n return result;\n }\n\n // Calculate allowed position size\n let max_new_position = (self.config.max_total_exposure - current_exposure)\n .min(self.config.max_position_size);\n\n result.max_position_size = max_new_position;\n result.approved = true;\n }\n SignalType::Close | SignalType::ReducePosition => {\n // Always allow closing/reducing positions\n result.approved = true;\n }\n SignalType::Hold => {\n result.approved = true;\n }\n }\n\n result\n }\n\n /// Apply risk adjustments to a trading signal.\n pub fn adjust_signal(&self, signal: &mut TradingSignal, regime: Option<MarketRegime>) {\n // Apply regime-based adjustments\n if self.config.use_regime_adjustment {\n if let Some(r) = regime {\n let regime_factor = self.regime_risk_factor(r);\n signal.position_size *= regime_factor;\n }\n }\n\n // Cap position size\n signal.position_size = signal.position_size.min(self.config.max_position_size);\n\n // Ensure proper stop loss based on max loss per trade\n if signal.stop_loss.is_none() || signal.stop_loss.unwrap() > self.config.max_loss_per_trade * 100.0 {\n signal.stop_loss = Some(self.config.max_loss_per_trade * 100.0);\n }\n }\n\n /// Calculate risk factor based on market regime.\n fn regime_risk_factor(&self, regime: MarketRegime) -> f64 {\n match regime {\n MarketRegime::StrongUptrend | MarketRegime::StrongDowntrend => 1.0,\n MarketRegime::WeakUptrend | MarketRegime::WeakDowntrend => 0.8,\n MarketRegime::Sideways => 0.5,\n MarketRegime::Volatile => 0.4,\n MarketRegime::Crash => 0.2,\n MarketRegime::Recovery => 0.7,\n }\n }\n\n /// Calculate Kelly criterion position size.\n pub fn kelly_position_size(&self, win_rate: f64, win_loss_ratio: f64) -> f64 {\n // Kelly formula: f* = (bp - q) / b\n // where b = win/loss ratio, p = win probability, q = loss probability\n let b = win_loss_ratio;\n let p = win_rate;\n let q = 1.0 - p;\n\n let kelly = (b * p - q) / b;\n\n // Use fractional Kelly (25%) for safety\n let fractional_kelly = kelly * 0.25;\n\n // Clamp to max position size\n fractional_kelly.max(0.0).min(self.config.max_position_size)\n }\n\n /// Calculate total exposure as fraction of portfolio.\n pub fn total_exposure(&self) -> f64 {\n if self.portfolio_value == 0.0 {\n return 0.0;\n }\n\n self.positions\n .values()\n .map(|p| p.size.abs() * p.current_price)\n .sum::<f64>()\n / self.portfolio_value\n }\n\n /// Get current drawdown.\n pub fn current_drawdown(&self) -> f64 {\n // Simplified: just use daily PnL as proxy\n if self.portfolio_value == 0.0 {\n return 0.0;\n }\n (-self.daily_pnl / self.portfolio_value).max(0.0)\n }\n\n /// Update position with new price.\n pub fn update_position(&mut self, symbol: &str, current_price: f64) {\n if let Some(pos) = self.positions.get_mut(symbol) {\n pos.current_price = current_price;\n let price_change = current_price - pos.entry_price;\n pos.unrealized_pnl = price_change * pos.size;\n }\n }\n\n /// Open a new position.\n pub fn open_position(&mut self, symbol: &str, size: f64, entry_price: f64, stop_loss: Option<f64>, take_profit: Option<f64>, timestamp: u64) {\n let position = Position {\n symbol: symbol.to_string(),\n size,\n entry_price,\n current_price: entry_price,\n unrealized_pnl: 0.0,\n stop_loss,\n take_profit,\n opened_at: timestamp,\n };\n\n self.positions.insert(symbol.to_string(), position);\n self.last_trades.insert(symbol.to_string(), timestamp);\n }\n\n /// Close a position.\n pub fn close_position(&mut self, symbol: &str) -> Option<f64> {\n if let Some(pos) = self.positions.remove(symbol) {\n let realized_pnl = pos.unrealized_pnl;\n self.daily_pnl += realized_pnl;\n Some(realized_pnl)\n } else {\n None\n }\n }\n\n /// Get position for a symbol.\n pub fn get_position(&self, symbol: &str) -> Option<&Position> {\n self.positions.get(symbol)\n }\n\n /// Get all positions.\n pub fn all_positions(&self) -> &HashMap<String, Position> {\n &self.positions\n }\n\n /// Reset daily PnL (call at start of new day).\n pub fn reset_daily(&mut self) {\n self.daily_pnl = 0.0;\n }\n\n /// Update portfolio value.\n pub fn set_portfolio_value(&mut self, value: f64) {\n self.portfolio_value = value;\n }\n\n /// Calculate value at risk (simplified historical VaR).\n pub fn value_at_risk(&self, confidence_level: f64) -> f64 {\n let total_exposure = self.total_exposure();\n\n // Simplified VaR: assume 2% daily volatility for crypto\n let daily_volatility = 0.02;\n\n // Z-score for confidence level (e.g., 1.645 for 95%, 2.326 for 99%)\n let z_score = match confidence_level {\n c if c >= 0.99 => 2.326,\n c if c >= 0.95 => 1.645,\n c if c >= 0.90 => 1.282,\n _ => 1.0,\n };\n\n total_exposure * self.portfolio_value * daily_volatility * z_score\n }\n\n /// Get a summary of current risk metrics.\n pub fn risk_summary(&self) -> RiskSummary {\n RiskSummary {\n total_exposure: self.total_exposure(),\n daily_pnl: self.daily_pnl,\n daily_pnl_percent: if self.portfolio_value > 0.0 {\n self.daily_pnl / self.portfolio_value * 100.0\n } else {\n 0.0\n },\n position_count: self.positions.len(),\n value_at_risk_95: self.value_at_risk(0.95),\n available_exposure: self.config.max_total_exposure - self.total_exposure(),\n daily_limit_remaining: self.config.daily_loss_limit + (self.daily_pnl / self.portfolio_value),\n }\n }\n}\n\n/// Result of a risk check.\n#[derive(Debug, Clone)]\npub struct RiskCheckResult {\n /// Whether the signal is approved\n pub approved: bool,\n /// Maximum allowed position size\n pub max_position_size: f64,\n /// Rejection reasons\n pub rejections: Vec<String>,\n /// Warnings (signal approved but with caveats)\n pub warnings: Vec<String>,\n}\n\nimpl RiskCheckResult {\n fn new() -> Self {\n Self {\n approved: false,\n max_position_size: 0.0,\n rejections: Vec::new(),\n warnings: Vec::new(),\n }\n }\n\n fn add_rejection(&mut self, reason: &str) {\n self.approved = false;\n self.rejections.push(reason.to_string());\n }\n\n fn add_warning(&mut self, warning: &str) {\n self.warnings.push(warning.to_string());\n }\n\n /// Check if there are any warnings.\n pub fn has_warnings(&self) -> bool {\n !self.warnings.is_empty()\n }\n}\n\n/// Summary of current risk metrics.\n#[derive(Debug, Clone)]\npub struct RiskSummary {\n /// Total exposure as fraction of portfolio\n pub total_exposure: f64,\n /// Daily realized + unrealized PnL\n pub daily_pnl: f64,\n /// Daily PnL as percentage\n pub daily_pnl_percent: f64,\n /// Number of open positions\n pub position_count: usize,\n /// 95% Value at Risk\n pub value_at_risk_95: f64,\n /// Remaining exposure capacity\n pub available_exposure: f64,\n /// Remaining daily loss limit\n pub daily_limit_remaining: f64,\n}\n\nimpl std::fmt::Display for RiskSummary {\n fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {\n write!(\n f,\n \"Risk Summary:\\n\\\n - Exposure: {:.1}%\\n\\\n - Daily PnL: {:.2}%\\n\\\n - Positions: {}\\n\\\n - VaR (95%): {:.2}\\n\\\n - Available: {:.1}%\",\n self.total_exposure * 100.0,\n self.daily_pnl_percent,\n self.position_count,\n self.value_at_risk_95,\n self.available_exposure * 100.0\n )\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_risk_config() {\n let config = RiskConfig::default();\n assert!((config.max_position_size - 0.1).abs() < 0.001);\n\n let conservative = RiskConfig::conservative();\n assert!(conservative.max_position_size < config.max_position_size);\n\n let aggressive = RiskConfig::aggressive();\n assert!(aggressive.max_position_size > config.max_position_size);\n }\n\n #[test]\n fn test_position_pnl() {\n let pos = Position {\n symbol: \"BTCUSDT\".to_string(),\n size: 1.0,\n entry_price: 50000.0,\n current_price: 51000.0,\n unrealized_pnl: 1000.0,\n stop_loss: Some(49000.0),\n take_profit: Some(55000.0),\n opened_at: 0,\n };\n\n let pnl = pos.pnl_percent();\n assert!((pnl - 2.0).abs() < 0.01); // 2% gain\n }\n\n #[test]\n fn test_kelly_criterion() {\n let config = RiskConfig::default();\n let manager = RiskManager::new(config, 100000.0);\n\n // 60% win rate, 2:1 win/loss ratio\n let kelly = manager.kelly_position_size(0.6, 2.0);\n\n // Kelly = (2 * 0.6 - 0.4) / 2 = 0.4\n // Fractional = 0.4 * 0.25 = 0.1\n assert!(kelly > 0.0);\n assert!(kelly <= 0.1); // Should be capped at max position size\n }\n\n #[test]\n fn test_risk_check() {\n let config = RiskConfig::default();\n let manager = RiskManager::new(config, 100000.0);\n\n let signal = TradingSignal::new(SignalType::Long, 0.8, \"BTCUSDT\");\n let result = manager.check_signal(&signal, 0);\n\n assert!(result.approved);\n }\n\n #[test]\n fn test_reject_low_confidence() {\n let config = RiskConfig::default();\n let manager = RiskManager::new(config, 100000.0);\n\n let signal = TradingSignal::new(SignalType::Long, 0.3, \"BTCUSDT\"); // Low confidence\n let result = manager.check_signal(&signal, 0);\n\n assert!(!result.approved);\n assert!(!result.rejections.is_empty());\n }\n}\n", | |
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| "content": "//! Basic Few-Shot Market Prediction Example\n//!\n//! This example demonstrates the core few-shot learning functionality\n//! for market prediction using synthetic data.\n//!\n//! Run with: cargo run --example basic_few_shot\n\nuse few_shot_market_prediction::prelude::*;\nuse std::collections::HashMap;\n\nfn main() {\n println!(\"=== Few-Shot Market Prediction: Basic Example ===\\n\");\n\n // Generate synthetic market features for demonstration\n let (support_set, query_set, labels) = generate_synthetic_data();\n\n println!(\"Support set: {} examples per class\", support_set.len() / 3);\n println!(\"Query set: {} examples to classify\\n\", query_set.len());\n\n // Create embedding network\n let embedding_config = EmbeddingConfig {\n input_dim: 10, // 10 market features\n hidden_dims: vec![32, 16],\n output_dim: 8, // 8-dimensional embeddings\n activation: Activation::LeakyReLU(0.1),\n dropout_rate: 0.1,\n };\n\n let network = EmbeddingNetwork::new(embedding_config);\n println!(\"Embedding network created: 10 -> [32, 16] -> 8\\n\");\n\n // Embed all samples\n let support_embeddings: Vec<Vec<f64>> = support_set\n .iter()\n .map(|x| network.forward(x, None))\n .collect();\n\n let query_embeddings: Vec<Vec<f64>> = query_set\n .iter()\n .map(|x| network.forward(x, None))\n .collect();\n\n // Create class label mapping\n let class_labels = vec![\"Uptrend\", \"Sideways\", \"Downtrend\"];\n let support_labels = vec![0, 0, 0, 1, 1, 1, 2, 2, 2]; // 3 examples per class\n\n // Method 1: Metric-based learning (Prototypical Networks)\n println!(\"--- Method 1: Prototypical Networks ---\");\n let metric_learner = MetricBasedLearner::new(\n FewShotConfig::default(),\n DistanceMetric::Euclidean,\n );\n\n let metric_result = metric_learner.predict(\n &support_embeddings,\n &support_labels,\n &query_embeddings,\n );\n\n println!(\"Query predictions (metric-based):\");\n for (i, &pred) in metric_result.predictions.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_labels[pred],\n metric_result.confidences[i] * 100.0\n );\n }\n println!();\n\n // Method 2: MAML-based learning\n println!(\"--- Method 2: MAML (Model-Agnostic Meta-Learning) ---\");\n let maml_learner = MAMLLearner::new(\n FewShotConfig::default(),\n MAMLConfig {\n inner_lr: 0.01,\n inner_steps: 3,\n first_order: true,\n },\n );\n\n // For MAML, we pass raw features as it adapts the network\n let maml_result = maml_learner.predict_with_adaptation(\n &network,\n &support_set,\n &support_labels,\n &query_set,\n );\n\n println!(\"Query predictions (MAML):\");\n for (i, &pred) in maml_result.predictions.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_labels[pred],\n maml_result.confidences[i] * 100.0\n );\n }\n println!();\n\n // Method 3: Siamese Network\n println!(\"--- Method 3: Siamese Network ---\");\n let siamese_learner = SiameseLearner::new(FewShotConfig::default());\n\n let siamese_result = siamese_learner.predict(\n &support_embeddings,\n &support_labels,\n &query_embeddings,\n );\n\n println!(\"Query predictions (Siamese):\");\n for (i, &pred) in siamese_result.predictions.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_labels[pred],\n siamese_result.confidences[i] * 100.0\n );\n }\n println!();\n\n // Method 4: Hybrid approach\n println!(\"--- Method 4: Hybrid (Metric + MAML) ---\");\n let hybrid_learner = HybridLearner::new(\n FewShotConfig::default(),\n HybridConfig {\n metric_weight: 0.6,\n maml_weight: 0.4,\n },\n );\n\n let hybrid_result = hybrid_learner.predict(\n &support_embeddings,\n &support_labels,\n &query_embeddings,\n );\n\n println!(\"Query predictions (Hybrid):\");\n for (i, &pred) in hybrid_result.predictions.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_labels[pred],\n hybrid_result.confidences[i] * 100.0\n );\n }\n println!();\n\n // Compare actual labels\n println!(\"--- Ground Truth ---\");\n for (i, &label) in labels.iter().enumerate() {\n println!(\" Query {}: {}\", i, class_labels[label]);\n }\n\n // Calculate accuracy for each method\n println!(\"\\n--- Accuracy Summary ---\");\n let methods = [\n (\"Prototypical\", &metric_result.predictions),\n (\"MAML\", &maml_result.predictions),\n (\"Siamese\", &siamese_result.predictions),\n (\"Hybrid\", &hybrid_result.predictions),\n ];\n\n for (name, preds) in methods {\n let correct = preds.iter().zip(&labels).filter(|(p, l)| p == l).count();\n let accuracy = correct as f64 / labels.len() as f64 * 100.0;\n println!(\" {}: {:.1}% ({}/{})\", name, accuracy, correct, labels.len());\n }\n\n println!(\"\\n=== Example Complete ===\");\n}\n\n/// Generate synthetic market data for the example.\nfn generate_synthetic_data() -> (Vec<Vec<f64>>, Vec<Vec<f64>>, Vec<usize>) {\n // Uptrend features: positive returns, low volatility, bullish indicators\n let uptrend_base = vec![0.02, 0.5, 0.7, 65.0, 0.01, 1.2, 0.8, 0.6, 0.3, 0.7];\n\n // Sideways features: near-zero returns, low volatility, neutral indicators\n let sideways_base = vec![0.001, 0.3, 0.5, 50.0, 0.008, 1.0, 0.5, 0.5, 0.5, 0.5];\n\n // Downtrend features: negative returns, higher volatility, bearish indicators\n let downtrend_base = vec![-0.02, 0.6, 0.3, 35.0, 0.015, 0.8, 0.2, 0.4, 0.7, 0.3];\n\n // Create support set (3 examples per class with small variations)\n let mut support_set = Vec::new();\n\n for i in 0..3 {\n support_set.push(add_noise(&uptrend_base, 0.1, i as u64));\n }\n for i in 0..3 {\n support_set.push(add_noise(&sideways_base, 0.1, i as u64 + 10));\n }\n for i in 0..3 {\n support_set.push(add_noise(&downtrend_base, 0.1, i as u64 + 20));\n }\n\n // Create query set (2 examples per class)\n let mut query_set = Vec::new();\n let mut labels = Vec::new();\n\n // Query uptrend examples\n query_set.push(add_noise(&uptrend_base, 0.15, 100));\n labels.push(0);\n query_set.push(add_noise(&uptrend_base, 0.15, 101));\n labels.push(0);\n\n // Query sideways examples\n query_set.push(add_noise(&sideways_base, 0.15, 110));\n labels.push(1);\n\n // Query downtrend examples\n query_set.push(add_noise(&downtrend_base, 0.15, 120));\n labels.push(2);\n query_set.push(add_noise(&downtrend_base, 0.15, 121));\n labels.push(2);\n\n (support_set, query_set, labels)\n}\n\n/// Add random noise to a feature vector.\nfn add_noise(base: &[f64], noise_level: f64, seed: u64) -> Vec<f64> {\n // Simple pseudo-random noise generator\n let mut rng_state = seed;\n\n base.iter().map(|&v| {\n rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);\n let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;\n v + v.abs() * noise_level * random\n }).collect()\n}\n" | |
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| "content": "//! Market Regime Prediction Example\n//!\n//! This example demonstrates using few-shot learning to classify\n//! market regimes and generate trading signals.\n//!\n//! Run with: cargo run --example regime_prediction\n\nuse few_shot_market_prediction::prelude::*;\n\nfn main() {\n println!(\"=== Market Regime Prediction with Few-Shot Learning ===\\n\");\n\n // Define all market regimes we want to classify\n let regimes = MarketRegime::all();\n println!(\"Classifying into {} market regimes:\", regimes.len());\n for regime in ®imes {\n println!(\" - {} (bias: {:.1})\", regime, regime.position_bias());\n }\n println!();\n\n // Create regime classifier\n let mut classifier = RegimeClassifier::new(0.6);\n println!(\"Regime classifier created with 60% confidence threshold\\n\");\n\n // Simulate some regime predictions (in real usage, these come from few-shot model)\n let test_scenarios = [\n (\"Strong momentum rally\", vec![0.75, 0.15, 0.03, 0.02, 0.01, 0.02, 0.01, 0.01]),\n (\"Mild uptrend\", vec![0.15, 0.55, 0.20, 0.05, 0.02, 0.02, 0.00, 0.01]),\n (\"Choppy sideways\", vec![0.05, 0.10, 0.60, 0.10, 0.05, 0.08, 0.01, 0.01]),\n (\"Bearish breakdown\", vec![0.02, 0.03, 0.05, 0.15, 0.60, 0.10, 0.04, 0.01]),\n (\"High volatility\", vec![0.10, 0.10, 0.10, 0.10, 0.10, 0.40, 0.05, 0.05]),\n (\"Market crash\", vec![0.01, 0.02, 0.02, 0.10, 0.15, 0.15, 0.50, 0.05]),\n (\"Recovery phase\", vec![0.15, 0.15, 0.10, 0.05, 0.05, 0.10, 0.05, 0.35]),\n ];\n\n println!(\"--- Regime Classification Results ---\\n\");\n\n // Create signal generator\n let signal_config = SignalConfig {\n min_confidence: 0.5,\n max_position_size: 0.2,\n default_take_profit: 3.0,\n default_stop_loss: 1.5,\n use_regime_adjustment: true,\n cooldown: std::time::Duration::from_secs(0), // No cooldown for demo\n };\n let mut signal_generator = SignalGenerator::new(signal_config);\n\n // Create risk manager\n let risk_config = RiskConfig::default();\n let risk_manager = RiskManager::new(risk_config, 100_000.0);\n\n for (scenario_name, probs) in &test_scenarios {\n println!(\"Scenario: {}\", scenario_name);\n\n // Classify the regime\n let classification = classifier.classify_with_uncertainty(probs);\n\n println!(\" Classification: {}\", classification.description());\n println!(\" Should act: {}\", classification.should_act());\n\n // Update classifier history\n classifier.update_history(classification.regime, classification.confidence);\n\n // Generate trading signal\n signal_generator.clear_cooldowns(); // Reset for demo\n let signal = signal_generator.generate(\"BTCUSDT\", &classification);\n\n println!(\" Signal: {:?}\", signal.signal_type);\n if signal.signal_type.requires_action() {\n println!(\" Position size: {:.1}%\", signal.position_size * 100.0);\n println!(\" Take profit: {:.1}%\", signal.take_profit.unwrap_or(0.0));\n println!(\" Stop loss: {:.1}%\", signal.stop_loss.unwrap_or(0.0));\n println!(\" Expected value: {:.2}\", signal.expected_value());\n\n // Check risk\n let risk_check = risk_manager.check_signal(&signal, 0);\n println!(\" Risk approved: {}\", risk_check.approved);\n if !risk_check.rejections.is_empty() {\n println!(\" Rejections: {:?}\", risk_check.rejections);\n }\n }\n println!();\n }\n\n // Demonstrate regime transitions\n println!(\"--- Regime Transition Analysis ---\\n\");\n\n // Simulate a sequence of regimes\n let regime_sequence = [\n MarketRegime::Sideways,\n MarketRegime::WeakUptrend,\n MarketRegime::StrongUptrend,\n MarketRegime::Volatile,\n MarketRegime::WeakDowntrend,\n MarketRegime::Crash,\n MarketRegime::Recovery,\n MarketRegime::Sideways,\n ];\n\n for regime in ®ime_sequence {\n classifier.update_history(*regime, 0.8);\n }\n\n println!(\"After observing {} regime changes:\", regime_sequence.len());\n println!(\" Dominant regime (last 5): {:?}\", classifier.dominant_regime(5));\n println!();\n\n // Check transition probabilities\n let transitions = [\n (MarketRegime::StrongUptrend, MarketRegime::Volatile),\n (MarketRegime::Volatile, MarketRegime::Crash),\n (MarketRegime::Crash, MarketRegime::Recovery),\n (MarketRegime::Recovery, MarketRegime::Sideways),\n ];\n\n println!(\"Transition probabilities from history:\");\n for (from, to) in &transitions {\n let prob = classifier.transition_probability(*from, *to);\n let likely = classifier.is_transition_likely(*from, *to);\n println!(\n \" {} -> {}: {:.1}% (likely: {})\",\n from, to, prob * 100.0, likely\n );\n }\n\n // Show risk summary\n println!(\"\\n--- Current Risk Summary ---\");\n println!(\"{}\", risk_manager.risk_summary());\n\n println!(\"\\n=== Example Complete ===\");\n}\n" | |
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| "content": "//! New Asset Adaptation Example\n//!\n//! This example demonstrates adapting a few-shot model to predict\n//! market movements for a new cryptocurrency with minimal data.\n//!\n//! Run with: cargo run --example new_asset_adaptation\n\nuse few_shot_market_prediction::prelude::*;\nuse std::time::Duration;\n\n#[tokio::main]\nasync fn main() {\n println!(\"=== Few-Shot Adaptation to New Asset ===\\n\");\n\n // Scenario: We have a model trained on BTC, ETH, and want to adapt to a new altcoin\n println!(\"Scenario: Adapting model trained on BTC/ETH to predict DOGE movements\\n\");\n\n // Step 1: Create the embedding network (pre-trained on major assets)\n let embedding_config = EmbeddingConfig {\n input_dim: 12, // 12 market features\n hidden_dims: vec![64, 32, 16],\n output_dim: 8,\n activation: Activation::GELU,\n dropout_rate: 0.1,\n };\n\n let network = EmbeddingNetwork::new(embedding_config);\n println!(\"Step 1: Pre-trained embedding network loaded\");\n println!(\" Architecture: 12 -> [64, 32, 16] -> 8\\n\");\n\n // Step 2: Generate synthetic \"historical\" data for major assets\n let (btc_features, btc_labels) = generate_asset_data(\"BTC\", 50);\n let (eth_features, eth_labels) = generate_asset_data(\"ETH\", 50);\n\n println!(\"Step 2: Historical data available\");\n println!(\" BTC: {} labeled samples\", btc_features.len());\n println!(\" ETH: {} labeled samples\", eth_features.len());\n println!();\n\n // Step 3: Get LIMITED data for new asset (few-shot scenario)\n let (doge_support, doge_support_labels) = generate_asset_data(\"DOGE\", 5); // Only 5 examples!\n let (doge_query, doge_query_labels) = generate_asset_data(\"DOGE_query\", 10);\n\n println!(\"Step 3: New asset (DOGE) - Few-shot data\");\n println!(\" Support set: {} examples (few-shot!)\", doge_support.len());\n println!(\" Query set: {} examples to predict\", doge_query.len());\n println!();\n\n // Step 4: Create few-shot learner with MAML for adaptation\n let config = FewShotConfig {\n n_way: 3, // 3-way classification (up, sideways, down)\n k_shot: 1, // 1-shot per class minimum (we have more)\n query_size: 5,\n embedding_dim: 8,\n };\n\n let maml_config = MAMLConfig {\n inner_lr: 0.01,\n inner_steps: 5, // More steps for better adaptation\n first_order: true,\n };\n\n let maml_learner = MAMLLearner::new(config.clone(), maml_config);\n println!(\"Step 4: MAML learner configured for few-shot adaptation\");\n println!(\" Inner learning rate: 0.01\");\n println!(\" Adaptation steps: 5\");\n println!();\n\n // Step 5: Adapt to new asset using few-shot learning\n println!(\"Step 5: Adapting model to DOGE...\\n\");\n\n let adaptation_result = maml_learner.predict_with_adaptation(\n &network,\n &doge_support,\n &doge_support_labels,\n &doge_query,\n );\n\n // Display results\n let class_names = [\"Uptrend\", \"Sideways\", \"Downtrend\"];\n\n println!(\"--- Adaptation Results for DOGE ---\\n\");\n println!(\"Predictions on query set:\");\n for (i, (&pred, &actual)) in adaptation_result.predictions.iter()\n .zip(doge_query_labels.iter())\n .enumerate()\n {\n let correct = if pred == actual { \"β\" } else { \"β\" };\n println!(\n \" Sample {}: Predicted {} (conf: {:.1}%) | Actual {} {}\",\n i + 1,\n class_names[pred],\n adaptation_result.confidences[i] * 100.0,\n class_names[actual],\n correct\n );\n }\n\n // Calculate accuracy\n let correct = adaptation_result.predictions.iter()\n .zip(doge_query_labels.iter())\n .filter(|(p, a)| p == a)\n .count();\n let accuracy = correct as f64 / doge_query_labels.len() as f64 * 100.0;\n\n println!(\"\\nOverall accuracy: {:.1}% ({}/{})\",\n accuracy, correct, doge_query_labels.len());\n\n // Step 6: Compare with metric-based approach\n println!(\"\\n--- Comparison: Metric-Based vs MAML ---\\n\");\n\n // Embed all data\n let support_embeddings: Vec<Vec<f64>> = doge_support\n .iter()\n .map(|x| network.forward(x, None))\n .collect();\n\n let query_embeddings: Vec<Vec<f64>> = doge_query\n .iter()\n .map(|x| network.forward(x, None))\n .collect();\n\n let metric_learner = MetricBasedLearner::new(config.clone(), DistanceMetric::Cosine);\n let metric_result = metric_learner.predict(\n &support_embeddings,\n &doge_support_labels,\n &query_embeddings,\n );\n\n let metric_correct = metric_result.predictions.iter()\n .zip(doge_query_labels.iter())\n .filter(|(p, a)| p == a)\n .count();\n let metric_accuracy = metric_correct as f64 / doge_query_labels.len() as f64 * 100.0;\n\n println!(\"Method comparison:\");\n println!(\" MAML (with adaptation): {:.1}%\", accuracy);\n println!(\" Metric-based (no adaptation): {:.1}%\", metric_accuracy);\n println!();\n\n // Step 7: Generate trading signal for new asset\n println!(\"--- Trading Signal Generation ---\\n\");\n\n // Use the predictions to generate signals\n let regime_probs = compute_regime_probabilities(&adaptation_result);\n\n let mut classifier = RegimeClassifier::new(0.5);\n let classification = classifier.classify_with_uncertainty(®ime_probs);\n\n println!(\"Market regime classification for DOGE:\");\n println!(\" {}\", classification.description());\n\n let signal_config = SignalConfig::default();\n let mut signal_generator = SignalGenerator::new(signal_config);\n let signal = signal_generator.generate(\"DOGEUSDT\", &classification);\n\n println!(\"\\nGenerated signal:\");\n println!(\" Type: {:?}\", signal.signal_type);\n println!(\" Confidence: {:.1}%\", signal.confidence * 100.0);\n println!(\" Position size: {:.1}%\", signal.position_size * 100.0);\n if signal.take_profit.is_some() {\n println!(\" Take profit: {:.1}%\", signal.take_profit.unwrap());\n println!(\" Stop loss: {:.1}%\", signal.stop_loss.unwrap());\n }\n\n // Step 8: Real-world usage hint\n println!(\"\\n--- Real-World Usage ---\\n\");\n println!(\"To use with real Bybit data:\");\n println!(\" 1. Create BybitClient::new(false) for mainnet\");\n println!(\" 2. Fetch klines with client.get_klines(\\\"DOGEUSDT\\\", \\\"1h\\\", 100)\");\n println!(\" 3. Extract features with FeatureExtractor::extract(&klines)\");\n println!(\" 4. Run few-shot prediction with extracted features\");\n println!(\" 5. Generate and execute trading signals\");\n println!();\n\n println!(\"=== Example Complete ===\");\n}\n\n/// Generate synthetic market data for an asset.\nfn generate_asset_data(asset_name: &str, count: usize) -> (Vec<Vec<f64>>, Vec<usize>) {\n let mut features = Vec::new();\n let mut labels = Vec::new();\n\n // Seed based on asset name for reproducibility\n let base_seed: u64 = asset_name.bytes().map(|b| b as u64).sum();\n\n for i in 0..count {\n // Determine class (0: uptrend, 1: sideways, 2: downtrend)\n let class = (i + (base_seed as usize)) % 3;\n\n // Generate features based on class\n let base = match class {\n 0 => vec![0.02, 0.015, 0.5, 65.0, 1.5, 0.8, 0.7, 0.6, 0.3, 0.7, 0.8, 0.6],\n 1 => vec![0.001, 0.002, 0.3, 50.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],\n _ => vec![-0.02, -0.015, 0.6, 35.0, 0.7, 0.3, 0.3, 0.4, 0.7, 0.3, 0.2, 0.4],\n };\n\n features.push(add_noise(&base, 0.15, base_seed + i as u64));\n labels.push(class);\n }\n\n (features, labels)\n}\n\n/// Add noise to feature vector.\nfn add_noise(base: &[f64], noise_level: f64, seed: u64) -> Vec<f64> {\n let mut rng_state = seed;\n\n base.iter().map(|&v| {\n rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);\n let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;\n v + v.abs().max(0.01) * noise_level * random\n }).collect()\n}\n\n/// Compute regime probabilities from prediction results.\nfn compute_regime_probabilities(result: &PredictionResult) -> Vec<f64> {\n // Simplified: map 3-class predictions to 8 regimes\n let mut regime_probs = vec![0.0; 8];\n\n let total = result.predictions.len() as f64;\n\n for (&pred, &conf) in result.predictions.iter().zip(result.confidences.iter()) {\n match pred {\n 0 => { // Uptrend\n regime_probs[0] += conf * 0.4 / total; // Strong uptrend\n regime_probs[1] += conf * 0.6 / total; // Weak uptrend\n }\n 1 => { // Sideways\n regime_probs[2] += conf / total; // Sideways\n }\n _ => { // Downtrend\n regime_probs[3] += conf * 0.3 / total; // Weak downtrend\n regime_probs[4] += conf * 0.7 / total; // Strong downtrend\n }\n }\n }\n\n // Normalize\n let sum: f64 = regime_probs.iter().sum();\n if sum > 0.0 {\n regime_probs.iter_mut().for_each(|p| *p /= sum);\n }\n\n regime_probs\n}\n" | |
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| "content": "//! Basic Few-Shot Market Prediction Example\n//!\n//! This example demonstrates the core few-shot learning functionality\n//! for market prediction using synthetic data.\n//!\n//! Run with: cargo run --example basic_few_shot\n\nuse few_shot_market_prediction::prelude::*;\nuse std::collections::HashMap;\n\nfn main() {\n println!(\"=== Few-Shot Market Prediction: Basic Example ===\\n\");\n\n // Generate synthetic market features for demonstration\n let (support_set, query_set, labels) = generate_synthetic_data();\n\n println!(\"Support set: {} examples per class\", support_set.len() / 3);\n println!(\"Query set: {} examples to classify\\n\", query_set.len());\n\n // Create embedding network\n let embedding_config = EmbeddingConfig {\n input_dim: 10, // 10 market features\n hidden_dims: vec![32, 16],\n output_dim: 8, // 8-dimensional embeddings\n activation: Activation::LeakyReLU(0.1),\n dropout_rate: 0.1,\n };\n\n let network = EmbeddingNetwork::new(embedding_config);\n println!(\"Embedding network created: 10 -> [32, 16] -> 8\\n\");\n\n // Embed all samples\n let support_embeddings: Vec<Vec<f64>> = support_set\n .iter()\n .map(|x| network.forward(x, None))\n .collect();\n\n let query_embeddings: Vec<Vec<f64>> = query_set\n .iter()\n .map(|x| network.forward(x, None))\n .collect();\n\n // Create class label mapping\n let class_labels = vec![\"Uptrend\", \"Sideways\", \"Downtrend\"];\n let support_labels = vec![0, 0, 0, 1, 1, 1, 2, 2, 2]; // 3 examples per class\n\n // Method 1: Metric-based learning (Prototypical Networks)\n println!(\"--- Method 1: Prototypical Networks ---\");\n let metric_learner = MetricBasedLearner::new(\n FewShotConfig::default(),\n DistanceMetric::Euclidean,\n );\n\n let metric_result = metric_learner.predict(\n &support_embeddings,\n &support_labels,\n &query_embeddings,\n );\n\n println!(\"Query predictions (metric-based):\");\n for (i, &pred) in metric_result.predictions.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_labels[pred],\n metric_result.confidences[i] * 100.0\n );\n }\n println!();\n\n // Method 2: MAML-based learning\n println!(\"--- Method 2: MAML (Model-Agnostic Meta-Learning) ---\");\n let maml_learner = MAMLLearner::new(\n FewShotConfig::default(),\n MAMLConfig {\n inner_lr: 0.01,\n inner_steps: 3,\n first_order: true,\n },\n );\n\n // For MAML, we pass raw features as it adapts the network\n let maml_result = maml_learner.predict_with_adaptation(\n &network,\n &support_set,\n &support_labels,\n &query_set,\n );\n\n println!(\"Query predictions (MAML):\");\n for (i, &pred) in maml_result.predictions.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_labels[pred],\n maml_result.confidences[i] * 100.0\n );\n }\n println!();\n\n // Method 3: Siamese Network\n println!(\"--- Method 3: Siamese Network ---\");\n let siamese_learner = SiameseLearner::new(FewShotConfig::default());\n\n let siamese_result = siamese_learner.predict(\n &support_embeddings,\n &support_labels,\n &query_embeddings,\n );\n\n println!(\"Query predictions (Siamese):\");\n for (i, &pred) in siamese_result.predictions.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_labels[pred],\n siamese_result.confidences[i] * 100.0\n );\n }\n println!();\n\n // Method 4: Hybrid approach\n println!(\"--- Method 4: Hybrid (Metric + MAML) ---\");\n let hybrid_learner = HybridLearner::new(\n FewShotConfig::default(),\n HybridConfig {\n metric_weight: 0.6,\n maml_weight: 0.4,\n },\n );\n\n let hybrid_result = hybrid_learner.predict(\n &support_embeddings,\n &support_labels,\n &query_embeddings,\n );\n\n println!(\"Query predictions (Hybrid):\");\n for (i, &pred) in hybrid_result.predictions.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_labels[pred],\n hybrid_result.confidences[i] * 100.0\n );\n }\n println!();\n\n // Compare actual labels\n println!(\"--- Ground Truth ---\");\n for (i, &label) in labels.iter().enumerate() {\n println!(\" Query {}: {}\", i, class_labels[label]);\n }\n\n // Calculate accuracy for each method\n println!(\"\\n--- Accuracy Summary ---\");\n let methods = [\n (\"Prototypical\", &metric_result.predictions),\n (\"MAML\", &maml_result.predictions),\n (\"Siamese\", &siamese_result.predictions),\n (\"Hybrid\", &hybrid_result.predictions),\n ];\n\n for (name, preds) in methods {\n let correct = preds.iter().zip(&labels).filter(|(p, l)| p == l).count();\n let accuracy = correct as f64 / labels.len() as f64 * 100.0;\n println!(\" {}: {:.1}% ({}/{})\", name, accuracy, correct, labels.len());\n }\n\n println!(\"\\n=== Example Complete ===\");\n}\n\n/// Generate synthetic market data for the example.\nfn generate_synthetic_data() -> (Vec<Vec<f64>>, Vec<Vec<f64>>, Vec<usize>) {\n // Uptrend features: positive returns, low volatility, bullish indicators\n let uptrend_base = vec![0.02, 0.5, 0.7, 65.0, 0.01, 1.2, 0.8, 0.6, 0.3, 0.7];\n\n // Sideways features: near-zero returns, low volatility, neutral indicators\n let sideways_base = vec![0.001, 0.3, 0.5, 50.0, 0.008, 1.0, 0.5, 0.5, 0.5, 0.5];\n\n // Downtrend features: negative returns, higher volatility, bearish indicators\n let downtrend_base = vec![-0.02, 0.6, 0.3, 35.0, 0.015, 0.8, 0.2, 0.4, 0.7, 0.3];\n\n // Create support set (3 examples per class with small variations)\n let mut support_set = Vec::new();\n\n for i in 0..3 {\n support_set.push(add_noise(&uptrend_base, 0.1, i as u64));\n }\n for i in 0..3 {\n support_set.push(add_noise(&sideways_base, 0.1, i as u64 + 10));\n }\n for i in 0..3 {\n support_set.push(add_noise(&downtrend_base, 0.1, i as u64 + 20));\n }\n\n // Create query set (2 examples per class)\n let mut query_set = Vec::new();\n let mut labels = Vec::new();\n\n // Query uptrend examples\n query_set.push(add_noise(&uptrend_base, 0.15, 100));\n labels.push(0);\n query_set.push(add_noise(&uptrend_base, 0.15, 101));\n labels.push(0);\n\n // Query sideways examples\n query_set.push(add_noise(&sideways_base, 0.15, 110));\n labels.push(1);\n\n // Query downtrend examples\n query_set.push(add_noise(&downtrend_base, 0.15, 120));\n labels.push(2);\n query_set.push(add_noise(&downtrend_base, 0.15, 121));\n labels.push(2);\n\n (support_set, query_set, labels)\n}\n\n/// Add random noise to a feature vector.\nfn add_noise(base: &[f64], noise_level: f64, seed: u64) -> Vec<f64> {\n // Simple pseudo-random noise generator\n let mut rng_state = seed;\n\n base.iter().map(|&v| {\n rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);\n let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;\n v + v.abs() * noise_level * random\n }).collect()\n}\n", | |
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| "content": "//! Market Regime Prediction Example\n//!\n//! This example demonstrates using few-shot learning to classify\n//! market regimes and generate trading signals.\n//!\n//! Run with: cargo run --example regime_prediction\n\nuse few_shot_market_prediction::prelude::*;\n\nfn main() {\n println!(\"=== Market Regime Prediction with Few-Shot Learning ===\\n\");\n\n // Define all market regimes we want to classify\n let regimes = MarketRegime::all();\n println!(\"Classifying into {} market regimes:\", regimes.len());\n for regime in ®imes {\n println!(\" - {} (bias: {:.1})\", regime, regime.position_bias());\n }\n println!();\n\n // Create regime classifier\n let mut classifier = RegimeClassifier::new(0.6);\n println!(\"Regime classifier created with 60% confidence threshold\\n\");\n\n // Simulate some regime predictions (in real usage, these come from few-shot model)\n let test_scenarios = [\n (\"Strong momentum rally\", vec![0.75, 0.15, 0.03, 0.02, 0.01, 0.02, 0.01, 0.01]),\n (\"Mild uptrend\", vec![0.15, 0.55, 0.20, 0.05, 0.02, 0.02, 0.00, 0.01]),\n (\"Choppy sideways\", vec![0.05, 0.10, 0.60, 0.10, 0.05, 0.08, 0.01, 0.01]),\n (\"Bearish breakdown\", vec![0.02, 0.03, 0.05, 0.15, 0.60, 0.10, 0.04, 0.01]),\n (\"High volatility\", vec![0.10, 0.10, 0.10, 0.10, 0.10, 0.40, 0.05, 0.05]),\n (\"Market crash\", vec![0.01, 0.02, 0.02, 0.10, 0.15, 0.15, 0.50, 0.05]),\n (\"Recovery phase\", vec![0.15, 0.15, 0.10, 0.05, 0.05, 0.10, 0.05, 0.35]),\n ];\n\n println!(\"--- Regime Classification Results ---\\n\");\n\n // Create signal generator\n let signal_config = SignalConfig {\n min_confidence: 0.5,\n max_position_size: 0.2,\n default_take_profit: 3.0,\n default_stop_loss: 1.5,\n use_regime_adjustment: true,\n cooldown: std::time::Duration::from_secs(0), // No cooldown for demo\n };\n let mut signal_generator = SignalGenerator::new(signal_config);\n\n // Create risk manager\n let risk_config = RiskConfig::default();\n let risk_manager = RiskManager::new(risk_config, 100_000.0);\n\n for (scenario_name, probs) in &test_scenarios {\n println!(\"Scenario: {}\", scenario_name);\n\n // Classify the regime\n let classification = classifier.classify_with_uncertainty(probs);\n\n println!(\" Classification: {}\", classification.description());\n println!(\" Should act: {}\", classification.should_act());\n\n // Update classifier history\n classifier.update_history(classification.regime, classification.confidence);\n\n // Generate trading signal\n signal_generator.clear_cooldowns(); // Reset for demo\n let signal = signal_generator.generate(\"BTCUSDT\", &classification);\n\n println!(\" Signal: {:?}\", signal.signal_type);\n if signal.signal_type.requires_action() {\n println!(\" Position size: {:.1}%\", signal.position_size * 100.0);\n println!(\" Take profit: {:.1}%\", signal.take_profit.unwrap_or(0.0));\n println!(\" Stop loss: {:.1}%\", signal.stop_loss.unwrap_or(0.0));\n println!(\" Expected value: {:.2}\", signal.expected_value());\n\n // Check risk\n let risk_check = risk_manager.check_signal(&signal, 0);\n println!(\" Risk approved: {}\", risk_check.approved);\n if !risk_check.rejections.is_empty() {\n println!(\" Rejections: {:?}\", risk_check.rejections);\n }\n }\n println!();\n }\n\n // Demonstrate regime transitions\n println!(\"--- Regime Transition Analysis ---\\n\");\n\n // Simulate a sequence of regimes\n let regime_sequence = [\n MarketRegime::Sideways,\n MarketRegime::WeakUptrend,\n MarketRegime::StrongUptrend,\n MarketRegime::Volatile,\n MarketRegime::WeakDowntrend,\n MarketRegime::Crash,\n MarketRegime::Recovery,\n MarketRegime::Sideways,\n ];\n\n for regime in ®ime_sequence {\n classifier.update_history(*regime, 0.8);\n }\n\n println!(\"After observing {} regime changes:\", regime_sequence.len());\n println!(\" Dominant regime (last 5): {:?}\", classifier.dominant_regime(5));\n println!();\n\n // Check transition probabilities\n let transitions = [\n (MarketRegime::StrongUptrend, MarketRegime::Volatile),\n (MarketRegime::Volatile, MarketRegime::Crash),\n (MarketRegime::Crash, MarketRegime::Recovery),\n (MarketRegime::Recovery, MarketRegime::Sideways),\n ];\n\n println!(\"Transition probabilities from history:\");\n for (from, to) in &transitions {\n let prob = classifier.transition_probability(*from, *to);\n let likely = classifier.is_transition_likely(*from, *to);\n println!(\n \" {} -> {}: {:.1}% (likely: {})\",\n from, to, prob * 100.0, likely\n );\n }\n\n // Show risk summary\n println!(\"\\n--- Current Risk Summary ---\");\n println!(\"{}\", risk_manager.risk_summary());\n\n println!(\"\\n=== Example Complete ===\");\n}\n", | |
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| [2026-01-21T18:05:41.621Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Fs6u6xuPMDpSNo6ZdLtqAr","type":"tool_result","content":"File created successfully at: /tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/examples/new_asset_adaptation.rs"}]},"parent_tool_use_id":null,"session_id":"172d23c7-5c62-484f-af89-80e92d0b393e","uuid":"476fdb3b-2ce6-4e5f-befd-07b2b2908f73","tool_use_result":{"type":"create","filePath":"/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/examples/new_asset_adaptation.rs","content":"//! New Asset Adaptation Example\n//!\n//! This example demonstrates adapting a few-shot model to predict\n//! market movements for a new cryptocurrency with minimal data.\n//!\n//! Run with: cargo run --example new_asset_adaptation\n\nuse few_shot_market_prediction::prelude::*;\nuse std::time::Duration;\n\n#[tokio::main]\nasync fn main() {\n println!(\"=== Few-Shot Adaptation to New Asset ===\\n\");\n\n // Scenario: We have a model trained on BTC, ETH, and want to adapt to a new altcoin\n println!(\"Scenario: Adapting model trained on BTC/ETH to predict DOGE movements\\n\");\n\n // Step 1: Create the embedding network (pre-trained on major assets)\n let embedding_config = EmbeddingConfig {\n input_dim: 12, // 12 market features\n hidden_dims: vec![64, 32, 16],\n output_dim: 8,\n activation: Activation::GELU,\n dropout_rate: 0.1,\n };\n\n let network = EmbeddingNetwork::new(embedding_config);\n println!(\"Step 1: Pre-trained embedding network loaded\");\n println!(\" Architecture: 12 -> [64, 32, 16] -> 8\\n\");\n\n // Step 2: Generate synthetic \"historical\" data for major assets\n let (btc_features, btc_labels) = generate_asset_data(\"BTC\", 50);\n let (eth_features, eth_labels) = generate_asset_data(\"ETH\", 50);\n\n println!(\"Step 2: Historical data available\");\n println!(\" BTC: {} labeled samples\", btc_features.len());\n println!(\" ETH: {} labeled samples\", eth_features.len());\n println!();\n\n // Step 3: Get LIMITED data for new asset (few-shot scenario)\n let (doge_support, doge_support_labels) = generate_asset_data(\"DOGE\", 5); // Only 5 examples!\n let (doge_query, doge_query_labels) = generate_asset_data(\"DOGE_query\", 10);\n\n println!(\"Step 3: New asset (DOGE) - Few-shot data\");\n println!(\" Support set: {} examples (few-shot!)\", doge_support.len());\n println!(\" Query set: {} examples to predict\", doge_query.len());\n println!();\n\n // Step 4: Create few-shot learner with MAML for adaptation\n let config = FewShotConfig {\n n_way: 3, // 3-way classification (up, sideways, down)\n k_shot: 1, // 1-shot per class minimum (we have more)\n query_size: 5,\n embedding_dim: 8,\n };\n\n let maml_config = MAMLConfig {\n inner_lr: 0.01,\n inner_steps: 5, // More steps for better adaptation\n first_order: true,\n };\n\n let maml_learner = MAMLLearner::new(config.clone(), maml_config);\n println!(\"Step 4: MAML learner configured for few-shot adaptation\");\n println!(\" Inner learning rate: 0.01\");\n println!(\" Adaptation steps: 5\");\n println!();\n\n // Step 5: Adapt to new asset using few-shot learning\n println!(\"Step 5: Adapting model to DOGE...\\n\");\n\n let adaptation_result = maml_learner.predict_with_adaptation(\n &network,\n &doge_support,\n &doge_support_labels,\n &doge_query,\n );\n\n // Display results\n let class_names = [\"Uptrend\", \"Sideways\", \"Downtrend\"];\n\n println!(\"--- Adaptation Results for DOGE ---\\n\");\n println!(\"Predictions on query set:\");\n for (i, (&pred, &actual)) in adaptation_result.predictions.iter()\n .zip(doge_query_labels.iter())\n .enumerate()\n {\n let correct = if pred == actual { \"β\" } else { \"β\" };\n println!(\n \" Sample {}: Pre | |
| [2026-01-21T18:05:41.628Z] [INFO] dicted {} (conf: {:.1}%) | Actual {} {}\",\n i + 1,\n class_names[pred],\n adaptation_result.confidences[i] * 100.0,\n class_names[actual],\n correct\n );\n }\n\n // Calculate accuracy\n let correct = adaptation_result.predictions.iter()\n .zip(doge_query_labels.iter())\n .filter(|(p, a)| p == a)\n .count();\n let accuracy = correct as f64 / doge_query_labels.len() as f64 * 100.0;\n\n println!(\"\\nOverall accuracy: {:.1}% ({}/{})\",\n accuracy, correct, doge_query_labels.len());\n\n // Step 6: Compare with metric-based approach\n println!(\"\\n--- Comparison: Metric-Based vs MAML ---\\n\");\n\n // Embed all data\n let support_embeddings: Vec<Vec<f64>> = doge_support\n .iter()\n .map(|x| network.forward(x, None))\n .collect();\n\n let query_embeddings: Vec<Vec<f64>> = doge_query\n .iter()\n .map(|x| network.forward(x, None))\n .collect();\n\n let metric_learner = MetricBasedLearner::new(config.clone(), DistanceMetric::Cosine);\n let metric_result = metric_learner.predict(\n &support_embeddings,\n &doge_support_labels,\n &query_embeddings,\n );\n\n let metric_correct = metric_result.predictions.iter()\n .zip(doge_query_labels.iter())\n .filter(|(p, a)| p == a)\n .count();\n let metric_accuracy = metric_correct as f64 / doge_query_labels.len() as f64 * 100.0;\n\n println!(\"Method comparison:\");\n println!(\" MAML (with adaptation): {:.1}%\", accuracy);\n println!(\" Metric-based (no adaptation): {:.1}%\", metric_accuracy);\n println!();\n\n // Step 7: Generate trading signal for new asset\n println!(\"--- Trading Signal Generation ---\\n\");\n\n // Use the predictions to generate signals\n let regime_probs = compute_regime_probabilities(&adaptation_result);\n\n let mut classifier = RegimeClassifier::new(0.5);\n let classification = classifier.classify_with_uncertainty(®ime_probs);\n\n println!(\"Market regime classification for DOGE:\");\n println!(\" {}\", classification.description());\n\n let signal_config = SignalConfig::default();\n let mut signal_generator = SignalGenerator::new(signal_config);\n let signal = signal_generator.generate(\"DOGEUSDT\", &classification);\n\n println!(\"\\nGenerated signal:\");\n println!(\" Type: {:?}\", signal.signal_type);\n println!(\" Confidence: {:.1}%\", signal.confidence * 100.0);\n println!(\" Position size: {:.1}%\", signal.position_size * 100.0);\n if signal.take_profit.is_some() {\n println!(\" Take profit: {:.1}%\", signal.take_profit.unwrap());\n println!(\" Stop loss: {:.1}%\", signal.stop_loss.unwrap());\n }\n\n // Step 8: Real-world usage hint\n println!(\"\\n--- Real-World Usage ---\\n\");\n println!(\"To use with real Bybit data:\");\n println!(\" 1. Create BybitClient::new(false) for mainnet\");\n println!(\" 2. Fetch klines with client.get_klines(\\\"DOGEUSDT\\\", \\\"1h\\\", 100)\");\n println!(\" 3. Extract features with FeatureExtractor::extract(&klines)\");\n println!(\" 4. Run few-shot prediction with extracted features\");\n println!(\" 5. Generate and execute trading signals\");\n println!();\n\n println!(\"=== Example Complete ===\");\n}\n\n/// Generate synthetic market data for an asset.\nfn generate_asset_data(asset_name: &str, count: usize) -> (Vec<Vec<f64>>, Vec<usize>) {\n let mut features = Vec::new();\n let mut labels = Vec::new();\n\n // Seed based on asset name for reproducibility\n let base_seed: u64 = asset_name.bytes().map(|b| b as u64).sum();\n\n for i in 0..count {\n // Determine class (0: uptrend, 1: sideways, 2: downtrend)\n let class = (i + (base_seed as usize)) % 3;\n\n // Generate features based on class\n let base = match class {\n 0 => vec![0.02, 0.015, 0.5, 65.0, 1.5, 0.8, 0.7, 0.6, 0.3, 0.7, 0.8, 0.6],\n 1 => vec![0.001, 0.002, 0.3, 50.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],\n _ => vec![-0.02, -0.015, 0.6, 35.0, 0.7, 0.3, 0.3, 0.4, 0.7, 0.3, 0.2, 0.4],\n };\n\n features.push(add_noise(&base, 0.15, base_seed + i as u64));\n labels.push(class);\n }\n\n (features, labels)\n}\n\n/// Add noise to feature vector.\nfn add_noise(base: &[f64], noise_level: f64, seed: u64) -> Vec<f64> {\n let mut rng_state = seed;\n\n base.iter().map(|&v| {\n rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);\n let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;\n v + v.abs().max(0.01) * noise_level * random\n }).collect()\n}\n\n/// Compute regime probabilities from prediction results.\nfn compute_regime_probabilities(result: &PredictionResult) -> Vec<f64> {\n // Simplified: map 3-class predictions to 8 regimes\n let mut regime_probs = vec![0.0; 8];\n\n let total = result.predictions.len() as f64;\n\n for (&pred, &conf) in result.predictions.iter().zip(result.confidences.iter()) {\n match pred {\n 0 => { // Uptrend\n regime_probs[0] += conf * 0.4 / total; // Strong uptrend\n regime_probs[1] += conf * 0.6 / total; // Weak uptrend\n }\n 1 => { // Sideways\n regime_probs[2] += conf / total; // Sideways\n }\n _ => { // Downtrend\n regime_probs[3] += conf * 0.3 / total; // Weak downtrend\n regime_probs[4] += conf * 0.7 / total; // Strong downtrend\n }\n }\n }\n\n // Normalize\n let sum: f64 = regime_probs.iter().sum();\n if sum > 0.0 {\n regime_probs.iter_mut().for_each(|p| *p /= sum);\n }\n\n regime_probs\n}\n","structuredPatch":[],"originalFile":null}} | |
| [2026-01-21T18:05:44.964Z] [INFO] { | |
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| "text": "Now I need to fix a few issues in the Rust code to make it compile properly. Let me check the lib.rs to understand what's exported and fix any import issues:" | |
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| [2026-01-21T18:05:45.437Z] [INFO] { | |
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| "content": " 1β//! # Few-Shot Market Prediction\n 2β//!\n 3β//! This library implements multiple few-shot learning approaches for financial market\n 4β//! prediction, including metric-based methods, MAML-inspired optimization, and hybrid\n 5β//! approaches.\n 6β//!\n 7β//! ## Overview\n 8β//!\n 9β//! Few-shot learning enables rapid adaptation to new market conditions with minimal\n 10β//! examples. This is particularly valuable for:\n 11β//!\n 12β//! - **New Asset Analysis**: Predict patterns for newly listed cryptocurrencies\n 13β//! - **Rare Event Detection**: Recognize crashes, squeezes with few historical examples\n 14β//! - **Cross-Asset Transfer**: Apply knowledge from one asset to another\n 15β//! - **Regime Change Adaptation**: Quickly adjust to market regime shifts\n 16β//!\n 17β//! ## Supported Methods\n 18β//!\n 19β//! 1. **Metric-Based** (Prototypical Networks): Classify based on distance to prototypes\n 20β//! 2. **MAML-Inspired**: Gradient-based quick adaptation\n 21β//! 3. **Siamese Networks**: Pairwise similarity learning\n 22β//! 4. **Hybrid**: Combine metric and optimization approaches\n 23β//!\n 24β//! ## Example Usage\n 25β//!\n 26β//! ```rust,ignore\n 27β//! use few_shot_market_prediction::prelude::*;\n 28β//!\n 29β//! // Create a metric-based few-shot predictor\n 30β//! let config = FewShotConfig::default()\n 31β//! .with_method(FewShotMethod::Metric)\n 32β//! .with_embedding_dim(32);\n 33β//!\n 34β//! let predictor = FewShotPredictor::new(config);\n 35β//!\n 36β//! // Fit on support set\n 37β//! predictor.fit(&support_features, &support_labels);\n 38β//!\n 39β//! // Predict on query\n 40β//! let (predictions, confidences) = predictor.predict(&query_features);\n 41β//! ```\n 42β//!\n 43β//! ## Modules\n 44β//!\n 45β//! - `network` - Neural network components (embedding, distance computation)\n 46β//! - `methods` - Few-shot learning methods (metric, MAML, siamese, hybrid)\n 47β//! - `data` - Bybit API integration and feature extraction\n 48β//! - `training` - Episodic training framework\n 49β//! - `strategy` - Trading signal generation and regime classification\n 50β\n 51βpub mod network;\n 52βpub mod methods;\n 53βpub mod data;\n 54βpub mod training;\n 55βpub mod strategy;\n 56β\n 57β/// Prelude module for convenient imports\n 58βpub mod prelude {\n 59β // Network components\n 60β pub use crate::network::{\n 61β EmbeddingNetwork, EmbeddingConfig, ActivationType,\n 62β DistanceComputer, DistanceMetric,\n 63β };\n 64β\n 65β // Few-shot methods\n 66β pub use crate::methods::{\n 67β FewShotMethod, FewShotConfig, FewShotPredictor,\n 68β MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n 69β PredictionResult, AdaptationResult,\n 70β };\n 71β\n 72β // Data components\n 73β pub use crate::data::{\n 74β BybitClient, BybitConfig,\n 75β MarketFeatures, FeatureExtractor, FeatureConfig,\n 76β Kline, Trade, OrderBook, FundingRate,\n 77β };\n 78β\n 79β // Training components\n 80β pub use crate::training::{\n 81β Episode, EpisodeGenerator, EpisodeConfig,\n 82β MetaTrainer, MetaTrainerConfig,\n 83β };\n 84β\n 85β // Strategy components\n 86β pub use crate::strategy::{\n 87β MarketRegime, RegimeClassifier,\n 88β TradingSignal, SignalType, SignalGenerator,\n 89β RiskManager, RiskConfig,\n 90β };\n 91β}\n 92β\n 93β/// Library version\n 94βpub const VERSION: &str = env!(\"CARGO_PKG_VERSION\");\n 95β\n 96β#[cfg(test)]\n 97βmod tests {\n 98β use super::*;\n 99β\n 100β #[test]\n 101β fn test_version() {\n 102β assert!(!VERSION.is_empty());\n 103β }\n 104β}\n 105β\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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| "filePath": "/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/src/lib.rs", | |
| "content": "//! # Few-Shot Market Prediction\n//!\n//! This library implements multiple few-shot learning approaches for financial market\n//! prediction, including metric-based methods, MAML-inspired optimization, and hybrid\n//! approaches.\n//!\n//! ## Overview\n//!\n//! Few-shot learning enables rapid adaptation to new market conditions with minimal\n//! examples. This is particularly valuable for:\n//!\n//! - **New Asset Analysis**: Predict patterns for newly listed cryptocurrencies\n//! - **Rare Event Detection**: Recognize crashes, squeezes with few historical examples\n//! - **Cross-Asset Transfer**: Apply knowledge from one asset to another\n//! - **Regime Change Adaptation**: Quickly adjust to market regime shifts\n//!\n//! ## Supported Methods\n//!\n//! 1. **Metric-Based** (Prototypical Networks): Classify based on distance to prototypes\n//! 2. **MAML-Inspired**: Gradient-based quick adaptation\n//! 3. **Siamese Networks**: Pairwise similarity learning\n//! 4. **Hybrid**: Combine metric and optimization approaches\n//!\n//! ## Example Usage\n//!\n//! ```rust,ignore\n//! use few_shot_market_prediction::prelude::*;\n//!\n//! // Create a metric-based few-shot predictor\n//! let config = FewShotConfig::default()\n//! .with_method(FewShotMethod::Metric)\n//! .with_embedding_dim(32);\n//!\n//! let predictor = FewShotPredictor::new(config);\n//!\n//! // Fit on support set\n//! predictor.fit(&support_features, &support_labels);\n//!\n//! // Predict on query\n//! let (predictions, confidences) = predictor.predict(&query_features);\n//! ```\n//!\n//! ## Modules\n//!\n//! - `network` - Neural network components (embedding, distance computation)\n//! - `methods` - Few-shot learning methods (metric, MAML, siamese, hybrid)\n//! - `data` - Bybit API integration and feature extraction\n//! - `training` - Episodic training framework\n//! - `strategy` - Trading signal generation and regime classification\n\npub mod network;\npub mod methods;\npub mod data;\npub mod training;\npub mod strategy;\n\n/// Prelude module for convenient imports\npub mod prelude {\n // Network components\n pub use crate::network::{\n EmbeddingNetwork, EmbeddingConfig, ActivationType,\n DistanceComputer, DistanceMetric,\n };\n\n // Few-shot methods\n pub use crate::methods::{\n FewShotMethod, FewShotConfig, FewShotPredictor,\n MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n PredictionResult, AdaptationResult,\n };\n\n // Data components\n pub use crate::data::{\n BybitClient, BybitConfig,\n MarketFeatures, FeatureExtractor, FeatureConfig,\n Kline, Trade, OrderBook, FundingRate,\n };\n\n // Training components\n pub use crate::training::{\n Episode, EpisodeGenerator, EpisodeConfig,\n MetaTrainer, MetaTrainerConfig,\n };\n\n // Strategy components\n pub use crate::strategy::{\n MarketRegime, RegimeClassifier,\n TradingSignal, SignalType, SignalGenerator,\n RiskManager, RiskConfig,\n };\n}\n\n/// Library version\npub const VERSION: &str = env!(\"CARGO_PKG_VERSION\");\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_version() {\n assert!(!VERSION.is_empty());\n }\n}\n", | |
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| "text": "I see some issues with the prelude module. Let me check and fix the methods/mod.rs and network/mod.rs to ensure they export everything properly:" | |
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| [2026-01-21T18:05:49.298Z] [INFO] { | |
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| "content": " 1β//! Few-shot learning methods\n 2β//!\n 3β//! This module provides multiple few-shot learning approaches:\n 4β//! - Metric-based (Prototypical Networks style)\n 5β//! - MAML-inspired optimization\n 6β//! - Siamese networks for pairwise similarity\n 7β//! - Hybrid approaches combining multiple methods\n 8β\n 9βmod metric;\n 10βmod maml;\n 11βmod siamese;\n 12βmod hybrid;\n 13βmod config;\n 14β\n 15βpub use metric::MetricBasedLearner;\n 16βpub use maml::MAMLLearner;\n 17βpub use siamese::SiameseLearner;\n 18βpub use hybrid::HybridLearner;\n 19βpub use config::{FewShotConfig, FewShotMethod};\n 20β\n 21βuse ndarray::Array1;\n 22βuse serde::{Deserialize, Serialize};\n 23β\n 24β/// Result of a few-shot prediction\n 25β#[derive(Debug, Clone, Serialize, Deserialize)]\n 26βpub struct PredictionResult {\n 27β /// Predicted class index\n 28β pub predicted_class: usize,\n 29β /// Confidence score (0-1)\n 30β pub confidence: f64,\n 31β /// Probabilities for each class\n 32β pub class_probabilities: Vec<f64>,\n 33β /// Whether the prediction is considered reliable\n 34β pub is_reliable: bool,\n 35β}\n 36β\n 37βimpl PredictionResult {\n 38β /// Create a new prediction result\n 39β pub fn new(\n 40β predicted_class: usize,\n 41β confidence: f64,\n 42β class_probabilities: Vec<f64>,\n 43β confidence_threshold: f64,\n 44β ) -> Self {\n 45β Self {\n 46β predicted_class,\n 47β confidence,\n 48β class_probabilities,\n 49β is_reliable: confidence >= confidence_threshold,\n 50β }\n 51β }\n 52β\n 53β /// Get entropy of the prediction distribution\n 54β pub fn entropy(&self) -> f64 {\n 55β -self.class_probabilities\n 56β .iter()\n 57β .filter(|&&p| p > 1e-10)\n 58β .map(|&p| p * p.ln())\n 59β .sum::<f64>()\n 60β }\n 61β}\n 62β\n 63β/// Result of MAML adaptation\n 64β#[derive(Debug, Clone)]\n 65βpub struct AdaptationResult {\n 66β /// Number of adaptation steps performed\n 67β pub steps: usize,\n 68β /// Loss before adaptation\n 69β pub initial_loss: f64,\n 70β /// Loss after adaptation\n 71β pub final_loss: f64,\n 72β /// Improvement ratio\n 73β pub improvement: f64,\n 74β}\n 75β\n 76βimpl AdaptationResult {\n 77β /// Create a new adaptation result\n 78β pub fn new(steps: usize, initial_loss: f64, final_loss: f64) -> Self {\n 79β let improvement = if initial_loss > 1e-10 {\n 80β (initial_loss - final_loss) / initial_loss\n 81β } else {\n 82β 0.0\n 83β };\n 84β Self {\n 85β steps,\n 86β initial_loss,\n 87β final_loss,\n 88β improvement,\n 89β }\n 90β }\n 91β}\n 92β\n 93β/// Trait for few-shot learners\n 94βpub trait FewShotLearner {\n 95β /// Fit the learner on a support set\n 96β fn fit(&mut self, support_features: &ndarray::Array2<f64>, support_labels: &[usize]);\n 97β\n 98β /// Predict on query features\n 99β fn predict(&self, query_features: &ndarray::Array2<f64>) -> Vec<PredictionResult>;\n 100β\n 101β /// Predict single sample\n 102β fn predict_single(&self, query: &Array1<f64>) -> PredictionResult;\n 103β}\n 104β\n 105β/// High-level few-shot predictor that can use different methods\n 106βpub struct FewShotPredictor {\n 107β config: FewShotConfig,\n 108β learner: Box<dyn FewShotLearner + Send + Sync>,\n 109β class_names: std::collections::HashMap<usize, String>,\n 110β}\n 111β\n 112βimpl FewShotPredictor {\n 113β /// Create a new few-shot predictor\n 114β pub fn new(config: FewShotConfig) -> Self {\n 115β let learner: Box<dyn FewShotLearner + Send + Sync> = match config.method {\n 116β FewShotMethod::Metric => Box::new(MetricBasedLearner::new(config.clone())),\n 117β FewShotMethod::MAML => Box::new(MAMLLearner::new(config.clone())),\n 118β FewShotMethod::Siamese => Box::new(SiameseLearner::new(config.clone())),\n 119β FewShotMethod::Hybrid => Box::new(HybridLearner::new(config.clone())),\n 120β };\n 121β\n 122β Self {\n 123β config,\n 124β learner,\n 125β class_names: std::collections::HashMap::new(),\n 126β }\n 127β }\n 128β\n 129β /// Set class names for interpretable predictions\n 130β pub fn with_class_names(mut self, names: std::collections::HashMap<usize, String>) -> Self {\n 131β self.class_names = names;\n 132β self\n 133β }\n 134β\n 135β /// Fit on support set\n 136β pub fn fit(&mut self, support_features: &ndarray::Array2<f64>, support_labels: &[usize]) {\n 137β self.learner.fit(support_features, support_labels);\n 138β\n 139β // Auto-populate class names if not set\n 140β if self.class_names.is_empty() {\n 141β for &label in support_labels {\n 142β self.class_names\n 143β .entry(label)\n 144β .or_insert_with(|| format!(\"Class_{}\", label));\n 145β }\n 146β }\n 147β }\n 148β\n 149β /// Predict on query features\n 150β pub fn predict(&self, query_features: &ndarray::Array2<f64>) -> Vec<PredictionResult> {\n 151β self.learner.predict(query_features)\n 152β }\n 153β\n 154β /// Predict single sample\n 155β pub fn predict_single(&self, query: &Array1<f64>) -> PredictionResult {\n 156β self.learner.predict_single(query)\n 157β }\n 158β\n 159β /// Get class name for a label\n 160β pub fn class_name(&self, label: usize) -> &str {\n 161β self.class_names\n 162β .get(&label)\n 163β .map(|s| s.as_str())\n 164β .unwrap_or(\"Unknown\")\n 165β }\n 166β\n 167β /// Get configuration\n 168β pub fn config(&self) -> &FewShotConfig {\n 169β &self.config\n 170β }\n 171β}\n 172β\n 173β#[cfg(test)]\n 174βmod tests {\n 175β use super::*;\n 176β use ndarray::Array2;\n 177β\n 178β fn create_synthetic_data() -> (Array2<f64>, Vec<usize>, Array2<f64>, Vec<usize>) {\n 179β // Create simple synthetic data for testing\n 180β let n_classes = 3;\n 181β let n_support = 5;\n 182β let n_query = 3;\n 183β let dim = 10;\n 184β\n 185β let mut support_data = Vec::new();\n 186β let mut support_labels = Vec::new();\n 187β let mut query_data = Vec::new();\n 188β let mut query_labels = Vec::new();\n 189β\n 190β for class in 0..n_classes {\n 191β let base = class as f64 * 0.5;\n 192β for _ in 0..n_support {\n 193β let sample: Vec<f64> = (0..dim).map(|_| base + rand::random::<f64>() * 0.1).collect();\n 194β support_data.extend(sample);\n 195β support_labels.push(class);\n 196β }\n 197β for _ in 0..n_query {\n 198β let sample: Vec<f64> = (0..dim).map(|_| base + rand::random::<f64>() * 0.1).collect();\n 199β query_data.extend(sample);\n 200β query_labels.push(class);\n 201β }\n 202β }\n 203β\n 204β let support = Array2::from_shape_vec((n_classes * n_support, dim), support_data).unwrap();\n 205β let query = Array2::from_shape_vec((n_classes * n_query, dim), query_data).unwrap();\n 206β\n 207β (support, support_labels, query, query_labels)\n 208β }\n 209β\n 210β #[test]\n 211β fn test_prediction_result() {\n 212β let result = PredictionResult::new(1, 0.8, vec![0.1, 0.8, 0.1], 0.5);\n 213β assert_eq!(result.predicted_class, 1);\n 214β assert!(result.is_reliable);\n 215β assert!(result.entropy() > 0.0);\n 216β }\n 217β\n 218β #[test]\n 219β fn test_few_shot_predictor_metric() {\n 220β let config = FewShotConfig::default().with_method(FewShotMethod::Metric);\n 221β let mut predictor = FewShotPredictor::new(config);\n 222β\n 223β let (support, support_labels, query, _) = create_synthetic_data();\n 224β predictor.fit(&support, &support_labels);\n 225β\n 226β let predictions = predictor.predict(&query);\n 227β assert_eq!(predictions.len(), query.nrows());\n 228β\n 229β for pred in predictions {\n 230β assert!(pred.confidence >= 0.0 && pred.confidence <= 1.0);\n 231β assert!((pred.class_probabilities.iter().sum::<f64>() - 1.0).abs() < 1e-6);\n 232β }\n 233β }\n 234β}\n 235β\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 learning methods\n//!\n//! This module provides multiple few-shot learning approaches:\n//! - Metric-based (Prototypical Networks style)\n//! - MAML-inspired optimization\n//! - Siamese networks for pairwise similarity\n//! - Hybrid approaches combining multiple methods\n\nmod metric;\nmod maml;\nmod siamese;\nmod hybrid;\nmod config;\n\npub use metric::MetricBasedLearner;\npub use maml::MAMLLearner;\npub use siamese::SiameseLearner;\npub use hybrid::HybridLearner;\npub use config::{FewShotConfig, FewShotMethod};\n\nuse ndarray::Array1;\nuse serde::{Deserialize, Serialize};\n\n/// Result of a few-shot prediction\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct PredictionResult {\n /// Predicted class index\n pub predicted_class: usize,\n /// Confidence score (0-1)\n pub confidence: f64,\n /// Probabilities for each class\n pub class_probabilities: Vec<f64>,\n /// Whether the prediction is considered reliable\n pub is_reliable: bool,\n}\n\nimpl PredictionResult {\n /// Create a new prediction result\n pub fn new(\n predicted_class: usize,\n confidence: f64,\n class_probabilities: Vec<f64>,\n confidence_threshold: f64,\n ) -> Self {\n Self {\n predicted_class,\n confidence,\n class_probabilities,\n is_reliable: confidence >= confidence_threshold,\n }\n }\n\n /// Get entropy of the prediction distribution\n pub fn entropy(&self) -> f64 {\n -self.class_probabilities\n .iter()\n .filter(|&&p| p > 1e-10)\n .map(|&p| p * p.ln())\n .sum::<f64>()\n }\n}\n\n/// Result of MAML adaptation\n#[derive(Debug, Clone)]\npub struct AdaptationResult {\n /// Number of adaptation steps performed\n pub steps: usize,\n /// Loss before adaptation\n pub initial_loss: f64,\n /// Loss after adaptation\n pub final_loss: f64,\n /// Improvement ratio\n pub improvement: f64,\n}\n\nimpl AdaptationResult {\n /// Create a new adaptation result\n pub fn new(steps: usize, initial_loss: f64, final_loss: f64) -> Self {\n let improvement = if initial_loss > 1e-10 {\n (initial_loss - final_loss) / initial_loss\n } else {\n 0.0\n };\n Self {\n steps,\n initial_loss,\n final_loss,\n improvement,\n }\n }\n}\n\n/// Trait for few-shot learners\npub trait FewShotLearner {\n /// Fit the learner on a support set\n fn fit(&mut self, support_features: &ndarray::Array2<f64>, support_labels: &[usize]);\n\n /// Predict on query features\n fn predict(&self, query_features: &ndarray::Array2<f64>) -> Vec<PredictionResult>;\n\n /// Predict single sample\n fn predict_single(&self, query: &Array1<f64>) -> PredictionResult;\n}\n\n/// High-level few-shot predictor that can use different methods\npub struct FewShotPredictor {\n config: FewShotConfig,\n learner: Box<dyn FewShotLearner + Send + Sync>,\n class_names: std::collections::HashMap<usize, String>,\n}\n\nimpl FewShotPredictor {\n /// Create a new few-shot predictor\n pub fn new(config: FewShotConfig) -> Self {\n let learner: Box<dyn FewShotLearner + Send + Sync> = match config.method {\n FewShotMethod::Metric => Box::new(MetricBasedLearner::new(config.clone())),\n FewShotMethod::MAML => Box::new(MAMLLearner::new(config.clone())),\n FewShotMethod::Siamese => Box::new(SiameseLearner::new(config.clone())),\n FewShotMethod::Hybrid => Box::new(HybridLearner::new(config.clone())),\n };\n\n Self {\n config,\n learner,\n class_names: std::collections::HashMap::new(),\n }\n }\n\n /// Set class names for interpretable predictions\n pub fn with_class_names(mut self, names: std::collections::HashMap<usize, String>) -> Self {\n self.class_names = names;\n self\n }\n\n /// Fit on support set\n pub fn fit(&mut self, support_features: &ndarray::Array2<f64>, support_labels: &[usize]) {\n self.learner.fit(support_features, support_labels);\n\n // Auto-populate class names if not set\n if self.class_names.is_empty() {\n for &label in support_labels {\n self.class_names\n .entry(label)\n .or_insert_with(|| format!(\"Class_{}\", label));\n }\n }\n }\n\n /// Predict on query features\n pub fn predict(&self, query_features: &ndarray::Array2<f64>) -> Vec<PredictionResult> {\n self.learner.predict(query_features)\n }\n\n /// Predict single sample\n pub fn predict_single(&self, query: &Array1<f64>) -> PredictionResult {\n self.learner.predict_single(query)\n }\n\n /// Get class name for a label\n pub fn class_name(&self, label: usize) -> &str {\n self.class_names\n .get(&label)\n .map(|s| s.as_str())\n .unwrap_or(\"Unknown\")\n }\n\n /// Get configuration\n pub fn config(&self) -> &FewShotConfig {\n &self.config\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n use ndarray::Array2;\n\n fn create_synthetic_data() -> (Array2<f64>, Vec<usize>, Array2<f64>, Vec<usize>) {\n // Create simple synthetic data for testing\n let n_classes = 3;\n let n_support = 5;\n let n_query = 3;\n let dim = 10;\n\n let mut support_data = Vec::new();\n let mut support_labels = Vec::new();\n let mut query_data = Vec::new();\n let mut query_labels = Vec::new();\n\n for class in 0..n_classes {\n let base = class as f64 * 0.5;\n for _ in 0..n_support {\n let sample: Vec<f64> = (0..dim).map(|_| base + rand::random::<f64>() * 0.1).collect();\n support_data.extend(sample);\n support_labels.push(class);\n }\n for _ in 0..n_query {\n let sample: Vec<f64> = (0..dim).map(|_| base + rand::random::<f64>() * 0.1).collect();\n query_data.extend(sample);\n query_labels.push(class);\n }\n }\n\n let support = Array2::from_shape_vec((n_classes * n_support, dim), support_data).unwrap();\n let query = Array2::from_shape_vec((n_classes * n_query, dim), query_data).unwrap();\n\n (support, support_labels, query, query_labels)\n }\n\n #[test]\n fn test_prediction_result() {\n let result = PredictionResult::new(1, 0.8, vec![0.1, 0.8, 0.1], 0.5);\n assert_eq!(result.predicted_class, 1);\n assert!(result.is_reliable);\n assert!(result.entropy() > 0.0);\n }\n\n #[test]\n fn test_few_shot_predictor_metric() {\n let config = FewShotConfig::default().with_method(FewShotMethod::Metric);\n let mut predictor = FewShotPredictor::new(config);\n\n let (support, support_labels, query, _) = create_synthetic_data();\n predictor.fit(&support, &support_labels);\n\n let predictions = predictor.predict(&query);\n assert_eq!(predictions.len(), query.nrows());\n\n for pred in predictions {\n assert!(pred.confidence >= 0.0 && pred.confidence <= 1.0);\n assert!((pred.class_probabilities.iter().sum::<f64>() - 1.0).abs() < 1e-6);\n }\n }\n}\n", | |
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| "content": " 1β//! Neural network components for few-shot learning\n 2β//!\n 3β//! This module provides:\n 4β//! - Embedding networks for converting market features to embeddings\n 5β//! - Distance computation for similarity-based classification\n 6β//! - Support for MAML-style gradient computation\n 7β\n 8βmod embedding;\n 9βmod distance;\n 10β\n 11βpub use embedding::{EmbeddingNetwork, EmbeddingConfig, ActivationType};\n 12βpub use distance::{DistanceComputer, DistanceMetric};\n 13β\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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| "filePath": "/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/src/network/mod.rs", | |
| "content": "//! Neural network components for few-shot learning\n//!\n//! This module provides:\n//! - Embedding networks for converting market features to embeddings\n//! - Distance computation for similarity-based classification\n//! - Support for MAML-style gradient computation\n\nmod embedding;\nmod distance;\n\npub use embedding::{EmbeddingNetwork, EmbeddingConfig, ActivationType};\npub use distance::{DistanceComputer, DistanceMetric};\n", | |
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| "text": "I see the issue - the prelude exports things with different names than what's defined. Let me check what's actually defined in the embedding.rs and update the lib.rs prelude:" | |
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| "content": " 1β//! Embedding network for few-shot learning\n 2β//!\n 3β//! Supports both forward pass and parameter adaptation for MAML-style training.\n 4β\n 5βuse ndarray::{Array1, Array2};\n 6βuse rand::Rng;\n 7βuse rand_distr::Normal;\n 8βuse serde::{Deserialize, Serialize};\n 9β\n 10β/// Activation function types\n 11β#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]\n 12βpub enum ActivationType {\n 13β /// ReLU activation: max(0, x)\n 14β ReLU,\n 15β /// Leaky ReLU: max(0.01*x, x)\n 16β LeakyReLU,\n 17β /// Tanh activation\n 18β Tanh,\n 19β /// Sigmoid activation\n 20β Sigmoid,\n 21β /// GELU (Gaussian Error Linear Unit)\n 22β GELU,\n 23β /// No activation (linear)\n 24β Linear,\n 25β}\n 26β\n 27βimpl Default for ActivationType {\n 28β fn default() -> Self {\n 29β Self::ReLU\n 30β }\n 31β}\n 32β\n 33β/// Configuration for embedding network\n 34β#[derive(Debug, Clone, Serialize, Deserialize)]\n 35βpub struct EmbeddingConfig {\n 36β /// Input feature dimension\n 37β pub input_dim: usize,\n 38β /// Hidden layer dimensions\n 39β pub hidden_dims: Vec<usize>,\n 40β /// Output embedding dimension\n 41β pub output_dim: usize,\n 42β /// Whether to L2 normalize output embeddings\n 43β pub normalize_embeddings: bool,\n 44β /// Dropout rate (0.0 = no dropout)\n 45β pub dropout_rate: f64,\n 46β /// Activation function type\n 47β pub activation: ActivationType,\n 48β /// Whether to use batch normalization\n 49β pub use_batch_norm: bool,\n 50β}\n 51β\n 52βimpl Default for EmbeddingConfig {\n 53β fn default() -> Self {\n 54β Self {\n 55β input_dim: 20,\n 56β hidden_dims: vec![128, 64],\n 57β output_dim: 32,\n 58β normalize_embeddings: true,\n 59β dropout_rate: 0.1,\n 60β activation: ActivationType::ReLU,\n 61β use_batch_norm: true,\n 62β }\n 63β }\n 64β}\n 65β\n 66βimpl EmbeddingConfig {\n 67β /// Create a new config with specified dimensions\n 68β pub fn new(input_dim: usize, hidden_dims: Vec<usize>, output_dim: usize) -> Self {\n 69β Self {\n 70β input_dim,\n 71β hidden_dims,\n 72β output_dim,\n 73β ..Default::default()\n 74β }\n 75β }\n 76β\n 77β /// Builder pattern: set input dimension\n 78β pub fn with_input_dim(mut self, dim: usize) -> Self {\n 79β self.input_dim = dim;\n 80β self\n 81β }\n 82β\n 83β /// Builder pattern: set output dimension\n 84β pub fn with_output_dim(mut self, dim: usize) -> Self {\n 85β self.output_dim = dim;\n 86β self\n 87β }\n 88β\n 89β /// Builder pattern: set activation\n 90β pub fn with_activation(mut self, activation: ActivationType) -> Self {\n 91β self.activation = activation;\n 92β self\n 93β }\n 94β}\n 95β\n 96β/// Network parameters that can be adapted during MAML training\n 97β#[derive(Debug, Clone)]\n 98βpub struct NetworkParams {\n 99β /// Weight matrices for each layer\n 100β pub weights: Vec<Array2<f64>>,\n 101β /// Bias vectors for each layer\n 102β pub biases: Vec<Array1<f64>>,\n 103β}\n 104β\n 105βimpl NetworkParams {\n 106β /// Create new random parameters\n 107β pub fn random(layer_dims: &[usize]) -> Self {\n 108β let mut weights = Vec::new();\n 109β let mut biases = Vec::new();\n 110β let mut rng = rand::thread_rng();\n 111β\n 112β for i in 0..layer_dims.len() - 1 {\n 113β let (in_dim, out_dim) = (layer_dims[i], layer_dims[i + 1]);\n 114β let std = (2.0 / (in_dim + out_dim) as f64).sqrt();\n 115β let normal = Normal::new(0.0, std).unwrap();\n 116β\n 117β let weight = Array2::from_shape_fn((in_dim, out_dim), |_| rng.sample(normal));\n 118β let bias = Array1::zeros(out_dim);\n 119β\n 120β weights.push(weight);\n 121β biases.push(bias);\n 122β }\n 123β\n 124β Self { weights, biases }\n 125β }\n 126β\n 127β /// Clone parameters\n 128β pub fn clone_params(&self) -> Self {\n 129β Self {\n 130β weights: self.weights.clone(),\n 131β biases: self.biases.clone(),\n 132β }\n 133β }\n 134β\n 135β /// Apply gradient update with given learning rate\n 136β pub fn apply_gradients(&mut self, weight_grads: &[Array2<f64>], bias_grads: &[Array1<f64>], lr: f64) {\n 137β for (i, (wg, bg)) in weight_grads.iter().zip(bias_grads.iter()).enumerate() {\n 138β self.weights[i] = &self.weights[i] - &(wg * lr);\n 139β self.biases[i] = &self.biases[i] - &(bg * lr);\n 140β }\n 141β }\n 142β\n 143β /// Scale all weights by a factor (for weight decay)\n 144β pub fn scale_weights(&mut self, factor: f64) {\n 145β for weight in &mut self.weights {\n 146β weight.mapv_inplace(|w| w * factor);\n 147β }\n 148β }\n 149β}\n 150β\n 151β/// Embedding network for converting market features to embedding vectors\n 152β///\n 153β/// Supports MAML-style training by allowing parameter injection during forward pass.\n 154β#[derive(Debug, Clone)]\n 155βpub struct EmbeddingNetwork {\n 156β config: EmbeddingConfig,\n 157β /// Default parameters (can be overridden during forward pass)\n 158β params: NetworkParams,\n 159β /// Layer dimensions for easy access\n 160β layer_dims: Vec<usize>,\n 161β}\n 162β\n 163βimpl EmbeddingNetwork {\n 164β /// Create a new embedding network with random initialization\n 165β pub fn new(config: EmbeddingConfig) -> Self {\n 166β // Build layer dimensions\n 167β let mut layer_dims = vec![config.input_dim];\n 168β layer_dims.extend(&config.hidden_dims);\n 169β layer_dims.push(config.output_dim);\n 170β\n 171β let params = NetworkParams::random(&layer_dims);\n 172β\n 173β Self {\n 174β config,\n 175β params,\n 176β layer_dims,\n 177β }\n 178β }\n 179β\n 180β /// Apply activation function\n 181β fn apply_activation(&self, x: &mut Array1<f64>) {\n 182β match self.config.activation {\n 183β ActivationType::ReLU => {\n 184β x.mapv_inplace(|v| v.max(0.0));\n 185β }\n 186β ActivationType::LeakyReLU => {\n 187β x.mapv_inplace(|v| if v > 0.0 { v } else { 0.01 * v });\n 188β }\n 189β ActivationType::Tanh => {\n 190β x.mapv_inplace(|v| v.tanh());\n 191β }\n 192β ActivationType::Sigmoid => {\n 193β x.mapv_inplace(|v| 1.0 / (1.0 + (-v).exp()));\n 194β }\n 195β ActivationType::GELU => {\n 196β // Approximation: 0.5 * x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))\n 197β x.mapv_inplace(|v| {\n 198β let inner = 0.7978845608 * (v + 0.044715 * v.powi(3));\n 199β 0.5 * v * (1.0 + inner.tanh())\n 200β });\n 201β }\n 202β ActivationType::Linear => {\n 203β // No-op\n 204β }\n 205β }\n 206β }\n 207β\n 208β /// Forward pass through the network with optional custom parameters\n 209β ///\n 210β /// # Arguments\n 211β /// * `input` - Input features\n 212β /// * `params` - Optional custom parameters (for MAML adaptation)\n 213β pub fn forward(&self, input: &Array1<f64>, params: Option<&NetworkParams>) -> Array1<f64> {\n 214β let params = params.unwrap_or(&self.params);\n 215β let mut x = input.clone();\n 216β\n 217β // Pass through all layers except the last\n 218β for i in 0..params.weights.len() - 1 {\n 219β // Linear transformation\n 220β x = params.weights[i].t().dot(&x) + ¶ms.biases[i];\n 221β // Apply activation\n 222β self.apply_activation(&mut x);\n 223β }\n 224β\n 225β // Last layer (no activation for embedding)\n 226β let last_idx = params.weights.len() - 1;\n 227β x = params.weights[last_idx].t().dot(&x) + ¶ms.biases[last_idx];\n 228β\n 229β // Optional L2 normalization\n 230β if self.config.normalize_embeddings {\n 231β let norm = x.dot(&x).sqrt();\n 232β if norm > 1e-8 {\n 233β x /= norm;\n 234β }\n 235β }\n 236β\n 237β x\n 238β }\n 239β\n 240β /// Forward pass for batch of inputs\n 241β pub fn forward_batch(&self, inputs: &Array2<f64>, params: Option<&NetworkParams>) -> Array2<f64> {\n 242β let n_samples = inputs.nrows();\n 243β let mut outputs = Array2::zeros((n_samples, self.config.output_dim));\n 244β\n 245β for i in 0..n_samples {\n 246β let input = inputs.row(i).to_owned();\n 247β let output = self.forward(&input, params);\n 248β outputs.row_mut(i).assign(&output);\n 249β }\n 250β\n 251β outputs\n 252β }\n 253β\n 254β /// Get current parameters\n 255β pub fn get_params(&self) -> &NetworkParams {\n 256β &self.params\n 257β }\n 258β\n 259β /// Get mutable reference to parameters\n 260β pub fn get_params_mut(&mut self) -> &mut NetworkParams {\n 261β &mut self.params\n 262β }\n 263β\n 264β /// Set parameters\n 265β pub fn set_params(&mut self, params: NetworkParams) {\n 266β self.params = params;\n 267β }\n 268β\n 269β /// Clone current parameters\n 270β pub fn clone_params(&self) -> NetworkParams {\n 271β self.params.clone_params()\n 272β }\n 273β\n 274β /// Get the output embedding dimension\n 275β pub fn output_dim(&self) -> usize {\n 276β self.config.output_dim\n 277β }\n 278β\n 279β /// Get the embedding dimension (alias for output_dim)\n 280β pub fn embedding_dim(&self) -> usize {\n 281β self.config.output_dim\n 282β }\n 283β\n 284β /// Get the input dimension\n 285β pub fn input_dim(&self) -> usize {\n 286β self.config.input_dim\n 287β }\n 288β\n 289β /// Get layer dimensions\n 290β pub fn layer_dims(&self) -> &[usize] {\n 291β &self.layer_dims\n 292β }\n 293β\n 294β /// Get reference to config\n 295β pub fn config(&self) -> &EmbeddingConfig {\n 296β &self.config\n 297β }\n 298β}\n 299β\n 300β#[cfg(test)]\n 301βmod tests {\n 302β use super::*;\n 303β\n 304β #[test]\n 305β fn test_network_creation() {\n 306β let config = EmbeddingConfig {\n 307β input_dim: 16,\n 308β hidden_dims: vec![32, 64],\n 309β output_dim: 32,\n 310β normalize_embeddings: true,\n 311β dropout_rate: 0.0,\n 312β activation: ActivationType::ReLU,\n 313β use_batch_norm: false,\n 314β };\n 315β let network = EmbeddingNetwork::new(config);\n 316β\n 317β assert_eq!(network.params.weights.len(), 3);\n 318β assert_eq!(network.input_dim(), 16);\n 319β assert_eq!(network.output_dim(), 32);\n 320β }\n 321β\n 322β #[test]\n 323β fn test_forward_pass() {\n 324β let config = EmbeddingConfig::new(8, vec![16], 4);\n 325β let network = EmbeddingNetwork::new(config);\n 326β\n 327β let input = Array1::from_vec(vec![1.0; 8]);\n 328β let output = network.forward(&input, None);\n 329β\n 330β assert_eq!(output.len(), 4);\n 331β }\n 332β\n 333β #[test]\n 334β fn test_normalized_output() {\n 335β let config = EmbeddingConfig {\n 336β input_dim: 8,\n 337β hidden_dims: vec![16],\n 338β output_dim: 4,\n 339β normalize_embeddings: true,\n 340β dropout_rate: 0.0,\n 341β activation: ActivationType::ReLU,\n 342β use_batch_norm: false,\n 343β };\n 344β let network = EmbeddingNetwork::new(config);\n 345β\n 346β let input = Array1::from_vec(vec![1.0; 8]);\n 347β let output = network.forward(&input, None);\n 348β\n 349β let norm = output.dot(&output).sqrt();\n 350β assert!((norm - 1.0).abs() < 0.01 || norm < 1e-6);\n 351β }\n 352β\n 353β #[test]\n 354β fn test_forward_with_custom_params() {\n 355β let config = EmbeddingConfig::new(4, vec![8], 4);\n 356β let network = EmbeddingNetwork::new(config);\n 357β\n 358β // Clone and modify parameters\n 359β let mut custom_params = network.clone_params();\n 360β custom_params.scale_weights(2.0);\n 361β\n 362β let input = Array1::from_vec(vec![0.5; 4]);\n 363β\n 364β let output_default = network.forward(&input, None);\n 365β let output_custom = network.forward(&input, Some(&custom_params));\n 366β\n 367β // Outputs should be different\n 368β let diff: f64 = (&output_default - &output_custom).mapv(|x| x.abs()).sum();\n 369β assert!(diff > 0.0);\n 370β }\n 371β\n 372β #[test]\n 373β fn test_batch_forward() {\n 374β let config = EmbeddingConfig::default();\n 375β let network = EmbeddingNetwork::new(config.clone());\n 376β\n 377β let batch_size = 5;\n 378β let inputs = Array2::from_shape_fn((batch_size, config.input_dim), |_| 0.5);\n 379β let outputs = network.forward_batch(&inputs, None);\n 380β\n 381β assert_eq!(outputs.nrows(), batch_size);\n 382β assert_eq!(outputs.ncols(), config.output_dim);\n 383β }\n 384β}\n 385β\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": "//! Embedding network for few-shot learning\n//!\n//! Supports both forward pass and parameter adaptation for MAML-style training.\n\nuse ndarray::{Array1, Array2};\nuse rand::Rng;\nuse rand_distr::Normal;\nuse serde::{Deserialize, Serialize};\n\n/// Activation function types\n#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]\npub enum ActivationType {\n /// ReLU activation: max(0, x)\n ReLU,\n /// Leaky ReLU: max(0.01*x, x)\n LeakyReLU,\n /// Tanh activation\n Tanh,\n /// Sigmoid activation\n Sigmoid,\n /// GELU (Gaussian Error Linear Unit)\n GELU,\n /// No activation (linear)\n Linear,\n}\n\nimpl Default for ActivationType {\n fn default() -> Self {\n Self::ReLU\n }\n}\n\n/// Configuration for embedding network\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct EmbeddingConfig {\n /// Input feature dimension\n pub input_dim: usize,\n /// Hidden layer dimensions\n pub hidden_dims: Vec<usize>,\n /// Output embedding dimension\n pub output_dim: usize,\n /// Whether to L2 normalize output embeddings\n pub normalize_embeddings: bool,\n /// Dropout rate (0.0 = no dropout)\n pub dropout_rate: f64,\n /// Activation function type\n pub activation: ActivationType,\n /// Whether to use batch normalization\n pub use_batch_norm: bool,\n}\n\nimpl Default for EmbeddingConfig {\n fn default() -> Self {\n Self {\n input_dim: 20,\n hidden_dims: vec![128, 64],\n output_dim: 32,\n normalize_embeddings: true,\n dropout_rate: 0.1,\n activation: ActivationType::ReLU,\n use_batch_norm: true,\n }\n }\n}\n\nimpl EmbeddingConfig {\n /// Create a new config with specified dimensions\n pub fn new(input_dim: usize, hidden_dims: Vec<usize>, output_dim: usize) -> Self {\n Self {\n input_dim,\n hidden_dims,\n output_dim,\n ..Default::default()\n }\n }\n\n /// Builder pattern: set input dimension\n pub fn with_input_dim(mut self, dim: usize) -> Self {\n self.input_dim = dim;\n self\n }\n\n /// Builder pattern: set output dimension\n pub fn with_output_dim(mut self, dim: usize) -> Self {\n self.output_dim = dim;\n self\n }\n\n /// Builder pattern: set activation\n pub fn with_activation(mut self, activation: ActivationType) -> Self {\n self.activation = activation;\n self\n }\n}\n\n/// Network parameters that can be adapted during MAML training\n#[derive(Debug, Clone)]\npub struct NetworkParams {\n /// Weight matrices for each layer\n pub weights: Vec<Array2<f64>>,\n /// Bias vectors for each layer\n pub biases: Vec<Array1<f64>>,\n}\n\nimpl NetworkParams {\n /// Create new random parameters\n pub fn random(layer_dims: &[usize]) -> Self {\n let mut weights = Vec::new();\n let mut biases = Vec::new();\n let mut rng = rand::thread_rng();\n\n for i in 0..layer_dims.len() - 1 {\n let (in_dim, out_dim) = (layer_dims[i], layer_dims[i + 1]);\n let std = (2.0 / (in_dim + out_dim) as f64).sqrt();\n let normal = Normal::new(0.0, std).unwrap();\n\n let weight = Array2::from_shape_fn((in_dim, out_dim), |_| rng.sample(normal));\n let bias = Array1::zeros(out_dim);\n\n weights.push(weight);\n biases.push(bias);\n }\n\n Self { weights, biases }\n }\n\n /// Clone parameters\n pub fn clone_params(&self) -> Self {\n Self {\n weights: self.weights.clone(),\n biases: self.biases.clone(),\n }\n }\n\n /// Apply gradient update with given learning rate\n pub fn apply_gradients(&mut self, weight_grads: &[Array2<f64>], bias_grads: &[Array1<f64>], lr: f64) {\n for (i, (wg, bg)) in weight_grads.iter().zip(bias_grads.iter()).enumerate() {\n self.weights[i] = &self.weights[i] - &(wg * lr);\n self.biases[i] = &self.biases[i] - &(bg * lr);\n }\n }\n\n /// Scale all weights by a factor (for weight decay)\n pub fn scale_weights(&mut self, factor: f64) {\n for weight in &mut self.weights {\n weight.mapv_inplace(|w| w * factor);\n }\n }\n}\n\n/// Embedding network for converting market features to embedding vectors\n///\n/// Supports MAML-style training by allowing parameter injection during forward pass.\n#[derive(Debug, Clone)]\npub struct EmbeddingNetwork {\n config: EmbeddingConfig,\n /// Default parameters (can be overridden during forward pass)\n params: NetworkParams,\n /// Layer dimensions for easy access\n layer_dims: Vec<usize>,\n}\n\nimpl EmbeddingNetwork {\n /// Create a new embedding network with random initialization\n pub fn new(config: EmbeddingConfig) -> Self {\n // Build layer dimensions\n let mut layer_dims = vec![config.input_dim];\n layer_dims.extend(&config.hidden_dims);\n layer_dims.push(config.output_dim);\n\n let params = NetworkParams::random(&layer_dims);\n\n Self {\n config,\n params,\n layer_dims,\n }\n }\n\n /// Apply activation function\n fn apply_activation(&self, x: &mut Array1<f64>) {\n match self.config.activation {\n ActivationType::ReLU => {\n x.mapv_inplace(|v| v.max(0.0));\n }\n ActivationType::LeakyReLU => {\n x.mapv_inplace(|v| if v > 0.0 { v } else { 0.01 * v });\n }\n ActivationType::Tanh => {\n x.mapv_inplace(|v| v.tanh());\n }\n ActivationType::Sigmoid => {\n x.mapv_inplace(|v| 1.0 / (1.0 + (-v).exp()));\n }\n ActivationType::GELU => {\n // Approximation: 0.5 * x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))\n x.mapv_inplace(|v| {\n let inner = 0.7978845608 * (v + 0.044715 * v.powi(3));\n 0.5 * v * (1.0 + inner.tanh())\n });\n }\n ActivationType::Linear => {\n // No-op\n }\n }\n }\n\n /// Forward pass through the network with optional custom parameters\n ///\n /// # Arguments\n /// * `input` - Input features\n /// * `params` - Optional custom parameters (for MAML adaptation)\n pub fn forward(&self, input: &Array1<f64>, params: Option<&NetworkParams>) -> Array1<f64> {\n let params = params.unwrap_or(&self.params);\n let mut x = input.clone();\n\n // Pass through all layers except the last\n for i in 0..params.weights.len() - 1 {\n // Linear transformation\n x = params.weights[i].t().dot(&x) + ¶ms.biases[i];\n // Apply activation\n self.apply_activation(&mut x);\n }\n\n // Last layer (no activation for embedding)\n let last_idx = params.weights.len() - 1;\n x = params.weights[last_idx].t().dot(&x) + ¶ms.biases[last_idx];\n\n // Optional L2 normalization\n if self.config.normalize_embeddings {\n let norm = x.dot(&x).sqrt();\n if norm > 1e-8 {\n x /= norm;\n }\n }\n\n x\n }\n\n /// Forward pass for batch of inputs\n pub fn forward_batch(&self, inputs: &Array2<f64>, params: Option<&NetworkParams>) -> Array2<f64> {\n let n_samples = inputs.nrows();\n let mut outputs = Array2::zeros((n_samples, self.config.output_dim));\n\n for i in 0..n_samples {\n let input = inputs.row(i).to_owned();\n let output = self.forward(&input, params);\n outputs.row_mut(i).assign(&output);\n }\n\n outputs\n }\n\n /// Get current parameters\n pub fn get_params(&self) -> &NetworkParams {\n &self.params\n }\n\n /// Get mutable reference to parameters\n pub fn get_params_mut(&mut self) -> &mut NetworkParams {\n &mut self.params\n }\n\n /// Set parameters\n pub fn set_params(&mut self, params: NetworkParams) {\n self.params = params;\n }\n\n /// Clone current parameters\n pub fn clone_params(&self) -> NetworkParams {\n self.params.clone_params()\n }\n\n /// Get the output embedding dimension\n pub fn output_dim(&self) -> usize {\n self.config.output_dim\n }\n\n /// Get the embedding dimension (alias for output_dim)\n pub fn embedding_dim(&self) -> usize {\n self.config.output_dim\n }\n\n /// Get the input dimension\n pub fn input_dim(&self) -> usize {\n self.config.input_dim\n }\n\n /// Get layer dimensions\n pub fn layer_dims(&self) -> &[usize] {\n &self.layer_dims\n }\n\n /// Get reference to config\n pub fn config(&self) -> &EmbeddingConfig {\n &self.config\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_network_creation() {\n let config = EmbeddingConfig {\n input_dim: 16,\n hidden_dims: vec![32, 64],\n output_dim: 32,\n normalize_embeddings: true,\n dropout_rate: 0.0,\n activation: ActivationType::ReLU,\n use_batch_norm: false,\n };\n let network = EmbeddingNetwork::new(config);\n\n assert_eq!(network.params.weights.len(), 3);\n assert_eq!(network.input_dim(), 16);\n assert_eq!(network.output_dim(), 32);\n }\n\n #[test]\n fn test_forward_pass() {\n let config = EmbeddingConfig::new(8, vec![16], 4);\n let network = EmbeddingNetwork::new(config);\n\n let input = Array1::from_vec(vec![1.0; 8]);\n let output = network.forward(&input, None);\n\n assert_eq!(output.len(), 4);\n }\n\n #[test]\n fn test_normalized_output() {\n let config = EmbeddingConfig {\n input_dim: 8,\n hidden_dims: vec![16],\n output_dim: 4,\n normalize_embeddings: true,\n dropout_rate: 0.0,\n activation: ActivationType::ReLU,\n use_batch_norm: false,\n };\n let network = EmbeddingNetwork::new(config);\n\n let input = Array1::from_vec(vec![1.0; 8]);\n let output = network.forward(&input, None);\n\n let norm = output.dot(&output).sqrt();\n assert!((norm - 1.0).abs() < 0.01 || norm < 1e-6);\n }\n\n #[test]\n fn test_forward_with_custom_params() {\n let config = EmbeddingConfig::new(4, vec![8], 4);\n let network = EmbeddingNetwork::new(config);\n\n // Clone and modify parameters\n let mut custom_params = network.clone_params();\n custom_params.scale_weights(2.0);\n\n let input = Array1::from_vec(vec![0.5; 4]);\n\n let output_default = network.forward(&input, None);\n let output_custom = network.forward(&input, Some(&custom_params));\n\n // Outputs should be different\n let diff: f64 = (&output_default - &output_custom).mapv(|x| x.abs()).sum();\n assert!(diff > 0.0);\n }\n\n #[test]\n fn test_batch_forward() {\n let config = EmbeddingConfig::default();\n let network = EmbeddingNetwork::new(config.clone());\n\n let batch_size = 5;\n let inputs = Array2::from_shape_fn((batch_size, config.input_dim), |_| 0.5);\n let outputs = network.forward_batch(&inputs, None);\n\n assert_eq!(outputs.nrows(), batch_size);\n assert_eq!(outputs.ncols(), config.output_dim);\n }\n}\n", | |
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| } | |
| [2026-01-21T18:05:58.023Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-5-20251101", | |
| "id": "msg_012eaaJK2BM1jeuz7w9jqytq", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": "I see the issue - the embedding.rs uses `ActivationType` but the examples and lib.rs use `Activation`. The examples also use structures that don't quite match. Let me update the lib.rs prelude and fix the examples to use the correct names:" | |
| } | |
| ], | |
| "stop_reason": null, | |
| "stop_sequence": null, | |
| "usage": { | |
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| "cache_read_input_tokens": 56753, | |
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| "service_tier": "standard" | |
| }, | |
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| "content": " 1β//! Configuration for few-shot learning methods\n 2β\n 3βuse crate::network::{DistanceMetric, EmbeddingConfig};\n 4βuse serde::{Deserialize, Serialize};\n 5β\n 6β/// Few-shot learning method types\n 7β#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]\n 8βpub enum FewShotMethod {\n 9β /// Metric-based learning (Prototypical Networks style)\n 10β Metric,\n 11β /// MAML-inspired gradient-based adaptation\n 12β MAML,\n 13β /// Siamese network for pairwise similarity\n 14β Siamese,\n 15β /// Hybrid combining metric and optimization\n 16β Hybrid,\n 17β}\n 18β\n 19βimpl Default for FewShotMethod {\n 20β fn default() -> Self {\n 21β Self::Metric\n 22β }\n 23β}\n 24β\n 25β/// Configuration for few-shot learning\n 26β#[derive(Debug, Clone, Serialize, Deserialize)]\n 27βpub struct FewShotConfig {\n 28β /// Few-shot learning method to use\n 29β pub method: FewShotMethod,\n 30β\n 31β /// Number of classes per episode (N-way)\n 32β pub n_way: usize,\n 33β\n 34β /// Number of examples per class (K-shot)\n 35β pub k_shot: usize,\n 36β\n 37β /// Number of query samples per class during training\n 38β pub n_query: usize,\n 39β\n 40β // Network configuration\n 41β /// Input feature dimension\n 42β pub input_dim: usize,\n 43β\n 44β /// Hidden layer dimensions\n 45β pub hidden_dims: Vec<usize>,\n 46β\n 47β /// Embedding dimension\n 48β pub embedding_dim: usize,\n 49β\n 50β // Distance/similarity\n 51β /// Distance metric for prototype comparison\n 52β pub distance_metric: DistanceMetric,\n 53β\n 54β /// Softmax temperature (higher = more uniform distribution)\n 55β pub temperature: f64,\n 56β\n 57β // MAML-specific\n 58β /// Number of adaptation steps for MAML\n 59β pub adaptation_steps: usize,\n 60β\n 61β /// Inner loop learning rate for MAML\n 62β pub adaptation_lr: f64,\n 63β\n 64β // Hybrid-specific\n 65β /// Weight for metric-based predictions in hybrid mode (0-1)\n 66β pub metric_weight: f64,\n 67β\n 68β // General training\n 69β /// Learning rate for meta-training\n 70β pub learning_rate: f64,\n 71β\n 72β /// Number of training episodes\n 73β pub n_episodes: usize,\n 74β\n 75β /// Dropout rate\n 76β pub dropout_rate: f64,\n 77β\n 78β /// L2 regularization weight\n 79β pub l2_reg: f64,\n 80β\n 81β // Prediction thresholds\n 82β /// Confidence threshold for reliable predictions\n 83β pub confidence_threshold: f64,\n 84β}\n 85β\n 86βimpl Default for FewShotConfig {\n 87β fn default() -> Self {\n 88β Self {\n 89β method: FewShotMethod::Metric,\n 90β n_way: 5,\n 91β k_shot: 5,\n 92β n_query: 10,\n 93β input_dim: 20,\n 94β hidden_dims: vec![128, 64],\n 95β embedding_dim: 32,\n 96β distance_metric: DistanceMetric::Euclidean,\n 97β temperature: 1.0,\n 98β adaptation_steps: 5,\n 99β adaptation_lr: 0.01,\n 100β metric_weight: 0.6,\n 101β learning_rate: 0.001,\n 102β n_episodes: 1000,\n 103β dropout_rate: 0.1,\n 104β l2_reg: 0.0001,\n 105β confidence_threshold: 0.5,\n 106β }\n 107β }\n 108β}\n 109β\n 110βimpl FewShotConfig {\n 111β /// Create a new config with default settings\n 112β pub fn new() -> Self {\n 113β Self::default()\n 114β }\n 115β\n 116β /// Builder: set the few-shot method\n 117β pub fn with_method(mut self, method: FewShotMethod) -> Self {\n 118β self.method = method;\n 119β self\n 120β }\n 121β\n 122β /// Builder: set N-way\n 123β pub fn with_n_way(mut self, n_way: usize) -> Self {\n 124β self.n_way = n_way;\n 125β self\n 126β }\n 127β\n 128β /// Builder: set K-shot\n 129β pub fn with_k_shot(mut self, k_shot: usize) -> Self {\n 130β self.k_shot = k_shot;\n 131β self\n 132β }\n 133β\n 134β /// Builder: set input dimension\n 135β pub fn with_input_dim(mut self, dim: usize) -> Self {\n 136β self.input_dim = dim;\n 137β self\n 138β }\n 139β\n 140β /// Builder: set embedding dimension\n 141β pub fn with_embedding_dim(mut self, dim: usize) -> Self {\n 142β self.embedding_dim = dim;\n 143β self\n 144β }\n 145β\n 146β /// Builder: set hidden dimensions\n 147β pub fn with_hidden_dims(mut self, dims: Vec<usize>) -> Self {\n 148β self.hidden_dims = dims;\n 149β self\n 150β }\n 151β\n 152β /// Builder: set distance metric\n 153β pub fn with_distance_metric(mut self, metric: DistanceMetric) -> Self {\n 154β self.distance_metric = metric;\n 155β self\n 156β }\n 157β\n 158β /// Builder: set temperature\n 159β pub fn with_temperature(mut self, temp: f64) -> Self {\n 160β self.temperature = temp;\n 161β self\n 162β }\n 163β\n 164β /// Builder: set MAML adaptation steps\n 165β pub fn with_adaptation_steps(mut self, steps: usize) -> Self {\n 166β self.adaptation_steps = steps;\n 167β self\n 168β }\n 169β\n 170β /// Builder: set MAML adaptation learning rate\n 171β pub fn with_adaptation_lr(mut self, lr: f64) -> Self {\n 172β self.adaptation_lr = lr;\n 173β self\n 174β }\n 175β\n 176β /// Builder: set confidence threshold\n 177β pub fn with_confidence_threshold(mut self, threshold: f64) -> Self {\n 178β self.confidence_threshold = threshold;\n 179β self\n 180β }\n 181β\n 182β /// Get embedding config derived from this config\n 183β pub fn to_embedding_config(&self) -> EmbeddingConfig {\n 184β EmbeddingConfig {\n 185β input_dim: self.input_dim,\n 186β hidden_dims: self.hidden_dims.clone(),\n 187β output_dim: self.embedding_dim,\n 188β normalize_embeddings: true,\n 189β dropout_rate: self.dropout_rate,\n 190β ..Default::default()\n 191β }\n 192β }\n 193β}\n 194β\n 195β#[cfg(test)]\n 196βmod tests {\n 197β use super::*;\n 198β\n 199β #[test]\n 200β fn test_config_builder() {\n 201β let config = FewShotConfig::new()\n 202β .with_method(FewShotMethod::MAML)\n 203β .with_n_way(3)\n 204β .with_k_shot(10)\n 205β .with_embedding_dim(64);\n 206β\n 207β assert_eq!(config.method, FewShotMethod::MAML);\n 208β assert_eq!(config.n_way, 3);\n 209β assert_eq!(config.k_shot, 10);\n 210β assert_eq!(config.embedding_dim, 64);\n 211β }\n 212β\n 213β #[test]\n 214β fn test_to_embedding_config() {\n 215β let config = FewShotConfig::default();\n 216β let emb_config = config.to_embedding_config();\n 217β\n 218β assert_eq!(emb_config.input_dim, config.input_dim);\n 219β assert_eq!(emb_config.output_dim, config.embedding_dim);\n 220β assert_eq!(emb_config.hidden_dims, config.hidden_dims);\n 221β }\n 222β}\n 223β\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": "//! Configuration for few-shot learning methods\n\nuse crate::network::{DistanceMetric, EmbeddingConfig};\nuse serde::{Deserialize, Serialize};\n\n/// Few-shot learning method types\n#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]\npub enum FewShotMethod {\n /// Metric-based learning (Prototypical Networks style)\n Metric,\n /// MAML-inspired gradient-based adaptation\n MAML,\n /// Siamese network for pairwise similarity\n Siamese,\n /// Hybrid combining metric and optimization\n Hybrid,\n}\n\nimpl Default for FewShotMethod {\n fn default() -> Self {\n Self::Metric\n }\n}\n\n/// Configuration for few-shot learning\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct FewShotConfig {\n /// Few-shot learning method to use\n pub method: FewShotMethod,\n\n /// Number of classes per episode (N-way)\n pub n_way: usize,\n\n /// Number of examples per class (K-shot)\n pub k_shot: usize,\n\n /// Number of query samples per class during training\n pub n_query: usize,\n\n // Network configuration\n /// Input feature dimension\n pub input_dim: usize,\n\n /// Hidden layer dimensions\n pub hidden_dims: Vec<usize>,\n\n /// Embedding dimension\n pub embedding_dim: usize,\n\n // Distance/similarity\n /// Distance metric for prototype comparison\n pub distance_metric: DistanceMetric,\n\n /// Softmax temperature (higher = more uniform distribution)\n pub temperature: f64,\n\n // MAML-specific\n /// Number of adaptation steps for MAML\n pub adaptation_steps: usize,\n\n /// Inner loop learning rate for MAML\n pub adaptation_lr: f64,\n\n // Hybrid-specific\n /// Weight for metric-based predictions in hybrid mode (0-1)\n pub metric_weight: f64,\n\n // General training\n /// Learning rate for meta-training\n pub learning_rate: f64,\n\n /// Number of training episodes\n pub n_episodes: usize,\n\n /// Dropout rate\n pub dropout_rate: f64,\n\n /// L2 regularization weight\n pub l2_reg: f64,\n\n // Prediction thresholds\n /// Confidence threshold for reliable predictions\n pub confidence_threshold: f64,\n}\n\nimpl Default for FewShotConfig {\n fn default() -> Self {\n Self {\n method: FewShotMethod::Metric,\n n_way: 5,\n k_shot: 5,\n n_query: 10,\n input_dim: 20,\n hidden_dims: vec![128, 64],\n embedding_dim: 32,\n distance_metric: DistanceMetric::Euclidean,\n temperature: 1.0,\n adaptation_steps: 5,\n adaptation_lr: 0.01,\n metric_weight: 0.6,\n learning_rate: 0.001,\n n_episodes: 1000,\n dropout_rate: 0.1,\n l2_reg: 0.0001,\n confidence_threshold: 0.5,\n }\n }\n}\n\nimpl FewShotConfig {\n /// Create a new config with default settings\n pub fn new() -> Self {\n Self::default()\n }\n\n /// Builder: set the few-shot method\n pub fn with_method(mut self, method: FewShotMethod) -> Self {\n self.method = method;\n self\n }\n\n /// Builder: set N-way\n pub fn with_n_way(mut self, n_way: usize) -> Self {\n self.n_way = n_way;\n self\n }\n\n /// Builder: set K-shot\n pub fn with_k_shot(mut self, k_shot: usize) -> Self {\n self.k_shot = k_shot;\n self\n }\n\n /// Builder: set input dimension\n pub fn with_input_dim(mut self, dim: usize) -> Self {\n self.input_dim = dim;\n self\n }\n\n /// Builder: set embedding dimension\n pub fn with_embedding_dim(mut self, dim: usize) -> Self {\n self.embedding_dim = dim;\n self\n }\n\n /// Builder: set hidden dimensions\n pub fn with_hidden_dims(mut self, dims: Vec<usize>) -> Self {\n self.hidden_dims = dims;\n self\n }\n\n /// Builder: set distance metric\n pub fn with_distance_metric(mut self, metric: DistanceMetric) -> Self {\n self.distance_metric = metric;\n self\n }\n\n /// Builder: set temperature\n pub fn with_temperature(mut self, temp: f64) -> Self {\n self.temperature = temp;\n self\n }\n\n /// Builder: set MAML adaptation steps\n pub fn with_adaptation_steps(mut self, steps: usize) -> Self {\n self.adaptation_steps = steps;\n self\n }\n\n /// Builder: set MAML adaptation learning rate\n pub fn with_adaptation_lr(mut self, lr: f64) -> Self {\n self.adaptation_lr = lr;\n self\n }\n\n /// Builder: set confidence threshold\n pub fn with_confidence_threshold(mut self, threshold: f64) -> Self {\n self.confidence_threshold = threshold;\n self\n }\n\n /// Get embedding config derived from this config\n pub fn to_embedding_config(&self) -> EmbeddingConfig {\n EmbeddingConfig {\n input_dim: self.input_dim,\n hidden_dims: self.hidden_dims.clone(),\n output_dim: self.embedding_dim,\n normalize_embeddings: true,\n dropout_rate: self.dropout_rate,\n ..Default::default()\n }\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_config_builder() {\n let config = FewShotConfig::new()\n .with_method(FewShotMethod::MAML)\n .with_n_way(3)\n .with_k_shot(10)\n .with_embedding_dim(64);\n\n assert_eq!(config.method, FewShotMethod::MAML);\n assert_eq!(config.n_way, 3);\n assert_eq!(config.k_shot, 10);\n assert_eq!(config.embedding_dim, 64);\n }\n\n #[test]\n fn test_to_embedding_config() {\n let config = FewShotConfig::default();\n let emb_config = config.to_embedding_config();\n\n assert_eq!(emb_config.input_dim, config.input_dim);\n assert_eq!(emb_config.output_dim, config.embedding_dim);\n assert_eq!(emb_config.hidden_dims, config.hidden_dims);\n }\n}\n", | |
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| [2026-01-21T18:05:59.916Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01F4hjPHv12EebT7wb1bjERT","type":"tool_result","content":" 1β//! MAML-inspired few-shot learning\n 2β//!\n 3β//! Model-Agnostic Meta-Learning uses gradient-based adaptation for quick\n 4β//! learning on new tasks. This implementation uses a simplified version\n 5β//! with finite-difference gradient approximation.\n 6β\n 7βuse super::{AdaptationResult, FewShotConfig, FewShotLearner, PredictionResult};\n 8βuse crate::network::{DistanceComputer, EmbeddingNetwork, NetworkParams};\n 9βuse ndarray::{Array1, Array2};\n 10βuse std::collections::HashMap;\n 11β\n 12β/// MAML-inspired few-shot learner\n 13β///\n 14β/// Performs gradient-based adaptation on the support set before making\n 15β/// predictions. This allows the model to quickly adapt to new task distributions.\n 16βpub struct MAMLLearner {\n 17β config: FewShotConfig,\n 18β network: EmbeddingNetwork,\n 19β distance_computer: DistanceComputer,\n 20β /// Adapted parameters after fitting\n 21β adapted_params: Option<NetworkParams>,\n 22β /// Prototypes computed with adapted parameters\n 23β prototypes: HashMap<usize, Array1<f64>>,\n 24β /// Last adaptation result\n 25β last_adaptation: Option<AdaptationResult>,\n 26β fitted: bool,\n 27β}\n 28β\n 29βimpl MAMLLearner {\n 30β /// Create a new MAML learner\n 31β pub fn new(config: FewShotConfig) -> Self {\n 32β let emb_config = config.to_embedding_config();\n 33β let network = EmbeddingNetwork::new(emb_config);\n 34β let distance_computer = DistanceComputer::new(config.distance_metric);\n 35β\n 36β Self {\n 37β config,\n 38β network,\n 39β distance_computer,\n 40β adapted_params: None,\n 41β prototypes: HashMap::new(),\n 42β last_adaptation: None,\n 43β fitted: false,\n 44β }\n 45β }\n 46β\n 47β /// Compute prototype-based loss for a set of embeddings and labels\n 48β fn compute_loss(\n 49β &self,\n 50β embeddings: &Array2<f64>,\n 51β labels: &[usize],\n 52β temperature: f64,\n 53β ) -> f64 {\n 54β // Compute prototypes\n 55β let mut class_embeddings: HashMap<usize, Vec<Array1<f64>>> = HashMap::new();\n 56β for (i, &label) in labels.iter().enumerate() {\n 57β let emb = embeddings.row(i).to_owned();\n 58β class_embeddings.entry(label).or_default().push(emb);\n 59β }\n 60β\n 61β let prototypes: HashMap<usize, Array1<f64>> = class_embeddings\n 62β .into_iter()\n 63β .map(|(label, embs)| {\n 64β let dim = embs[0].len();\n 65β let mut sum = Array1::zeros(dim);\n 66β for emb in &embs {\n 67β sum = sum + emb;\n 68β }\n 69β (label, sum / embs.len() as f64)\n 70β })\n 71β .collect();\n 72β\n 73β // Compute loss as negative log probability of correct class\n 74β let mut class_indices: Vec<_> = prototypes.keys().cloned().collect();\n 75β class_indices.sort();\n 76β\n 77β let mut total_loss = 0.0;\n 78β for (i, &true_label) in labels.iter().enumerate() {\n 79β let emb = embeddings.row(i).to_owned();\n 80β\n 81β // Compute distances to all prototypes\n 82β let distances: Vec<f64> = class_indices\n 83β .iter()\n 84β .map(|&label| {\n 85β let prototype = &prototypes[&label];\n 86β self.distance_computer.distance(&emb, prototype)\n 87β })\n 88β .collect();\n 89β\n 90β // Convert to probabilities\n 91β let probs = DistanceComputer::distances_to_probs(&distances, temperature);\n 92β\n 93β // Find index of true class\n 94β let true_idx = class_indices.iter().position(|&l| l == true_label).unwrap();\n 95β\n 96β // Negative log probability\n 97β total_loss -= (probs[true_idx] + 1e-10).ln();\n 98β }\n 99β\n 100β total_loss / labels.len() as f64\n 101β }\n 102β\n 103β /// Perform gradient-based adaptation using finite differences\n 104β ///\n 105β /// This is a simplified implementation. In practice, you would use\n 106β /// automatic differentiation for better performance.\n 107β fn adapt(\n 108β &self,\n 109β support_features: &Array2<f64>,\n 110β support_labels: &[usize],\n 111β ) -> (NetworkParams, AdaptationResult) {\n 112β let mut params = self.network.clone_params();\n 113β let eps = 1e-5;\n 114β\n 115β // Compute initial loss\n 116β let initial_embeddings = self.network.forward_batch(support_features, Some(¶ms));\n 117β let initial_loss = self.compute_loss(&initial_embeddings, support_labels, self.config.temperature);\n 118β\n 119β // Perform adaptation steps\n 120β for _step in 0..self.config.adaptation_steps {\n 121β // Compute gradients using finite differences (simplified)\n 122β let mut weight_grads: Vec<Array2<f64>> = Vec::new();\n 123β let mut bias_grads: Vec<Array1<f64>> = Vec::new();\n 124β\n 125β // For each layer, compute approximate gradients\n 126β for layer_idx in 0..params.weights.len() {\n 127β let (rows, cols) = params.weights[layer_idx].dim();\n 128β let mut w_grad = Array2::zeros((rows, cols));\n 129β\n 130β // Sample random indices for stochastic gradient approximation\n 131β // (full gradient would be too expensive)\n 132β let n_samples = std::cmp::min(10, rows * cols);\n 133β let mut rng = rand::thread_rng();\n 134β\n 135β for _ in 0..n_samples {\n 136β let i = rand::Rng::gen_range(&mut rng, 0..rows);\n 137β let j = rand::Rng::gen_range(&mut rng, 0..cols);\n 138β\n 139β // Finite difference approximation\n 140β params.weights[layer_idx][[i, j]] += eps;\n 141β let emb_plus = self.network.forward_batch(support_features, Some(¶ms));\n 142β let loss_plus = self.compute_loss(&emb_plus, support_labels, self.config.temperature);\n 143β params.weights[layer_idx][[i, j]] -= eps;\n 144β\n 145β w_grad[[i, j]] = (loss_plus - initial_loss) / eps;\n 146β }\n 147β weight_grads.push(w_grad);\n 148β\n 149β // Bias gradients\n 150β let bias_len = params.biases[layer_idx].len();\n 151β let mut b_grad = Array1::zeros(bias_len);\n 152β\n 153β for idx in 0..std::cmp::min(5, bias_len) {\n 154β params.biases[layer_idx][idx] += eps;\n 155β let emb_plus = self.network.forward_batch(support_features, Some(¶ms));\n 156β let loss_plus = self.compute_loss(&emb_plus, support_labels, self.config.temperature);\n 157β params.biases[layer_idx][idx] -= eps;\n 158β\n 159β b_grad[idx] = (loss_plus - initial_loss) / eps;\n 160β }\n 161β bias_grads.push(b_grad);\n 162β }\n 163β\n 164β // Apply gradients\n 165β params.apply_gradients(&weight_grads, &bias_grads, self.config.adaptation_lr);\n 166β }\n 167β\n 168β // Compute final loss\n 169β let final_embeddings = self.network.forward_batch(support_features, Some(¶ms));\n 170β let final_loss = self.compute_loss(&final_embeddings, support_labels, self.config.temperature);\n 171β\n 172β let result = AdaptationResult::new(self.config.adaptation_steps, initial_loss, final_loss);\n 173β\n 174β (params, result)\n 175β }\n 176β\n 177β /// Get last adaptation result\n 178β pub fn last_adaptation(&self) -> Option<&AdaptationResult> {\n 179β self.last_adaptation.as_ref()\n 180β }\n 181β\n 182β /// Get prototypes\n 183β pub fn prototypes(&self) -> &HashMap<usize, Array1<f64>> {\n 184β &self.prototypes\n 185β }\n 186β}\n 187β\n 188βimpl FewShotLearner for MAMLLearner {\n 189β fn fit(&mut self, support_features: &Array2<f64>, support_labels: &[usize]) {\n 190β // Perform adaptation\n 191β let (adapted_params, adaptation_result) = self.adapt(support_features, support_labels);\n 192β\n 193β // Store adapted parameters\n 194β self.adapted_params = Some(adapted_params.clone());\n 195β self.last_adaptation = Some(adaptation_result);\n 196β\n 197β // Compute prototypes using adapted parameters\n 198β let embeddings = self.network.forward_batch(support_features, Some(&adapted_params));\n 199β\n 200β let mut class_embeddings: HashMap<usize, Vec<Array1<f64>>> = HashMap::new();\n 201β for (i, &label) in support_labels.iter().enumerate() {\n 202β let emb = embeddings.row(i).to_owned();\n 203β class_embeddings.entry(label).or_default().push(emb);\n 204β }\n 205β\n 206β self.prototypes.clear();\n 207β for (label, embs) in class_embeddings {\n 208β let dim = embs[0].len();\n 209β let mut sum = Array1::zeros(dim);\n 210β for emb in &embs {\n 211β sum = sum + emb;\n 212β }\n 213β self.prototypes.insert(label, sum / embs.len() as f64);\n 214β }\n 215β\n 216β self.fitted = true;\n 217β }\n 218β\n 219β fn predict(&self, query_features: &Array2<f64>) -> Vec<PredictionResult> {\n 220β if !self.fitted {\n 221β panic!(\"Model not fitted. Call fit() first.\");\n 222β }\n 223β\n 224β let params = self.adapted_params.as_ref().unwrap();\n 225β let n_queries = query_features.nrows();\n 226β let mut results = Vec::with_capacity(n_queries);\n 227β\n 228β // Embed query features using adapted parameters\n 229β let query_embeddings = self.network.forward_batch(query_features, Some(params));\n 230β\n 231β // Get sorted class indices\n 232β let mut class_indices: Vec<_> = self.prototypes.keys().cloned().collect();\n 233β class_indices.sort();\n 234β\n 235β for i in 0..n_queries {\n 236β let query_emb = query_embeddings.row(i).to_owned();\n 237β\n 238β // Compute distances to all prototypes\n 239β let distances: Vec<f64> = class_indices\n 240β .iter()\n 241β .map(|&label| {\n 242β let prototype = &self.prototypes[&label];\n 243β self.distance_computer.distance(&query_emb, prototype)\n 244β })\n 245β .collect();\n 246β\n 247β // Convert to probabilities\n 248β let probs = DistanceComputer::distances_to_probs(&distances, self.config.temperature);\n 249β\n 250β // Find best class\n 251β let (best_idx, &confidence) = probs\n 252β .iter()\n 253β .enumerate()\n 254β .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n 255β .unwrap();\n 256β\n 257β let predicted_class = class_indices[best_idx];\n 258β\n 259β results.push(PredictionResult::new(\n 260β predicted_class,\n 261β confidence,\n 262β probs,\n 263β self.config.confidence_threshold,\n 264β ));\n 265β }\n 266β\n 267β results\n 268β }\n 269β\n 270β fn predict_single(&self, query: &Array1<f64>) -> PredictionResult {\n 271β if !self.fitted {\n 272β panic!(\"Model not fitted. Call fit() first.\");\n 273β }\n 274β\n 275β let params = self.adapted_params.as_ref().unwrap();\n 276β\n 277β // Embed query using adapted parameters\n 278β let query_emb = self.network.forward(query, Some(params));\n 279β\n 280β // Get sorted class indices\n 281β let mut class_indices: Vec<_> = self.prototypes.keys().cloned().collect();\n 282β class_indices.sort();\n 283β\n 284β // Compute distances to all prototypes\n 285β let distances: Vec<f64> = class_indices\n 286β .iter()\n 287β .map(|&label| {\n 288β let prototype = &self.prototypes[&label];\n 289β self.distance_computer.distance(&query_emb, prototype)\n 290β })\n 291β .collect();\n 292β\n 293β // Convert to probabilities\n 294β let probs = DistanceComputer::distances_to_probs(&distances, self.config.temperature);\n 295β\n 296β // Find best class\n 297β let (best_idx, &confidence) = probs\n 298β .iter()\n 299β .enumerate()\n 300β .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n 301β .unwrap();\n 302β\n 303β let predicted_class = class_indices[best_idx];\n 304β\n 305β PredictionResult::new(\n 306β predicted_class,\n 307β confidence,\n 308β probs,\n 309β self.config.confidence_threshold,\n 310β )\n 311β }\n 312β}\n 313β\n 314β#[cfg(test)]\n 315βmod tests {\n 316β use super::*;\n 317β use rand::Rng;\n 318β\n 319β fn create_test_data() -> (Array2<f64>, Vec<usize>) {\n 320β let mut rng = rand::thread_rng();\n 321β let n_classes = 3;\n 322β let n_per_class = 5;\n 323β let dim = 10;\n 324β\n 325β let mut data = Vec::new();\n 326β let mut labels = Vec::new();\n 327β\n 328β for class in 0..n_classes {\n 329β let base = class as f64 * 2.0;\n 330β for _ in 0..n_per_class {\n 331β for _ in 0..dim {\n 332β data.push(base + rng.gen::<f64>() * 0.3);\n 333β }\n 334β labels.push(class);\n 335β }\n 336β }\n 337β\n 338β let features = Array2::from_shape_vec((n_classes * n_per_class, dim), data).unwrap();\n 339β (features, labels)\n 340β }\n 341β\n 342β #[test]\n 343β fn test_maml_learner_fit() {\n 344β let config = FewShotConfig::default()\n 345β .with_input_dim(10)\n 346β .with_adaptation_steps(2);\n 347β let mut learner = MAMLLearner::new(config);\n 348β\n 349β let (features, labels) = create_test_data();\n 350β learner.fit(&features, &labels);\n 351β\n 352β assert!(learner.fitted);\n 353β assert!(learner.adapted_params.is_some());\n 354β assert!(learner.last_adaptation.is_some());\n 355β }\n 356β\n 357β #[test]\n 358β fn test_maml_learner_predict() {\n 359β let config = FewShotConfig::default()\n 360β .with_input_dim(10)\n 361β .with_adaptation_steps(1);\n 362β let mut learner = MAMLLearner::new(config);\n 363β\n 364β let (features, labels) = create_test_data();\n 365β learner.fit(&features, &labels);\n | |
| [2026-01-21T18:05:59.922Z] [INFO] 366β\n 367β let query = Array2::from_shape_vec((2, 10), vec![0.1; 20]).unwrap();\n 368β let predictions = learner.predict(&query);\n 369β\n 370β assert_eq!(predictions.len(), 2);\n 371β }\n 372β}\n 373β\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":"172d23c7-5c62-484f-af89-80e92d0b393e","uuid":"71c43a8d-1224-4265-9f1c-e3bc2b4460f3","tool_use_result":{"type":"text","file":{"filePath":"/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/src/methods/maml.rs","content":"//! MAML-inspired few-shot learning\n//!\n//! Model-Agnostic Meta-Learning uses gradient-based adaptation for quick\n//! learning on new tasks. This implementation uses a simplified version\n//! with finite-difference gradient approximation.\n\nuse super::{AdaptationResult, FewShotConfig, FewShotLearner, PredictionResult};\nuse crate::network::{DistanceComputer, EmbeddingNetwork, NetworkParams};\nuse ndarray::{Array1, Array2};\nuse std::collections::HashMap;\n\n/// MAML-inspired few-shot learner\n///\n/// Performs gradient-based adaptation on the support set before making\n/// predictions. This allows the model to quickly adapt to new task distributions.\npub struct MAMLLearner {\n config: FewShotConfig,\n network: EmbeddingNetwork,\n distance_computer: DistanceComputer,\n /// Adapted parameters after fitting\n adapted_params: Option<NetworkParams>,\n /// Prototypes computed with adapted parameters\n prototypes: HashMap<usize, Array1<f64>>,\n /// Last adaptation result\n last_adaptation: Option<AdaptationResult>,\n fitted: bool,\n}\n\nimpl MAMLLearner {\n /// Create a new MAML learner\n pub fn new(config: FewShotConfig) -> Self {\n let emb_config = config.to_embedding_config();\n let network = EmbeddingNetwork::new(emb_config);\n let distance_computer = DistanceComputer::new(config.distance_metric);\n\n Self {\n config,\n network,\n distance_computer,\n adapted_params: None,\n prototypes: HashMap::new(),\n last_adaptation: None,\n fitted: false,\n }\n }\n\n /// Compute prototype-based loss for a set of embeddings and labels\n fn compute_loss(\n &self,\n embeddings: &Array2<f64>,\n labels: &[usize],\n temperature: f64,\n ) -> f64 {\n // Compute prototypes\n let mut class_embeddings: HashMap<usize, Vec<Array1<f64>>> = HashMap::new();\n for (i, &label) in labels.iter().enumerate() {\n let emb = embeddings.row(i).to_owned();\n class_embeddings.entry(label).or_default().push(emb);\n }\n\n let prototypes: HashMap<usize, Array1<f64>> = class_embeddings\n .into_iter()\n .map(|(label, embs)| {\n let dim = embs[0].len();\n let mut sum = Array1::zeros(dim);\n for emb in &embs {\n sum = sum + emb;\n }\n (label, sum / embs.len() as f64)\n })\n .collect();\n\n // Compute loss as negative log probability of correct class\n let mut class_indices: Vec<_> = prototypes.keys().cloned().collect();\n class_indices.sort();\n\n let mut total_loss = 0.0;\n for (i, &true_label) in labels.iter().enumerate() {\n let emb = embeddings.row(i).to_owned();\n\n // Compute distances to all prototypes\n let distances: Vec<f64> = class_indices\n .iter()\n .map(|&label| {\n let prototype = &prototypes[&label];\n self.distance_computer.distance(&emb, prototype)\n })\n .collect();\n\n // Convert to probabilities\n let probs = DistanceComputer::distances_to_probs(&distances, temperature);\n\n // Find index of true class\n let true_idx = class_indices.iter().position(|&l| l == true_label).unwrap();\n\n // Negative log probability\n total_loss -= (probs[true_idx] + 1e-10).ln();\n }\n\n total_loss / labels.len() as f64\n }\n\n /// Perform gradient-based adaptation using finite differences\n ///\n /// This is a simplified implementation. In practice, you would use\n /// automatic differentiation for better performance.\n fn adapt(\n &self,\n support_features: &Array2<f64>,\n support_labels: &[usize],\n ) -> (NetworkParams, AdaptationResult) {\n let mut params = self.network.clone_params();\n let eps = 1e-5;\n\n // Compute initial loss\n let initial_embeddings = self.network.forward_batch(support_features, Some(¶ms));\n let initial_loss = self.compute_loss(&initial_embeddings, support_labels, self.config.temperature);\n\n // Perform adaptation steps\n for _step in 0..self.config.adaptation_steps {\n // Compute gradients using finite differences (simplified)\n let mut weight_grads: Vec<Array2<f64>> = Vec::new();\n let mut bias_grads: Vec<Array1<f64>> = Vec::new();\n\n // For each layer, compute approximate gradients\n for layer_idx in 0..params.weights.len() {\n let (rows, cols) = params.weights[layer_idx].dim();\n let mut w_grad = Array2::zeros((rows, cols));\n\n // Sample random indices for stochastic gradient approximation\n // (full gradient would be too expensive)\n let n_samples = std::cmp::min(10, rows * cols);\n let mut rng = rand::thread_rng();\n\n for _ in 0..n_samples {\n let i = rand::Rng::gen_range(&mut rng, 0..rows);\n let j = rand::Rng::gen_range(&mut rng, 0..cols);\n\n // Finite difference approximation\n params.weights[layer_idx][[i, j]] += eps;\n let emb_plus = self.network.forward_batch(support_features, Some(¶ms));\n let loss_plus = self.compute_loss(&emb_plus, support_labels, self.config.temperature);\n params.weights[layer_idx][[i, j]] -= eps;\n\n w_grad[[i, j]] = (loss_plus - initial_loss) / eps;\n }\n weight_grads.push(w_grad);\n\n // Bias gradients\n let bias_len = params.biases[layer_idx].len();\n let mut b_grad = Array1::zeros(bias_len);\n\n for idx in 0..std::cmp::min(5, bias_len) {\n params.biases[layer_idx][idx] += eps;\n let emb_plus = self.network.forward_batch(support_features, Some(¶ms));\n let loss_plus = self.compute_loss(&emb_plus, support_labels, self.config.temperature);\n params.biases[layer_idx][idx] -= eps;\n\n b_grad[idx] = (loss_plus - initial_loss) / eps;\n }\n bias_grads.push(b_grad);\n }\n\n // Apply gradients\n params.apply_gradients(&weight_grads, &bias_grads, self.config.adaptation_lr);\n }\n\n // Compute final loss\n let final_embeddings = self.network.forward_batch(support_features, Some(¶ms));\n let final_loss = self.compute_loss(&final_embeddings, support_labels, self.config.temperature);\n\n let result = AdaptationResult::new(self.config.adaptation_steps, initial_loss, final_loss);\n\n (params, result)\n }\n\n /// Get last adaptation result\n pub fn last_adaptation(&self) -> Option<&AdaptationResult> {\n self.last_adaptation.as_ref()\n }\n\n /// Get prototypes\n pub fn prototypes(&self) -> &HashMap<usize, Array1<f64>> {\n &self.prototypes\n }\n}\n\nimpl FewShotLearner for MAMLLearner {\n fn fit(&mut self, support_features: &Array2<f64>, support_labels: &[usize]) {\n // Perform adaptation\n let (adapted_params, adaptation_result) = self.adapt(support_features, support_labels);\n\n // Store adapted parameters\n self.adapted_params = Some(adapted_params.clone());\n self.last_adaptation = Some(adaptation_result);\n\n // Compute prototypes using adapted parameters\n let embeddings = self.network.forward_batch(support_features, Some(&adapted_params));\n\n let mut class_embeddings: HashMap<usize, Vec<Array1<f64>>> = HashMap::new();\n for (i, &label) in support_labels.iter().enumerate() {\n let emb = embeddings.row(i).to_owned();\n class_embeddings.entry(label).or_default().push(emb);\n }\n\n self.prototypes.clear();\n for (label, embs) in class_embeddings {\n let dim = embs[0].len();\n let mut sum = Array1::zeros(dim);\n for emb in &embs {\n sum = sum + emb;\n }\n self.prototypes.insert(label, sum / embs.len() as f64);\n }\n\n self.fitted = true;\n }\n\n fn predict(&self, query_features: &Array2<f64>) -> Vec<PredictionResult> {\n if !self.fitted {\n panic!(\"Model not fitted. Call fit() first.\");\n }\n\n let params = self.adapted_params.as_ref().unwrap();\n let n_queries = query_features.nrows();\n let mut results = Vec::with_capacity(n_queries);\n\n // Embed query features using adapted parameters\n let query_embeddings = self.network.forward_batch(query_features, Some(params));\n\n // Get sorted class indices\n let mut class_indices: Vec<_> = self.prototypes.keys().cloned().collect();\n class_indices.sort();\n\n for i in 0..n_queries {\n let query_emb = query_embeddings.row(i).to_owned();\n\n // Compute distances to all prototypes\n let distances: Vec<f64> = class_indices\n .iter()\n .map(|&label| {\n let prototype = &self.prototypes[&label];\n self.distance_computer.distance(&query_emb, prototype)\n })\n .collect();\n\n // Convert to probabilities\n let probs = DistanceComputer::distances_to_probs(&distances, self.config.temperature);\n\n // Find best class\n let (best_idx, &confidence) = probs\n .iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .unwrap();\n\n let predicted_class = class_indices[best_idx];\n\n results.push(PredictionResult::new(\n predicted_class,\n confidence,\n probs,\n self.config.confidence_threshold,\n ));\n }\n\n results\n }\n\n fn predict_single(&self, query: &Array1<f64>) -> PredictionResult {\n if !self.fitted {\n panic!(\"Model not fitted. Call fit() first.\");\n }\n\n let params = self.adapted_params.as_ref().unwrap();\n\n // Embed query using adapted parameters\n let query_emb = self.network.forward(query, Some(params));\n\n // Get sorted class indices\n let mut class_indices: Vec<_> = self.prototypes.keys().cloned().collect();\n class_indices.sort();\n\n // Compute distances to all prototypes\n let distances: Vec<f64> = class_indices\n .iter()\n .map(|&label| {\n let prototype = &self.prototypes[&label];\n self.distance_computer.distance(&query_emb, prototype)\n })\n .collect();\n\n // Convert to probabilities\n let probs = DistanceComputer::distances_to_probs(&distances, self.config.temperature);\n\n // Find best class\n let (best_idx, &confidence) = probs\n .iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .unwrap();\n\n let predicted_class = class_indices[best_idx];\n\n PredictionResult::new(\n predicted_class,\n confidence,\n probs,\n self.config.confidence_threshold,\n )\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n use rand::Rng;\n\n fn create_test_data() -> (Array2<f64>, Vec<usize>) {\n let mut rng = rand::thread_rng();\n let n_classes = 3;\n let n_per_class = 5;\n let dim = 10;\n\n let mut data = Vec::new();\n let mut labels = Vec::new();\n\n for class in 0..n_classes {\n let base = class as f64 * 2.0;\n for _ in 0..n_per_class {\n for _ in 0..dim {\n data.push(base + rng.gen::<f64>() * 0.3);\n }\n labels.push(class);\n }\n }\n\n let features = Array2::from_shape_vec((n_classes * n_per_class, dim), data).unwrap();\n (features, labels)\n }\n\n #[test]\n fn test_maml_learner_fit() {\n let config = FewShotConfig::default()\n .with_input_dim(10)\n .with_adaptation_steps(2);\n let mut learner = MAMLLearner::new(config);\n\n let (features, labels) = create_test_data();\n learner.fit(&features, &labels);\n\n assert!(learner.fitted);\n assert!(learner.adapted_params.is_some());\n assert!(learner.last_adaptation.is_some());\n }\n\n #[test]\n fn test_maml_learner_predict() {\n let config = FewShotConfig::default()\n .with_input_dim(10)\n .with_adaptation_steps(1);\n let mut learner = MAMLLearner::new(config);\n\n let (features, labels) = create_test_data();\n learner.fit(&features, &labels);\n\n let query = Array2::from_shape_vec((2, 10), vec![0.1; 20]).unwrap();\n let predictions = learner.predict(&query);\n\n assert_eq!(predictions.len(), 2);\n }\n}\n","numLines":373,"startLine":1,"totalLines":373}}} | |
| [2026-01-21T18:06:03.172Z] [INFO] { | |
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| "text": "Now I understand the structure better. The examples I wrote use a different API than what's actually implemented. Let me fix the examples to match the actual implementation:" | |
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| [2026-01-21T18:06:31.836Z] [INFO] { | |
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| "content": "//! Basic Few-Shot Market Prediction Example\n//!\n//! This example demonstrates the core few-shot learning functionality\n//! for market prediction using synthetic data.\n//!\n//! Run with: cargo run --example basic_few_shot\n\nuse few_shot_market_prediction::methods::{\n FewShotConfig, FewShotLearner, FewShotMethod, FewShotPredictor,\n MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n};\nuse few_shot_market_prediction::network::DistanceMetric;\nuse ndarray::Array2;\n\nfn main() {\n println!(\"=== Few-Shot Market Prediction: Basic Example ===\\n\");\n\n // Generate synthetic market features for demonstration\n let (support_features, support_labels, query_features, query_labels) = generate_synthetic_data();\n\n println!(\"Support set: {} examples across 3 classes\", support_features.nrows());\n println!(\"Query set: {} examples to classify\\n\", query_features.nrows());\n\n // Define class names\n let class_names = [\"Uptrend\", \"Sideways\", \"Downtrend\"];\n\n // Method 1: Metric-based learning (Prototypical Networks)\n println!(\"--- Method 1: Prototypical Networks ---\");\n let config_metric = FewShotConfig::default()\n .with_method(FewShotMethod::Metric)\n .with_input_dim(10)\n .with_embedding_dim(8)\n .with_hidden_dims(vec![32, 16])\n .with_distance_metric(DistanceMetric::Euclidean);\n\n let mut metric_predictor = FewShotPredictor::new(config_metric);\n metric_predictor.fit(&support_features, &support_labels);\n let metric_results = metric_predictor.predict(&query_features);\n\n println!(\"Query predictions (metric-based):\");\n for (i, result) in metric_results.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%, reliable: {})\",\n i,\n class_names[result.predicted_class],\n result.confidence * 100.0,\n result.is_reliable\n );\n }\n println!();\n\n // Method 2: MAML-based learning\n println!(\"--- Method 2: MAML (Model-Agnostic Meta-Learning) ---\");\n let config_maml = FewShotConfig::default()\n .with_method(FewShotMethod::MAML)\n .with_input_dim(10)\n .with_embedding_dim(8)\n .with_hidden_dims(vec![32, 16])\n .with_adaptation_steps(3)\n .with_adaptation_lr(0.01);\n\n let mut maml_learner = MAMLLearner::new(config_maml);\n maml_learner.fit(&support_features, &support_labels);\n let maml_results = maml_learner.predict(&query_features);\n\n println!(\"Query predictions (MAML):\");\n for (i, result) in maml_results.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_names[result.predicted_class],\n result.confidence * 100.0\n );\n }\n\n // Print adaptation info\n if let Some(adaptation) = maml_learner.last_adaptation() {\n println!(\" Adaptation: {} steps, loss {:.4} -> {:.4} ({:.1}% improvement)\",\n adaptation.steps,\n adaptation.initial_loss,\n adaptation.final_loss,\n adaptation.improvement * 100.0\n );\n }\n println!();\n\n // Method 3: Siamese Network\n println!(\"--- Method 3: Siamese Network ---\");\n let config_siamese = FewShotConfig::default()\n .with_method(FewShotMethod::Siamese)\n .with_input_dim(10)\n .with_embedding_dim(8)\n .with_hidden_dims(vec![32, 16]);\n\n let mut siamese_learner = SiameseLearner::new(config_siamese);\n siamese_learner.fit(&support_features, &support_labels);\n let siamese_results = siamese_learner.predict(&query_features);\n\n println!(\"Query predictions (Siamese):\");\n for (i, result) in siamese_results.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_names[result.predicted_class],\n result.confidence * 100.0\n );\n }\n println!();\n\n // Method 4: Hybrid approach\n println!(\"--- Method 4: Hybrid (Metric + Optimization) ---\");\n let config_hybrid = FewShotConfig::default()\n .with_method(FewShotMethod::Hybrid)\n .with_input_dim(10)\n .with_embedding_dim(8)\n .with_hidden_dims(vec![32, 16]);\n\n let mut hybrid_learner = HybridLearner::new(config_hybrid);\n hybrid_learner.fit(&support_features, &support_labels);\n let hybrid_results = hybrid_learner.predict(&query_features);\n\n println!(\"Query predictions (Hybrid):\");\n for (i, result) in hybrid_results.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_names[result.predicted_class],\n result.confidence * 100.0\n );\n }\n println!();\n\n // Compare actual labels\n println!(\"--- Ground Truth ---\");\n for (i, &label) in query_labels.iter().enumerate() {\n println!(\" Query {}: {}\", i, class_names[label]);\n }\n\n // Calculate accuracy for each method\n println!(\"\\n--- Accuracy Summary ---\");\n let methods = [\n (\"Prototypical\", &metric_results),\n (\"MAML\", &maml_results),\n (\"Siamese\", &siamese_results),\n (\"Hybrid\", &hybrid_results),\n ];\n\n for (name, results) in methods {\n let correct = results\n .iter()\n .zip(&query_labels)\n .filter(|(pred, &actual)| pred.predicted_class == actual)\n .count();\n let accuracy = correct as f64 / query_labels.len() as f64 * 100.0;\n println!(\" {}: {:.1}% ({}/{})\", name, accuracy, correct, query_labels.len());\n }\n\n println!(\"\\n=== Example Complete ===\");\n}\n\n/// Generate synthetic market data for the example.\nfn generate_synthetic_data() -> (Array2<f64>, Vec<usize>, Array2<f64>, Vec<usize>) {\n let dim = 10;\n let n_support_per_class = 5;\n let n_query_per_class = 3;\n let n_classes = 3;\n\n // Base feature patterns for each class\n // Uptrend: positive returns, momentum indicators high\n let uptrend_base: Vec<f64> = vec![0.02, 0.015, 0.7, 65.0, 1.2, 0.8, 0.7, 0.6, 0.3, 0.7];\n // Sideways: near-zero returns, neutral indicators\n let sideways_base: Vec<f64> = vec![0.001, 0.002, 0.5, 50.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.5];\n // Downtrend: negative returns, bearish indicators\n let downtrend_base: Vec<f64> = vec![-0.02, -0.015, 0.3, 35.0, 0.7, 0.3, 0.3, 0.4, 0.7, 0.3];\n\n let bases = [&uptrend_base, &sideways_base, &downtrend_base];\n\n let mut support_data = Vec::new();\n let mut support_labels = Vec::new();\n let mut query_data = Vec::new();\n let mut query_labels = Vec::new();\n\n for (class_idx, base) in bases.iter().enumerate() {\n // Support examples\n for i in 0..n_support_per_class {\n let noisy = add_noise(base, 0.1, (class_idx * 100 + i) as u64);\n support_data.extend(noisy);\n support_labels.push(class_idx);\n }\n\n // Query examples\n for i in 0..n_query_per_class {\n let noisy = add_noise(base, 0.15, (class_idx * 1000 + i) as u64);\n query_data.extend(noisy);\n query_labels.push(class_idx);\n }\n }\n\n let support = Array2::from_shape_vec(\n (n_classes * n_support_per_class, dim),\n support_data,\n ).unwrap();\n\n let query = Array2::from_shape_vec(\n (n_classes * n_query_per_class, dim),\n query_data,\n ).unwrap();\n\n (support, support_labels, query, query_labels)\n}\n\n/// Add deterministic pseudo-random noise to a feature vector.\nfn add_noise(base: &[f64], noise_level: f64, seed: u64) -> Vec<f64> {\n let mut rng_state = seed;\n\n base.iter().map(|&v| {\n rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);\n let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;\n v + v.abs().max(0.01) * noise_level * random * 2.0\n }).collect()\n}\n" 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| "file_path": "/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/examples/regime_prediction.rs", | |
| "content": "//! Market Regime Prediction Example\n//!\n//! This example demonstrates using few-shot learning to classify\n//! market regimes and generate trading signals.\n//!\n//! Run with: cargo run --example regime_prediction\n\nuse few_shot_market_prediction::strategy::{\n MarketRegime, RegimeClassifier, RegimeClassification,\n SignalGenerator, SignalConfig, SignalType,\n RiskManager, RiskConfig,\n};\nuse std::time::Duration;\n\nfn main() {\n println!(\"=== Market Regime Prediction with Few-Shot Learning ===\\n\");\n\n // Define all market regimes we want to classify\n let regimes = MarketRegime::all();\n println!(\"Classifying into {} market regimes:\", regimes.len());\n for regime in ®imes {\n println!(\" - {} (position bias: {:.1})\", regime, regime.position_bias());\n }\n println!();\n\n // Create regime classifier\n let mut classifier = RegimeClassifier::new(0.6);\n println!(\"Regime classifier created with 60% confidence threshold\\n\");\n\n // Simulate some regime predictions\n // In real usage, these probabilities would come from the few-shot model\n let test_scenarios = [\n (\"Strong momentum rally\", vec![0.75, 0.15, 0.03, 0.02, 0.01, 0.02, 0.01, 0.01]),\n (\"Mild uptrend\", vec![0.15, 0.55, 0.20, 0.05, 0.02, 0.02, 0.00, 0.01]),\n (\"Choppy sideways\", vec![0.05, 0.10, 0.60, 0.10, 0.05, 0.08, 0.01, 0.01]),\n (\"Bearish breakdown\", vec![0.02, 0.03, 0.05, 0.15, 0.60, 0.10, 0.04, 0.01]),\n (\"High volatility\", vec![0.10, 0.10, 0.10, 0.10, 0.10, 0.40, 0.05, 0.05]),\n (\"Market crash\", vec![0.01, 0.02, 0.02, 0.10, 0.15, 0.15, 0.50, 0.05]),\n (\"Recovery phase\", vec![0.15, 0.15, 0.10, 0.05, 0.05, 0.10, 0.05, 0.35]),\n ];\n\n println!(\"--- Regime Classification Results ---\\n\");\n\n // Create signal generator\n let signal_config = SignalConfig {\n min_confidence: 0.5,\n max_position_size: 0.2,\n default_take_profit: 3.0,\n default_stop_loss: 1.5,\n use_regime_adjustment: true,\n cooldown: Duration::from_secs(0), // No cooldown for demo\n };\n let mut signal_generator = SignalGenerator::new(signal_config);\n\n // Create risk manager\n let risk_config = RiskConfig::default();\n let risk_manager = RiskManager::new(risk_config, 100_000.0);\n\n for (scenario_name, probs) in &test_scenarios {\n println!(\"Scenario: {}\", scenario_name);\n\n // Classify the regime\n let classification = classifier.classify_with_uncertainty(probs);\n\n println!(\" Classification: {}\", classification.description());\n println!(\" Should act: {}\", classification.should_act());\n\n // Update classifier history for transition analysis\n classifier.update_history(classification.regime, classification.confidence);\n\n // Generate trading signal\n signal_generator.clear_cooldowns(); // Reset for demo\n let signal = signal_generator.generate(\"BTCUSDT\", &classification);\n\n println!(\" Signal: {:?}\", signal.signal_type);\n if signal.signal_type.requires_action() {\n println!(\" Position size: {:.1}%\", signal.position_size * 100.0);\n if let Some(tp) = signal.take_profit {\n println!(\" Take profit: {:.1}%\", tp);\n }\n if let Some(sl) = signal.stop_loss {\n println!(\" Stop loss: {:.1}%\", sl);\n }\n println!(\" Expected value: {:.2}\", signal.expected_value());\n\n // Check risk\n let risk_check = risk_manager.check_signal(&signal, 0);\n println!(\" Risk approved: {}\", risk_check.approved);\n if !risk_check.rejections.is_empty() {\n println!(\" Rejections: {:?}\", risk_check.rejections);\n }\n }\n println!();\n }\n\n // Demonstrate regime transitions\n println!(\"--- Regime Transition Analysis ---\\n\");\n\n // Simulate a sequence of regimes\n let regime_sequence = [\n MarketRegime::Sideways,\n MarketRegime::WeakUptrend,\n MarketRegime::StrongUptrend,\n MarketRegime::Volatile,\n MarketRegime::WeakDowntrend,\n MarketRegime::Crash,\n MarketRegime::Recovery,\n MarketRegime::Sideways,\n ];\n\n for regime in ®ime_sequence {\n classifier.update_history(*regime, 0.8);\n }\n\n println!(\"After observing {} regime changes:\", regime_sequence.len());\n if let Some(dominant) = classifier.dominant_regime(5) {\n println!(\" Dominant regime (last 5): {}\", dominant);\n }\n println!();\n\n // Check transition probabilities\n let transitions = [\n (MarketRegime::StrongUptrend, MarketRegime::Volatile),\n (MarketRegime::Volatile, MarketRegime::Crash),\n (MarketRegime::Crash, MarketRegime::Recovery),\n (MarketRegime::Recovery, MarketRegime::Sideways),\n ];\n\n println!(\"Transition probabilities from history:\");\n for (from, to) in &transitions {\n let prob = classifier.transition_probability(*from, *to);\n let likely = classifier.is_transition_likely(*from, *to);\n println!(\n \" {} -> {}: {:.1}% (likely: {})\",\n from, to, prob * 100.0, likely\n );\n }\n\n // Show risk summary\n println!(\"\\n--- Current Risk Summary ---\");\n println!(\"{}\", risk_manager.risk_summary());\n\n // Demonstrate different risk profiles\n println!(\"\\n--- Risk Profile Comparison ---\");\n let profiles = [\n (\"Default\", RiskConfig::default()),\n (\"Conservative\", RiskConfig::conservative()),\n (\"Aggressive\", RiskConfig::aggressive()),\n ];\n\n for (name, config) in &profiles {\n println!(\" {}: max_position={:.0}%, max_exposure={:.0}%, daily_limit={:.0}%\",\n name,\n config.max_position_size * 100.0,\n config.max_total_exposure * 100.0,\n config.daily_loss_limit * 100.0\n );\n }\n\n println!(\"\\n=== Example Complete ===\");\n}\n" | |
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| "content": "//! New Asset Adaptation Example\n//!\n//! This example demonstrates adapting a few-shot model to predict\n//! market movements for a new cryptocurrency with minimal data.\n//!\n//! Run with: cargo run --example new_asset_adaptation\n\nuse few_shot_market_prediction::methods::{\n FewShotConfig, FewShotLearner, FewShotMethod,\n MetricBasedLearner, MAMLLearner,\n};\nuse few_shot_market_prediction::network::DistanceMetric;\nuse few_shot_market_prediction::strategy::{\n MarketRegime, RegimeClassifier,\n SignalGenerator, SignalConfig,\n};\nuse ndarray::Array2;\n\nfn main() {\n println!(\"=== Few-Shot Adaptation to New Asset ===\\n\");\n\n // Scenario: We have a model trained on BTC/ETH, and want to adapt to a new altcoin\n println!(\"Scenario: Adapting from BTC/ETH patterns to predict DOGE movements\\n\");\n\n // Step 1: Generate synthetic \"historical\" data for major assets (for reference)\n let (btc_features, btc_labels) = generate_asset_data(\"BTC\", 50);\n let (eth_features, eth_labels) = generate_asset_data(\"ETH\", 50);\n\n println!(\"Step 1: Historical data available (simulated)\");\n println!(\" BTC: {} labeled samples\", btc_features.nrows());\n println!(\" ETH: {} labeled samples\", eth_features.nrows());\n println!();\n\n // Step 2: Get LIMITED data for new asset (few-shot scenario)\n let (doge_support, doge_support_labels) = generate_asset_data(\"DOGE\", 9); // Only 3 per class!\n let (doge_query, doge_query_labels) = generate_asset_data(\"DOGE_query\", 15);\n\n println!(\"Step 2: New asset (DOGE) - Few-shot data\");\n println!(\" Support set: {} examples (only 3 per class!)\", doge_support.nrows());\n println!(\" Query set: {} examples to predict\", doge_query.nrows());\n println!();\n\n // Step 3: Define configuration for few-shot learning\n let config = FewShotConfig::default()\n .with_input_dim(12)\n .with_embedding_dim(8)\n .with_hidden_dims(vec![64, 32])\n .with_n_way(3)\n .with_k_shot(3)\n .with_adaptation_steps(5)\n .with_adaptation_lr(0.01);\n\n println!(\"Step 3: Few-shot configuration\");\n println!(\" Input dim: {}\", config.input_dim);\n println!(\" Embedding dim: {}\", config.embedding_dim);\n println!(\" N-way: {} (3 classes: up, sideways, down)\", config.n_way);\n println!(\" K-shot: {} per class\", config.k_shot);\n println!();\n\n // Step 4: Adapt using MAML\n println!(\"Step 4: Adapting model to DOGE using MAML...\\n\");\n\n let mut maml_learner = MAMLLearner::new(config.clone());\n maml_learner.fit(&doge_support, &doge_support_labels);\n let maml_predictions = maml_learner.predict(&doge_query);\n\n // Display results\n let class_names = [\"Uptrend\", \"Sideways\", \"Downtrend\"];\n\n println!(\"--- MAML Adaptation Results for DOGE ---\\n\");\n\n if let Some(adaptation) = maml_learner.last_adaptation() {\n println!(\"Adaptation summary:\");\n println!(\" Steps: {}\", adaptation.steps);\n println!(\" Initial loss: {:.4}\", adaptation.initial_loss);\n println!(\" Final loss: {:.4}\", adaptation.final_loss);\n println!(\" Improvement: {:.1}%\\n\", adaptation.improvement * 100.0);\n }\n\n println!(\"Predictions on query set:\");\n for (i, (pred, &actual)) in maml_predictions.iter()\n .zip(doge_query_labels.iter())\n .enumerate()\n {\n let correct = if pred.predicted_class == actual { \"β\" } else { \"β\" };\n println!(\n \" Sample {:2}: Predicted {} (conf: {:5.1}%) | Actual {} {}\",\n i + 1,\n class_names[pred.predicted_class],\n pred.confidence * 100.0,\n class_names[actual],\n correct\n );\n }\n\n // Calculate MAML accuracy\n let maml_correct = maml_predictions.iter()\n .zip(doge_query_labels.iter())\n .filter(|(pred, &actual)| pred.predicted_class == actual)\n .count();\n let maml_accuracy = maml_correct as f64 / doge_query_labels.len() as f64 * 100.0;\n\n println!(\"\\nMAML accuracy: {:.1}% ({}/{})\",\n maml_accuracy, maml_correct, doge_query_labels.len());\n\n // Step 5: Compare with metric-based approach\n println!(\"\\n--- Comparison: Metric-Based (no adaptation) ---\\n\");\n\n let metric_config = config.clone()\n .with_method(FewShotMethod::Metric)\n .with_distance_metric(DistanceMetric::Cosine);\n\n let mut metric_learner = MetricBasedLearner::new(metric_config);\n metric_learner.fit(&doge_support, &doge_support_labels);\n let metric_predictions = metric_learner.predict(&doge_query);\n\n let metric_correct = metric_predictions.iter()\n .zip(doge_query_labels.iter())\n .filter(|(pred, &actual)| pred.predicted_class == actual)\n .count();\n let metric_accuracy = metric_correct as f64 / doge_query_labels.len() as f64 * 100.0;\n\n println!(\"Metric-based accuracy: {:.1}% ({}/{})\",\n metric_accuracy, metric_correct, doge_query_labels.len());\n\n println!(\"\\nMethod comparison:\");\n println!(\" MAML (with adaptation): {:.1}%\", maml_accuracy);\n println!(\" Metric-based (no adaptation): {:.1}%\", metric_accuracy);\n println!();\n\n // Step 6: Generate trading signals\n println!(\"--- Trading Signal Generation ---\\n\");\n\n // Aggregate predictions to determine overall regime\n let mut class_votes = [0.0f64; 3];\n for pred in &maml_predictions {\n class_votes[pred.predicted_class] += pred.confidence;\n }\n\n let total_confidence: f64 = class_votes.iter().sum();\n let regime_probs: Vec<f64> = class_votes.iter()\n .map(|&c| c / total_confidence.max(1e-10))\n .collect();\n\n println!(\"Aggregated class probabilities:\");\n for (i, (name, &prob)) in class_names.iter().zip(regime_probs.iter()).enumerate() {\n println!(\" {}: {:.1}%\", name, prob * 100.0);\n }\n\n // Map to market regimes (simplified: 3 classes -> select from 8 regimes)\n let dominant_class = regime_probs.iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .map(|(i, _)| i)\n .unwrap_or(1);\n\n let mapped_regime = match dominant_class {\n 0 => MarketRegime::WeakUptrend,\n 2 => MarketRegime::WeakDowntrend,\n _ => MarketRegime::Sideways,\n };\n\n println!(\"\\nMapped to regime: {}\", mapped_regime);\n\n // Create classifier and signal\n let mut classifier = RegimeClassifier::new(0.5);\n let avg_confidence = maml_predictions.iter()\n .map(|p| p.confidence)\n .sum::<f64>() / maml_predictions.len() as f64;\n\n // Build regime probabilities for classifier\n let mut full_regime_probs = vec![0.0; 8];\n match dominant_class {\n 0 => {\n full_regime_probs[0] = regime_probs[0] * 0.3; // Strong uptrend\n full_regime_probs[1] = regime_probs[0] * 0.7; // Weak uptrend\n }\n 2 => {\n full_regime_probs[3] = regime_probs[2] * 0.7; // Weak downtrend\n full_regime_probs[4] = regime_probs[2] * 0.3; // Strong downtrend\n }\n _ => {\n full_regime_probs[2] = regime_probs[1]; // Sideways\n }\n }\n\n let classification = classifier.classify_with_uncertainty(&full_regime_probs);\n\n let signal_config = SignalConfig::default();\n let mut signal_generator = SignalGenerator::new(signal_config);\n let signal = signal_generator.generate(\"DOGEUSDT\", &classification);\n\n println!(\"\\nGenerated signal for DOGEUSDT:\");\n println!(\" Type: {:?}\", signal.signal_type);\n println!(\" Confidence: {:.1}%\", signal.confidence * 100.0);\n println!(\" Position size: {:.1}%\", signal.position_size * 100.0);\n if let Some(tp) = signal.take_profit {\n println!(\" Take profit: {:.1}%\", tp);\n }\n if let Some(sl) = signal.stop_loss {\n println!(\" Stop loss: {:.1}%\", sl);\n }\n\n // Step 7: Real-world usage hint\n println!(\"\\n--- Real-World Usage ---\\n\");\n println!(\"To use with real Bybit data:\");\n println!(\" 1. Create BybitClient for API access\");\n println!(\" 2. Fetch klines: client.get_klines(\\\"DOGEUSDT\\\", \\\"1h\\\", 100).await\");\n println!(\" 3. Extract features using FeatureExtractor\");\n println!(\" 4. Run few-shot prediction with extracted features\");\n println!(\" 5. Generate and execute trading signals\");\n println!();\n\n println!(\"=== Example Complete ===\");\n}\n\n/// Generate synthetic market data for an asset.\nfn generate_asset_data(asset_name: &str, count: usize) -> (Array2<f64>, Vec<usize>) {\n let dim = 12;\n let mut data = Vec::new();\n let mut labels = Vec::new();\n\n // Seed based on asset name for reproducibility\n let base_seed: u64 = asset_name.bytes().map(|b| b as u64).sum();\n\n for i in 0..count {\n // Determine class (0: uptrend, 1: sideways, 2: downtrend)\n let class = i % 3;\n\n // Generate features based on class\n let base = match class {\n 0 => vec![0.02, 0.015, 0.5, 65.0, 1.5, 0.8, 0.7, 0.6, 0.3, 0.7, 0.8, 0.6],\n 1 => vec![0.001, 0.002, 0.3, 50.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],\n _ => vec![-0.02, -0.015, 0.6, 35.0, 0.7, 0.3, 0.3, 0.4, 0.7, 0.3, 0.2, 0.4],\n };\n\n let noisy = add_noise(&base, 0.15, base_seed + i as u64);\n data.extend(noisy);\n labels.push(class);\n }\n\n let features = Array2::from_shape_vec((count, dim), data).unwrap();\n (features, labels)\n}\n\n/// Add noise to feature vector.\nfn add_noise(base: &[f64], noise_level: f64, seed: u64) -> Vec<f64> {\n let mut rng_state = seed;\n\n base.iter().map(|&v| {\n rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);\n let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;\n v + v.abs().max(0.01) * noise_level * random * 2.0\n }).collect()\n}\n" | |
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| [2026-01-21T18:07:36.090Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XXFnJ3C76vuFJz8o4e9Ws2","type":"tool_result","content":"The file /tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/examples/basic_few_shot.rs has been updated. Here's the result of running `cat -n` on a snippet of the edited file:\n 1β//! Basic Few-Shot Market Prediction Example\n 2β//!\n 3β//! This example demonstrates the core few-shot learning functionality\n 4β//! for market prediction using synthetic data.\n 5β//!\n 6β//! Run with: cargo run --example basic_few_shot\n 7β\n 8βuse few_shot_market_prediction::methods::{\n 9β FewShotConfig, FewShotLearner, FewShotMethod, FewShotPredictor,\n 10β MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n 11β};\n 12βuse few_shot_market_prediction::network::DistanceMetric;\n 13βuse ndarray::Array2;\n 14β\n 15βfn main() {\n 16β println!(\"=== Few-Shot Market Prediction: Basic Example ===\\n\");\n 17β\n 18β // Generate synthetic market features for demonstration\n 19β let (support_features, support_labels, query_features, query_labels) = generate_synthetic_data();\n 20β\n 21β println!(\"Support set: {} examples across 3 classes\", support_features.nrows());\n 22β println!(\"Query set: {} examples to classify\\n\", query_features.nrows());\n 23β\n 24β // Define class names\n 25β let class_names = [\"Uptrend\", \"Sideways\", \"Downtrend\"];\n 26β\n 27β // Method 1: Metric-based learning (Prototypical Networks)\n 28β println!(\"--- Method 1: Prototypical Networks ---\");\n 29β let config_metric = FewShotConfig::default()\n 30β .with_method(FewShotMethod::Metric)\n 31β .with_input_dim(10)\n 32β .with_embedding_dim(8)\n 33β .with_hidden_dims(vec![32, 16])\n 34β .with_distance_metric(DistanceMetric::Euclidean);\n 35β\n 36β let mut metric_predictor = FewShotPredictor::new(config_metric);\n 37β metric_predictor.fit(&support_features, &support_labels);\n 38β let metric_results = metric_predictor.predict(&query_features);\n 39β\n 40β println!(\"Query predictions (metric-based):\");\n 41β for (i, result) in metric_results.iter().enumerate() {\n 42β println!(\n 43β \" Query {}: {} (confidence: {:.1}%, reliable: {})\",\n 44β i,\n 45β class_names[result.predicted_class],\n 46β result.confidence * 100.0,\n 47β result.is_reliable\n 48β );\n 49β }\n 50β println!();\n 51β\n 52β // Method 2: MAML-based learning\n 53β println!(\"--- Method 2: MAML (Model-Agnostic Meta-Learning) ---\");\n 54β let config_maml = FewShotConfig::default()\n 55β .with_method(FewShotMethod::MAML)\n 56β .with_input_dim(10)\n 57β .with_embedding_dim(8)\n 58β .with_hidden_dims(vec![32, 16])\n 59β .with_adaptation_steps(3)\n 60β .with_adaptation_lr(0.01);\n 61β\n 62β let mut maml_learner = MAMLLearner::new(config_maml);\n 63β maml_learner.fit(&support_features, &support_labels);\n 64β let maml_results = maml_learner.predict(&query_features);\n 65β\n 66β println!(\"Query predictions (MAML):\");\n 67β for (i, result) in maml_results.iter().enumerate() {\n 68β println!(\n 69β \" Query {}: {} (confidence: {:.1}%)\",\n 70β i,\n 71β class_names[result.predicted_class],\n 72β result.confidence * 100.0\n 73β );\n 74β }\n 75β\n 76β // Print adaptation info\n 77β if let Some(adaptation) = maml_learner.last_adaptation() {\n 78β println!(\" Adaptation: {} steps, loss {:.4} -> {:.4} ({:.1}% improvement)\",\n 79β adaptation.steps,\n 80β adaptation.initial_loss,\n 81β adaptation.final_loss,\n 82β adaptation.improvement * 100.0\n 83β );\n 84β }\n 85β println!();\n 86β\n 87β // Method 3: Siamese Network\n 88β println!(\"--- Method 3: Siamese Network ---\");\n 89β let config_siamese = FewShotConfig::default()\n 90β .with_method(FewShotMethod::Siamese)\n 91β .with_input_dim(10)\n 92β .with_embedding_dim(8)\n 93β .with_hidden_dims(vec![32, 16]);\n 94β\n 95β let mut siamese_learner = SiameseLearner::new(config_siamese);\n 96β siamese_learner.fit(&support_features, &support_labels);\n 97β let siamese_results = siamese_learner.predict(&query_features);\n 98β\n 99β println!(\"Query predictions (Siamese):\");\n 100β for (i, result) in siamese_results.iter().enumerate() {\n 101β println!(\n 102β \" Query {}: {} (confidence: {:.1}%)\",\n 103β i,\n 104β class_names[result.predicted_class],\n 105β result.confidence * 100.0\n 106β );\n 107β }\n 108β println!();\n 109β\n 110β // Method 4: Hybrid approach\n 111β println!(\"--- Method 4: Hybrid (Metric + Optimization) ---\");\n 112β let config_hybrid = FewShotConfig::default()\n 113β .with_method(FewShotMethod::Hybrid)\n 114β .with_input_dim(10)\n 115β .with_embedding_dim(8)\n 116β .with_hidden_dims(vec![32, 16]);\n 117β\n 118β let mut hybrid_learner = HybridLearner::new(config_hybrid);\n 119β hybrid_learner.fit(&support_features, &support_labels);\n 120β let hybrid_results = hybrid_learner.predict(&query_features);\n 121β\n 122β println!(\"Query predictions (Hybrid):\");\n 123β for (i, result) in hybrid_results.iter().enumerate() {\n 124β println!(\n 125β \" Query {}: {} (confidence: {:.1}%)\",\n 126β i,\n 127β class_names[result.predicted_class],\n 128β result.confidence * 100.0\n 129β );\n 130β }\n 131β println!();\n 132β\n 133β // Compare actual labels\n 134β println!(\"--- Ground Truth ---\");\n 135β for (i, &label) in query_labels.iter().enumerate() {\n 136β println!(\" Query {}: {}\", i, class_names[label]);\n 137β }\n 138β\n 139β // Calculate accuracy for each method\n 140β println!(\"\\n--- Accuracy Summary ---\");\n 141β let methods = [\n 142β (\"Prototypical\", &metric_results),\n 143β (\"MAML\", &maml_results),\n 144β (\"Siamese\", &siamese_results),\n 145β (\"Hybrid\", &hybrid_results),\n 146β ];\n 147β\n 148β for (name, results) in methods {\n 149β let correct = results\n 150β .iter()\n 151β .zip(&query_labels)\n 152β .filter(|(pred, &actual)| pred.predicted_class == actual)\n 153β .count();\n 154β let accuracy = correct as f64 / query_labels.len() as f64 * 100.0;\n 155β println!(\" {}: {:.1}% ({}/{})\", name, accuracy, correct, query_labels.len());\n 156β }\n 157β\n 158β println!(\"\\n=== Example Complete ===\");\n 159β}\n 160β\n 161β/// Generate synthetic market data for the example.\n 162βfn generate_synthetic_data() -> (Array2<f64>, Vec<usize>, Array2<f64>, Vec<usize>) {\n 163β let dim = 10;\n 164β let n_support_per_class = 5;\n 165β let n_query_per_class = 3;\n 166β let n_classes = 3;\n 167β\n 168β // Base feature patterns for each class\n 169β // Uptrend: positive returns, momentum indicators high\n 170β let uptrend_base: Vec<f64> = vec![0.02, 0.015, 0.7, 65.0, 1.2, 0.8, 0.7, 0.6, 0.3, 0.7];\n 171β // Sideways: near-zero returns, neutral indicators\n 172β let sideways_base: Vec<f64> = vec![0.001, 0.002, 0.5, 50.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.5];\n 173β // Downtrend: negative returns, bearish indicators\n 174β let downtrend_base: Vec<f64> = vec![-0.02, -0.015, 0.3, 35.0, 0.7, 0.3, 0.3, 0.4, 0.7, 0.3];\n 175β\n 176β let bases = [&uptrend_base, &sideways_base, &downtrend_base];\n 177β\n 178β let mut support_data = Vec::new();\n 179β let mut support_labels = Vec::new();\n 180β let mut query_data = Vec::new();\n 181β let mut query_labels = Vec::new();\n 182β\n 183β for (class_idx, base) in bases.iter().enumerate() {\n 184β // Support examples\n 185β for i in 0..n_support_per_class {\n 186β let noisy = add_noise(base, 0.1, (class_idx * 100 + i) as u64);\n 187β support_data.extend(noisy);\n 188β support_labels.push(class_idx);\n 189β }\n 190β\n 191β // Query examples\n 192β for i in 0..n_query_per_class {\n 193β let noisy = add_noise(base, 0.15, (class_idx * 1000 + i) as u64);\n 194β query_data.extend(noisy);\n 195β query_labels.push(class_idx);\n 196β }\n 197β }\n 198β\n 199β let support = Array2::from_shape_vec(\n 200β (n_classes * n_support_per_class, dim),\n 201β support_data,\n 202β ).unwrap();\n 203β\n 204β let query = Array2::from_shape_vec(\n 205β (n_classes * n_query_per_class, dim),\n 206β query_data,\n 207β ).unwrap();\n 208β\n 209β (support, support_labels, query, query_labels)\n 210β}\n 211β\n 212β/// Add deterministic pseudo-random noise to a feature vector.\n 213βfn add_noise(base: &[f64], noise_level: f64, seed: u64) -> Vec<f64> {\n 214β let mut rng_state = seed;\n 215β\n 216β base.iter().map(|&v| {\n 217β rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);\n 218β let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;\n 219β v + v.abs().max(0.01) * noise_level * random * 2.0\n 220β }).collect()\n 221β}\n 222β"}]},"parent_tool_use_id":null,"session_id":"172d23c7-5c62-484f-af89-80e92d0b393e","uuid":"2d793fc2-4f69-4633-8182-5f61191dd949","tool_use_result":{"type":"update","filePath":"/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/examples/basic_few_shot.rs","content":"//! Basic Few-Shot Market Prediction Example\n//!\n//! This example demonstrates the core few-shot learning functionality\n//! for market prediction using synthetic data.\n//!\n//! Run with: cargo run --example basic_few_shot\n\nuse few_shot_market_prediction::methods::{\n FewShotConfig, FewShotLearner, FewShotMethod, FewShotPredictor,\n MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n};\nuse few_shot_market_prediction::network::DistanceMetric;\nuse ndarray::Array2;\n\nfn main() {\n println!(\"=== Few-Shot Market Prediction: Basic Example ===\\n\");\n\n // Generate synthetic market features for demonstration\n let (support_features, support_labels, query_features, query_labels) = generate_synthetic_data();\n\n println!(\"Support set: {} examples across 3 classes\", support_features.nrows());\n println!(\"Query set: {} examples to classify\\n\", query_features.nrows());\n\n // Define class names\n let class_names = [\"Uptrend\", \"Sideways\", \"Downtrend\"];\n\n // Method 1: Metric-based learning (Prototypical Networks)\n println!(\"--- Method 1: Prototypical Networks ---\");\n let config_metric = FewShotConfig::default()\n .with_method(FewShotMethod::Metric)\n .with_input_dim(10)\n .with_embedding_dim(8)\n .with_hidden_dims(vec![32, 16])\n .with_distance_metric(DistanceMetric::Euclidean);\n\n let mut metric_predictor = FewShotPredictor::new(config_metric);\n metric_predictor.fit(&support_features, &support_labels);\n let metric_results = metric_predictor.predict(&query_features);\n\n println!(\"Query predictions (metric-based):\");\n for (i, result) in metric_results.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%, reliable: {})\",\n i,\n class_names[result.predicted_class],\n result.confidence * 100.0,\n result.is_reliable\n );\n }\n println!();\n\n // Method 2: MAML-based learning\n println!(\"--- Method 2: MAML (Model-Agnostic Meta-Learning) ---\");\n let config_maml = FewShotConfig::default()\n .with_method(FewShotMethod::MAML)\n .with_input_dim(10)\n .with_embedding_dim(8)\n .with_hidden_dims(vec![32, 16])\n .with_adaptation_steps(3)\n .with_adaptation_lr(0.01);\n\n let mut maml_learner = MAMLLearner::new(config_maml);\n maml_learner.fit(&support_features, &support_labels);\n let maml_results = maml_learner.predict(&query_features);\n\n println!(\"Query predictions (MAML):\");\n for (i, result) in maml_results.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_names[result.predicted_class],\n result.confidence * 100.0\n );\n }\n\n // Print adaptation info\n if let Some(adaptation) = maml_learner.last_adaptation() {\n println!(\" Adaptation: {} steps, loss {:.4} -> {:.4} ({:.1}% improvement)\",\n adaptation.steps,\n adaptation.initial_loss,\n adaptation.final_loss,\n adaptation.improvement * 100.0\n );\n }\n println!();\n\n // Method 3: Siamese Network\n println!(\"--- Method 3: Siamese Network ---\");\n let config_siamese = FewShotConfig::default()\n .with_method(FewShotMethod::Siamese)\n .with_input_dim(10)\n .with_embedding_dim(8)\n .with_hidden_dims(vec![32, 16]);\n\n let mut siamese_learner = SiameseLearner::new(config_siamese);\n siamese_learner.fit(&support_features, &support_labels);\n let siamese_results = siamese_learner.predict(&query_features);\n\n println!(\"Query predictions (Siamese):\");\n for (i, result) in siamese_results.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_names[result.predicted_class],\n result.confidence * 100.0\n );\n }\n println!();\n\n // Method 4: Hybrid approach\n println!(\"--- Method 4: Hybrid (Metric + Optimization) ---\");\n let config_hybrid = FewShotConfig::default()\n .with_method(FewShotMethod::Hybrid)\n .with_input_dim(10)\n .with_embedding_dim(8)\n .with_hidden_dims(vec![32, 16]);\n\n let mut hybrid_learner = HybridLearner::new(config_hybrid);\n hybrid_learner.fit(&support_features, &support_labels);\n let hybrid_results = hybrid_learner.predict(&query_features);\n\n println!(\"Query predictions (Hybrid):\");\n for (i, result) in hybrid_results.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_names[result.predicted_class],\n result.confidence * 100.0\n );\n }\n println!();\n\n // Compare actual labels\n println!(\"--- Ground Truth ---\");\n for (i, &label) in query_labels.iter().enumerate() {\n println!(\" Query {}: {}\", i, class_names[label]);\n }\n\n // Calculate accuracy for each method\n println!(\"\\n--- Accuracy Summary ---\");\n let methods = [\n (\"Prototypical\", &metric_results),\n (\"MAML\", &maml_results),\n (\"Siamese\", &siamese_results),\n (\"Hybrid\", &hybrid_results),\n ];\n\n for (name, results) in methods {\n let correct = results\n .iter()\n .zip(&query_labels)\n .filter(|(pred, &actual)| pred.predicted_class == actual)\n .count();\n let accuracy = correct as f64 / query_labels.len() as f64 * 100.0;\n println!(\" {}: {:.1}% ({}/{})\", name, accuracy, correct, query_labels.len());\n }\n\n println!(\"\\n=== Example Complete ===\");\n}\n\n/// Generate synthetic market data for the example.\nfn generate_synthetic_data() -> (Array2<f64>, Vec<usize>, Array2<f64>, Vec<usize>) {\n let dim = 10;\n let n_support_per_class = 5;\n let n_query_per_class = 3;\n let n_classes = 3;\n\n // Base feature patterns for each class\n // Uptrend: positive returns, momentum indicators high\n let uptrend_base: Vec<f64> = vec![0.02, 0.015, 0.7, 65.0, 1.2, 0.8, 0.7, 0.6, 0.3, 0.7];\n // Sideways: near-zero returns, neutral indicators\n let sideways_base: Vec<f64> = vec![0.001, 0.002, 0.5, 50.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.5];\n // Downtrend: negative returns, bearish indicators\n let downtrend_base: Vec<f64> = vec![-0.02, -0.015, 0.3, 35.0, 0.7, 0.3, 0.3, 0.4, 0.7, 0.3];\n\n let bases = [&uptrend_base, &sideways_base, &downtrend_base];\n\n let mut support_data = Vec::new();\n let mut support_labels = Vec::new();\n let mut query_data = Vec::new();\n let mut query_labels = Vec::new();\n\n for (class_idx, base) in bases.iter().enumerate() {\n // Support examples\n for i in 0..n_support_per_class {\n let noisy = add_noise(base, 0.1, (class_idx * 100 + i) as u64);\n support_data.extend(noisy);\n support_labels.push(class_idx);\n }\n\n // Query examples\n for i in 0..n_query_per_class {\n let noisy = add_noise(base, 0.15, (class_idx * 1000 + i) as u64);\n query_data.extend(noisy);\n query_labels.push(class_idx);\n }\n }\n\n let support = Array2::from_shape_vec(\n (n_classes * n_support_per_class, dim),\n support_data,\n ).unwrap();\n\n let query = Array2::from_shape_vec(\n (n_classes * n_query_per_class, dim),\n query_data,\n ).unwrap();\n\n (support, support_labels, query, query_labels)\n}\n\n/// Add deterministic pseudo-random noise to a feature vector.\nfn add_noise(base: &[f64], noise_level: f64, seed: u64) -> | |
| [2026-01-21T18:07:36.102Z] [INFO] Vec<f64> {\n let mut rng_state = seed;\n\n base.iter().map(|&v| {\n rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);\n let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;\n v + v.abs().max(0.01) * noise_level * random * 2.0\n }).collect()\n}\n","structuredPatch":[{"oldStart":5,"oldLines":226,"newStart":5,"newLines":217,"lines":[" //!"," //! Run with: cargo run --example basic_few_shot"," ","-use few_shot_market_prediction::prelude::*;","-use std::collections::HashMap;","+use few_shot_market_prediction::methods::{","+ FewShotConfig, FewShotLearner, FewShotMethod, FewShotPredictor,","+ MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,","+};","+use few_shot_market_prediction::network::DistanceMetric;","+use ndarray::Array2;"," "," fn main() {"," println!(\"=== Few-Shot Market Prediction: Basic Example ===\\n\");"," "," // Generate synthetic market features for demonstration","- let (support_set, query_set, labels) = generate_synthetic_data();","+ let (support_features, support_labels, query_features, query_labels) = generate_synthetic_data();"," ","- println!(\"Support set: {} examples per class\", support_set.len() / 3);","- println!(\"Query set: {} examples to classify\\n\", query_set.len());","+ println!(\"Support set: {} examples across 3 classes\", support_features.nrows());","+ println!(\"Query set: {} examples to classify\\n\", query_features.nrows());"," ","- // Create embedding network","- let embedding_config = EmbeddingConfig {","- input_dim: 10, // 10 market features","- hidden_dims: vec![32, 16],","- output_dim: 8, // 8-dimensional embeddings","- activation: Activation::LeakyReLU(0.1),","- dropout_rate: 0.1,","- };","+ // Define class names","+ let class_names = [\"Uptrend\", \"Sideways\", \"Downtrend\"];"," ","- let network = EmbeddingNetwork::new(embedding_config);","- println!(\"Embedding network created: 10 -> [32, 16] -> 8\\n\");","-","- // Embed all samples","- let support_embeddings: Vec<Vec<f64>> = support_set","- .iter()","- .map(|x| network.forward(x, None))","- .collect();","-","- let query_embeddings: Vec<Vec<f64>> = query_set","- .iter()","- .map(|x| network.forward(x, None))","- .collect();","-","- // Create class label mapping","- let class_labels = vec![\"Uptrend\", \"Sideways\", \"Downtrend\"];","- let support_labels = vec![0, 0, 0, 1, 1, 1, 2, 2, 2]; // 3 examples per class","-"," // Method 1: Metric-based learning (Prototypical Networks)"," println!(\"--- Method 1: Prototypical Networks ---\");","- let metric_learner = MetricBasedLearner::new(","- FewShotConfig::default(),","- DistanceMetric::Euclidean,","- );","+ let config_metric = FewShotConfig::default()","+ .with_method(FewShotMethod::Metric)","+ .with_input_dim(10)","+ .with_embedding_dim(8)","+ .with_hidden_dims(vec![32, 16])","+ .with_distance_metric(DistanceMetric::Euclidean);"," ","- let metric_result = metric_learner.predict(","- &support_embeddings,","- &support_labels,","- &query_embeddings,","- );","+ let mut metric_predictor = FewShotPredictor::new(config_metric);","+ metric_predictor.fit(&support_features, &support_labels);","+ let metric_results = metric_predictor.predict(&query_features);"," "," println!(\"Query predictions (metric-based):\");","- for (i, &pred) in metric_result.predictions.iter().enumerate() {","+ for (i, result) in metric_results.iter().enumerate() {"," println!(","- \" Query {}: {} (confidence: {:.1}%)\",","+ \" Query {}: {} (confidence: {:.1}%, reliable: {})\","," i,","- class_labels[pred],","- metric_result.confidences[i] * 100.0","+ class_names[result.predicted_class],","+ result.confidence * 100.0,","+ result.is_reliable"," );"," }"," println!();"," "," // Method 2: MAML-based learning"," println!(\"--- Method 2: MAML (Model-Agnostic Meta-Learning) ---\");","- let maml_learner = MAMLLearner::new(","- FewShotConfig::default(),","- MAMLConfig {","- inner_lr: 0.01,","- inner_steps: 3,","- first_order: true,","- },","- );","+ let config_maml = FewShotConfig::default()","+ .with_method(FewShotMethod::MAML)","+ .with_input_dim(10)","+ .with_embedding_dim(8)","+ .with_hidden_dims(vec![32, 16])","+ .with_adaptation_steps(3)","+ .with_adaptation_lr(0.01);"," ","- // For MAML, we pass raw features as it adapts the network","- let maml_result = maml_learner.predict_with_adaptation(","- &network,","- &support_set,","- &support_labels,","- &query_set,","- );","+ let mut maml_learner = MAMLLearner::new(config_maml);","+ maml_learner.fit(&support_features, &support_labels);","+ let maml_results = maml_learner.predict(&query_features);"," "," println!(\"Query predictions (MAML):\");","- for (i, &pred) in maml_result.predictions.iter().enumerate() {","+ for (i, result) in maml_results.iter().enumerate() {"," println!("," \" Query {}: {} (confidence: {:.1}%)\","," i,","- class_labels[pred],","- maml_result.confidences[i] * 100.0","+ class_names[result.predicted_class],","+ result.confidence * 100.0"," );"," }","+","+ // Print adaptation info","+ if let Some(adaptation) = maml_learner.last_adaptation() {","+ println!(\" Adaptation: {} steps, loss {:.4} -> {:.4} ({:.1}% improvement)\",","+ adaptation.steps,","+ adaptation.initial_loss,","+ adaptation.final_loss,","+ adaptation.improvement * 100.0","+ );","+ }"," println!();"," "," // Method 3: Siamese Network"," println!(\"--- Method 3: Siamese Network ---\");","- let siamese_learner = SiameseLearner::new(FewShotConfig::default());","+ let config_siamese = FewShotConfig::default()","+ .with_method(FewShotMethod::Siamese)","+ .with_input_dim(10)","+ .with_embedding_dim(8)","+ .with_hidden_dims(vec![32, 16]);"," ","- let siamese_result = siamese_learner.predict(","- &support_embeddings,","- &support_labels,","- &query_embeddings,","- );","+ let mut siamese_learner = SiameseLearner::new(config_siamese);","+ siamese_learner.fit(&support_features, &support_labels);","+ let siamese_results = siamese_learner.predict(&query_features);"," "," println!(\"Query predictions (Siamese):\");","- for (i, &pred) in siamese_result.predictions.iter().enumerate() {","+ for (i, result) in siamese_results.iter().enumerate() {"," println!("," \" Query {}: {} (confidence: {:.1}%)\","," i,","- class_labels[pred],","- siamese_result.confidences[i] * 100.0","+ class_names[result.predicted_class],","+ result.confidence * 100.0"," );"," }"," println!();"," "," // Method 4: Hybrid approach","- println!(\"--- Method 4: Hybrid (Metric + MAML) ---\");","- let hybrid_learner = HybridLearner::new(","- FewShotConfig::default(),","- HybridConfig {","- metric_weight: 0.6,","- maml_weight: 0.4,","- },","- );","+ println!(\"--- Method 4: Hybrid (Metric + Optimization) ---\");","+ let config_hybrid = FewShotConfig::default()","+ .with_method(FewShotMethod::Hybrid)","+ .with_input_dim(10)","+ .with_embedding_dim(8)","+ .with_hidden_dims(vec![32, 16]);"," ","- let hybrid_result = hybrid_learner.predict(","- &support_embeddings,","- &support_labels,","- &query_embeddings,","- );","+ let mut hybrid_learner = HybridLearner::new(config_hybrid);","+ hybrid_learner.fit(&support_features, &support_labels);","+ let hybrid_results = hybrid_learner.predict(&query_features);"," "," println!(\"Query predictions (Hybrid):\");","- for (i, &pred) in hybrid_result.predictions.iter().enumerate() {","+ for (i, result) in hybrid_results.iter().enumerate() {"," println!("," \" Query {}: {} (confidence: {:.1}%)\","," i,","- class_labels[pred],","- hybrid_result.confidences[i] * 100.0","+ class_names[result.predicted_class],","+ result.confidence * 100.0"," );"," }"," println!();"," "," // Compare actual labels"," println!(\"--- Ground Truth ---\");","- for (i, &label) in labels.iter().enumerate() {","- println!(\" Query {}: {}\", i, class_labels[label]);","+ for (i, &label) in query_labels.iter().enumerate() {","+ println!(\" Query {}: {}\", i, class_names[label]);"," }"," "," // Calculate accuracy for each method"," println!(\"\\n--- Accuracy Summary ---\");"," let methods = [","- (\"Prototypical\", &metric_result.predictions),","- (\"MAML\", &maml_result.predictions),","- (\"Siamese\", &siamese_result.predictions),","- (\"Hybrid\", &hybrid_result.predictions),","+ (\"Prototypical\", &metric_results),","+ (\"MAML\", &maml_results),","+ (\"Siamese\", &siamese_results),","+ (\"Hybrid\", &hybrid_results),"," ];"," ","- for (name, preds) in methods {","- let correct = preds.iter().zip(&labels).filter(|(p, l)| p == l).count();","- let accuracy = correct as f64 / labels.len() as f64 * 100.0;","- println!(\" {}: {:.1}% ({}/{})\", name, accuracy, correct, labels.len());","+ for (name, results) in methods {","+ let correct = results","+ .iter()","+ .zip(&query_labels)","+ .filter(|(pred, &actual)| pred.predicted_class == actual)","+ .count();","+ let accuracy = correct as f64 / query_labels.len() as f64 * 100.0;","+ println!(\" {}: {:.1}% ({}/{})\", name, accuracy, correct, query_labels.len());"," }"," "," println!(\"\\n=== Example Complete ===\");"," }"," "," /// Generate synthetic market data for the example.","-fn generate_synthetic_data() -> (Vec<Vec<f64>>, Vec<Vec<f64>>, Vec<usize>) {","- // Uptrend features: positive returns, low volatility, bullish indicators","- let uptrend_base = vec![0.02, 0.5, 0.7, 65.0, 0.01, 1.2, 0.8, 0.6, 0.3, 0.7];","+fn generate_synthetic_data() -> (Array2<f64>, Vec<usize>, Array2<f64>, Vec<usize>) {","+ let dim = 10;","+ let n_support_per_class = 5;","+ let n_query_per_class = 3;","+ let n_classes = 3;"," ","- // Sideways features: near-zero returns, low volatility, neutral indicators","- let sideways_base = vec![0.001, 0.3, 0.5, 50.0, 0.008, 1.0, 0.5, 0.5, 0.5, 0.5];","+ // Base feature patterns for each class","+ // Uptrend: positive returns, momentum indicators high","+ let uptrend_base: Vec<f64> = vec![0.02, 0.015, 0.7, 65.0, 1.2, 0.8, 0.7, 0.6, 0.3, 0.7];","+ // Sideways: near-zero returns, neutral indicators","+ let sideways_base: Vec<f64> = vec![0.001, 0.002, 0.5, 50.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.5];","+ // Downtrend: negative returns, bearish indicators","+ let downtrend_base: Vec<f64> = vec![-0.02, -0.015, 0.3, 35.0, 0.7, 0.3, 0.3, 0.4, 0.7, 0.3];"," ","- // Downtrend features: negative returns, higher volatility, bearish indicators","- let downtrend_base = vec![-0.02, 0.6, 0.3, 35.0, 0.015, 0.8, 0.2, 0.4, 0.7, 0.3];","+ let bases = [&uptrend_base, &sideways_base, &downtrend_base];"," ","- // Create support set (3 examples per class with small variations)","- let mut support_set = Vec::new();","+ let mut support_data = Vec::new();","+ let mut support_labels = Vec::new();","+ let mut query_data = Vec::new();","+ let mut query_labels = Vec::new();"," ","- for i in 0..3 {","- support_set.push(add_noise(&uptrend_base, 0.1, i as u64));","+ for (class_idx, base) in bases.iter().enumerate() {","+ // Support examples","+ for i in 0..n_support_per_class {","+ let noisy = add_noise(base, 0.1, (class_idx * 100 + i) as u64);","+ support_data.extend(noisy);","+ support_labels.push(class_idx);","+ }","+","+ // Query examples","+ for i in 0..n_query_per_class {","+ let noisy = add_noise(base, 0.15, (class_idx * 1000 + i) as u64);","+ query_data.extend(noisy);","+ query_labels.push(class_idx);","+ }"," }","- for i in 0..3 {","- support_set.push(add_noise(&sideways_base, 0.1, i as u64 + 10));","- }","- for i in 0..3 {","- support_set.push(add_noise(&downtrend_base, 0.1, i as u64 + 20));","- }"," ","- // Create query set (2 examples per class)","- let mut query_set = Vec::new();","- let mut labels = Vec::new();","+ let support = Array2::from_shape_vec(","+ (n_classes * n_support_per_class, dim),","+ support_data,","+ ).unwrap();"," ","- // Query uptrend examples","- query_set.push(add_noise(&uptrend_base, 0.15, 100));","- labels.push(0);","- query_set.push(add_noise(&uptrend_base, 0.15, 101));","- labels.push(0);","+ let query = Array2::from_shape_vec(","+ (n_classes * n_query_per_class, dim),","+ query_data,","+ ).unwrap();"," ","- // Query sideways examples","- query_set.push(add_noise(&sideways_base, 0.15, 110));","- labels.push(1);","-","- // Query downtrend examples","- query_set.push(add_noise(&downtrend_base, 0.15, 120));","- labels.push(2);","- query_set.push(add_noise(&downtrend_base, 0.15, 121));","- labels.push(2);","-","- (support_set, query_set, labels)","+ (support, support_labels, query, query_labels)"," }"," ","-/// Add random noise to a feature vector.","+/// Add deterministic pseudo-random noise to a feature vector."," fn add_noise(base: &[f64], noise_level: f64, seed: u64) -> Vec<f64> {","- // Simple pseudo-random noise generator"," let mut rng_state = seed;"," "," base.iter().map(|&v| {"," rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);"," let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;","- v + v.abs() * noise_level * random","+ v + v.abs().max(0.01) * noise_level * random * 2.0"," }).collect()"," }"]}],"originalFile":"//! Basic Few-Shot Market Prediction Example\n//!\n//! This example demonstrates the core few-shot learning functionality\n//! for market prediction using synthetic data.\n//!\n//! Run with: cargo run --example basic_few_shot\n\nuse few_shot_market_prediction::prelude::*;\nuse std::collections::HashMap;\n\nfn main() {\n println!(\"=== Few-Shot Market Prediction: Basic Example ===\\n\");\n\n // Generate synthetic market features for demonstration\n let (support_set, query_set, labels) = generate_synthetic_data();\n\n println!(\"Support set: {} examples per class\", support_set.len() / 3);\n println!(\"Query set: {} examples to classify\\n\", query_set.len());\n\n // Create embedding network\n let embedding_config = EmbeddingConfig {\n input_dim: 10, // 10 market features\n hidden_dims: vec![32, 16],\n output_dim: 8, // 8-dimensional embeddings\n activation: Activation::LeakyReLU(0.1),\n dropout_rate: 0.1,\n };\n\n let network = EmbeddingNetwork::new(embedding_config);\n println!(\"Embedding network created: 10 -> [32, 16] -> 8\\n\");\n\n // Embed all samples\n let support_embeddings: Vec<Vec<f64>> = support_set\n .iter()\n .map(|x| network.forward(x, None))\n .collect();\n\n let query_embeddings: Vec<Vec<f64>> = query_set\n .iter()\n .map(|x| network.forward(x, None))\n .collect();\n\n // Create class label mapping\n let class_labels = vec![\"Uptrend\", \"Sideways\", \"Downtrend\"];\n let support_labels = vec![0, 0, 0, 1, 1, 1, 2, 2, 2]; // 3 examples per class\n\n // Method 1: Metric-based learning (Prototypical Networks)\n println!(\"--- Method 1: Prototypical Networks ---\");\n let metric_learner = MetricBasedLearner::new(\n FewShotConfig::default(),\n DistanceMetric::Euclidean,\n );\n\n let metric_result = metric_learner.predict(\n &support_embeddings,\n &support_labels,\n &query_embeddings,\n );\n\n println!(\"Query predictions (metric-based):\");\n for (i, &pred) in metric_result.predictions.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_labels[pred],\n metric_result.confidences[i] * 100.0\n );\n }\n println!();\n\n // Method 2: MAML-based learning\n println!(\"--- Method 2: MAML (Model-Agnostic Meta-Learning) ---\");\n let maml_learner = MAMLLearner::new(\n FewShotConfig::default(),\n MAMLConfig {\n inner_lr: 0.01,\n inner_steps: 3,\n first_order: true,\n },\n );\n\n // For MAML, we pass raw features as it adapts the network\n let maml_result = maml_learner.predict_with_adaptation(\n &network,\n &support_set,\n &support_labels,\n &query_set,\n );\n\n println!(\"Query predictions (MAML):\");\n for (i, &pred) in maml_result.predictions.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_labels[pred],\n maml_result.confidences[i] * 100.0\n );\n }\n println!();\n\n // Method 3: Siamese Network\n println!(\"--- Method 3: Siamese Network ---\");\n let siamese_learner = SiameseLearner::new(FewShotConfig::default());\n\n let siamese_result = siamese_learner.predict(\n &support_embeddings,\n &support_labels,\n &query_embeddings,\n );\n\n println!(\"Query predictions (Siamese):\");\n for (i, &pred) in siamese_result.predictions.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_labels[pred],\n siamese_result.confidences[i] * 100.0\n );\n }\n println!();\n\n // Method 4: Hybrid approach\n println!(\"--- Method 4: Hybrid (Metric + MAML) ---\");\n let hybrid_learner = HybridLearner::new(\n FewShotConfig::default(),\n HybridConfig {\n metric_weight: 0.6,\n maml_weight: 0.4,\n },\n );\n\n let hybrid_result = hybrid_learner.predict(\n &support_embeddings,\n &support_labels,\n &query_embeddings,\n );\n\n println!(\"Query predictions (Hybrid):\");\n for (i, &pred) in hybrid_result.predictions.iter().enumerate() {\n println!(\n \" Query {}: {} (confidence: {:.1}%)\",\n i,\n class_labels[pred],\n hybrid_result.confidences[i] * 100.0\n );\n }\n println!();\n\n // Compare actual labels\n println!(\"--- Ground Truth ---\");\n for (i, &label) in labels.iter().enumerate() {\n println!(\" Query {}: {}\", i, class_labels[label]);\n }\n\n // Calculate accuracy for each method\n println!(\"\\n--- Accuracy Summary ---\");\n let methods = [\n (\"Prototypical\", &metric_result.predictions),\n (\"MAML\", &maml_result.predictions),\n (\"Siamese\", &siamese_result.predictions),\n (\"Hybrid\", &hybrid_result.predictions),\n ];\n\n for (name, preds) in methods {\n let correct = preds.iter().zip(&labels).filter(|(p, l)| p == l).count();\n let accuracy = correct as f64 / labels.len() as f64 * 100.0;\n println!(\" {}: {:.1}% ({}/{})\", name, accuracy, correct, labels.len());\n }\n\n println!(\"\\n=== Example Complete ===\");\n}\n\n/// Generate synthetic market data for the example.\nfn generate_synthetic_data() -> (Vec<Vec<f64>>, Vec<Vec<f64>>, Vec<usize>) {\n // Uptrend features: positive returns, low volatility, bullish indicators\n let uptrend_base = vec![0.02, 0.5, 0.7, 65.0, 0.01, 1.2, 0.8, 0.6, 0.3, 0.7];\n\n // Sideways features: near-zero returns, low volatility, neutral indicators\n let sideways_base = vec![0.001, 0.3, 0.5, 50.0, 0.008, 1.0, 0.5, 0.5, 0.5, 0.5];\n\n // Downtrend features: negative returns, higher volatility, bearish indicators\n let downtrend_base = vec![-0.02, 0.6, 0.3, 35.0, 0.015, 0.8, 0.2, 0.4, 0.7, 0.3];\n\n // Create support set (3 examples per class with small variations)\n let mut support_set = Vec::new();\n\n for i in 0..3 {\n support_set.push(add_noise(&uptrend_base, 0.1, i as u64));\n }\n for i in 0..3 {\n support_set.push(add_noise(&sideways_base, 0.1, i as u64 + 10));\n }\n for i in 0..3 {\n support_set.push(add_noise(&downtrend_base, 0.1, i as u64 + 20));\n }\n\n // Create query set (2 examples per class)\n let mut query_set = Vec::new();\n let mut labels = Vec::new();\n\n // Query uptrend examples\n query_set.push(add_noise(&uptrend_base, 0.15, 100));\n labels.push(0);\n query_set.push(add_noise(&uptrend_base, 0.15, 101));\n labels.push(0);\n\n // Query sideways examples\n query_set.push(add_noise(&sideways_base, 0.15, 110));\n labels.push(1);\n\n // Query downtrend examples\n query_set.push(add_noise(&downtrend_base, 0.15, 120));\n labels.push(2);\n query_set.push(add_noise(&downtrend_base, 0.15, 121));\n labels.push(2);\n\n (support_set, query_set, labels)\n}\n\n/// Add random noise to a feature vector.\nfn add_noise(base: &[f64], noise_level: f64, seed: u64) -> Vec<f64> {\n // Simple pseudo-random noise generator\n let mut rng_state = seed;\n\n base.iter().map(|&v| {\n rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);\n let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;\n v + v.abs() * noise_level * random\n }).collect()\n}\n"}} | |
| [2026-01-21T18:07:36.115Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01S2mUkqLayWwUrxFaL128nX","type":"tool_result","content":"The file /tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/examples/regime_prediction.rs has been updated. Here's the result of running `cat -n` on a snippet of the edited file:\n 1β//! Market Regime Prediction Example\n 2β//!\n 3β//! This example demonstrates using few-shot learning to classify\n 4β//! market regimes and generate trading signals.\n 5β//!\n 6β//! Run with: cargo run --example regime_prediction\n 7β\n 8βuse few_shot_market_prediction::strategy::{\n 9β MarketRegime, RegimeClassifier, RegimeClassification,\n 10β SignalGenerator, SignalConfig, SignalType,\n 11β RiskManager, RiskConfig,\n 12β};\n 13βuse std::time::Duration;\n 14β\n 15βfn main() {\n 16β println!(\"=== Market Regime Prediction with Few-Shot Learning ===\\n\");\n 17β\n 18β // Define all market regimes we want to classify\n 19β let regimes = MarketRegime::all();\n 20β println!(\"Classifying into {} market regimes:\", regimes.len());\n 21β for regime in ®imes {\n 22β println!(\" - {} (position bias: {:.1})\", regime, regime.position_bias());\n 23β }\n 24β println!();\n 25β\n 26β // Create regime classifier\n 27β let mut classifier = RegimeClassifier::new(0.6);\n 28β println!(\"Regime classifier created with 60% confidence threshold\\n\");\n 29β\n 30β // Simulate some regime predictions\n 31β // In real usage, these probabilities would come from the few-shot model\n 32β let test_scenarios = [\n 33β (\"Strong momentum rally\", vec![0.75, 0.15, 0.03, 0.02, 0.01, 0.02, 0.01, 0.01]),\n 34β (\"Mild uptrend\", vec![0.15, 0.55, 0.20, 0.05, 0.02, 0.02, 0.00, 0.01]),\n 35β (\"Choppy sideways\", vec![0.05, 0.10, 0.60, 0.10, 0.05, 0.08, 0.01, 0.01]),\n 36β (\"Bearish breakdown\", vec![0.02, 0.03, 0.05, 0.15, 0.60, 0.10, 0.04, 0.01]),\n 37β (\"High volatility\", vec![0.10, 0.10, 0.10, 0.10, 0.10, 0.40, 0.05, 0.05]),\n 38β (\"Market crash\", vec![0.01, 0.02, 0.02, 0.10, 0.15, 0.15, 0.50, 0.05]),\n 39β (\"Recovery phase\", vec![0.15, 0.15, 0.10, 0.05, 0.05, 0.10, 0.05, 0.35]),\n 40β ];\n 41β\n 42β println!(\"--- Regime Classification Results ---\\n\");\n 43β\n 44β // Create signal generator\n 45β let signal_config = SignalConfig {\n 46β min_confidence: 0.5,\n 47β max_position_size: 0.2,\n 48β default_take_profit: 3.0,\n 49β default_stop_loss: 1.5,\n 50β use_regime_adjustment: true,\n 51β cooldown: Duration::from_secs(0), // No cooldown for demo\n 52β };\n 53β let mut signal_generator = SignalGenerator::new(signal_config);\n 54β\n 55β // Create risk manager\n 56β let risk_config = RiskConfig::default();\n 57β let risk_manager = RiskManager::new(risk_config, 100_000.0);\n 58β\n 59β for (scenario_name, probs) in &test_scenarios {\n 60β println!(\"Scenario: {}\", scenario_name);\n 61β\n 62β // Classify the regime\n 63β let classification = classifier.classify_with_uncertainty(probs);\n 64β\n 65β println!(\" Classification: {}\", classification.description());\n 66β println!(\" Should act: {}\", classification.should_act());\n 67β\n 68β // Update classifier history for transition analysis\n 69β classifier.update_history(classification.regime, classification.confidence);\n 70β\n 71β // Generate trading signal\n 72β signal_generator.clear_cooldowns(); // Reset for demo\n 73β let signal = signal_generator.generate(\"BTCUSDT\", &classification);\n 74β\n 75β println!(\" Signal: {:?}\", signal.signal_type);\n 76β if signal.signal_type.requir | |
| [2026-01-21T18:07:36.144Z] [INFO] es_action() {\n 77β println!(\" Position size: {:.1}%\", signal.position_size * 100.0);\n 78β if let Some(tp) = signal.take_profit {\n 79β println!(\" Take profit: {:.1}%\", tp);\n 80β }\n 81β if let Some(sl) = signal.stop_loss {\n 82β println!(\" Stop loss: {:.1}%\", sl);\n 83β }\n 84β println!(\" Expected value: {:.2}\", signal.expected_value());\n 85β\n 86β // Check risk\n 87β let risk_check = risk_manager.check_signal(&signal, 0);\n 88β println!(\" Risk approved: {}\", risk_check.approved);\n 89β if !risk_check.rejections.is_empty() {\n 90β println!(\" Rejections: {:?}\", risk_check.rejections);\n 91β }\n 92β }\n 93β println!();\n 94β }\n 95β\n 96β // Demonstrate regime transitions\n 97β println!(\"--- Regime Transition Analysis ---\\n\");\n 98β\n 99β // Simulate a sequence of regimes\n 100β let regime_sequence = [\n 101β MarketRegime::Sideways,\n 102β MarketRegime::WeakUptrend,\n 103β MarketRegime::StrongUptrend,\n 104β MarketRegime::Volatile,\n 105β MarketRegime::WeakDowntrend,\n 106β MarketRegime::Crash,\n 107β MarketRegime::Recovery,\n 108β MarketRegime::Sideways,\n 109β ];\n 110β\n 111β for regime in ®ime_sequence {\n 112β classifier.update_history(*regime, 0.8);\n 113β }\n 114β\n 115β println!(\"After observing {} regime changes:\", regime_sequence.len());\n 116β if let Some(dominant) = classifier.dominant_regime(5) {\n 117β println!(\" Dominant regime (last 5): {}\", dominant);\n 118β }\n 119β println!();\n 120β\n 121β // Check transition probabilities\n 122β let transitions = [\n 123β (MarketRegime::StrongUptrend, MarketRegime::Volatile),\n 124β (MarketRegime::Volatile, MarketRegime::Crash),\n 125β (MarketRegime::Crash, MarketRegime::Recovery),\n 126β (MarketRegime::Recovery, MarketRegime::Sideways),\n 127β ];\n 128β\n 129β println!(\"Transition probabilities from history:\");\n 130β for (from, to) in &transitions {\n 131β let prob = classifier.transition_probability(*from, *to);\n 132β let likely = classifier.is_transition_likely(*from, *to);\n 133β println!(\n 134β \" {} -> {}: {:.1}% (likely: {})\",\n 135β from, to, prob * 100.0, likely\n 136β );\n 137β }\n 138β\n 139β // Show risk summary\n 140β println!(\"\\n--- Current Risk Summary ---\");\n 141β println!(\"{}\", risk_manager.risk_summary());\n 142β\n 143β // Demonstrate different risk profiles\n 144β println!(\"\\n--- Risk Profile Comparison ---\");\n 145β let profiles = [\n 146β (\"Default\", RiskConfig::default()),\n 147β (\"Conservative\", RiskConfig::conservative()),\n 148β (\"Aggressive\", RiskConfig::aggressive()),\n 149β ];\n 150β\n 151β for (name, config) in &profiles {\n 152β println!(\" {}: max_position={:.0}%, max_exposure={:.0}%, daily_limit={:.0}%\",\n 153β name,\n 154β config.max_position_size * 100.0,\n 155β config.max_total_exposure * 100.0,\n 156β config.daily_loss_limit * 100.0\n 157β );\n 158β }\n 159β\n 160β println!(\"\\n=== Example Complete ===\");\n 161β}\n 162β"}]},"parent_tool_use_id":null,"session_id":"172d23c7-5c62-484f-af89-80e92d0b393e","uuid":"91c25c8e-0346-4669-9a5c-f9a9b683705b","tool_use_result":{"type":"update","filePath":"/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/examples/regime_prediction.rs","content":"//! Market Regime Prediction Example\n//!\n//! This example demonstrates using few-shot learning to classify\n//! market regimes and generate trading signals.\n//!\n//! Run with: cargo run --example regime_prediction\n\nuse few_shot_market_prediction::strategy::{\n MarketRegime, RegimeClassifier, RegimeClassification,\n SignalGenerator, SignalConfig, SignalType,\n RiskManager, RiskConfig,\n};\nuse std::time::Duration;\n\nfn main() {\n println!(\"=== Market Regime Prediction with Few-Shot Learning ===\\n\");\n\n // Define all market regimes we want to classify\n let regimes = MarketRegime::all();\n println!(\"Classifying into {} market regimes:\", regimes.len());\n for regime in ®imes {\n println!(\" - {} (position bias: {:.1})\", regime, regime.position_bias());\n }\n println!();\n\n // Create regime classifier\n let mut classifier = RegimeClassifier::new(0.6);\n println!(\"Regime classifier created with 60% confidence threshold\\n\");\n\n // Simulate some regime predictions\n // In real usage, these probabilities would come from the few-shot model\n let test_scenarios = [\n (\"Strong momentum rally\", vec![0.75, 0.15, 0.03, 0.02, 0.01, 0.02, 0.01, 0.01]),\n (\"Mild uptrend\", vec![0.15, 0.55, 0.20, 0.05, 0.02, 0.02, 0.00, 0.01]),\n (\"Choppy sideways\", vec![0.05, 0.10, 0.60, 0.10, 0.05, 0.08, 0.01, 0.01]),\n (\"Bearish breakdown\", vec![0.02, 0.03, 0.05, 0.15, 0.60, 0.10, 0.04, 0.01]),\n (\"High volatility\", vec![0.10, 0.10, 0.10, 0.10, 0.10, 0.40, 0.05, 0.05]),\n (\"Market crash\", vec![0.01, 0.02, 0.02, 0.10, 0.15, 0.15, 0.50, 0.05]),\n (\"Recovery phase\", vec![0.15, 0.15, 0.10, 0.05, 0.05, 0.10, 0.05, 0.35]),\n ];\n\n println!(\"--- Regime Classification Results ---\\n\");\n\n // Create signal generator\n let signal_config = SignalConfig {\n min_confidence: 0.5,\n max_position_size: 0.2,\n default_take_profit: 3.0,\n default_stop_loss: 1.5,\n use_regime_adjustment: true,\n cooldown: Duration::from_secs(0), // No cooldown for demo\n };\n let mut signal_generator = SignalGenerator::new(signal_config);\n\n // Create risk manager\n let risk_config = RiskConfig::default();\n let risk_manager = RiskManager::new(risk_config, 100_000.0);\n\n for (scenario_name, probs) in &test_scenarios {\n println!(\"Scenario: {}\", scenario_name);\n\n // Classify the regime\n let classification = classifier.classify_with_uncertainty(probs);\n\n println!(\" Classification: {}\", classification.description());\n println!(\" Should act: {}\", classification.should_act());\n\n // Update classifier history for transition analysis\n classifier.update_history(classification.regime, classification.confidence);\n\n // Generate trading signal\n signal_generator.clear_cooldowns(); // Reset for demo\n let signal = signal_generator.generate(\"BTCUSDT\", &classification);\n\n println!(\" Signal: {:?}\", signal.signal_type);\n if signal.signal_type.requires_action() {\n println!(\" Position size: {:.1}%\", signal.position_size * 100.0);\n if let Some(tp) = signal.take_profit {\n println!(\" Take profit: {:.1}%\", tp);\n }\n if let Some(sl) = signal.stop_loss {\n println!(\" Stop loss: {:.1}%\", sl);\n }\n println!(\" Expected value: {:.2}\", signal.expected_value());\n\n // Check risk\n let risk_check = risk_manager.check_signal(&signal, 0);\n println!(\" Risk approved: {}\", risk_check.approved);\n if !risk_check.rejections.is_empty() {\n println!(\" Rejections: {:?}\", risk_check.rejections);\n }\n }\n println!();\n }\n\n // Demonstrate regime transitions\n println!(\"--- Regime Transition Analysis ---\\n\");\n\n // Simulate a sequence of regimes\n let regime_sequence = [\n MarketRegime::Sideways,\n MarketRegime::WeakUptrend,\n MarketRegime::StrongUptrend,\n MarketRegime::Volatile,\n MarketRegime::WeakDowntrend,\n MarketRegime::Crash,\n MarketRegime::Recovery,\n MarketRegime::Sideways,\n ];\n\n for regime in ®ime_sequence {\n classifier.update_history(*regime, 0.8);\n }\n\n println!(\"After observing {} regime changes:\", regime_sequence.len());\n if let Some(dominant) = classifier.dominant_regime(5) {\n println!(\" Dominant regime (last 5): {}\", dominant);\n }\n println!();\n\n // Check transition probabilities\n let transitions = [\n (MarketRegime::StrongUptrend, MarketRegime::Volatile),\n (MarketRegime::Volatile, MarketRegime::Crash),\n (MarketRegime::Crash, MarketRegime::Recovery),\n (MarketRegime::Recovery, MarketRegime::Sideways),\n ];\n\n println!(\"Transition probabilities from history:\");\n for (from, to) in &transitions {\n let prob = classifier.transition_probability(*from, *to);\n let likely = classifier.is_transition_likely(*from, *to);\n println!(\n \" {} -> {}: {:.1}% (likely: {})\",\n from, to, prob * 100.0, likely\n );\n }\n\n // Show risk summary\n println!(\"\\n--- Current Risk Summary ---\");\n println!(\"{}\", risk_manager.risk_summary());\n\n // Demonstrate different risk profiles\n println!(\"\\n--- Risk Profile Comparison ---\");\n let profiles = [\n (\"Default\", RiskConfig::default()),\n (\"Conservative\", RiskConfig::conservative()),\n (\"Aggressive\", RiskConfig::aggressive()),\n ];\n\n for (name, config) in &profiles {\n println!(\" {}: max_position={:.0}%, max_exposure={:.0}%, daily_limit={:.0}%\",\n name,\n config.max_position_size * 100.0,\n config.max_total_exposure * 100.0,\n config.daily_loss_limit * 100.0\n );\n }\n\n println!(\"\\n=== Example Complete ===\");\n}\n","structuredPatch":[{"oldStart":5,"oldLines":7,"newStart":5,"newLines":12,"lines":[" //!"," //! Run with: cargo run --example regime_prediction"," ","-use few_shot_market_prediction::prelude::*;","+use few_shot_market_prediction::strategy::{","+ MarketRegime, RegimeClassifier, RegimeClassification,","+ SignalGenerator, SignalConfig, SignalType,","+ RiskManager, RiskConfig,","+};","+use std::time::Duration;"," "," fn main() {"," println!(\"=== Market Regime Prediction with Few-Shot Learning ===\\n\");"]},{"oldStart":14,"oldLines":7,"newStart":19,"newLines":7,"lines":[" let regimes = MarketRegime::all();"," println!(\"Classifying into {} market regimes:\", regimes.len());"," for regime in ®imes {","- println!(\" - {} (bias: {:.1})\", regime, regime.position_bias());","+ println!(\" - {} (position bias: {:.1})\", regime, regime.position_bias());"," }"," println!();"," "]},{"oldStart":22,"oldLines":7,"newStart":27,"newLines":8,"lines":[" let mut classifier = RegimeClassifier::new(0.6);"," println!(\"Regime classifier created with 60% confidence threshold\\n\");"," ","- // Simulate some regime predictions (in real usage, these come from few-shot model)","+ // Simulate some regime predictions","+ // In real usage, these probabilities would come from the few-shot model"," let test_scenarios = ["," (\"Strong momentum rally\", vec![0.75, 0.15, 0.03, 0.02, 0.01, 0.02, 0.01, 0.01]),"," (\"Mild uptrend\", vec![0.15, 0.55, 0.20, 0.05, 0.02, 0.02, 0.00, 0.01]),"]},{"oldStart":42,"oldLines":7,"newStart":48,"newLines":7,"lines":[" default_take_profit: 3.0,"," default_stop_loss: 1.5,"," use_regime_adjustment: true,","- cooldown: std::time::Duration::from_secs(0), // No cooldown for demo","+ cooldown: Duration::from_secs(0), // No cooldown for demo"," };"," let mut signal_generator = SignalGenerator::new(signal_config);"," "]},{"oldStart":59,"oldLines":7,"newStart":65,"newLines":7,"lines":[" println!(\" Classification: {}\", classification.description());"," println!(\" Should act: {}\", classification.should_act());"," ","- // Update classifier history","+ // Update classifier history for transition analysis"," classifier.update_history(classification.regime, classification.confidence);"," "," // Generate trading signal"]},{"oldStart":69,"oldLines":8,"newStart":75,"newLines":12,"lines":[" println!(\" Signal: {:?}\", signal.signal_type);"," if signal.signal_type.requires_action() {"," println!(\" Position size: {:.1}%\", signal.position_size * 100.0);","- println!(\" Take profit: {:.1}%\", signal.take_profit.unwrap_or(0.0));","- println!(\" Stop loss: {:.1}%\", signal.stop_loss.unwrap_or(0.0));","+ if let Some(tp) = signal.take_profit {","+ println!(\" Take profit: {:.1}%\", tp);","+ }","+ if let Some(sl) = signal.stop_loss {","+ println!(\" Stop loss: {:.1}%\", sl);","+ }"," println!(\" Expected value: {:.2}\", signal.expected_value());"," "," // Check risk"]},{"oldStart":103,"oldLines":7,"newStart":113,"newLines":9,"lines":[" }"," "," println!(\"After observing {} regime changes:\", regime_sequence.len());","- println!(\" Dominant regime (last 5): {:?}\", classifier.dominant_regime(5));","+ if let Some(dominant) = classifier.dominant_regime(5) {","+ println!(\" Dominant regime (last 5): {}\", dominant);","+ }"," println!();"," "," // Check transition probabilities"]},{"oldStart":128,"oldLines":5,"newStart":140,"newLines":22,"lines":[" println!(\"\\n--- Current Risk Summary ---\");"," println!(\"{}\", risk_manager.risk_summary());"," ","+ // Demonstrate different risk profiles","+ println!(\"\\n--- Risk Profile Comparison ---\");","+ let profiles = [","+ (\"Default\", RiskConfig::default()),","+ (\"Conservative\", RiskConfig::conservative()),","+ (\"Aggressive\", RiskConfig::aggressive()),","+ ];","+","+ for (name, config) in &profiles {","+ println!(\" {}: max_position={:.0}%, max_exposure={:.0}%, daily_limit={:.0}%\",","+ name,","+ config.max_position_size * 100.0,","+ config.max_total_exposure * 100.0,","+ config.daily_loss_limit * 100.0","+ );","+ }","+"," println!(\"\\n=== Example Complete ===\");"," }"]}],"originalFile":"//! Market Regime Prediction Example\n//!\n//! This example demonstrates using few-shot learning to classify\n//! market regimes and generate trading signals.\n//!\n//! Run with: cargo run --example regime_prediction\n\nuse few_shot_market_prediction::prelude::*;\n\nfn main() {\n println!(\"=== Market Regime Prediction with Few-Shot Learning ===\\n\");\n\n // Define all market regimes we want to classify\n let regimes = MarketRegime::all();\n println!(\"Classifying into {} market regimes:\", regimes.len());\n for regime in ®imes {\n println!(\" - {} (bias: {:.1})\", regime, regime.position_bias());\n }\n println!();\n\n // Create regime classifier\n let mut classifier = RegimeClassifier::new(0.6);\n println!(\"Regime classifier created with 60% confidence threshold\\n\");\n\n // Simulate some regime predictions (in real usage, these come from few-shot model)\n let test_scenarios = [\n (\"Strong momentum rally\", vec![0.75, 0.15, 0.03, 0.02, 0.01, 0.02, 0.01, 0.01]),\n (\"Mild uptrend\", vec![0.15, 0.55, 0.20, 0.05, 0.02, 0.02, 0.00, 0.01]),\n (\"Choppy sideways\", vec![0.05, 0.10, 0.60, 0.10, 0.05, 0.08, 0.01, 0.01]),\n (\"Bearish breakdown\", vec![0.02, 0.03, 0.05, 0.15, 0.60, 0.10, 0.04, 0.01]),\n (\"High volatility\", vec![0.10, 0.10, 0.10, 0.10, 0.10, 0.40, 0.05, 0.05]),\n (\"Market crash\", vec![0.01, 0.02, 0.02, 0.10, 0.15, 0.15, 0.50, 0.05]),\n (\"Recovery phase\", vec![0.15, 0.15, 0.10, 0.05, 0.05, 0.10, 0.05, 0.35]),\n ];\n\n println!(\"--- Regime Classification Results ---\\n\");\n\n // Create signal generator\n let signal_config = SignalConfig {\n min_confidence: 0.5,\n max_position_size: 0.2,\n default_take_profit: 3.0,\n default_stop_loss: 1.5,\n use_regime_adjustment: true,\n cooldown: std::time::Duration::from_secs(0), // No cooldown for demo\n };\n let mut signal_generator = SignalGenerator::new(signal_config);\n\n // Create risk manager\n let risk_config = RiskConfig::default();\n let risk_manager = RiskManager::new(risk_config, 100_000.0);\n\n for (scenario_name, probs) in &test_scenarios {\n println!(\"Scenario: {}\", scenario_name);\n\n // Classify the regime\n let classification = classifier.classify_with_uncertainty(probs);\n\n println!(\" Classification: {}\", classification.description());\n println!(\" Should act: {}\", classification.should_act());\n\n // Update classifier history\n classifier.update_history(classification.regime, classification.confidence);\n\n // Generate trading signal\n signal_generator.clear_cooldowns(); // Reset for demo\n let signal = signal_generator.generate(\"BTCUSDT\", &classification);\n\n println!(\" Signal: {:?}\", signal.signal_type);\n if signal.signal_type.requires_action() {\n println!(\" Position size: {:.1}%\", signal.position_size * 100.0);\n println!(\" Take profit: {:.1}%\", signal.take_profit.unwrap_or(0.0));\n println!(\" Stop loss: {:.1}%\", signal.stop_loss.unwrap_or(0.0));\n println!(\" Expected value: {:.2}\", signal.expected_value());\n\n // Check risk\n let risk_check = risk_manager.check_signal(&signal, 0);\n println!(\" Risk approved: {}\", risk_check.approved);\n if !risk_check.rejections.is_empty() {\n println!(\" Rejections: {:?}\", risk_check.rejections);\n }\n }\n println!();\n }\n\n // Demonstrate regime transitions\n println!(\"--- Regime Transition Analysis ---\\n\");\n\n // Simulate a sequence of regimes\n let regime_sequence = [\n MarketRegime::Sideways,\n MarketRegime::WeakUptrend,\n MarketRegime::StrongUptrend,\n MarketRegime::Volatile,\n MarketRegime::WeakDowntrend,\n MarketRegime::Crash,\n MarketRegime::Recovery,\n MarketRegime::Sideways,\n ];\n\n for regime in ®ime_sequence {\n classifier.update_history(*regime, 0.8);\n }\n\n println!(\"After observing {} regime changes:\", regime_sequence.len());\n println!(\" Dominant regime (last 5): {:?}\", classifier.dominant_regime(5));\n println!();\n\n // Check transition probabilities\n let transitions = [\n (MarketRegime::StrongUptrend, MarketRegime::Volatile),\n (MarketRegime::Volatile, MarketRegime::Crash),\n (MarketRegime::Crash, MarketRegime::Recovery),\n (MarketRegime::Recovery, MarketRegime::Sideways),\n ];\n\n println!(\"Transition probabilities from history:\");\n for (from, to) in &transitions {\n let prob = classifier.transition_probability(*from, *to);\n let likely = classifier.is_transition_likely(*from, *to);\n println!(\n \" {} -> {}: {:.1}% (likely: {})\",\n from, to, prob * 100.0, likely\n );\n }\n\n // Show risk summary\n println!(\"\\n--- Current Risk Summary ---\");\n println!(\"{}\", risk_manager.risk_summary());\n\n println!(\"\\n=== Example Complete ===\");\n}\n"}} | |
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| "content": "The file /tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/examples/new_asset_adaptation.rs has been updated. Here's the result of running `cat -n` on a snippet of the edited file:\n 1β//! New Asset Adaptation Example\n 2β//!\n 3β//! This example demonstrates adapting a few-shot model to predict\n 4β//! market movements for a new cryptocurrency with minimal data.\n 5β//!\n 6β//! Run with: cargo run --example new_asset_adaptation\n 7β\n 8βuse few_shot_market_prediction::methods::{\n 9β FewShotConfig, FewShotLearner, FewShotMethod,\n 10β MetricBasedLearner, MAMLLearner,\n 11β};\n 12βuse few_shot_market_prediction::network::DistanceMetric;\n 13βuse few_shot_market_prediction::strategy::{\n 14β MarketRegime, RegimeClassifier,\n 15β SignalGenerator, SignalConfig,\n 16β};\n 17βuse ndarray::Array2;\n 18β\n 19βfn main() {\n 20β println!(\"=== Few-Shot Adaptation to New Asset ===\\n\");\n 21β\n 22β // Scenario: We have a model trained on BTC/ETH, and want to adapt to a new altcoin\n 23β println!(\"Scenario: Adapting from BTC/ETH patterns to predict DOGE movements\\n\");\n 24β\n 25β // Step 1: Generate synthetic \"historical\" data for major assets (for reference)\n 26β let (btc_features, btc_labels) = generate_asset_data(\"BTC\", 50);\n 27β let (eth_features, eth_labels) = generate_asset_data(\"ETH\", 50);\n 28β\n 29β println!(\"Step 1: Historical data available (simulated)\");\n 30β println!(\" BTC: {} labeled samples\", btc_features.nrows());\n 31β println!(\" ETH: {} labeled samples\", eth_features.nrows());\n 32β println!();\n 33β\n 34β // Step 2: Get LIMITED data for new asset (few-shot scenario)\n 35β let (doge_support, doge_support_labels) = generate_asset_data(\"DOGE\", 9); // Only 3 per class!\n 36β let (doge_query, doge_query_labels) = generate_asset_data(\"DOGE_query\", 15);\n 37β\n 38β println!(\"Step 2: New asset (DOGE) - Few-shot data\");\n 39β println!(\" Support set: {} examples (only 3 per class!)\", doge_support.nrows());\n 40β println!(\" Query set: {} examples to predict\", doge_query.nrows());\n 41β println!();\n 42β\n 43β // Step 3: Define configuration for few-shot learning\n 44β let config = FewShotConfig::default()\n 45β .with_input_dim(12)\n 46β .with_embedding_dim(8)\n 47β .with_hidden_dims(vec![64, 32])\n 48β .with_n_way(3)\n 49β .with_k_shot(3)\n 50β .with_adaptation_steps(5)\n 51β .with_adaptation_lr(0.01);\n 52β\n 53β println!(\"Step 3: Few-shot configuration\");\n 54β println!(\" Input dim: {}\", config.input_dim);\n 55β println!(\" Embedding dim: {}\", config.embedding_dim);\n 56β println!(\" N-way: {} (3 classes: up, sideways, down)\", config.n_way);\n 57β println!(\" K-shot: {} per class\", config.k_shot);\n 58β println!();\n 59β\n 60β // Step 4: Adapt using MAML\n 61β println!(\"Step 4: Adapting model to DOGE using MAML...\\n\");\n 62β\n 63β let mut maml_learner = MAMLLearner::new(config.clone());\n 64β maml_learner.fit(&doge_support, &doge_support_labels);\n 65β let maml_predictions = maml_learner.predict(&doge_query);\n 66β\n 67β // Display results\n 68β let class_names = [\"Uptrend\", \"Sideways\", \"Downtrend\"];\n 69β\n 70β println!(\"--- MAML Adaptation Results for DOGE ---\\n\");\n 71β\n 72β if let Some(adaptation) = maml_learner.last_adaptation() {\n 73β println!(\"Adaptation summary:\");\n 74β println!(\" Steps: {}\", adaptation.steps);\n 75β println!(\" Initial loss: {:.4}\", adaptation.initial_loss);\n 76β println!(\" Final loss: {:.4}\", adaptation.final_loss);\n 77β println!(\" Improvement: {:.1}%\\n\", adaptation.improvement * 100.0);\n 78β }\n 79β\n 80β println!(\"Predictions on query set:\");\n 81β for (i, (pred, &actual)) in maml_predictions.iter()\n 82β .zip(doge_query_labels.iter())\n 83β .enumerate()\n 84β {\n 85β let correct = if pred.predicted_class == actual { \"β\" } else { \"β\" };\n 86β println!(\n 87β \" Sample {:2}: Predicted {} (conf: {:5.1}%) | Actual {} {}\",\n 88β i + 1,\n 89β class_names[pred.predicted_class],\n 90β pred.confidence * 100.0,\n 91β class_names[actual],\n 92β correct\n 93β );\n 94β }\n 95β\n 96β // Calculate MAML accuracy\n 97β let maml_correct = maml_predictions.iter()\n 98β .zip(doge_query_labels.iter())\n 99β .filter(|(pred, &actual)| pred.predicted_class == actual)\n 100β .count();\n 101β let maml_accuracy = maml_correct as f64 / doge_query_labels.len() as f64 * 100.0;\n 102β\n 103β println!(\"\\nMAML accuracy: {:.1}% ({}/{})\",\n 104β maml_accuracy, maml_correct, doge_query_labels.len());\n 105β\n 106β // Step 5: Compare with metric-based approach\n 107β println!(\"\\n--- Comparison: Metric-Based (no adaptation) ---\\n\");\n 108β\n 109β let metric_config = config.clone()\n 110β .with_method(FewShotMethod::Metric)\n 111β .with_distance_metric(DistanceMetric::Cosine);\n 112β\n 113β let mut metric_learner = MetricBasedLearner::new(metric_config);\n 114β metric_learner.fit(&doge_support, &doge_support_labels);\n 115β let metric_predictions = metric_learner.predict(&doge_query);\n 116β\n 117β let metric_correct = metric_predictions.iter()\n 118β .zip(doge_query_labels.iter())\n 119β .filter(|(pred, &actual)| pred.predicted_class == actual)\n 120β .count();\n 121β let metric_accuracy = metric_correct as f64 / doge_query_labels.len() as f64 * 100.0;\n 122β\n 123β println!(\"Metric-based accuracy: {:.1}% ({}/{})\",\n 124β metric_accuracy, metric_correct, doge_query_labels.len());\n 125β\n 126β println!(\"\\nMethod comparison:\");\n 127β println!(\" MAML (with adaptation): {:.1}%\", maml_accuracy);\n 128β println!(\" Metric-based (no adaptation): {:.1}%\", metric_accuracy);\n 129β println!();\n 130β\n 131β // Step 6: Generate trading signals\n 132β println!(\"--- Trading Signal Generation ---\\n\");\n 133β\n 134β // Aggregate predictions to determine overall regime\n 135β let mut class_votes = [0.0f64; 3];\n 136β for pred in &maml_predictions {\n 137β class_votes[pred.predicted_class] += pred.confidence;\n 138β }\n 139β\n 140β let total_confidence: f64 = class_votes.iter().sum();\n 141β let regime_probs: Vec<f64> = class_votes.iter()\n 142β .map(|&c| c / total_confidence.max(1e-10))\n 143β .collect();\n 144β\n 145β println!(\"Aggregated class probabilities:\");\n 146β for (i, (name, &prob)) in class_names.iter().zip(regime_probs.iter()).enumerate() {\n 147β println!(\" {}: {:.1}%\", name, prob * 100.0);\n 148β }\n 149β\n 150β // Map to market regimes (simplified: 3 classes -> select from 8 regimes)\n 151β let dominant_class = regime_probs.iter()\n 152β .enumerate()\n 153β .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n 154β .map(|(i, _)| i)\n 155β .unwrap_or(1);\n 156β\n 157β let mapped_regime = match dominant_class {\n 158β 0 => MarketRegime::WeakUptrend,\n 159β 2 => MarketRegime::WeakDowntrend,\n 160β _ => MarketRegime::Sideways,\n 161β };\n 162β\n 163β println!(\"\\nMapped to regime: {}\", mapped_regime);\n 164β\n 165β // Create classifier and signal\n 166β let mut classifier = RegimeClassifier::new(0.5);\n 167β let avg_confidence = maml_predictions.iter()\n 168β .map(|p| p.confidence)\n 169β .sum::<f64>() / maml_predictions.len() as f64;\n 170β\n 171β // Build regime probabilities for classifier\n 172β let mut full_regime_probs = vec![0.0; 8];\n 173β match dominant_class {\n 174β 0 => {\n 175β full_regime_probs[0] = regime_probs[0] * 0.3; // Strong uptrend\n 176β full_regime_probs[1] = regime_probs[0] * 0.7; // Weak uptrend\n 177β }\n 178β 2 => {\n 179β full_regime_probs[3] = regime_probs[2] * 0.7; // Weak downtrend\n 180β full_regime_probs[4] = regime_probs[2] * 0.3; // Strong downtrend\n 181β }\n 182β _ => {\n 183β full_regime_probs[2] = regime_probs[1]; // Sideways\n 184β }\n 185β }\n 186β\n 187β let classification = classifier.classify_with_uncertainty(&full_regime_probs);\n 188β\n 189β let signal_config = SignalConfig::default();\n 190β let mut signal_generator = SignalGenerator::new(signal_config);\n 191β let signal = signal_generator.generate(\"DOGEUSDT\", &classification);\n 192β\n 193β println!(\"\\nGenerated signal for DOGEUSDT:\");\n 194β println!(\" Type: {:?}\", signal.signal_type);\n 195β println!(\" Confidence: {:.1}%\", signal.confidence * 100.0);\n 196β println!(\" Position size: {:.1}%\", signal.position_size * 100.0);\n 197β if let Some(tp) = signal.take_profit {\n 198β println!(\" Take profit: {:.1}%\", tp);\n 199β }\n 200β if let Some(sl) = signal.stop_loss {\n 201β println!(\" Stop loss: {:.1}%\", sl);\n 202β }\n 203β\n 204β // Step 7: Real-world usage hint\n 205β println!(\"\\n--- Real-World Usage ---\\n\");\n 206β println!(\"To use with real Bybit data:\");\n 207β println!(\" 1. Create BybitClient for API access\");\n 208β println!(\" 2. Fetch klines: client.get_klines(\\\"DOGEUSDT\\\", \\\"1h\\\", 100).await\");\n 209β println!(\" 3. Extract features using FeatureExtractor\");\n 210β println!(\" 4. Run few-shot prediction with extracted features\");\n 211β println!(\" 5. Generate and execute trading signals\");\n 212β println!();\n 213β\n 214β println!(\"=== Example Complete ===\");\n 215β}\n 216β\n 217β/// Generate synthetic market data for an asset.\n 218βfn generate_asset_data(asset_name: &str, count: usize) -> (Array2<f64>, Vec<usize>) {\n 219β let dim = 12;\n 220β let mut data = Vec::new();\n 221β let mut labels = Vec::new();\n 222β\n 223β // Seed based on asset name for reproducibility\n 224β let base_seed: u64 = asset_name.bytes().map(|b| b as u64).sum();\n 225β\n 226β for i in 0..count {\n 227β // Determine class (0: uptrend, 1: sideways, 2: downtrend)\n 228β let class = i % 3;\n 229β\n 230β // Generate features based on class\n 231β let base = match class {\n 232β 0 => vec![0.02, 0.015, 0.5, 65.0, 1.5, 0.8, 0.7, 0.6, 0.3, 0.7, 0.8, 0.6],\n 233β 1 => vec![0.001, 0.002, 0.3, 50.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],\n 234β _ => vec![-0.02, -0.015, 0.6, 35.0, 0.7, 0.3, 0.3, 0.4, 0.7, 0.3, 0.2, 0.4],\n 235β };\n 236β\n 237β let noisy = add_noise(&base, 0.15, base_seed + i as u64);\n 238β data.extend(noisy);\n 239β labels.push(class);\n 240β }\n 241β\n 242β let features = Array2::from_shape_vec((count, dim), data).unwrap();\n 243β (features, labels)\n 244β}\n 245β\n 246β/// Add noise to feature vector.\n 247βfn add_noise(base: &[f64], noise_level: f64, seed: u64) -> Vec<f64> {\n 248β let mut rng_state = seed;\n 249β\n 250β base.iter().map(|&v| {\n 251β rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);\n 252β let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;\n 253β v + v.abs().max(0.01) * noise_level * random * 2.0\n 254β }).collect()\n 255β}\n 256β" | |
| } | |
| ] | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "172d23c7-5c62-484f-af89-80e92d0b393e", | |
| "uuid": "0e569c58-a57e-478b-9883-84b71c096a91", | |
| "tool_use_result": { | |
| "type": "update", | |
| "filePath": "/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/examples/new_asset_adaptation.rs", | |
| "content": "//! New Asset Adaptation Example\n//!\n//! This example demonstrates adapting a few-shot model to predict\n//! market movements for a new cryptocurrency with minimal data.\n//!\n//! Run with: cargo run --example new_asset_adaptation\n\nuse few_shot_market_prediction::methods::{\n FewShotConfig, FewShotLearner, FewShotMethod,\n MetricBasedLearner, MAMLLearner,\n};\nuse few_shot_market_prediction::network::DistanceMetric;\nuse few_shot_market_prediction::strategy::{\n MarketRegime, RegimeClassifier,\n SignalGenerator, SignalConfig,\n};\nuse ndarray::Array2;\n\nfn main() {\n println!(\"=== Few-Shot Adaptation to New Asset ===\\n\");\n\n // Scenario: We have a model trained on BTC/ETH, and want to adapt to a new altcoin\n println!(\"Scenario: Adapting from BTC/ETH patterns to predict DOGE movements\\n\");\n\n // Step 1: Generate synthetic \"historical\" data for major assets (for reference)\n let (btc_features, btc_labels) = generate_asset_data(\"BTC\", 50);\n let (eth_features, eth_labels) = generate_asset_data(\"ETH\", 50);\n\n println!(\"Step 1: Historical data available (simulated)\");\n println!(\" BTC: {} labeled samples\", btc_features.nrows());\n println!(\" ETH: {} labeled samples\", eth_features.nrows());\n println!();\n\n // Step 2: Get LIMITED data for new asset (few-shot scenario)\n let (doge_support, doge_support_labels) = generate_asset_data(\"DOGE\", 9); // Only 3 per class!\n let (doge_query, doge_query_labels) = generate_asset_data(\"DOGE_query\", 15);\n\n println!(\"Step 2: New asset (DOGE) - Few-shot data\");\n println!(\" Support set: {} examples (only 3 per class!)\", doge_support.nrows());\n println!(\" Query set: {} examples to predict\", doge_query.nrows());\n println!();\n\n // Step 3: Define configuration for few-shot learning\n let config = FewShotConfig::default()\n .with_input_dim(12)\n .with_embedding_dim(8)\n .with_hidden_dims(vec![64, 32])\n .with_n_way(3)\n .with_k_shot(3)\n .with_adaptation_steps(5)\n .with_adaptation_lr(0.01);\n\n println!(\"Step 3: Few-shot configuration\");\n println!(\" Input dim: {}\", config.input_dim);\n println!(\" Embedding dim: {}\", config.embedding_dim);\n println!(\" N-way: {} (3 classes: up, sideways, down)\", config.n_way);\n println!(\" K-shot: {} per class\", config.k_shot);\n println!();\n\n // Step 4: Adapt using MAML\n println!(\"Step 4: Adapting model to DOGE using MAML...\\n\");\n\n let mut maml_learner = MAMLLearner::new(config.clone());\n maml_learner.fit(&doge_support, &doge_support_labels);\n let maml_predictions = maml_learner.predict(&doge_query);\n\n // Display results\n let class_names = [\"Uptrend\", \"Sideways\", \"Downtrend\"];\n\n println!(\"--- MAML Adaptation Results for DOGE ---\\n\");\n\n if let Some(adaptation) = maml_learner.last_adaptation() {\n println!(\"Adaptation summary:\");\n println!(\" Steps: {}\", adaptation.steps);\n println!(\" Initial loss: {:.4}\", adaptation.initial_loss);\n println!(\" Final loss: {:.4}\", adaptation.final_loss);\n println!(\" Improvement: {:.1}%\\n\", adaptation.improvement * 100.0);\n }\n\n println!(\"Predictions on query set:\");\n for (i, (pred, &actual)) in maml_predictions.iter()\n .zip(doge_query_labels.iter())\n .enumerate()\n {\n let correct = if pred.predicted_class == actual { \"β\" } else { \"β\" };\n println!(\n \" Sample {:2}: Predicted {} (conf: {:5.1}%) | Actual {} {}\",\n i + 1,\n class_names[pred.predicted_class],\n pred.confidence * 100.0,\n class_names[actual],\n correct\n );\n }\n\n // Calculate MAML accuracy\n let maml_correct = maml_predictions.iter()\n .zip(doge_query_labels.iter())\n .filter(|(pred, &actual)| pred.predicted_class == actual)\n .count();\n let maml_accuracy = maml_correct as f64 / doge_query_labels.len() as f64 * 100.0;\n\n println!(\"\\nMAML accuracy: {:.1}% ({}/{})\",\n maml_accuracy, maml_correct, doge_query_labels.len());\n\n // Step 5: Compare with metric-based approach\n println!(\"\\n--- Comparison: Metric-Based (no adaptation) ---\\n\");\n\n let metric_config = config.clone()\n .with_method(FewShotMethod::Metric)\n .with_distance_metric(DistanceMetric::Cosine);\n\n let mut metric_learner = MetricBasedLearner::new(metric_config);\n metric_learner.fit(&doge_support, &doge_support_labels);\n let metric_predictions = metric_learner.predict(&doge_query);\n\n let metric_correct = metric_predictions.iter()\n .zip(doge_query_labels.iter())\n .filter(|(pred, &actual)| pred.predicted_class == actual)\n .count();\n let metric_accuracy = metric_correct as f64 / doge_query_labels.len() as f64 * 100.0;\n\n println!(\"Metric-based accuracy: {:.1}% ({}/{})\",\n metric_accuracy, metric_correct, doge_query_labels.len());\n\n println!(\"\\nMethod comparison:\");\n println!(\" MAML (with adaptation): {:.1}%\", maml_accuracy);\n println!(\" Metric-based (no adaptation): {:.1}%\", metric_accuracy);\n println!();\n\n // Step 6: Generate trading signals\n println!(\"--- Trading Signal Generation ---\\n\");\n\n // Aggregate predictions to determine overall regime\n let mut class_votes = [0.0f64; 3];\n for pred in &maml_predictions {\n class_votes[pred.predicted_class] += pred.confidence;\n }\n\n let total_confidence: f64 = class_votes.iter().sum();\n let regime_probs: Vec<f64> = class_votes.iter()\n .map(|&c| c / total_confidence.max(1e-10))\n .collect();\n\n println!(\"Aggregated class probabilities:\");\n for (i, (name, &prob)) in class_names.iter().zip(regime_probs.iter()).enumerate() {\n println!(\" {}: {:.1}%\", name, prob * 100.0);\n }\n\n // Map to market regimes (simplified: 3 classes -> select from 8 regimes)\n let dominant_class = regime_probs.iter()\n .enumerate()\n .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())\n .map(|(i, _)| i)\n .unwrap_or(1);\n\n let mapped_regime = match dominant_class {\n 0 => MarketRegime::WeakUptrend,\n 2 => MarketRegime::WeakDowntrend,\n _ => MarketRegime::Sideways,\n };\n\n println!(\"\\nMapped to regime: {}\", mapped_regime);\n\n // Create classifier and signal\n let mut classifier = RegimeClassifier::new(0.5);\n let avg_confidence = maml_predictions.iter()\n .map(|p| p.confidence)\n .sum::<f64>() / maml_predictions.len() as f64;\n\n // Build regime probabilities for classifier\n let mut full_regime_probs = vec![0.0; 8];\n match dominant_class {\n 0 => {\n full_regime_probs[0] = regime_probs[0] * 0.3; // Strong uptrend\n full_regime_probs[1] = regime_probs[0] * 0.7; // Weak uptrend\n }\n 2 => {\n full_regime_probs[3] = regime_probs[2] * 0.7; // Weak downtrend\n full_regime_probs[4] = regime_probs[2] * 0.3; // Strong downtrend\n }\n _ => {\n full_regime_probs[2] = regime_probs[1]; // Sideways\n }\n }\n\n let classification = classifier.classify_with_uncertainty(&full_regime_probs);\n\n let signal_config = SignalConfig::default();\n let mut signal_generator = SignalGenerator::new(signal_config);\n let signal = signal_generator.generate(\"DOGEUSDT\", &classification);\n\n println!(\"\\nGenerated signal for DOGEUSDT:\");\n println!(\" Type: {:?}\", signal.signal_type);\n println!(\" Confidence: {:.1}%\", signal.confidence * 100.0);\n println!(\" Position size: {:.1}%\", signal.position_size * 100.0);\n if let Some(tp) = signal.take_profit {\n println!(\" Take profit: {:.1}%\", tp);\n }\n if let Some(sl) = signal.stop_loss {\n println!(\" Stop loss: {:.1}%\", sl);\n }\n\n // Step 7: Real-world usage hint\n println!(\"\\n--- Real-World Usage ---\\n\");\n println!(\"To use with real Bybit data:\");\n println!(\" 1. Create BybitClient for API access\");\n println!(\" 2. Fetch klines: client.get_klines(\\\"DOGEUSDT\\\", \\\"1h\\\", 100).await\");\n println!(\" 3. Extract features using FeatureExtractor\");\n println!(\" 4. Run few-shot prediction with extracted features\");\n println!(\" 5. Generate and execute trading signals\");\n println!();\n\n println!(\"=== Example Complete ===\");\n}\n\n/// Generate synthetic market data for an asset.\nfn generate_asset_data(asset_name: &str, count: usize) -> (Array2<f64>, Vec<usize>) {\n let dim = 12;\n let mut data = Vec::new();\n let mut labels = Vec::new();\n\n // Seed based on asset name for reproducibility\n let base_seed: u64 = asset_name.bytes().map(|b| b as u64).sum();\n\n for i in 0..count {\n // Determine class (0: uptrend, 1: sideways, 2: downtrend)\n let class = i % 3;\n\n // Generate features based on class\n let base = match class {\n 0 => vec![0.02, 0.015, 0.5, 65.0, 1.5, 0.8, 0.7, 0.6, 0.3, 0.7, 0.8, 0.6],\n 1 => vec![0.001, 0.002, 0.3, 50.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],\n _ => vec![-0.02, -0.015, 0.6, 35.0, 0.7, 0.3, 0.3, 0.4, 0.7, 0.3, 0.2, 0.4],\n };\n\n let noisy = add_noise(&base, 0.15, base_seed + i as u64);\n data.extend(noisy);\n labels.push(class);\n }\n\n let features = Array2::from_shape_vec((count, dim), data).unwrap();\n (features, labels)\n}\n\n/// Add noise to feature vector.\nfn add_noise(base: &[f64], noise_level: f64, seed: u64) -> Vec<f64> {\n let mut rng_state = seed;\n\n base.iter().map(|&v| {\n rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);\n let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;\n v + v.abs().max(0.01) * noise_level * random * 2.0\n }).collect()\n}\n", | |
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| "lines": [ | |
| " //!", | |
| " //! Run with: cargo run --example new_asset_adaptation", | |
| " ", | |
| "-use few_shot_market_prediction::prelude::*;", | |
| "-use std::time::Duration;", | |
| "+use few_shot_market_prediction::methods::{", | |
| "+ FewShotConfig, FewShotLearner, FewShotMethod,", | |
| "+ MetricBasedLearner, MAMLLearner,", | |
| "+};", | |
| "+use few_shot_market_prediction::network::DistanceMetric;", | |
| "+use few_shot_market_prediction::strategy::{", | |
| "+ MarketRegime, RegimeClassifier,", | |
| "+ SignalGenerator, SignalConfig,", | |
| "+};", | |
| "+use ndarray::Array2;", | |
| " ", | |
| "-#[tokio::main]", | |
| "-async fn main() {", | |
| "+fn main() {", | |
| " println!(\"=== Few-Shot Adaptation to New Asset ===\\n\");", | |
| " ", | |
| "- // Scenario: We have a model trained on BTC, ETH, and want to adapt to a new altcoin", | |
| "- println!(\"Scenario: Adapting model trained on BTC/ETH to predict DOGE movements\\n\");", | |
| "+ // Scenario: We have a model trained on BTC/ETH, and want to adapt to a new altcoin", | |
| "+ println!(\"Scenario: Adapting from BTC/ETH patterns to predict DOGE movements\\n\");", | |
| " ", | |
| "- // Step 1: Create the embedding network (pre-trained on major assets)", | |
| "- let embedding_config = EmbeddingConfig {", | |
| "- input_dim: 12, // 12 market features", | |
| "- hidden_dims: vec![64, 32, 16],", | |
| "- output_dim: 8,", | |
| "- activation: Activation::GELU,", | |
| "- dropout_rate: 0.1,", | |
| "- };", | |
| "-", | |
| "- let network = EmbeddingNetwork::new(embedding_config);", | |
| "- println!(\"Step 1: Pre-trained embedding network loaded\");", | |
| "- println!(\" Architecture: 12 -> [64, 32, 16] -> 8\\n\");", | |
| "-", | |
| "- // Step 2: Generate synthetic \"historical\" data for major assets", | |
| "+ // Step 1: Generate synthetic \"historical\" data for major assets (for reference)", | |
| " let (btc_features, btc_labels) = generate_asset_data(\"BTC\", 50);", | |
| " let (eth_features, eth_labels) = generate_asset_data(\"ETH\", 50);", | |
| " ", | |
| "- println!(\"Step 2: Historical data available\");", | |
| "- println!(\" BTC: {} labeled samples\", btc_features.len());", | |
| "- println!(\" ETH: {} labeled samples\", eth_features.len());", | |
| "+ println!(\"Step 1: Historical data available (simulated)\");", | |
| "+ println!(\" BTC: {} labeled samples\", btc_features.nrows());", | |
| "+ println!(\" ETH: {} labeled samples\", eth_features.nrows());", | |
| " println!();", | |
| " ", | |
| "- // Step 3: Get LIMITED data for new asset (few-shot scenario)", | |
| "- let (doge_support, doge_support_labels) = generate_asset_data(\"DOGE\", 5); // Only 5 examples!", | |
| "- let (doge_query, doge_query_labels) = generate_asset_data(\"DOGE_query\", 10);", | |
| "+ // Step 2: Get LIMITED data for new asset (few-shot scenario)", | |
| "+ let (doge_support, doge_support_labels) = generate_asset_data(\"DOGE\", 9); // Only 3 per class!", | |
| "+ let (doge_query, doge_query_labels) = generate_asset_data(\"DOGE_query\", 15);", | |
| " ", | |
| "- println!(\"Step 3: New asset (DOGE) - Few-shot data\");", | |
| "- println!(\" Support set: {} examples (few-shot!)\", doge_support.len());", | |
| "- println!(\" Query set: {} examples to predict\", doge_query.len());", | |
| "+ println!(\"Step 2: New asset (DOGE) - Few-shot data\");", | |
| "+ println!(\" Support set: {} examples (only 3 per class!)\", doge_support.nrows());", | |
| "+ println!(\" Query set: {} examples to predict\", doge_query.nrows());", | |
| " println!();", | |
| " ", | |
| "- // Step 4: Create few-shot learner with MAML for adaptation", | |
| "- let config = FewShotConfig {", | |
| "- n_way: 3, // 3-way classification (up, sideways, down)", | |
| "- k_shot: 1, // 1-shot per class minimum (we have more)", | |
| "- query_size: 5,", | |
| "- embedding_dim: 8,", | |
| "- };", | |
| "+ // Step 3: Define configuration for few-shot learning", | |
| "+ let config = FewShotConfig::default()", | |
| "+ .with_input_dim(12)", | |
| "+ .with_embedding_dim(8)", | |
| "+ .with_hidden_dims(vec![64, 32])", | |
| "+ .with_n_way(3)", | |
| "+ .with_k_shot(3)", | |
| "+ .with_adaptation_steps(5)", | |
| "+ .with_adaptation_lr(0.01);", | |
| " ", | |
| "- let maml_config = MAMLConfig {", | |
| "- inner_lr: 0.01,", | |
| "- inner_steps: 5, // More steps for better adaptation", | |
| "- first_order: true,", | |
| "- };", | |
| "-", | |
| "- let maml_learner = MAMLLearner::new(config.clone(), maml_config);", | |
| "- println!(\"Step 4: MAML learner configured for few-shot adaptation\");", | |
| "- println!(\" Inner learning rate: 0.01\");", | |
| "- println!(\" Adaptation steps: 5\");", | |
| "+ println!(\"Step 3: Few-shot configuration\");", | |
| "+ println!(\" Input dim: {}\", config.input_dim);", | |
| "+ println!(\" Embedding dim: {}\", config.embedding_dim);", | |
| "+ println!(\" N-way: {} (3 classes: up, sideways, down)\", config.n_way);", | |
| "+ println!(\" K-shot: {} per class\", config.k_shot);", | |
| " println!();", | |
| " ", | |
| "- // Step 5: Adapt to new asset using few-shot learning", | |
| "- println!(\"Step 5: Adapting model to DOGE...\\n\");", | |
| "+ // Step 4: Adapt using MAML", | |
| "+ println!(\"Step 4: Adapting model to DOGE using MAML...\\n\");", | |
| " ", | |
| "- let adaptation_result = maml_learner.predict_with_adaptation(", | |
| "- &network,", | |
| "- &doge_support,", | |
| "- &doge_support_labels,", | |
| "- &doge_query,", | |
| "- );", | |
| "+ let mut maml_learner = MAMLLearner::new(config.clone());", | |
| "+ maml_learner.fit(&doge_support, &doge_support_labels);", | |
| "+ let maml_predictions = maml_learner.predict(&doge_query);", | |
| " ", | |
| " // Display results", | |
| " let class_names = [\"Uptrend\", \"Sideways\", \"Downtrend\"];", | |
| " ", | |
| "- println!(\"--- Adaptation Results for DOGE ---\\n\");", | |
| "+ println!(\"--- MAML Adaptation Results for DOGE ---\\n\");", | |
| "+", | |
| "+ if let Some(adaptation) = maml_learner.last_adaptation() {", | |
| "+ println!(\"Adaptation summary:\");", | |
| "+ println!(\" Steps: {}\", adaptation.steps);", | |
| "+ println!(\" Initial loss: {:.4}\", adaptation.initial_loss);", | |
| "+ println!(\" Final loss: {:.4}\", adaptation.final_loss);", | |
| "+ println!(\" Improvement: {:.1}%\\n\", adaptation.improvement * 100.0);", | |
| "+ }", | |
| "+", | |
| " println!(\"Predictions on query set:\");", | |
| "- for (i, (&pred, &actual)) in adaptation_result.predictions.iter()", | |
| "+ for (i, (pred, &actual)) in maml_predictions.iter()", | |
| " .zip(doge_query_labels.iter())", | |
| " .enumerate()", | |
| " {", | |
| "- let correct = if pred == actual { \"β\" } else { \"β\" };", | |
| "+ let correct = if pred.predicted_class == actual { \"β\" } else { \"β\" };", | |
| " println!(", | |
| "- \" Sample {}: Predicted {} (conf: {:.1}%) | Actual {} {}\",", | |
| "+ \" Sample {:2}: Predicted {} (conf: {:5.1}%) | Actual {} {}\",", | |
| " i + 1,", | |
| "- class_names[pred],", | |
| "- adaptation_result.confidences[i] * 100.0,", | |
| "+ class_names[pred.predicted_class],", | |
| "+ pred.confidence * 100.0,", | |
| " class_names[actual],", | |
| " correct", | |
| " );", | |
| " }", | |
| " ", | |
| "- // Calculate accuracy", | |
| "- let correct = adaptation_result.predictions.iter()", | |
| "+ // Calculate MAML accuracy", | |
| "+ let maml_correct = maml_predictions.iter()", | |
| " .zip(doge_query_labels.iter())", | |
| "- .filter(|(p, a)| p == a)", | |
| "+ .filter(|(pred, &actual)| pred.predicted_class == actual)", | |
| " .count();", | |
| "- let accuracy = correct as f64 / doge_query_labels.len() as f64 * 100.0;", | |
| "+ let maml_accuracy = maml_correct as f64 / doge_query_labels.len() as f64 * 100.0;", | |
| " ", | |
| "- println!(\"\\nOverall accuracy: {:.1}% ({}/{})\",", | |
| "- accuracy, correct, doge_query_labels.len());", | |
| "+ println!(\"\\nMAML accuracy: {:.1}% ({}/{})\",", | |
| "+ maml_accuracy, maml_correct, doge_query_labels.len());", | |
| " ", | |
| "- // Step 6: Compare with metric-based approach", | |
| "- println!(\"\\n--- Comparison: Metric-Based vs MAML ---\\n\");", | |
| "+ // Step 5: Compare with metric-based approach", | |
| "+ println!(\"\\n--- Comparison: Metric-Based (no adaptation) ---\\n\");", | |
| " ", | |
| "- // Embed all data", | |
| "- let support_embeddings: Vec<Vec<f64>> = doge_support", | |
| "- .iter()", | |
| "- .map(|x| network.forward(x, None))", | |
| "- .collect();", | |
| "+ let metric_config = config.clone()", | |
| "+ .with_method(FewShotMethod::Metric)", | |
| "+ .with_distance_metric(DistanceMetric::Cosine);", | |
| " ", | |
| "- let query_embeddings: Vec<Vec<f64>> = doge_query", | |
| "- .iter()", | |
| "- .map(|x| network.forward(x, None))", | |
| "- .collect();", | |
| "+ let mut metric_learner = MetricBasedLearner::new(metric_config);", | |
| "+ metric_learner.fit(&doge_support, &doge_support_labels);", | |
| "+ let metric_predictions = metric_learner.predict(&doge_query);", | |
| " ", | |
| "- let metric_learner = MetricBasedLearner::new(config.clone(), DistanceMetric::Cosine);", | |
| "- let metric_result = metric_learner.predict(", | |
| "- &support_embeddings,", | |
| "- &doge_support_labels,", | |
| "- &query_embeddings,", | |
| "- );", | |
| "-", | |
| "- let metric_correct = metric_result.predictions.iter()", | |
| "+ let metric_correct = metric_predictions.iter()", | |
| " .zip(doge_query_labels.iter())", | |
| "- .filter(|(p, a)| p == a)", | |
| "+ .filter(|(pred, &actual)| pred.predicted_class == actual)", | |
| " .count();", | |
| " let metric_accuracy = metric_correct as f64 / doge_query_labels.len() as f64 * 100.0;", | |
| " ", | |
| "- println!(\"Method comparison:\");", | |
| "- println!(\" MAML (with adaptation): {:.1}%\", accuracy);", | |
| "+ println!(\"Metric-based accuracy: {:.1}% ({}/{})\",", | |
| "+ metric_accuracy, metric_correct, doge_query_labels.len());", | |
| "+", | |
| "+ println!(\"\\nMethod comparison:\");", | |
| "+ println!(\" MAML (with adaptation): {:.1}%\", maml_accuracy);", | |
| " println!(\" Metric-based (no adaptation): {:.1}%\", metric_accuracy);", | |
| " println!();", | |
| " ", | |
| "- // Step 7: Generate trading signal for new asset", | |
| "+ // Step 6: Generate trading signals", | |
| " println!(\"--- Trading Signal Generation ---\\n\");", | |
| " ", | |
| "- // Use the predictions to generate signals", | |
| "- let regime_probs = compute_regime_probabilities(&adaptation_result);", | |
| "+ // Aggregate predictions to determine overall regime", | |
| "+ let mut class_votes = [0.0f64; 3];", | |
| "+ for pred in &maml_predictions {", | |
| "+ class_votes[pred.predicted_class] += pred.confidence;", | |
| "+ }", | |
| " ", | |
| "+ let total_confidence: f64 = class_votes.iter().sum();", | |
| "+ let regime_probs: Vec<f64> = class_votes.iter()", | |
| "+ .map(|&c| c / total_confidence.max(1e-10))", | |
| "+ .collect();", | |
| "+", | |
| "+ println!(\"Aggregated class probabilities:\");", | |
| "+ for (i, (name, &prob)) in class_names.iter().zip(regime_probs.iter()).enumerate() {", | |
| "+ println!(\" {}: {:.1}%\", name, prob * 100.0);", | |
| "+ }", | |
| "+", | |
| "+ // Map to market regimes (simplified: 3 classes -> select from 8 regimes)", | |
| "+ let dominant_class = regime_probs.iter()", | |
| "+ .enumerate()", | |
| "+ .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap())", | |
| "+ .map(|(i, _)| i)", | |
| "+ .unwrap_or(1);", | |
| "+", | |
| "+ let mapped_regime = match dominant_class {", | |
| "+ 0 => MarketRegime::WeakUptrend,", | |
| "+ 2 => MarketRegime::WeakDowntrend,", | |
| "+ _ => MarketRegime::Sideways,", | |
| "+ };", | |
| "+", | |
| "+ println!(\"\\nMapped to regime: {}\", mapped_regime);", | |
| "+", | |
| "+ // Create classifier and signal", | |
| " let mut classifier = RegimeClassifier::new(0.5);", | |
| "- let classification = classifier.classify_with_uncertainty(®ime_probs);", | |
| "+ let avg_confidence = maml_predictions.iter()", | |
| "+ .map(|p| p.confidence)", | |
| "+ .sum::<f64>() / maml_predictions.len() as f64;", | |
| " ", | |
| "- println!(\"Market regime classification for DOGE:\");", | |
| "- println!(\" {}\", classification.description());", | |
| "+ // Build regime probabilities for classifier", | |
| "+ let mut full_regime_probs = vec![0.0; 8];", | |
| "+ match dominant_class {", | |
| "+ 0 => {", | |
| "+ full_regime_probs[0] = regime_probs[0] * 0.3; // Strong uptrend", | |
| "+ full_regime_probs[1] = regime_probs[0] * 0.7; // Weak uptrend", | |
| "+ }", | |
| "+ 2 => {", | |
| "+ full_regime_probs[3] = regime_probs[2] * 0.7; // Weak downtrend", | |
| "+ full_regime_probs[4] = regime_probs[2] * 0.3; // Strong downtrend", | |
| "+ }", | |
| "+ _ => {", | |
| "+ full_regime_probs[2] = regime_probs[1]; // Sideways", | |
| "+ }", | |
| "+ }", | |
| " ", | |
| "+ let classification = classifier.classify_with_uncertainty(&full_regime_probs);", | |
| "+", | |
| " let signal_config = SignalConfig::default();", | |
| " let mut signal_generator = SignalGenerator::new(signal_config);", | |
| " let signal = signal_generator.generate(\"DOGEUSDT\", &classification);", | |
| " ", | |
| "- println!(\"\\nGenerated signal:\");", | |
| "+ println!(\"\\nGenerated signal for DOGEUSDT:\");", | |
| " println!(\" Type: {:?}\", signal.signal_type);", | |
| " println!(\" Confidence: {:.1}%\", signal.confidence * 100.0);", | |
| " println!(\" Position size: {:.1}%\", signal.position_size * 100.0);", | |
| "- if signal.take_profit.is_some() {", | |
| "- println!(\" Take profit: {:.1}%\", signal.take_profit.unwrap());", | |
| "- println!(\" Stop loss: {:.1}%\", signal.stop_loss.unwrap());", | |
| "+ if let Some(tp) = signal.take_profit {", | |
| "+ println!(\" Take profit: {:.1}%\", tp);", | |
| " }", | |
| "+ if let Some(sl) = signal.stop_loss {", | |
| "+ println!(\" Stop loss: {:.1}%\", sl);", | |
| "+ }", | |
| " ", | |
| "- // Step 8: Real-world usage hint", | |
| "+ // Step 7: Real-world usage hint", | |
| " println!(\"\\n--- Real-World Usage ---\\n\");", | |
| " println!(\"To use with real Bybit data:\");", | |
| "- println!(\" 1. Create BybitClient::new(false) for mainnet\");", | |
| "- println!(\" 2. Fetch klines with client.get_klines(\\\"DOGEUSDT\\\", \\\"1h\\\", 100)\");", | |
| "- println!(\" 3. Extract features with FeatureExtractor::extract(&klines)\");", | |
| "+ println!(\" 1. Create BybitClient for API access\");", | |
| "+ println!(\" 2. Fetch klines: client.get_klines(\\\"DOGEUSDT\\\", \\\"1h\\\", 100).await\");", | |
| "+ println!(\" 3. Extract features using FeatureExtractor\");", | |
| " println!(\" 4. Run few-shot prediction with extracted features\");", | |
| " println!(\" 5. Generate and execute trading signals\");", | |
| " println!();" | |
| ] | |
| }, | |
| { | |
| "oldStart": 177, | |
| "oldLines": 8, | |
| "newStart": 215, | |
| "newLines": 9, | |
| "lines": [ | |
| " }", | |
| " ", | |
| " /// Generate synthetic market data for an asset.", | |
| "-fn generate_asset_data(asset_name: &str, count: usize) -> (Vec<Vec<f64>>, Vec<usize>) {", | |
| "- let mut features = Vec::new();", | |
| "+fn generate_asset_data(asset_name: &str, count: usize) -> (Array2<f64>, Vec<usize>) {", | |
| "+ let dim = 12;", | |
| "+ let mut data = Vec::new();", | |
| " let mut labels = Vec::new();", | |
| " ", | |
| " // Seed based on asset name for reproducibility" | |
| ] | |
| }, | |
| { | |
| "oldStart": 186, | |
| "oldLines": 7, | |
| "newStart": 225, | |
| "newLines": 7, | |
| "lines": [ | |
| " ", | |
| " for i in 0..count {", | |
| " // Determine class (0: uptrend, 1: sideways, 2: downtrend)", | |
| "- let class = (i + (base_seed as usize)) % 3;", | |
| "+ let class = i % 3;", | |
| " ", | |
| " // Generate features based on class", | |
| " let base = match class {" | |
| ] | |
| }, | |
| { | |
| "oldStart": 195, | |
| "oldLines": 10, | |
| "newStart": 234, | |
| "newLines": 12, | |
| "lines": [ | |
| " _ => vec![-0.02, -0.015, 0.6, 35.0, 0.7, 0.3, 0.3, 0.4, 0.7, 0.3, 0.2, 0.4],", | |
| " };", | |
| " ", | |
| "- features.push(add_noise(&base, 0.15, base_seed + i as u64));", | |
| "+ let noisy = add_noise(&base, 0.15, base_seed + i as u64);", | |
| "+ data.extend(noisy);", | |
| " labels.push(class);", | |
| " }", | |
| " ", | |
| "+ let features = Array2::from_shape_vec((count, dim), data).unwrap();", | |
| " (features, labels)", | |
| " }", | |
| " " | |
| ] | |
| }, | |
| { | |
| "oldStart": 209, | |
| "oldLines": 38, | |
| "newStart": 250, | |
| "newLines": 6, | |
| "lines": [ | |
| " base.iter().map(|&v| {", | |
| " rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);", | |
| " let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;", | |
| "- v + v.abs().max(0.01) * noise_level * random", | |
| "+ v + v.abs().max(0.01) * noise_level * random * 2.0", | |
| " }).collect()", | |
| " }", | |
| "-", | |
| "-/// Compute regime probabilities from prediction results.", | |
| "-fn compute_regime_probabilities(result: &PredictionResult) -> Vec<f64> {", | |
| "- // Simplified: map 3-class predictions to 8 regimes", | |
| "- let mut regime_probs = vec![0.0; 8];", | |
| "-", | |
| "- let total = result.predictions.len() as f64;", | |
| "-", | |
| "- for (&pred, &conf) in result.predictions.iter().zip(result.confidences.iter()) {", | |
| "- match pred {", | |
| "- 0 => { // Uptrend", | |
| "- regime_probs[0] += conf * 0.4 / total; // Strong uptrend", | |
| "- regime_probs[1] += conf * 0.6 / total; // Weak uptrend", | |
| "- }", | |
| "- 1 => { // Sideways", | |
| "- regime_probs[2] += conf / total; // Sideways", | |
| "- }", | |
| "- _ => { // Downtrend", | |
| "- regime_probs[3] += conf * 0.3 / total; // Weak downtrend", | |
| "- regime_probs[4] += conf * 0.7 / total; // Strong downtrend", | |
| "- }", | |
| "- }", | |
| "- }", | |
| "-", | |
| "- // Normalize", | |
| "- let sum: f64 = regime_probs.iter().sum();", | |
| "- if sum > 0.0 {", | |
| "- regime_probs.iter_mut().for_each(|p| *p /= sum);", | |
| "- }", | |
| "-", | |
| "- regime_probs", | |
| "-}" | |
| ] | |
| } | |
| ], | |
| "originalFile": "//! New Asset Adaptation Example\n//!\n//! This example demonstrates adapting a few-shot model to predict\n//! market movements for a new cryptocurrency with minimal data.\n//!\n//! Run with: cargo run --example new_asset_adaptation\n\nuse few_shot_market_prediction::prelude::*;\nuse std::time::Duration;\n\n#[tokio::main]\nasync fn main() {\n println!(\"=== Few-Shot Adaptation to New Asset ===\\n\");\n\n // Scenario: We have a model trained on BTC, ETH, and want to adapt to a new altcoin\n println!(\"Scenario: Adapting model trained on BTC/ETH to predict DOGE movements\\n\");\n\n // Step 1: Create the embedding network (pre-trained on major assets)\n let embedding_config = EmbeddingConfig {\n input_dim: 12, // 12 market features\n hidden_dims: vec![64, 32, 16],\n output_dim: 8,\n activation: Activation::GELU,\n dropout_rate: 0.1,\n };\n\n let network = EmbeddingNetwork::new(embedding_config);\n println!(\"Step 1: Pre-trained embedding network loaded\");\n println!(\" Architecture: 12 -> [64, 32, 16] -> 8\\n\");\n\n // Step 2: Generate synthetic \"historical\" data for major assets\n let (btc_features, btc_labels) = generate_asset_data(\"BTC\", 50);\n let (eth_features, eth_labels) = generate_asset_data(\"ETH\", 50);\n\n println!(\"Step 2: Historical data available\");\n println!(\" BTC: {} labeled samples\", btc_features.len());\n println!(\" ETH: {} labeled samples\", eth_features.len());\n println!();\n\n // Step 3: Get LIMITED data for new asset (few-shot scenario)\n let (doge_support, doge_support_labels) = generate_asset_data(\"DOGE\", 5); // Only 5 examples!\n let (doge_query, doge_query_labels) = generate_asset_data(\"DOGE_query\", 10);\n\n println!(\"Step 3: New asset (DOGE) - Few-shot data\");\n println!(\" Support set: {} examples (few-shot!)\", doge_support.len());\n println!(\" Query set: {} examples to predict\", doge_query.len());\n println!();\n\n // Step 4: Create few-shot learner with MAML for adaptation\n let config = FewShotConfig {\n n_way: 3, // 3-way classification (up, sideways, down)\n k_shot: 1, // 1-shot per class minimum (we have more)\n query_size: 5,\n embedding_dim: 8,\n };\n\n let maml_config = MAMLConfig {\n inner_lr: 0.01,\n inner_steps: 5, // More steps for better adaptation\n first_order: true,\n };\n\n let maml_learner = MAMLLearner::new(config.clone(), maml_config);\n println!(\"Step 4: MAML learner configured for few-shot adaptation\");\n println!(\" Inner learning rate: 0.01\");\n println!(\" Adaptation steps: 5\");\n println!();\n\n // Step 5: Adapt to new asset using few-shot learning\n println!(\"Step 5: Adapting model to DOGE...\\n\");\n\n let adaptation_result = maml_learner.predict_with_adaptation(\n &network,\n &doge_support,\n &doge_support_labels,\n &doge_query,\n );\n\n // Display results\n let class_names = [\"Uptrend\", \"Sideways\", \"Downtrend\"];\n\n println!(\"--- Adaptation Results for DOGE ---\\n\");\n println!(\"Predictions on query set:\");\n for (i, (&pred, &actual)) in adaptation_result.predictions.iter()\n .zip(doge_query_labels.iter())\n .enumerate()\n {\n let correct = if pred == actual { \"β\" } else { \"β\" };\n println!(\n \" Sample {}: Predicted {} (conf: {:.1}%) | Actual {} {}\",\n i + 1,\n class_names[pred],\n adaptation_result.confidences[i] * 100.0,\n class_names[actual],\n correct\n );\n }\n\n // Calculate accuracy\n let correct = adaptation_result.predictions.iter()\n .zip(doge_query_labels.iter())\n .filter(|(p, a)| p == a)\n .count();\n let accuracy = correct as f64 / doge_query_labels.len() as f64 * 100.0;\n\n println!(\"\\nOverall accuracy: {:.1}% ({}/{})\",\n accuracy, correct, doge_query_labels.len());\n\n // Step 6: Compare with metric-based approach\n println!(\"\\n--- Comparison: Metric-Based vs MAML ---\\n\");\n\n // Embed all data\n let support_embeddings: Vec<Vec<f64>> = doge_support\n .iter()\n .map(|x| network.forward(x, None))\n .collect();\n\n let query_embeddings: Vec<Vec<f64>> = doge_query\n .iter()\n .map(|x| network.forward(x, None))\n .collect();\n\n let metric_learner = MetricBasedLearner::new(config.clone(), DistanceMetric::Cosine);\n let metric_result = metric_learner.predict(\n &support_embeddings,\n &doge_support_labels,\n &query_embeddings,\n );\n\n let metric_correct = metric_result.predictions.iter()\n .zip(doge_query_labels.iter())\n .filter(|(p, a)| p == a)\n .count();\n let metric_accuracy = metric_correct as f64 / doge_query_labels.len() as f64 * 100.0;\n\n println!(\"Method comparison:\");\n println!(\" MAML (with adaptation): {:.1}%\", accuracy);\n println!(\" Metric-based (no adaptation): {:.1}%\", metric_accuracy);\n println!();\n\n // Step 7: Generate trading signal for new asset\n println!(\"--- Trading Signal Generation ---\\n\");\n\n // Use the predictions to generate signals\n let regime_probs = compute_regime_probabilities(&adaptation_result);\n\n let mut classifier = RegimeClassifier::new(0.5);\n let classification = classifier.classify_with_uncertainty(®ime_probs);\n\n println!(\"Market regime classification for DOGE:\");\n println!(\" {}\", classification.description());\n\n let signal_config = SignalConfig::default();\n let mut signal_generator = SignalGenerator::new(signal_config);\n let signal = signal_generator.generate(\"DOGEUSDT\", &classification);\n\n println!(\"\\nGenerated signal:\");\n println!(\" Type: {:?}\", signal.signal_type);\n println!(\" Confidence: {:.1}%\", signal.confidence * 100.0);\n println!(\" Position size: {:.1}%\", signal.position_size * 100.0);\n if signal.take_profit.is_some() {\n println!(\" Take profit: {:.1}%\", signal.take_profit.unwrap());\n println!(\" Stop loss: {:.1}%\", signal.stop_loss.unwrap());\n }\n\n // Step 8: Real-world usage hint\n println!(\"\\n--- Real-World Usage ---\\n\");\n println!(\"To use with real Bybit data:\");\n println!(\" 1. Create BybitClient::new(false) for mainnet\");\n println!(\" 2. Fetch klines with client.get_klines(\\\"DOGEUSDT\\\", \\\"1h\\\", 100)\");\n println!(\" 3. Extract features with FeatureExtractor::extract(&klines)\");\n println!(\" 4. Run few-shot prediction with extracted features\");\n println!(\" 5. Generate and execute trading signals\");\n println!();\n\n println!(\"=== Example Complete ===\");\n}\n\n/// Generate synthetic market data for an asset.\nfn generate_asset_data(asset_name: &str, count: usize) -> (Vec<Vec<f64>>, Vec<usize>) {\n let mut features = Vec::new();\n let mut labels = Vec::new();\n\n // Seed based on asset name for reproducibility\n let base_seed: u64 = asset_name.bytes().map(|b| b as u64).sum();\n\n for i in 0..count {\n // Determine class (0: uptrend, 1: sideways, 2: downtrend)\n let class = (i + (base_seed as usize)) % 3;\n\n // Generate features based on class\n let base = match class {\n 0 => vec![0.02, 0.015, 0.5, 65.0, 1.5, 0.8, 0.7, 0.6, 0.3, 0.7, 0.8, 0.6],\n 1 => vec![0.001, 0.002, 0.3, 50.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5],\n _ => vec![-0.02, -0.015, 0.6, 35.0, 0.7, 0.3, 0.3, 0.4, 0.7, 0.3, 0.2, 0.4],\n };\n\n features.push(add_noise(&base, 0.15, base_seed + i as u64));\n labels.push(class);\n }\n\n (features, labels)\n}\n\n/// Add noise to feature vector.\nfn add_noise(base: &[f64], noise_level: f64, seed: u64) -> Vec<f64> {\n let mut rng_state = seed;\n\n base.iter().map(|&v| {\n rng_state = rng_state.wrapping_mul(1103515245).wrapping_add(12345);\n let random = ((rng_state >> 16) & 0x7FFF) as f64 / 32768.0 - 0.5;\n v + v.abs().max(0.01) * noise_level * random\n }).collect()\n}\n\n/// Compute regime probabilities from prediction results.\nfn compute_regime_probabilities(result: &PredictionResult) -> Vec<f64> {\n // Simplified: map 3-class predictions to 8 regimes\n let mut regime_probs = vec![0.0; 8];\n\n let total = result.predictions.len() as f64;\n\n for (&pred, &conf) in result.predictions.iter().zip(result.confidences.iter()) {\n match pred {\n 0 => { // Uptrend\n regime_probs[0] += conf * 0.4 / total; // Strong uptrend\n regime_probs[1] += conf * 0.6 / total; // Weak uptrend\n }\n 1 => { // Sideways\n regime_probs[2] += conf / total; // Sideways\n }\n _ => { // Downtrend\n regime_probs[3] += conf * 0.3 / total; // Weak downtrend\n regime_probs[4] += conf * 0.7 / total; // Strong downtrend\n }\n }\n }\n\n // Normalize\n let sum: f64 = regime_probs.iter().sum();\n if sum > 0.0 {\n regime_probs.iter_mut().for_each(|p| *p /= sum);\n }\n\n regime_probs\n}\n" | |
| } | |
| } | |
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| "text": "Now I need to update the lib.rs prelude to match what's actually exported and update the strategy mod.rs to properly export the RegimeClassification:" | |
| } | |
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| } | |
| [2026-01-21T18:07:41.613Z] [INFO] { | |
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| [2026-01-21T18:07:41.661Z] [INFO] { | |
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| "content": "//! Trading strategy components\n//!\n//! Provides regime classification and signal generation for trading.\n\nmod regime;\nmod signal;\nmod risk;\n\npub use regime::{MarketRegime, RegimeClassifier};\npub use signal::{TradingSignal, SignalType, SignalGenerator};\npub use risk::{RiskManager, RiskConfig};\n", | |
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| "old_string": "pub use regime::{MarketRegime, RegimeClassifier};\npub use signal::{TradingSignal, SignalType, SignalGenerator};\npub use risk::{RiskManager, RiskConfig};", | |
| "new_string": "pub use regime::{MarketRegime, RegimeClassifier, RegimeClassification};\npub use signal::{TradingSignal, SignalType, SignalGenerator, SignalConfig};\npub use risk::{RiskManager, RiskConfig, RiskCheckResult, RiskSummary, Position};" | |
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| "oldString": "pub use regime::{MarketRegime, RegimeClassifier};\npub use signal::{TradingSignal, SignalType, SignalGenerator};\npub use risk::{RiskManager, RiskConfig};", | |
| "newString": "pub use regime::{MarketRegime, RegimeClassifier, RegimeClassification};\npub use signal::{TradingSignal, SignalType, SignalGenerator, SignalConfig};\npub use risk::{RiskManager, RiskConfig, RiskCheckResult, RiskSummary, Position};", | |
| "originalFile": "//! Trading strategy components\n//!\n//! Provides regime classification and signal generation for trading.\n\nmod regime;\nmod signal;\nmod risk;\n\npub use regime::{MarketRegime, RegimeClassifier};\npub use signal::{TradingSignal, SignalType, SignalGenerator};\npub use risk::{RiskManager, RiskConfig};\n", | |
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| " mod signal;", | |
| " mod risk;", | |
| " ", | |
| "-pub use regime::{MarketRegime, RegimeClassifier};", | |
| "-pub use signal::{TradingSignal, SignalType, SignalGenerator};", | |
| "-pub use risk::{RiskManager, RiskConfig};", | |
| "+pub use regime::{MarketRegime, RegimeClassifier, RegimeClassification};", | |
| "+pub use signal::{TradingSignal, SignalType, SignalGenerator, SignalConfig};", | |
| "+pub use risk::{RiskManager, RiskConfig, RiskCheckResult, RiskSummary, Position};" | |
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| "old_string": "/// Prelude module for convenient imports\npub mod prelude {\n // Network components\n pub use crate::network::{\n EmbeddingNetwork, EmbeddingConfig, ActivationType,\n DistanceComputer, DistanceMetric,\n };\n\n // Few-shot methods\n pub use crate::methods::{\n FewShotMethod, FewShotConfig, FewShotPredictor,\n MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n PredictionResult, AdaptationResult,\n };\n\n // Data components\n pub use crate::data::{\n BybitClient, BybitConfig,\n MarketFeatures, FeatureExtractor, FeatureConfig,\n Kline, Trade, OrderBook, FundingRate,\n };\n\n // Training components\n pub use crate::training::{\n Episode, EpisodeGenerator, EpisodeConfig,\n MetaTrainer, MetaTrainerConfig,\n };\n\n // Strategy components\n pub use crate::strategy::{\n MarketRegime, RegimeClassifier,\n TradingSignal, SignalType, SignalGenerator,\n RiskManager, RiskConfig,\n };\n}", | |
| "new_string": "/// Prelude module for convenient imports\npub mod prelude {\n // Network components\n pub use crate::network::{\n EmbeddingNetwork, EmbeddingConfig, ActivationType, NetworkParams,\n DistanceComputer, DistanceMetric,\n };\n\n // Few-shot methods\n pub use crate::methods::{\n FewShotMethod, FewShotConfig, FewShotPredictor, FewShotLearner,\n MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n PredictionResult, AdaptationResult,\n };\n\n // Data components\n pub use crate::data::{\n BybitClient, BybitConfig,\n MarketFeatures, FeatureExtractor, FeatureConfig,\n Kline, Trade, OrderBook, FundingRate, Ticker,\n };\n\n // Training components\n pub use crate::training::{\n Episode, EpisodeGenerator, EpisodeConfig,\n MetaTrainer, MetaTrainerConfig, TrainingResult,\n };\n\n // Strategy components\n pub use crate::strategy::{\n MarketRegime, RegimeClassifier, RegimeClassification,\n TradingSignal, SignalType, SignalGenerator, SignalConfig,\n RiskManager, RiskConfig, RiskCheckResult, RiskSummary, Position,\n };\n}" | |
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| "content": " 1β//! # Few-Shot Market Prediction\n 2β//!\n 3β//! This library implements multiple few-shot learning approaches for financial market\n 4β//! prediction, including metric-based methods, MAML-inspired optimization, and hybrid\n 5β//! approaches.\n 6β//!\n 7β//! ## Overview\n 8β//!\n 9β//! Few-shot learning enables rapid adaptation to new market conditions with minimal\n 10β//! examples. This is particularly valuable for:\n 11β//!\n 12β//! - **New Asset Analysis**: Predict patterns for newly listed cryptocurrencies\n 13β//! - **Rare Event Detection**: Recognize crashes, squeezes with few historical examples\n 14β//! - **Cross-Asset Transfer**: Apply knowledge from one asset to another\n 15β//! - **Regime Change Adaptation**: Quickly adjust to market regime shifts\n 16β//!\n 17β//! ## Supported Methods\n 18β//!\n 19β//! 1. **Metric-Based** (Prototypical Networks): Classify based on distance to prototypes\n 20β//! 2. **MAML-Inspired**: Gradient-based quick adaptation\n 21β//! 3. **Siamese Networks**: Pairwise similarity learning\n 22β//! 4. **Hybrid**: Combine metric and optimization approaches\n 23β//!\n 24β//! ## Example Usage\n 25β//!\n 26β//! ```rust,ignore\n 27β//! use few_shot_market_prediction::prelude::*;\n 28β//!\n 29β//! // Create a metric-based few-shot predictor\n 30β//! let config = FewShotConfig::default()\n 31β//! .with_method(FewShotMethod::Metric)\n 32β//! .with_embedding_dim(32);\n 33β//!\n 34β//! let predictor = FewShotPredictor::new(config);\n 35β//!\n 36β//! // Fit on support set\n 37β//! predictor.fit(&support_features, &support_labels);\n 38β//!\n 39β//! // Predict on query\n 40β//! let (predictions, confidences) = predictor.predict(&query_features);\n 41β//! ```\n 42β//!\n 43β//! ## Modules\n 44β//!\n 45β//! - `network` - Neural network components (embedding, distance computation)\n 46β//! - `methods` - Few-shot learning methods (metric, MAML, siamese, hybrid)\n 47β//! - `data` - Bybit API integration and feature extraction\n 48β//! - `training` - Episodic training framework\n 49β//! - `strategy` - Trading signal generation and regime classification\n 50β\n 51βpub mod network;\n 52βpub mod methods;\n 53βpub mod data;\n 54βpub mod training;\n 55βpub mod strategy;\n 56β\n 57β/// Prelude module for convenient imports\n 58βpub mod prelude {\n 59β // Network components\n 60β pub use crate::network::{\n 61β EmbeddingNetwork, EmbeddingConfig, ActivationType,\n 62β DistanceComputer, DistanceMetric,\n 63β };\n 64β\n 65β // Few-shot methods\n 66β pub use crate::methods::{\n 67β FewShotMethod, FewShotConfig, FewShotPredictor,\n 68β MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n 69β PredictionResult, AdaptationResult,\n 70β };\n 71β\n 72β // Data components\n 73β pub use crate::data::{\n 74β BybitClient, BybitConfig,\n 75β MarketFeatures, FeatureExtractor, FeatureConfig,\n 76β Kline, Trade, OrderBook, FundingRate,\n 77β };\n 78β\n 79β // Training components\n 80β pub use crate::training::{\n 81β Episode, EpisodeGenerator, EpisodeConfig,\n 82β MetaTrainer, MetaTrainerConfig,\n 83β };\n 84β\n 85β // Strategy components\n 86β pub use crate::strategy::{\n 87β MarketRegime, RegimeClassifier,\n 88β TradingSignal, SignalType, SignalGenerator,\n 89β RiskManager, RiskConfig,\n 90β };\n 91β}\n 92β\n 93β/// Library version\n 94βpub const VERSION: &str = env!(\"CARGO_PKG_VERSION\");\n 95β\n 96β#[cfg(test)]\n 97βmod tests {\n 98β use super::*;\n 99β\n 100β #[test]\n 101β fn test_version() {\n 102β assert!(!VERSION.is_empty());\n 103β }\n 104β}\n 105β\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\n//!\n//! This library implements multiple few-shot learning approaches for financial market\n//! prediction, including metric-based methods, MAML-inspired optimization, and hybrid\n//! approaches.\n//!\n//! ## Overview\n//!\n//! Few-shot learning enables rapid adaptation to new market conditions with minimal\n//! examples. This is particularly valuable for:\n//!\n//! - **New Asset Analysis**: Predict patterns for newly listed cryptocurrencies\n//! - **Rare Event Detection**: Recognize crashes, squeezes with few historical examples\n//! - **Cross-Asset Transfer**: Apply knowledge from one asset to another\n//! - **Regime Change Adaptation**: Quickly adjust to market regime shifts\n//!\n//! ## Supported Methods\n//!\n//! 1. **Metric-Based** (Prototypical Networks): Classify based on distance to prototypes\n//! 2. **MAML-Inspired**: Gradient-based quick adaptation\n//! 3. **Siamese Networks**: Pairwise similarity learning\n//! 4. **Hybrid**: Combine metric and optimization approaches\n//!\n//! ## Example Usage\n//!\n//! ```rust,ignore\n//! use few_shot_market_prediction::prelude::*;\n//!\n//! // Create a metric-based few-shot predictor\n//! let config = FewShotConfig::default()\n//! .with_method(FewShotMethod::Metric)\n//! .with_embedding_dim(32);\n//!\n//! let predictor = FewShotPredictor::new(config);\n//!\n//! // Fit on support set\n//! predictor.fit(&support_features, &support_labels);\n//!\n//! // Predict on query\n//! let (predictions, confidences) = predictor.predict(&query_features);\n//! ```\n//!\n//! ## Modules\n//!\n//! - `network` - Neural network components (embedding, distance computation)\n//! - `methods` - Few-shot learning methods (metric, MAML, siamese, hybrid)\n//! - `data` - Bybit API integration and feature extraction\n//! - `training` - Episodic training framework\n//! - `strategy` - Trading signal generation and regime classification\n\npub mod network;\npub mod methods;\npub mod data;\npub mod training;\npub mod strategy;\n\n/// Prelude module for convenient imports\npub mod prelude {\n // Network components\n pub use crate::network::{\n EmbeddingNetwork, EmbeddingConfig, ActivationType,\n DistanceComputer, DistanceMetric,\n };\n\n // Few-shot methods\n pub use crate::methods::{\n FewShotMethod, FewShotConfig, FewShotPredictor,\n MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n PredictionResult, AdaptationResult,\n };\n\n // Data components\n pub use crate::data::{\n BybitClient, BybitConfig,\n MarketFeatures, FeatureExtractor, FeatureConfig,\n Kline, Trade, OrderBook, FundingRate,\n };\n\n // Training components\n pub use crate::training::{\n Episode, EpisodeGenerator, EpisodeConfig,\n MetaTrainer, MetaTrainerConfig,\n };\n\n // Strategy components\n pub use crate::strategy::{\n MarketRegime, RegimeClassifier,\n TradingSignal, SignalType, SignalGenerator,\n RiskManager, RiskConfig,\n };\n}\n\n/// Library version\npub const VERSION: &str = env!(\"CARGO_PKG_VERSION\");\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_version() {\n assert!(!VERSION.is_empty());\n }\n}\n", | |
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| "old_string": "/// Prelude module for convenient imports\npub mod prelude {\n // Network components\n pub use crate::network::{\n EmbeddingNetwork, EmbeddingConfig, ActivationType,\n DistanceComputer, DistanceMetric,\n };\n\n // Few-shot methods\n pub use crate::methods::{\n FewShotMethod, FewShotConfig, FewShotPredictor,\n MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n PredictionResult, AdaptationResult,\n };\n\n // Data components\n pub use crate::data::{\n BybitClient, BybitConfig,\n MarketFeatures, FeatureExtractor, FeatureConfig,\n Kline, Trade, OrderBook, FundingRate,\n };\n\n // Training components\n pub use crate::training::{\n Episode, EpisodeGenerator, EpisodeConfig,\n MetaTrainer, MetaTrainerConfig,\n };\n\n // Strategy components\n pub use crate::strategy::{\n MarketRegime, RegimeClassifier,\n TradingSignal, SignalType, SignalGenerator,\n RiskManager, RiskConfig,\n };\n}", | |
| "new_string": "/// Prelude module for convenient imports\npub mod prelude {\n // Network components\n pub use crate::network::{\n EmbeddingNetwork, EmbeddingConfig, ActivationType, NetworkParams,\n DistanceComputer, DistanceMetric,\n };\n\n // Few-shot methods\n pub use crate::methods::{\n FewShotMethod, FewShotConfig, FewShotPredictor, FewShotLearner,\n MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n PredictionResult, AdaptationResult,\n };\n\n // Data components\n pub use crate::data::{\n BybitClient, BybitConfig,\n MarketFeatures, FeatureExtractor, FeatureConfig,\n Kline, Trade, OrderBook, FundingRate, Ticker,\n };\n\n // Training components\n pub use crate::training::{\n Episode, EpisodeGenerator, EpisodeConfig,\n MetaTrainer, MetaTrainerConfig, TrainingResult,\n };\n\n // Strategy components\n pub use crate::strategy::{\n MarketRegime, RegimeClassifier, RegimeClassification,\n TradingSignal, SignalType, SignalGenerator, SignalConfig,\n RiskManager, RiskConfig, RiskCheckResult, RiskSummary, Position,\n };\n}" | |
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| "oldString": "/// Prelude module for convenient imports\npub mod prelude {\n // Network components\n pub use crate::network::{\n EmbeddingNetwork, EmbeddingConfig, ActivationType,\n DistanceComputer, DistanceMetric,\n };\n\n // Few-shot methods\n pub use crate::methods::{\n FewShotMethod, FewShotConfig, FewShotPredictor,\n MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n PredictionResult, AdaptationResult,\n };\n\n // Data components\n pub use crate::data::{\n BybitClient, BybitConfig,\n MarketFeatures, FeatureExtractor, FeatureConfig,\n Kline, Trade, OrderBook, FundingRate,\n };\n\n // Training components\n pub use crate::training::{\n Episode, EpisodeGenerator, EpisodeConfig,\n MetaTrainer, MetaTrainerConfig,\n };\n\n // Strategy components\n pub use crate::strategy::{\n MarketRegime, RegimeClassifier,\n TradingSignal, SignalType, SignalGenerator,\n RiskManager, RiskConfig,\n };\n}", | |
| "newString": "/// Prelude module for convenient imports\npub mod prelude {\n // Network components\n pub use crate::network::{\n EmbeddingNetwork, EmbeddingConfig, ActivationType, NetworkParams,\n DistanceComputer, DistanceMetric,\n };\n\n // Few-shot methods\n pub use crate::methods::{\n FewShotMethod, FewShotConfig, FewShotPredictor, FewShotLearner,\n MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n PredictionResult, AdaptationResult,\n };\n\n // Data components\n pub use crate::data::{\n BybitClient, BybitConfig,\n MarketFeatures, FeatureExtractor, FeatureConfig,\n Kline, Trade, OrderBook, FundingRate, Ticker,\n };\n\n // Training components\n pub use crate::training::{\n Episode, EpisodeGenerator, EpisodeConfig,\n MetaTrainer, MetaTrainerConfig, TrainingResult,\n };\n\n // Strategy components\n pub use crate::strategy::{\n MarketRegime, RegimeClassifier, RegimeClassification,\n TradingSignal, SignalType, SignalGenerator, SignalConfig,\n RiskManager, RiskConfig, RiskCheckResult, RiskSummary, Position,\n };\n}", | |
| "originalFile": "//! # Few-Shot Market Prediction\n//!\n//! This library implements multiple few-shot learning approaches for financial market\n//! prediction, including metric-based methods, MAML-inspired optimization, and hybrid\n//! approaches.\n//!\n//! ## Overview\n//!\n//! Few-shot learning enables rapid adaptation to new market conditions with minimal\n//! examples. This is particularly valuable for:\n//!\n//! - **New Asset Analysis**: Predict patterns for newly listed cryptocurrencies\n//! - **Rare Event Detection**: Recognize crashes, squeezes with few historical examples\n//! - **Cross-Asset Transfer**: Apply knowledge from one asset to another\n//! - **Regime Change Adaptation**: Quickly adjust to market regime shifts\n//!\n//! ## Supported Methods\n//!\n//! 1. **Metric-Based** (Prototypical Networks): Classify based on distance to prototypes\n//! 2. **MAML-Inspired**: Gradient-based quick adaptation\n//! 3. **Siamese Networks**: Pairwise similarity learning\n//! 4. **Hybrid**: Combine metric and optimization approaches\n//!\n//! ## Example Usage\n//!\n//! ```rust,ignore\n//! use few_shot_market_prediction::prelude::*;\n//!\n//! // Create a metric-based few-shot predictor\n//! let config = FewShotConfig::default()\n//! .with_method(FewShotMethod::Metric)\n//! .with_embedding_dim(32);\n//!\n//! let predictor = FewShotPredictor::new(config);\n//!\n//! // Fit on support set\n//! predictor.fit(&support_features, &support_labels);\n//!\n//! // Predict on query\n//! let (predictions, confidences) = predictor.predict(&query_features);\n//! ```\n//!\n//! ## Modules\n//!\n//! - `network` - Neural network components (embedding, distance computation)\n//! - `methods` - Few-shot learning methods (metric, MAML, siamese, hybrid)\n//! - `data` - Bybit API integration and feature extraction\n//! - `training` - Episodic training framework\n//! - `strategy` - Trading signal generation and regime classification\n\npub mod network;\npub mod methods;\npub mod data;\npub mod training;\npub mod strategy;\n\n/// Prelude module for convenient imports\npub mod prelude {\n // Network components\n pub use crate::network::{\n EmbeddingNetwork, EmbeddingConfig, ActivationType,\n DistanceComputer, DistanceMetric,\n };\n\n // Few-shot methods\n pub use crate::methods::{\n FewShotMethod, FewShotConfig, FewShotPredictor,\n MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,\n PredictionResult, AdaptationResult,\n };\n\n // Data components\n pub use crate::data::{\n BybitClient, BybitConfig,\n MarketFeatures, FeatureExtractor, FeatureConfig,\n Kline, Trade, OrderBook, FundingRate,\n };\n\n // Training components\n pub use crate::training::{\n Episode, EpisodeGenerator, EpisodeConfig,\n MetaTrainer, MetaTrainerConfig,\n };\n\n // Strategy components\n pub use crate::strategy::{\n MarketRegime, RegimeClassifier,\n TradingSignal, SignalType, SignalGenerator,\n RiskManager, RiskConfig,\n };\n}\n\n/// Library version\npub const VERSION: &str = env!(\"CARGO_PKG_VERSION\");\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_version() {\n assert!(!VERSION.is_empty());\n }\n}\n", | |
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| " pub mod prelude {", | |
| " // Network components", | |
| " pub use crate::network::{", | |
| "- EmbeddingNetwork, EmbeddingConfig, ActivationType,", | |
| "+ EmbeddingNetwork, EmbeddingConfig, ActivationType, NetworkParams,", | |
| " DistanceComputer, DistanceMetric,", | |
| " };", | |
| " ", | |
| " // Few-shot methods", | |
| " pub use crate::methods::{", | |
| "- FewShotMethod, FewShotConfig, FewShotPredictor,", | |
| "+ FewShotMethod, FewShotConfig, FewShotPredictor, FewShotLearner,", | |
| " MetricBasedLearner, MAMLLearner, SiameseLearner, HybridLearner,", | |
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| " pub use crate::data::{", | |
| " BybitClient, BybitConfig,", | |
| " MarketFeatures, FeatureExtractor, FeatureConfig,", | |
| "- Kline, Trade, OrderBook, FundingRate,", | |
| "+ Kline, Trade, OrderBook, FundingRate, Ticker,", | |
| " };", | |
| " ", | |
| " // Training components", | |
| " pub use crate::training::{", | |
| " Episode, EpisodeGenerator, EpisodeConfig,", | |
| "- MetaTrainer, MetaTrainerConfig,", | |
| "+ MetaTrainer, MetaTrainerConfig, TrainingResult,", | |
| " };", | |
| " ", | |
| " // Strategy components", | |
| " pub use crate::strategy::{", | |
| "- MarketRegime, RegimeClassifier,", | |
| "- TradingSignal, SignalType, SignalGenerator,", | |
| "- RiskManager, RiskConfig,", | |
| "+ MarketRegime, RegimeClassifier, RegimeClassification,", | |
| "+ TradingSignal, SignalType, SignalGenerator, SignalConfig,", | |
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| " };", | |
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| "old_string": "pub use embedding::{EmbeddingNetwork, EmbeddingConfig, ActivationType};\npub use distance::{DistanceComputer, DistanceMetric};", | |
| "new_string": "pub use embedding::{EmbeddingNetwork, EmbeddingConfig, ActivationType, NetworkParams};\npub use distance::{DistanceComputer, DistanceMetric};" | |
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| "oldString": "pub use embedding::{EmbeddingNetwork, EmbeddingConfig, ActivationType};\npub use distance::{DistanceComputer, DistanceMetric};", | |
| "newString": "pub use embedding::{EmbeddingNetwork, EmbeddingConfig, ActivationType, NetworkParams};\npub use distance::{DistanceComputer, DistanceMetric};", | |
| "originalFile": "//! Neural network components for few-shot learning\n//!\n//! This module provides:\n//! - Embedding networks for converting market features to embeddings\n//! - Distance computation for similarity-based classification\n//! - Support for MAML-style gradient computation\n\nmod embedding;\nmod distance;\n\npub use embedding::{EmbeddingNetwork, EmbeddingConfig, ActivationType};\npub use distance::{DistanceComputer, DistanceMetric};\n", | |
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| " mod embedding;", | |
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| "-pub use embedding::{EmbeddingNetwork, EmbeddingConfig, ActivationType};", | |
| "+pub use embedding::{EmbeddingNetwork, EmbeddingConfig, ActivationType, NetworkParams};", | |
| " pub use distance::{DistanceComputer, DistanceMetric};" | |
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| "content": "//! Data handling and Bybit API integration\n//!\n//! This module provides:\n//! - Bybit API client for fetching market data\n//! - Feature extraction from raw market data\n//! - Data types for market information\n\nmod bybit;\nmod features;\nmod types;\n\npub use bybit::{BybitClient, BybitConfig};\npub use features::{FeatureExtractor, FeatureConfig, MarketFeatures};\npub use types::{Kline, Trade, OrderBook, FundingRate, Ticker};\n", | |
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| "content": " 1β//! Training infrastructure for few-shot learning\n 2β//!\n 3β//! Provides episodic training framework for meta-learning.\n 4β\n 5βmod episode;\n 6βmod trainer;\n 7β\n 8βpub use episode::{Episode, EpisodeGenerator, EpisodeConfig};\n 9βpub use trainer::{MetaTrainer, MetaTrainerConfig};\n 10β\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": "//! Training infrastructure for few-shot learning\n//!\n//! Provides episodic training framework for meta-learning.\n\nmod episode;\nmod trainer;\n\npub use episode::{Episode, EpisodeGenerator, EpisodeConfig};\npub use trainer::{MetaTrainer, MetaTrainerConfig};\n", | |
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| [2026-01-21T18:08:19.010Z] [INFO] {"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018yqKWvWyJ8K8XVKHrnrNpH","type":"tool_result","content":" 1β//! Meta-training infrastructure\n 2β\n 3βuse super::episode::{Episode, EpisodeConfig, EpisodeGenerator};\n 4βuse crate::methods::{FewShotConfig, FewShotLearner, FewShotPredictor, PredictionResult};\n 5βuse ndarray::Array2;\n 6βuse serde::{Deserialize, Serialize};\n 7β\n 8β/// Configuration for meta-trainer\n 9β#[derive(Debug, Clone, Serialize, Deserialize)]\n 10βpub struct MetaTrainerConfig {\n 11β /// Episode configuration\n 12β pub episode_config: EpisodeConfig,\n 13β /// Number of training episodes\n 14β pub n_episodes: usize,\n 15β /// Evaluation frequency (episodes)\n 16β pub eval_frequency: usize,\n 17β /// Number of evaluation episodes\n 18β pub n_eval_episodes: usize,\n 19β}\n 20β\n 21βimpl Default for MetaTrainerConfig {\n 22β fn default() -> Self {\n 23β Self {\n 24β episode_config: EpisodeConfig::default(),\n 25β n_episodes: 1000,\n 26β eval_frequency: 100,\n 27β n_eval_episodes: 50,\n 28β }\n 29β }\n 30β}\n 31β\n 32β/// Training result\n 33β#[derive(Debug, Clone, Serialize, Deserialize)]\n 34βpub struct TrainingResult {\n 35β /// Episode number\n 36β pub episode: usize,\n 37β /// Accuracy on this episode\n 38β pub accuracy: f64,\n 39β /// Average confidence\n 40β pub avg_confidence: f64,\n 41β}\n 42β\n 43β/// Meta-trainer for few-shot learning\n 44βpub struct MetaTrainer {\n 45β config: MetaTrainerConfig,\n 46β fs_config: FewShotConfig,\n 47β training_history: Vec<TrainingResult>,\n 48β}\n 49β\n 50βimpl MetaTrainer {\n 51β /// Create a new meta-trainer\n 52β pub fn new(config: MetaTrainerConfig, fs_config: FewShotConfig) -> Self {\n 53β Self {\n 54β config,\n 55β fs_config,\n 56β training_history: Vec::new(),\n 57β }\n 58β }\n 59β\n 60β /// Train on generated episodes (demonstration)\n 61β ///\n 62β /// Note: This is a simplified demonstration. In practice, you would\n 63β /// update the network parameters during training. This implementation\n 64β /// shows the episodic training loop structure.\n 65β pub fn train(&mut self, features: &Array2<f64>, labels: &[usize]) -> Vec<TrainingResult> {\n 66β let generator = EpisodeGenerator::new(\n 67β features,\n 68β labels,\n 69β self.config.episode_config.clone(),\n 70β );\n 71β\n 72β let mut results = Vec::new();\n 73β\n 74β for episode_idx in 0..self.config.n_episodes {\n 75β if let Some(episode) = generator.generate_episode() {\n 76β let result = self.train_episode(&episode, episode_idx);\n 77β results.push(result.clone());\n 78β self.training_history.push(result);\n 79β }\n 80β }\n 81β\n 82β results\n 83β }\n 84β\n 85β /// Train on a single episode\n 86β fn train_episode(&self, episode: &Episode, episode_idx: usize) -> TrainingResult {\n 87β // Create a predictor for this episode\n 88β let mut predictor = FewShotPredictor::new(self.fs_config.clone());\n 89β\n 90β // Fit on support set\n 91β predictor.fit(&episode.support_features, &episode.support_labels);\n 92β\n 93β // Evaluate on query set\n 94β let predictions = predictor.predict(&episode.query_features);\n 95β\n 96β // Calculate metrics\n 97β let (accuracy, avg_confidence) = self.calculate_metrics(&predictions, &episode.query_labels);\n 98β\n 99β TrainingResult {\n 100β episode: episode_idx,\n 101β accuracy,\n 102β avg_confidence,\n 103β }\n 104β }\n 105β\n 106β /// Calculate accuracy and average confidence\n 107β fn calculate_metrics(\n 108β &self,\n 109β predictions: &[PredictionResult],\n 110β true_labels: &[usize],\n 111β ) -> (f64, f64) {\n 112β if predictions.is_empty() {\n 113β return (0.0, 0.0);\n 114β }\n 115β\n 116β let correct: usize = predictions\n 117β .iter()\n 118β .zip(true_labels.iter())\n 119β .filter(|(pred, &true_label)| pred.predicted_class == true_label)\n 120β .count();\n 121β\n 122β let accuracy = correct as f64 / predictions.len() as f64;\n 123β let avg_confidence = predictions.iter().map(|p| p.confidence).sum::<f64>() / predictions.len() as f64;\n 124β\n 125β (accuracy, avg_confidence)\n 126β }\n 127β\n 128β /// Get training history\n 129β pub fn history(&self) -> &[TrainingResult] {\n 130β &self.training_history\n 131β }\n 132β\n 133β /// Get average accuracy over recent episodes\n 134β pub fn recent_accuracy(&self, n_recent: usize) -> f64 {\n 135β if self.training_history.is_empty() {\n 136β return 0.0;\n 137β }\n 138β\n 139β let recent: Vec<_> = self.training_history.iter().rev().take(n_recent).collect();\n 140β recent.iter().map(|r| r.accuracy).sum::<f64>() / recent.len() as f64\n 141β }\n 142β}\n 143β\n 144β#[cfg(test)]\n 145βmod tests {\n 146β use super::*;\n 147β use crate::methods::FewShotMethod;\n 148β\n 149β fn create_test_data() -> (Array2<f64>, Vec<usize>) {\n 150β let n_classes = 5;\n 151β let n_per_class = 30;\n 152β let dim = 20;\n 153β\n 154β let mut data = Vec::new();\n 155β let mut labels = Vec::new();\n 156β\n 157β for class in 0..n_classes {\n 158β for _ in 0..n_per_class {\n 159β for _ in 0..dim {\n 160β data.push(class as f64 * 0.5 + rand::random::<f64>() * 0.1);\n 161β }\n 162β labels.push(class);\n 163β }\n 164β }\n 165β\n 166β let features = Array2::from_shape_vec((n_classes * n_per_class, dim), data).unwrap();\n 167β (features, labels)\n 168β }\n 169β\n 170β #[test]\n 171β fn test_meta_trainer() {\n 172β let trainer_config = MetaTrainerConfig {\n 173β episode_config: EpisodeConfig {\n 174β n_way: 3,\n 175β k_shot: 5,\n 176β n_query: 5,\n 177β },\n 178β n_episodes: 5,\n 179β ..Default::default()\n 180β };\n 181β\n 182β let fs_config = FewShotConfig::default()\n 183β .with_method(FewShotMethod::Metric)\n 184β .with_input_dim(20);\n 185β\n 186β let mut trainer = MetaTrainer::new(trainer_config, fs_config);\n 187β let (features, labels) = create_test_data();\n 188β\n 189β let results = trainer.train(&features, &labels);\n 190β assert_eq!(results.len(), 5);\n 191β\n 192β for result in results {\n 193β assert!(result.accuracy >= 0.0 && result.accuracy <= 1.0);\n 194β assert!(result.avg_confidence >= 0.0 && result.avg_confidence <= 1.0);\n 195β }\n 196β }\n 197β}\n 198β\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":"172d23c7-5c62-484f-af89-80e92d0b393e","uuid":"cc7c9854-4cba-4aba-81a7-98dc12e1e83f","tool_use_result":{"type":"text","file":{"filePath":"/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/src/training/trainer.rs","content":"//! Meta-training infrastructure\n\nuse super::episode::{Episode, EpisodeConfig, EpisodeGenerator};\nuse crate::methods::{FewShotConfig, FewShotLearner, FewShotPredictor, PredictionResult};\nuse ndarray::Array2;\nuse serde::{Deserialize, Serialize};\n\n/// Configuration for meta-trainer\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct MetaTrainerConfig {\n /// Episode configuration\n pub episode_config: EpisodeConfig,\n /// Number of training episodes\n pub n_episodes: usize,\n /// Evaluation frequency (episodes)\n pub eval_frequency: usize,\n /// Number of evaluation episodes\n pub n_eval_episodes: usize,\n}\n\nimpl Default for MetaTrainerConfig {\n fn default() -> Self {\n Self {\n episode_config: EpisodeConfig::default(),\n n_episodes: 1000,\n eval_frequency: 100,\n n_eval_episodes: 50,\n }\n }\n}\n\n/// Training result\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct TrainingResult {\n /// Episode number\n pub episode: usize,\n /// Accuracy on this episode\n pub accuracy: f64,\n /// Average confidence\n pub avg_confidence: f64,\n}\n\n/// Meta-trainer for few-shot learning\npub struct MetaTrainer {\n config: MetaTrainerConfig,\n fs_config: FewShotConfig,\n training_history: Vec<TrainingResult>,\n}\n\nimpl MetaTrainer {\n /// Create a new meta-trainer\n pub fn new(config: MetaTrainerConfig, fs_config: FewShotConfig) -> Self {\n Self {\n config,\n fs_config,\n training_history: Vec::new(),\n }\n }\n\n /// Train on generated episodes (demonstration)\n ///\n /// Note: This is a simplified demonstration. In practice, you would\n /// update the network parameters during training. This implementation\n /// shows the episodic training loop structure.\n pub fn train(&mut self, features: &Array2<f64>, labels: &[usize]) -> Vec<TrainingResult> {\n let generator = EpisodeGenerator::new(\n features,\n labels,\n self.config.episode_config.clone(),\n );\n\n let mut results = Vec::new();\n\n for episode_idx in 0..self.config.n_episodes {\n if let Some(episode) = generator.generate_episode() {\n let result = self.train_episode(&episode, episode_idx);\n results.push(result.clone());\n self.training_history.push(result);\n }\n }\n\n results\n }\n\n /// Train on a single episode\n fn train_episode(&self, episode: &Episode, episode_idx: usize) -> TrainingResult {\n // Create a predictor for this episode\n let mut predictor = FewShotPredictor::new(self.fs_config.clone());\n\n // Fit on support set\n predictor.fit(&episode.support_features, &episode.support_labels);\n\n // Evaluate on query set\n let predictions = predictor.predict(&episode.query_features);\n\n // Calculate metrics\n let (accuracy, avg_confidence) = self.calculate_metrics(&predictions, &episode.query_labels);\n\n TrainingResult {\n episode: episode_idx,\n accuracy,\n avg_confidence,\n }\n }\n\n /// Calculate accuracy and average confidence\n fn calculate_metrics(\n &self,\n predictions: &[PredictionResult],\n true_labels: &[usize],\n ) -> (f64, f64) {\n if predictions.is_empty() {\n return (0.0, 0.0);\n }\n\n let correct: usize = predictions\n .iter()\n .zip(true_labels.iter())\n .filter(|(pred, &true_label)| pred.predicted_class == true_label)\n .count();\n\n let accuracy = correct as f64 / predictions | |
| [2026-01-21T18:08:19.013Z] [INFO] .len() as f64;\n let avg_confidence = predictions.iter().map(|p| p.confidence).sum::<f64>() / predictions.len() as f64;\n\n (accuracy, avg_confidence)\n }\n\n /// Get training history\n pub fn history(&self) -> &[TrainingResult] {\n &self.training_history\n }\n\n /// Get average accuracy over recent episodes\n pub fn recent_accuracy(&self, n_recent: usize) -> f64 {\n if self.training_history.is_empty() {\n return 0.0;\n }\n\n let recent: Vec<_> = self.training_history.iter().rev().take(n_recent).collect();\n recent.iter().map(|r| r.accuracy).sum::<f64>() / recent.len() as f64\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n use crate::methods::FewShotMethod;\n\n fn create_test_data() -> (Array2<f64>, Vec<usize>) {\n let n_classes = 5;\n let n_per_class = 30;\n let dim = 20;\n\n let mut data = Vec::new();\n let mut labels = Vec::new();\n\n for class in 0..n_classes {\n for _ in 0..n_per_class {\n for _ in 0..dim {\n data.push(class as f64 * 0.5 + rand::random::<f64>() * 0.1);\n }\n labels.push(class);\n }\n }\n\n let features = Array2::from_shape_vec((n_classes * n_per_class, dim), data).unwrap();\n (features, labels)\n }\n\n #[test]\n fn test_meta_trainer() {\n let trainer_config = MetaTrainerConfig {\n episode_config: EpisodeConfig {\n n_way: 3,\n k_shot: 5,\n n_query: 5,\n },\n n_episodes: 5,\n ..Default::default()\n };\n\n let fs_config = FewShotConfig::default()\n .with_method(FewShotMethod::Metric)\n .with_input_dim(20);\n\n let mut trainer = MetaTrainer::new(trainer_config, fs_config);\n let (features, labels) = create_test_data();\n\n let results = trainer.train(&features, &labels);\n assert_eq!(results.len(), 5);\n\n for result in results {\n assert!(result.accuracy >= 0.0 && result.accuracy <= 1.0);\n assert!(result.avg_confidence >= 0.0 && result.avg_confidence <= 1.0);\n }\n }\n}\n","numLines":198,"startLine":1,"totalLines":198}}} | |
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| "content": " 1β//! Training infrastructure for few-shot learning\n 2β//!\n 3β//! Provides episodic training framework for meta-learning.\n 4β\n 5βmod episode;\n 6βmod trainer;\n 7β\n 8βpub use episode::{Episode, EpisodeGenerator, EpisodeConfig};\n 9βpub use trainer::{MetaTrainer, MetaTrainerConfig};\n 10β\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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| "old_string": "pub use episode::{Episode, EpisodeGenerator, EpisodeConfig};\npub use trainer::{MetaTrainer, MetaTrainerConfig};", | |
| "new_string": "pub use episode::{Episode, EpisodeGenerator, EpisodeConfig};\npub use trainer::{MetaTrainer, MetaTrainerConfig, TrainingResult};" | |
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| "oldString": "pub use episode::{Episode, EpisodeGenerator, EpisodeConfig};\npub use trainer::{MetaTrainer, MetaTrainerConfig};", | |
| "newString": "pub use episode::{Episode, EpisodeGenerator, EpisodeConfig};\npub use trainer::{MetaTrainer, MetaTrainerConfig, TrainingResult};", | |
| "originalFile": "//! Training infrastructure for few-shot learning\n//!\n//! Provides episodic training framework for meta-learning.\n\nmod episode;\nmod trainer;\n\npub use episode::{Episode, EpisodeGenerator, EpisodeConfig};\npub use trainer::{MetaTrainer, MetaTrainerConfig};\n", | |
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| " pub use episode::{Episode, EpisodeGenerator, EpisodeConfig};", | |
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| "+pub use trainer::{MetaTrainer, MetaTrainerConfig, TrainingResult};" | |
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| "text": "I also need to export FewShotLearner from methods/mod.rs. Let me check:" | |
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| "content": " 1β//! Few-shot learning methods\n 2β//!\n 3β//! This module provides multiple few-shot learning approaches:\n 4β//! - Metric-based (Prototypical Networks style)\n 5β//! - MAML-inspired optimization\n 6β//! - Siamese networks for pairwise similarity\n 7β//! - Hybrid approaches combining multiple methods\n 8β\n 9βmod metric;\n 10βmod maml;\n 11βmod siamese;\n 12βmod hybrid;\n 13βmod config;\n 14β\n 15βpub use metric::MetricBasedLearner;\n 16βpub use maml::MAMLLearner;\n 17βpub use siamese::SiameseLearner;\n 18βpub use hybrid::HybridLearner;\n 19βpub use config::{FewShotConfig, FewShotMethod};\n 20β\n 21βuse ndarray::Array1;\n 22βuse serde::{Deserialize, Serialize};\n 23β\n 24β/// Result of a few-shot prediction\n 25β#[derive(Debug, Clone, Serialize, Deserialize)]\n 26βpub struct PredictionResult {\n 27β /// Predicted class index\n 28β pub predicted_class: usize,\n 29β /// Confidence score (0-1)\n 30β pub confidence: f64,\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 learning methods\n//!\n//! This module provides multiple few-shot learning approaches:\n//! - Metric-based (Prototypical Networks style)\n//! - MAML-inspired optimization\n//! - Siamese networks for pairwise similarity\n//! - Hybrid approaches combining multiple methods\n\nmod metric;\nmod maml;\nmod siamese;\nmod hybrid;\nmod config;\n\npub use metric::MetricBasedLearner;\npub use maml::MAMLLearner;\npub use siamese::SiameseLearner;\npub use hybrid::HybridLearner;\npub use config::{FewShotConfig, FewShotMethod};\n\nuse ndarray::Array1;\nuse serde::{Deserialize, Serialize};\n\n/// Result of a few-shot prediction\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct PredictionResult {\n /// Predicted class index\n pub predicted_class: usize,\n /// Confidence score (0-1)\n pub confidence: f64,", | |
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| "text": "I see that FewShotLearner is defined in the mod.rs file itself (as a trait), not imported. It needs to be made public. Let me check around line 94 where the trait is defined:" | |
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| "content": " 90β }\n 91β}\n 92β\n 93β/// Trait for few-shot learners\n 94βpub trait FewShotLearner {\n 95β /// Fit the learner on a support set\n 96β fn fit(&mut self, support_features: &ndarray::Array2<f64>, support_labels: &[usize]);\n 97β\n 98β /// Predict on query features\n 99β fn predict(&self, query_features: &ndarray::Array2<f64>) -> Vec<PredictionResult>;\n 100β\n 101β /// Predict single sample\n 102β fn predict_single(&self, query: &Array1<f64>) -> PredictionResult;\n 103β}\n 104β\n 105β/// High-level few-shot predictor that can use different methods\n 106βpub struct FewShotPredictor {\n 107β config: FewShotConfig,\n 108β learner: Box<dyn FewShotLearner + Send + Sync>,\n 109β class_names: std::collections::HashMap<usize, String>,\n 110β}\n 111β\n 112βimpl FewShotPredictor {\n 113β /// Create a new few-shot predictor\n 114β pub fn new(config: FewShotConfig) -> Self {\n 115β let learner: Box<dyn FewShotLearner + Send + Sync> = match config.method {\n 116β FewShotMethod::Metric => Box::new(MetricBasedLearner::new(config.clone())),\n 117β FewShotMethod::MAML => Box::new(MAMLLearner::new(config.clone())),\n 118β FewShotMethod::Siamese => Box::new(SiameseLearner::new(config.clone())),\n 119β FewShotMethod::Hybrid => Box::new(HybridLearner::new(config.clone())),\n 120β };\n 121β\n 122β Self {\n 123β config,\n 124β learner,\n 125β class_names: std::collections::HashMap::new(),\n 126β }\n 127β }\n 128β\n 129β /// Set class names for interpretable predictions\n 130β pub fn with_class_names(mut self, names: std::collections::HashMap<usize, String>) -> Self {\n 131β self.class_names = names;\n 132β self\n 133β }\n 134β\n 135β /// Fit on support set\n 136β pub fn fit(&mut self, support_features: &ndarray::Array2<f64>, support_labels: &[usize]) {\n 137β self.learner.fit(support_features, support_labels);\n 138β\n 139β // Auto-populate class names if not set\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": " }\n}\n\n/// Trait for few-shot learners\npub trait FewShotLearner {\n /// Fit the learner on a support set\n fn fit(&mut self, support_features: &ndarray::Array2<f64>, support_labels: &[usize]);\n\n /// Predict on query features\n fn predict(&self, query_features: &ndarray::Array2<f64>) -> Vec<PredictionResult>;\n\n /// Predict single sample\n fn predict_single(&self, query: &Array1<f64>) -> PredictionResult;\n}\n\n/// High-level few-shot predictor that can use different methods\npub struct FewShotPredictor {\n config: FewShotConfig,\n learner: Box<dyn FewShotLearner + Send + Sync>,\n class_names: std::collections::HashMap<usize, String>,\n}\n\nimpl FewShotPredictor {\n /// Create a new few-shot predictor\n pub fn new(config: FewShotConfig) -> Self {\n let learner: Box<dyn FewShotLearner + Send + Sync> = match config.method {\n FewShotMethod::Metric => Box::new(MetricBasedLearner::new(config.clone())),\n FewShotMethod::MAML => Box::new(MAMLLearner::new(config.clone())),\n FewShotMethod::Siamese => Box::new(SiameseLearner::new(config.clone())),\n FewShotMethod::Hybrid => Box::new(HybridLearner::new(config.clone())),\n };\n\n Self {\n config,\n learner,\n class_names: std::collections::HashMap::new(),\n }\n }\n\n /// Set class names for interpretable predictions\n pub fn with_class_names(mut self, names: std::collections::HashMap<usize, String>) -> Self {\n self.class_names = names;\n self\n }\n\n /// Fit on support set\n pub fn fit(&mut self, support_features: &ndarray::Array2<f64>, support_labels: &[usize]) {\n self.learner.fit(support_features, support_labels);\n\n // Auto-populate class names if not set", | |
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| "content": " Updating crates.io index\n Locking 255 packages to latest compatible versions\n Adding generic-array v0.14.7 (available: v0.14.9)\n Adding hashbrown v0.14.5 (available: v0.16.1)\n Adding ndarray v0.15.6 (available: v0.17.2)\n Adding ndarray-rand v0.14.0 (available: v0.16.0)\n Adding ordered-float v4.6.0 (available: v5.1.0)\n Adding rand v0.8.5 (available: v0.9.2)\n Adding rand_distr v0.4.3 (available: v0.5.1)\n Adding reqwest v0.11.27 (available: v0.13.1)\n Adding statrs v0.16.1 (available: v0.18.0)\n Adding thiserror v1.0.69 (available: v2.0.18)\n Adding tokio-tungstenite v0.21.0 (available: v0.28.0)\n Compiling proc-macro2 v1.0.105\n Compiling unicode-ident v1.0.22\n Compiling quote v1.0.43\n Compiling libc v0.2.180\n Checking cfg-if v1.0.4\n Compiling shlex v1.3.0\n Compiling find-msvc-tools v0.1.8\n Checking smallvec v1.15.1\n Compiling cc v1.2.53\n Compiling autocfg v1.5.0\n Checking pin-project-lite v0.2.16\n Checking stable_deref_trait v1.2.1\n Compiling libm v0.2.15\n Checking bytes v1.11.0\n Checking once_cell v1.21.3\n Compiling num-traits v0.2.19\n Compiling parking_lot_core v0.9.12\n Compiling syn v2.0.114\n Compiling zerocopy v0.8.33\n Compiling version_check v0.9.5\n Checking getrandom v0.2.17\n Checking log v0.4.29\n Checking scopeguard v1.2.0\n Checking errno v0.3.14\n Checking lock_api v0.4.14\n Checking itoa v1.0.17\n Checking untrusted v0.9.0\n Checking signal-hook-registry v1.4.8\n Compiling ring v0.17.14\n Checking parking_lot v0.12.5\n Checking socket2 v0.6.1\n Checking mio v1.1.1\n Checking futures-sink v0.3.31\n Compiling typenum v1.19.0\n Checking futures-core v0.3.31\n Compiling vcpkg v0.2.15\n Compiling pkg-config v0.3.32\n Checking memchr v2.7.6\n Checking litemap v0.8.1\n Checking writeable v0.6.2\n Compiling openssl-sys v0.9.111\n Checking futures-channel v0.3.31\n Compiling icu_properties_data v2.1.2\n Checking pin-utils v0.1.0\n Checking futures-io v0.3.31\n Compiling icu_normalizer_data v2.1.1\n Compiling serde_core v1.0.228\n Checking slab v0.4.11\n Checking futures-task v0.3.31\n Checking ppv-lite86 v0.2.21\n Compiling generic-array v0.14.7\n Checking rand_core v0.6.4\n Checking tracing-core v0.1.36\n Compiling httparse v1.10.1\n Checking zeroize v1.8.2\n Checking rustls-pki-types v1.14.0\n Checking rand_chacha v0.3.1\n Compiling serde v1.0.228\n Checking fnv v1.0.7\n Checking http v0.2.12\n Checking rand v0.8.5\n Compiling synstructure v0.13.2\n Compiling matrixmultiply v0.3.10\n Checking equivalent v1.0.2\n Checking hashbrown v0.16.1\n Compiling openssl v0.10.75\n Checking foreign-types-shared v0.1.1\n Checking percent-encoding v2.3.2\n Checking form_urlencoded v1.2.2\n Checking foreign-types v0.3.2\n Checking num-integer v0.1.46\n Checking indexmap v2.13.0\n Checking num-complex v0.4.6\n Checking bytemuck v1.24.0\n Compiling zerofrom-derive v0.1.6\n Compiling yoke-derive v0.8.1\n Compiling zerovec-derive v0.11.2\n Compiling displaydoc v0.2.5\n Compiling tokio-macros v2.6.0\n Compiling futures-macro v0.3.31\n Compiling tracing-attributes v0.1.31\n Checking tokio v1.49.0\n Checking zerofrom v0.1.6\n Compiling serde_derive v1.0.228\n Compiling openssl-macros v0.1.1\n Checking yoke v0.8.1\n Checking zerovec v0.11.5", | |
| "is_error": false | |
| } | |
| ] | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "172d23c7-5c62-484f-af89-80e92d0b393e", | |
| "uuid": "188dcf3a-ed2c-4ab3-a1f5-097fd77865d5", | |
| "tool_use_result": { | |
| "stdout": " Updating crates.io index\n Locking 255 packages to latest compatible versions\n Adding generic-array v0.14.7 (available: v0.14.9)\n Adding hashbrown v0.14.5 (available: v0.16.1)\n Adding ndarray v0.15.6 (available: v0.17.2)\n Adding ndarray-rand v0.14.0 (available: v0.16.0)\n Adding ordered-float v4.6.0 (available: v5.1.0)\n Adding rand v0.8.5 (available: v0.9.2)\n Adding rand_distr v0.4.3 (available: v0.5.1)\n Adding reqwest v0.11.27 (available: v0.13.1)\n Adding statrs v0.16.1 (available: v0.18.0)\n Adding thiserror v1.0.69 (available: v2.0.18)\n Adding tokio-tungstenite v0.21.0 (available: v0.28.0)\n Compiling proc-macro2 v1.0.105\n Compiling unicode-ident v1.0.22\n Compiling quote v1.0.43\n Compiling libc v0.2.180\n Checking cfg-if v1.0.4\n Compiling shlex v1.3.0\n Compiling find-msvc-tools v0.1.8\n Checking smallvec v1.15.1\n Compiling cc v1.2.53\n Compiling autocfg v1.5.0\n Checking pin-project-lite v0.2.16\n Checking stable_deref_trait v1.2.1\n Compiling libm v0.2.15\n Checking bytes v1.11.0\n Checking once_cell v1.21.3\n Compiling num-traits v0.2.19\n Compiling parking_lot_core v0.9.12\n Compiling syn v2.0.114\n Compiling zerocopy v0.8.33\n Compiling version_check v0.9.5\n Checking getrandom v0.2.17\n Checking log v0.4.29\n Checking scopeguard v1.2.0\n Checking errno v0.3.14\n Checking lock_api v0.4.14\n Checking itoa v1.0.17\n Checking untrusted v0.9.0\n Checking signal-hook-registry v1.4.8\n Compiling ring v0.17.14\n Checking parking_lot v0.12.5\n Checking socket2 v0.6.1\n Checking mio v1.1.1\n Checking futures-sink v0.3.31\n Compiling typenum v1.19.0\n Checking futures-core v0.3.31\n Compiling vcpkg v0.2.15\n Compiling pkg-config v0.3.32\n Checking memchr v2.7.6\n Checking litemap v0.8.1\n Checking writeable v0.6.2\n Compiling openssl-sys v0.9.111\n Checking futures-channel v0.3.31\n Compiling icu_properties_data v2.1.2\n Checking pin-utils v0.1.0\n Checking futures-io v0.3.31\n Compiling icu_normalizer_data v2.1.1\n Compiling serde_core v1.0.228\n Checking slab v0.4.11\n Checking futures-task v0.3.31\n Checking ppv-lite86 v0.2.21\n Compiling generic-array v0.14.7\n Checking rand_core v0.6.4\n Checking tracing-core v0.1.36\n Compiling httparse v1.10.1\n Checking zeroize v1.8.2\n Checking rustls-pki-types v1.14.0\n Checking rand_chacha v0.3.1\n Compiling serde v1.0.228\n Checking fnv v1.0.7\n Checking http v0.2.12\n Checking rand v0.8.5\n Compiling synstructure v0.13.2\n Compiling matrixmultiply v0.3.10\n Checking equivalent v1.0.2\n Checking hashbrown v0.16.1\n Compiling openssl v0.10.75\n Checking foreign-types-shared v0.1.1\n Checking percent-encoding v2.3.2\n Checking form_urlencoded v1.2.2\n Checking foreign-types v0.3.2\n Checking num-integer v0.1.46\n Checking indexmap v2.13.0\n Checking num-complex v0.4.6\n Checking bytemuck v1.24.0\n Compiling zerofrom-derive v0.1.6\n Compiling yoke-derive v0.8.1\n Compiling zerovec-derive v0.11.2\n Compiling displaydoc v0.2.5\n Compiling tokio-macros v2.6.0\n Compiling futures-macro v0.3.31\n Compiling tracing-attributes v0.1.31\n Checking tokio v1.49.0\n Checking zerofrom v0.1.6\n Compiling serde_derive v1.0.228\n Compiling openssl-macros v0.1.1\n Checking yoke v0.8.1\n Checking zerovec v0.11.5", | |
| "stderr": "", | |
| "interrupted": false, | |
| "isImage": false | |
| } | |
| } | |
| [2026-01-21T18:09:03.759Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-5-20251101", | |
| "id": "msg_01RuQPzxeZboshwGPKSGzteg", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": "The build is still going. Let me wait for it to complete:" | |
| } | |
| ], | |
| "stop_reason": null, | |
| "stop_sequence": null, | |
| "usage": { | |
| "input_tokens": 1, | |
| "cache_creation_input_tokens": 1597, | |
| "cache_read_input_tokens": 98360, | |
| "cache_creation": { | |
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| "service_tier": "standard" | |
| }, | |
| "context_management": null | |
| }, | |
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| "session_id": "172d23c7-5c62-484f-af89-80e92d0b393e", | |
| "uuid": "2b387519-5afc-492e-adc2-bdbfec99ff09" | |
| } | |
| [2026-01-21T18:09:04.655Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-5-20251101", | |
| "id": "msg_01RuQPzxeZboshwGPKSGzteg", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "tool_use", | |
| "id": "toolu_01DxUyBjtkZMKBMsFTu1qzXc", | |
| "name": "Bash", | |
| "input": { | |
| "command": "cargo check 2>&1 | tail -100", | |
| "description": "Check Rust code compilation - wait for completion", | |
| "timeout": 600000 | |
| } | |
| } | |
| ], | |
| "stop_reason": null, | |
| "stop_sequence": null, | |
| "usage": { | |
| "input_tokens": 1, | |
| "cache_creation_input_tokens": 1597, | |
| "cache_read_input_tokens": 98360, | |
| "cache_creation": { | |
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| }, | |
| "context_management": null | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "172d23c7-5c62-484f-af89-80e92d0b393e", | |
| "uuid": "5a237149-4125-4f56-8452-88f8df72e041" | |
| } | |
| [2026-01-21T18:09:19.522Z] [INFO] { | |
| "type": "user", | |
| "message": { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "tool_use_id": "toolu_01DxUyBjtkZMKBMsFTu1qzXc", | |
| "type": "tool_result", | |
| "content": " Checking tungstenite v0.21.0\n Checking serde_urlencoded v0.7.1\n Compiling nalgebra-macros v0.1.0\n Checking rustls-pemfile v1.0.4\n Checking matchers v0.2.0\n Checking webpki-roots v0.26.11\n Checking hyper-tls v0.5.0\n Checking hyper-rustls v0.24.2\n Checking tokio-rustls v0.25.0\n Checking sharded-slab v0.1.7\n Checking futures-executor v0.3.31\n Checking ndarray v0.15.6\n Checking tracing-log v0.2.0\n Checking thread_local v1.1.9\n Checking encoding_rs v0.8.35\n Checking webpki-roots v0.25.4\n Checking allocator-api2 v0.2.21\n Checking iana-time-zone v0.1.64\n Checking mime v0.3.17\n Checking sync_wrapper v0.1.2\n Checking nu-ansi-term v0.50.3\n Checking ipnet v2.11.0\n Checking hashbrown v0.14.5\n Checking tracing-subscriber v0.3.22\n Checking chrono v0.4.43\n Checking futures v0.3.31\n Checking reqwest v0.11.27\n Checking nalgebra v0.29.0\n Checking tokio-tungstenite v0.21.0\n Checking ordered-float v4.6.0\n Compiling async-trait v0.1.89\n Checking ndarray-rand v0.14.0\n Checking statrs v0.16.1\n Checking few_shot_market_prediction v0.1.0 (/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction)\nwarning: unused imports: `FundingRate` and `Trade`\n --> src/data/bybit.rs:3:20\n |\n3 | use super::types::{FundingRate, Kline, OrderBook, OrderBookLevel, Ticker, Trade};\n | ^^^^^^^^^^^ ^^^^^\n |\n = note: `#[warn(unused_imports)]` (part of `#[warn(unused)]`) on by default\n\nwarning: unused import: `DateTime`\n --> src/data/bybit.rs:4:14\n |\n4 | use chrono::{DateTime, TimeZone, Utc};\n | ^^^^^^^^\n\nwarning: unused import: `FewShotLearner`\n --> src/training/trainer.rs:4:37\n |\n4 | use crate::methods::{FewShotConfig, FewShotLearner, FewShotPredictor, PredictionResult};\n | ^^^^^^^^^^^^^^\n\nwarning: function `compute_prototype` is never used\n --> src/network/distance.rs:179:8\n |\n179 | pub fn compute_prototype(embeddings: &[Array1<f64>]) -> Array1<f64> {\n | ^^^^^^^^^^^^^^^^^\n |\n = note: `#[warn(dead_code)]` (part of `#[warn(unused)]`) on by default\n\nwarning: function `compute_class_prototypes` is never used\n --> src/network/distance.rs:195:8\n |\n195 | pub fn compute_class_prototypes(\n | ^^^^^^^^^^^^^^^^^^^^^^^^\n\nwarning: field `min_samples` is never read\n --> src/strategy/regime.rs:135:5\n |\n131 | pub struct RegimeClassifier {\n | ---------------- field in this struct\n...\n135 | min_samples: usize,\n | ^^^^^^^^^^^\n\nwarning: struct `SignalAggregator` is never constructed\n --> src/strategy/signal.rs:356:12\n |\n356 | pub struct SignalAggregator {\n | ^^^^^^^^^^^^^^^^\n\nwarning: associated items `new`, `with_weights`, and `aggregate` are never used\n --> src/strategy/signal.rs:365:12\n |\n363 | impl SignalAggregator {\n | --------------------- associated items in this implementation\n364 | /// Create a new aggregator.\n365 | pub fn new(min_signals: usize) -> Self {\n | ^^^\n...\n373 | pub fn with_weights(mut self, weights: Vec<f64>) -> Self {\n | ^^^^^^^^^^^^\n...\n379 | pub fn aggregate(&self, signals: &[TradingSignal]) -> Option<TradingSignal> {\n | ^^^^^^^^^\n\nwarning: `few_shot_market_prediction` (lib) generated 8 warnings (run `cargo fix --lib -p few_shot_market_prediction` to apply 2 suggestions)\n Finished `dev` profile [optimized + debuginfo] target(s) in 14.08s", | |
| "is_error": false | |
| } | |
| ] | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "172d23c7-5c62-484f-af89-80e92d0b393e", | |
| "uuid": "7d48d419-488d-4c52-bfdc-f96885cb1fb2", | |
| "tool_use_result": { | |
| "stdout": " Checking tungstenite v0.21.0\n Checking serde_urlencoded v0.7.1\n Compiling nalgebra-macros v0.1.0\n Checking rustls-pemfile v1.0.4\n Checking matchers v0.2.0\n Checking webpki-roots v0.26.11\n Checking hyper-tls v0.5.0\n Checking hyper-rustls v0.24.2\n Checking tokio-rustls v0.25.0\n Checking sharded-slab v0.1.7\n Checking futures-executor v0.3.31\n Checking ndarray v0.15.6\n Checking tracing-log v0.2.0\n Checking thread_local v1.1.9\n Checking encoding_rs v0.8.35\n Checking webpki-roots v0.25.4\n Checking allocator-api2 v0.2.21\n Checking iana-time-zone v0.1.64\n Checking mime v0.3.17\n Checking sync_wrapper v0.1.2\n Checking nu-ansi-term v0.50.3\n Checking ipnet v2.11.0\n Checking hashbrown v0.14.5\n Checking tracing-subscriber v0.3.22\n Checking chrono v0.4.43\n Checking futures v0.3.31\n Checking reqwest v0.11.27\n Checking nalgebra v0.29.0\n Checking tokio-tungstenite v0.21.0\n Checking ordered-float v4.6.0\n Compiling async-trait v0.1.89\n Checking ndarray-rand v0.14.0\n Checking statrs v0.16.1\n Checking few_shot_market_prediction v0.1.0 (/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction)\nwarning: unused imports: `FundingRate` and `Trade`\n --> src/data/bybit.rs:3:20\n |\n3 | use super::types::{FundingRate, Kline, OrderBook, OrderBookLevel, Ticker, Trade};\n | ^^^^^^^^^^^ ^^^^^\n |\n = note: `#[warn(unused_imports)]` (part of `#[warn(unused)]`) on by default\n\nwarning: unused import: `DateTime`\n --> src/data/bybit.rs:4:14\n |\n4 | use chrono::{DateTime, TimeZone, Utc};\n | ^^^^^^^^\n\nwarning: unused import: `FewShotLearner`\n --> src/training/trainer.rs:4:37\n |\n4 | use crate::methods::{FewShotConfig, FewShotLearner, FewShotPredictor, PredictionResult};\n | ^^^^^^^^^^^^^^\n\nwarning: function `compute_prototype` is never used\n --> src/network/distance.rs:179:8\n |\n179 | pub fn compute_prototype(embeddings: &[Array1<f64>]) -> Array1<f64> {\n | ^^^^^^^^^^^^^^^^^\n |\n = note: `#[warn(dead_code)]` (part of `#[warn(unused)]`) on by default\n\nwarning: function `compute_class_prototypes` is never used\n --> src/network/distance.rs:195:8\n |\n195 | pub fn compute_class_prototypes(\n | ^^^^^^^^^^^^^^^^^^^^^^^^\n\nwarning: field `min_samples` is never read\n --> src/strategy/regime.rs:135:5\n |\n131 | pub struct RegimeClassifier {\n | ---------------- field in this struct\n...\n135 | min_samples: usize,\n | ^^^^^^^^^^^\n\nwarning: struct `SignalAggregator` is never constructed\n --> src/strategy/signal.rs:356:12\n |\n356 | pub struct SignalAggregator {\n | ^^^^^^^^^^^^^^^^\n\nwarning: associated items `new`, `with_weights`, and `aggregate` are never used\n --> src/strategy/signal.rs:365:12\n |\n363 | impl SignalAggregator {\n | --------------------- associated items in this implementation\n364 | /// Create a new aggregator.\n365 | pub fn new(min_signals: usize) -> Self {\n | ^^^\n...\n373 | pub fn with_weights(mut self, weights: Vec<f64>) -> Self {\n | ^^^^^^^^^^^^\n...\n379 | pub fn aggregate(&self, signals: &[TradingSignal]) -> Option<TradingSignal> {\n | ^^^^^^^^^\n\nwarning: `few_shot_market_prediction` (lib) generated 8 warnings (run `cargo fix --lib -p few_shot_market_prediction` to apply 2 suggestions)\n Finished `dev` profile [optimized + debuginfo] target(s) in 14.08s", | |
| "stderr": "", | |
| "interrupted": false, | |
| "isImage": false | |
| } | |
| } | |
| [2026-01-21T18:09:22.320Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-5-20251101", | |
| "id": "msg_013Zwsyqf4GeNHYfMLVeeUy1", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": "The code compiles successfully with only warnings (no errors). Now let me also run the tests:" | |
| } | |
| ], | |
| "stop_reason": null, | |
| "stop_sequence": null, | |
| "usage": { | |
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| "uuid": "2ded98fa-b117-4c4f-8bb7-10136e0e48fa" | |
| } | |
| [2026-01-21T18:09:23.131Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-5-20251101", | |
| "id": "msg_013Zwsyqf4GeNHYfMLVeeUy1", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "tool_use", | |
| "id": "toolu_01L1LxyaXSLnRNEqewFoWfbT", | |
| "name": "Bash", | |
| "input": { | |
| "command": "cargo test 2>&1 | tail -80", | |
| "description": "Run Rust tests", | |
| "timeout": 300000 | |
| } | |
| } | |
| ], | |
| "stop_reason": null, | |
| "stop_sequence": null, | |
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| "uuid": "002285ac-a037-4da2-9388-63f848fff8c4" | |
| } | |
| [2026-01-21T18:10:23.144Z] [INFO] { | |
| "type": "user", | |
| "message": { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "tool_use_id": "toolu_01L1LxyaXSLnRNEqewFoWfbT", | |
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| "content": " | ^^^^^^^^^^^^^^^^^^^^^^^^\n |\n = note: `#[warn(dead_code)]` (part of `#[warn(unused)]`) on by default\n\nwarning: `few_shot_market_prediction` (lib test) generated 7 warnings (6 duplicates)\n Finished `test` profile [optimized + debuginfo] target(s) in 59.13s\n Running unittests src/lib.rs (target/debug/deps/few_shot_market_prediction-097aef9c4a5f9ca5)\n\nrunning 49 tests\ntest data::bybit::tests::test_config_default ... ok\ntest data::features::tests::test_rsi_calculation ... ok\ntest data::features::tests::test_feature_extraction ... ok\ntest data::bybit::tests::test_config_testnet ... ok\ntest data::types::tests::test_kline_calculations ... ok\ntest methods::config::tests::test_config_builder ... ok\ntest methods::config::tests::test_to_embedding_config ... ok\ntest data::types::tests::test_order_book ... ok\ntest data::features::tests::test_std_dev ... ok\ntest methods::hybrid::tests::test_combine_probs ... ok\ntest methods::metric::tests::test_metric_learner_predict ... ok\ntest methods::metric::tests::test_metric_learner_predict_single ... ok\ntest methods::metric::tests::test_metric_learner_fit ... ok\ntest methods::siamese::tests::test_siamese_learner_fit ... ok\ntest methods::siamese::tests::test_siamese_learner_predict ... ok\ntest methods::tests::test_few_shot_predictor_metric ... FAILED\ntest methods::tests::test_prediction_result ... ok\ntest network::distance::tests::test_compute_prototype ... ok\ntest network::distance::tests::test_cosine_distance ... ok\ntest network::distance::tests::test_distance_matrix ... ok\ntest network::distance::tests::test_distances_to_probs ... ok\ntest network::distance::tests::test_euclidean_distance ... ok\ntest network::embedding::tests::test_batch_forward ... ok\ntest network::embedding::tests::test_forward_pass ... ok\ntest network::embedding::tests::test_forward_with_custom_params ... FAILED\ntest network::embedding::tests::test_network_creation ... ok\ntest network::embedding::tests::test_normalized_output ... ok\ntest strategy::regime::tests::test_classification_with_uncertainty ... ok\ntest strategy::regime::tests::test_classifier ... ok\ntest strategy::regime::tests::test_regime_index_conversion ... ok\ntest strategy::regime::tests::test_regime_properties ... ok\ntest strategy::risk::tests::test_kelly_criterion ... ok\ntest strategy::risk::tests::test_position_pnl ... ok\ntest strategy::risk::tests::test_reject_low_confidence ... ok\ntest strategy::risk::tests::test_risk_check ... ok\ntest strategy::risk::tests::test_risk_config ... ok\ntest strategy::signal::tests::test_hold_on_low_confidence ... ok\ntest strategy::signal::tests::test_signal_creation ... ok\ntest network::distance::tests::test_manhattan_distance ... ok\ntest strategy::signal::tests::test_signal_expected_value ... ok\ntest strategy::signal::tests::test_signal_generator ... ok\ntest tests::test_version ... ok\ntest training::episode::tests::test_episode_generation ... ok\ntest training::episode::tests::test_multiple_episodes ... ok\ntest training::trainer::tests::test_meta_trainer ... ok\ntest methods::maml::tests::test_maml_learner_predict ... ok\ntest methods::hybrid::tests::test_hybrid_learner_predict ... ok\ntest methods::hybrid::tests::test_hybrid_learner_fit ... ok\ntest methods::maml::tests::test_maml_learner_fit ... ok\n\nfailures:\n\n---- methods::tests::test_few_shot_predictor_metric stdout ----\n\nthread 'methods::tests::test_few_shot_predictor_metric' (1059517) panicked at /home/hive/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/ndarray-0.15.6/src/linalg/impl_linalg.rs:299:5:\nndarray: inputs 128 Γ 20 and 10 Γ 1 are not compatible for matrix multiplication\nnote: run with `RUST_BACKTRACE=1` environment variable to display a backtrace\n\n---- network::embedding::tests::test_forward_with_custom_params stdout ----\n\nthread 'network::embedding::tests::test_forward_with_custom_params' (1059527) panicked at src/network/embedding.rs:369:9:\nassertion failed: diff > 0.0\n\n\nfailures:\n methods::tests::test_few_shot_predictor_metric\n network::embedding::tests::test_forward_with_custom_params\n\ntest result: FAILED. 47 passed; 2 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.06s\n\nerror: test failed, to rerun pass `--lib`", | |
| "is_error": false | |
| } | |
| ] | |
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| "parent_tool_use_id": null, | |
| "session_id": "172d23c7-5c62-484f-af89-80e92d0b393e", | |
| "uuid": "6ac2af76-cedd-4aeb-a501-cc99dd484e5e", | |
| "tool_use_result": { | |
| "stdout": " | ^^^^^^^^^^^^^^^^^^^^^^^^\n |\n = note: `#[warn(dead_code)]` (part of `#[warn(unused)]`) on by default\n\nwarning: `few_shot_market_prediction` (lib test) generated 7 warnings (6 duplicates)\n Finished `test` profile [optimized + debuginfo] target(s) in 59.13s\n Running unittests src/lib.rs (target/debug/deps/few_shot_market_prediction-097aef9c4a5f9ca5)\n\nrunning 49 tests\ntest data::bybit::tests::test_config_default ... ok\ntest data::features::tests::test_rsi_calculation ... ok\ntest data::features::tests::test_feature_extraction ... ok\ntest data::bybit::tests::test_config_testnet ... ok\ntest data::types::tests::test_kline_calculations ... ok\ntest methods::config::tests::test_config_builder ... ok\ntest methods::config::tests::test_to_embedding_config ... ok\ntest data::types::tests::test_order_book ... ok\ntest data::features::tests::test_std_dev ... ok\ntest methods::hybrid::tests::test_combine_probs ... ok\ntest methods::metric::tests::test_metric_learner_predict ... ok\ntest methods::metric::tests::test_metric_learner_predict_single ... ok\ntest methods::metric::tests::test_metric_learner_fit ... ok\ntest methods::siamese::tests::test_siamese_learner_fit ... ok\ntest methods::siamese::tests::test_siamese_learner_predict ... ok\ntest methods::tests::test_few_shot_predictor_metric ... FAILED\ntest methods::tests::test_prediction_result ... ok\ntest network::distance::tests::test_compute_prototype ... ok\ntest network::distance::tests::test_cosine_distance ... ok\ntest network::distance::tests::test_distance_matrix ... ok\ntest network::distance::tests::test_distances_to_probs ... ok\ntest network::distance::tests::test_euclidean_distance ... ok\ntest network::embedding::tests::test_batch_forward ... ok\ntest network::embedding::tests::test_forward_pass ... ok\ntest network::embedding::tests::test_forward_with_custom_params ... FAILED\ntest network::embedding::tests::test_network_creation ... ok\ntest network::embedding::tests::test_normalized_output ... ok\ntest strategy::regime::tests::test_classification_with_uncertainty ... ok\ntest strategy::regime::tests::test_classifier ... ok\ntest strategy::regime::tests::test_regime_index_conversion ... ok\ntest strategy::regime::tests::test_regime_properties ... ok\ntest strategy::risk::tests::test_kelly_criterion ... ok\ntest strategy::risk::tests::test_position_pnl ... ok\ntest strategy::risk::tests::test_reject_low_confidence ... ok\ntest strategy::risk::tests::test_risk_check ... ok\ntest strategy::risk::tests::test_risk_config ... ok\ntest strategy::signal::tests::test_hold_on_low_confidence ... ok\ntest strategy::signal::tests::test_signal_creation ... ok\ntest network::distance::tests::test_manhattan_distance ... ok\ntest strategy::signal::tests::test_signal_expected_value ... ok\ntest strategy::signal::tests::test_signal_generator ... ok\ntest tests::test_version ... ok\ntest training::episode::tests::test_episode_generation ... ok\ntest training::episode::tests::test_multiple_episodes ... ok\ntest training::trainer::tests::test_meta_trainer ... ok\ntest methods::maml::tests::test_maml_learner_predict ... ok\ntest methods::hybrid::tests::test_hybrid_learner_predict ... ok\ntest methods::hybrid::tests::test_hybrid_learner_fit ... ok\ntest methods::maml::tests::test_maml_learner_fit ... ok\n\nfailures:\n\n---- methods::tests::test_few_shot_predictor_metric stdout ----\n\nthread 'methods::tests::test_few_shot_predictor_metric' (1059517) panicked at /home/hive/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/ndarray-0.15.6/src/linalg/impl_linalg.rs:299:5:\nndarray: inputs 128 Γ 20 and 10 Γ 1 are not compatible for matrix multiplication\nnote: run with `RUST_BACKTRACE=1` environment variable to display a backtrace\n\n---- network::embedding::tests::test_forward_with_custom_params stdout ----\n\nthread 'network::embedding::tests::test_forward_with_custom_params' (1059527) panicked at src/network/embedding.rs:369:9:\nassertion failed: diff > 0.0\n\n\nfailures:\n methods::tests::test_few_shot_predictor_metric\n network::embedding::tests::test_forward_with_custom_params\n\ntest result: FAILED. 47 passed; 2 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.06s\n\nerror: test failed, to rerun pass `--lib`", | |
| "stderr": "", | |
| "interrupted": false, | |
| "isImage": false | |
| } | |
| } | |
| [2026-01-21T18:10:27.617Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-5-20251101", | |
| "id": "msg_01CP4kRSAEBv8uKtLyZcwG5q", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": "There are 2 test failures. Let me examine and fix them:\n\n1. `test_few_shot_predictor_metric` - dimension mismatch (128 Γ 20 and 10 Γ 1)\n2. `test_forward_with_custom_params` - the diff assertion failed\n\nLet me look at these tests:" | |
| } | |
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| } | |
| [2026-01-21T18:10:28.483Z] [INFO] { | |
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| [2026-01-21T18:10:28.516Z] [INFO] { | |
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| "content": " 175β use super::*;\n 176β use ndarray::Array2;\n 177β\n 178β fn create_synthetic_data() -> (Array2<f64>, Vec<usize>, Array2<f64>, Vec<usize>) {\n 179β // Create simple synthetic data for testing\n 180β let n_classes = 3;\n 181β let n_support = 5;\n 182β let n_query = 3;\n 183β let dim = 10;\n 184β\n 185β let mut support_data = Vec::new();\n 186β let mut support_labels = Vec::new();\n 187β let mut query_data = Vec::new();\n 188β let mut query_labels = Vec::new();\n 189β\n 190β for class in 0..n_classes {\n 191β let base = class as f64 * 0.5;\n 192β for _ in 0..n_support {\n 193β let sample: Vec<f64> = (0..dim).map(|_| base + rand::random::<f64>() * 0.1).collect();\n 194β support_data.extend(sample);\n 195β support_labels.push(class);\n 196β }\n 197β for _ in 0..n_query {\n 198β let sample: Vec<f64> = (0..dim).map(|_| base + rand::random::<f64>() * 0.1).collect();\n 199β query_data.extend(sample);\n 200β query_labels.push(class);\n 201β }\n 202β }\n 203β\n 204β let support = Array2::from_shape_vec((n_classes * n_support, dim), support_data).unwrap();\n 205β let query = Array2::from_shape_vec((n_classes * n_query, dim), query_data).unwrap();\n 206β\n 207β (support, support_labels, query, query_labels)\n 208β }\n 209β\n 210β #[test]\n 211β fn test_prediction_result() {\n 212β let result = PredictionResult::new(1, 0.8, vec![0.1, 0.8, 0.1], 0.5);\n 213β assert_eq!(result.predicted_class, 1);\n 214β assert!(result.is_reliable);\n 215β assert!(result.entropy() > 0.0);\n 216β }\n 217β\n 218β #[test]\n 219β fn test_few_shot_predictor_metric() {\n 220β let config = FewShotConfig::default().with_method(FewShotMethod::Metric);\n 221β let mut predictor = FewShotPredictor::new(config);\n 222β\n 223β let (support, support_labels, query, _) = create_synthetic_data();\n 224β predictor.fit(&support, &support_labels);\n 225β\n 226β let predictions = predictor.predict(&query);\n 227β assert_eq!(predictions.len(), query.nrows());\n 228β\n 229β for pred in predictions {\n 230β assert!(pred.confidence >= 0.0 && pred.confidence <= 1.0);\n 231β assert!((pred.class_probabilities.iter().sum::<f64>() - 1.0).abs() < 1e-6);\n 232β }\n 233β }\n 234β}\n 235β\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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| "file": { | |
| "filePath": "/tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/src/methods/mod.rs", | |
| "content": " use super::*;\n use ndarray::Array2;\n\n fn create_synthetic_data() -> (Array2<f64>, Vec<usize>, Array2<f64>, Vec<usize>) {\n // Create simple synthetic data for testing\n let n_classes = 3;\n let n_support = 5;\n let n_query = 3;\n let dim = 10;\n\n let mut support_data = Vec::new();\n let mut support_labels = Vec::new();\n let mut query_data = Vec::new();\n let mut query_labels = Vec::new();\n\n for class in 0..n_classes {\n let base = class as f64 * 0.5;\n for _ in 0..n_support {\n let sample: Vec<f64> = (0..dim).map(|_| base + rand::random::<f64>() * 0.1).collect();\n support_data.extend(sample);\n support_labels.push(class);\n }\n for _ in 0..n_query {\n let sample: Vec<f64> = (0..dim).map(|_| base + rand::random::<f64>() * 0.1).collect();\n query_data.extend(sample);\n query_labels.push(class);\n }\n }\n\n let support = Array2::from_shape_vec((n_classes * n_support, dim), support_data).unwrap();\n let query = Array2::from_shape_vec((n_classes * n_query, dim), query_data).unwrap();\n\n (support, support_labels, query, query_labels)\n }\n\n #[test]\n fn test_prediction_result() {\n let result = PredictionResult::new(1, 0.8, vec![0.1, 0.8, 0.1], 0.5);\n assert_eq!(result.predicted_class, 1);\n assert!(result.is_reliable);\n assert!(result.entropy() > 0.0);\n }\n\n #[test]\n fn test_few_shot_predictor_metric() {\n let config = FewShotConfig::default().with_method(FewShotMethod::Metric);\n let mut predictor = FewShotPredictor::new(config);\n\n let (support, support_labels, query, _) = create_synthetic_data();\n predictor.fit(&support, &support_labels);\n\n let predictions = predictor.predict(&query);\n assert_eq!(predictions.len(), query.nrows());\n\n for pred in predictions {\n assert!(pred.confidence >= 0.0 && pred.confidence <= 1.0);\n assert!((pred.class_probabilities.iter().sum::<f64>() - 1.0).abs() < 1e-6);\n }\n }\n}\n", | |
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| [2026-01-21T18:10:31.437Z] [INFO] { | |
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| "content": [ | |
| { | |
| "type": "text", | |
| "text": "The issue is that the test data has `dim=10` but the default FewShotConfig has `input_dim=20`. Let me fix the test:" | |
| } | |
| ], | |
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| [2026-01-21T18:10:33.754Z] [INFO] { | |
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| "old_string": " #[test]\n fn test_few_shot_predictor_metric() {\n let config = FewShotConfig::default().with_method(FewShotMethod::Metric);\n let mut predictor = FewShotPredictor::new(config);", | |
| "new_string": " #[test]\n fn test_few_shot_predictor_metric() {\n let config = FewShotConfig::default()\n .with_method(FewShotMethod::Metric)\n .with_input_dim(10); // Match test data dimension\n let mut predictor = FewShotPredictor::new(config);" | |
| } | |
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| [2026-01-21T18:10:33.788Z] [INFO] { | |
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| "tool_use_id": "toolu_011CzXx6NoBuWzCwVDVjf7yE", | |
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| "content": "The file /tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/src/methods/mod.rs has been updated successfully." | |
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| "oldString": " #[test]\n fn test_few_shot_predictor_metric() {\n let config = FewShotConfig::default().with_method(FewShotMethod::Metric);\n let mut predictor = FewShotPredictor::new(config);", | |
| "newString": " #[test]\n fn test_few_shot_predictor_metric() {\n let config = FewShotConfig::default()\n .with_method(FewShotMethod::Metric)\n .with_input_dim(10); // Match test data dimension\n let mut predictor = FewShotPredictor::new(config);", | |
| "originalFile": "//! Few-shot learning methods\n//!\n//! This module provides multiple few-shot learning approaches:\n//! - Metric-based (Prototypical Networks style)\n//! - MAML-inspired optimization\n//! - Siamese networks for pairwise similarity\n//! - Hybrid approaches combining multiple methods\n\nmod metric;\nmod maml;\nmod siamese;\nmod hybrid;\nmod config;\n\npub use metric::MetricBasedLearner;\npub use maml::MAMLLearner;\npub use siamese::SiameseLearner;\npub use hybrid::HybridLearner;\npub use config::{FewShotConfig, FewShotMethod};\n\nuse ndarray::Array1;\nuse serde::{Deserialize, Serialize};\n\n/// Result of a few-shot prediction\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct PredictionResult {\n /// Predicted class index\n pub predicted_class: usize,\n /// Confidence score (0-1)\n pub confidence: f64,\n /// Probabilities for each class\n pub class_probabilities: Vec<f64>,\n /// Whether the prediction is considered reliable\n pub is_reliable: bool,\n}\n\nimpl PredictionResult {\n /// Create a new prediction result\n pub fn new(\n predicted_class: usize,\n confidence: f64,\n class_probabilities: Vec<f64>,\n confidence_threshold: f64,\n ) -> Self {\n Self {\n predicted_class,\n confidence,\n class_probabilities,\n is_reliable: confidence >= confidence_threshold,\n }\n }\n\n /// Get entropy of the prediction distribution\n pub fn entropy(&self) -> f64 {\n -self.class_probabilities\n .iter()\n .filter(|&&p| p > 1e-10)\n .map(|&p| p * p.ln())\n .sum::<f64>()\n }\n}\n\n/// Result of MAML adaptation\n#[derive(Debug, Clone)]\npub struct AdaptationResult {\n /// Number of adaptation steps performed\n pub steps: usize,\n /// Loss before adaptation\n pub initial_loss: f64,\n /// Loss after adaptation\n pub final_loss: f64,\n /// Improvement ratio\n pub improvement: f64,\n}\n\nimpl AdaptationResult {\n /// Create a new adaptation result\n pub fn new(steps: usize, initial_loss: f64, final_loss: f64) -> Self {\n let improvement = if initial_loss > 1e-10 {\n (initial_loss - final_loss) / initial_loss\n } else {\n 0.0\n };\n Self {\n steps,\n initial_loss,\n final_loss,\n improvement,\n }\n }\n}\n\n/// Trait for few-shot learners\npub trait FewShotLearner {\n /// Fit the learner on a support set\n fn fit(&mut self, support_features: &ndarray::Array2<f64>, support_labels: &[usize]);\n\n /// Predict on query features\n fn predict(&self, query_features: &ndarray::Array2<f64>) -> Vec<PredictionResult>;\n\n /// Predict single sample\n fn predict_single(&self, query: &Array1<f64>) -> PredictionResult;\n}\n\n/// High-level few-shot predictor that can use different methods\npub struct FewShotPredictor {\n config: FewShotConfig,\n learner: Box<dyn FewShotLearner + Send + Sync>,\n class_names: std::collections::HashMap<usize, String>,\n}\n\nimpl FewShotPredictor {\n /// Create a new few-shot predictor\n pub fn new(config: FewShotConfig) -> Self {\n let learner: Box<dyn FewShotLearner + Send + Sync> = match config.method {\n FewShotMethod::Metric => Box::new(MetricBasedLearner::new(config.clone())),\n FewShotMethod::MAML => Box::new(MAMLLearner::new(config.clone())),\n FewShotMethod::Siamese => Box::new(SiameseLearner::new(config.clone())),\n FewShotMethod::Hybrid => Box::new(HybridLearner::new(config.clone())),\n };\n\n Self {\n config,\n learner,\n class_names: std::collections::HashMap::new(),\n }\n }\n\n /// Set class names for interpretable predictions\n pub fn with_class_names(mut self, names: std::collections::HashMap<usize, String>) -> Self {\n self.class_names = names;\n self\n }\n\n /// Fit on support set\n pub fn fit(&mut self, support_features: &ndarray::Array2<f64>, support_labels: &[usize]) {\n self.learner.fit(support_features, support_labels);\n\n // Auto-populate class names if not set\n if self.class_names.is_empty() {\n for &label in support_labels {\n self.class_names\n .entry(label)\n .or_insert_with(|| format!(\"Class_{}\", label));\n }\n }\n }\n\n /// Predict on query features\n pub fn predict(&self, query_features: &ndarray::Array2<f64>) -> Vec<PredictionResult> {\n self.learner.predict(query_features)\n }\n\n /// Predict single sample\n pub fn predict_single(&self, query: &Array1<f64>) -> PredictionResult {\n self.learner.predict_single(query)\n }\n\n /// Get class name for a label\n pub fn class_name(&self, label: usize) -> &str {\n self.class_names\n .get(&label)\n .map(|s| s.as_str())\n .unwrap_or(\"Unknown\")\n }\n\n /// Get configuration\n pub fn config(&self) -> &FewShotConfig {\n &self.config\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n use ndarray::Array2;\n\n fn create_synthetic_data() -> (Array2<f64>, Vec<usize>, Array2<f64>, Vec<usize>) {\n // Create simple synthetic data for testing\n let n_classes = 3;\n let n_support = 5;\n let n_query = 3;\n let dim = 10;\n\n let mut support_data = Vec::new();\n let mut support_labels = Vec::new();\n let mut query_data = Vec::new();\n let mut query_labels = Vec::new();\n\n for class in 0..n_classes {\n let base = class as f64 * 0.5;\n for _ in 0..n_support {\n let sample: Vec<f64> = (0..dim).map(|_| base + rand::random::<f64>() * 0.1).collect();\n support_data.extend(sample);\n support_labels.push(class);\n }\n for _ in 0..n_query {\n let sample: Vec<f64> = (0..dim).map(|_| base + rand::random::<f64>() * 0.1).collect();\n query_data.extend(sample);\n query_labels.push(class);\n }\n }\n\n let support = Array2::from_shape_vec((n_classes * n_support, dim), support_data).unwrap();\n let query = Array2::from_shape_vec((n_classes * n_query, dim), query_data).unwrap();\n\n (support, support_labels, query, query_labels)\n }\n\n #[test]\n fn test_prediction_result() {\n let result = PredictionResult::new(1, 0.8, vec![0.1, 0.8, 0.1], 0.5);\n assert_eq!(result.predicted_class, 1);\n assert!(result.is_reliable);\n assert!(result.entropy() > 0.0);\n }\n\n #[test]\n fn test_few_shot_predictor_metric() {\n let config = FewShotConfig::default().with_method(FewShotMethod::Metric);\n let mut predictor = FewShotPredictor::new(config);\n\n let (support, support_labels, query, _) = create_synthetic_data();\n predictor.fit(&support, &support_labels);\n\n let predictions = predictor.predict(&query);\n assert_eq!(predictions.len(), query.nrows());\n\n for pred in predictions {\n assert!(pred.confidence >= 0.0 && pred.confidence <= 1.0);\n assert!((pred.class_probabilities.iter().sum::<f64>() - 1.0).abs() < 1e-6);\n }\n }\n}\n", | |
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| " ", | |
| " #[test]", | |
| " fn test_few_shot_predictor_metric() {", | |
| "- let config = FewShotConfig::default().with_method(FewShotMethod::Metric);", | |
| "+ let config = FewShotConfig::default()", | |
| "+ .with_method(FewShotMethod::Metric)", | |
| "+ .with_input_dim(10); // Match test data dimension", | |
| " let mut predictor = FewShotPredictor::new(config);", | |
| " ", | |
| " let (support, support_labels, query, _) = create_synthetic_data();" | |
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| "text": "Now let me check the second failing test:" | |
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| [2026-01-21T18:10:37.602Z] [INFO] { | |
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| "content": " 353β #[test]\n 354β fn test_forward_with_custom_params() {\n 355β let config = EmbeddingConfig::new(4, vec![8], 4);\n 356β let network = EmbeddingNetwork::new(config);\n 357β\n 358β // Clone and modify parameters\n 359β let mut custom_params = network.clone_params();\n 360β custom_params.scale_weights(2.0);\n 361β\n 362β let input = Array1::from_vec(vec![0.5; 4]);\n 363β\n 364β let output_default = network.forward(&input, None);\n 365β let output_custom = network.forward(&input, Some(&custom_params));\n 366β\n 367β // Outputs should be different\n 368β let diff: f64 = (&output_default - &output_custom).mapv(|x| x.abs()).sum();\n 369β assert!(diff > 0.0);\n 370β }\n 371β\n 372β #[test]\n 373β fn test_batch_forward() {\n 374β let config = EmbeddingConfig::default();\n 375β let network = EmbeddingNetwork::new(config.clone());\n 376β\n 377β let batch_size = 5;\n 378β let inputs = Array2::from_shape_fn((batch_size, config.input_dim), |_| 0.5);\n 379β let outputs = network.forward_batch(&inputs, None);\n 380β\n 381β assert_eq!(outputs.nrows(), batch_size);\n 382β assert_eq!(outputs.ncols(), config.output_dim);\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": " #[test]\n fn test_forward_with_custom_params() {\n let config = EmbeddingConfig::new(4, vec![8], 4);\n let network = EmbeddingNetwork::new(config);\n\n // Clone and modify parameters\n let mut custom_params = network.clone_params();\n custom_params.scale_weights(2.0);\n\n let input = Array1::from_vec(vec![0.5; 4]);\n\n let output_default = network.forward(&input, None);\n let output_custom = network.forward(&input, Some(&custom_params));\n\n // Outputs should be different\n let diff: f64 = (&output_default - &output_custom).mapv(|x| x.abs()).sum();\n assert!(diff > 0.0);\n }\n\n #[test]\n fn test_batch_forward() {\n let config = EmbeddingConfig::default();\n let network = EmbeddingNetwork::new(config.clone());\n\n let batch_size = 5;\n let inputs = Array2::from_shape_fn((batch_size, config.input_dim), |_| 0.5);\n let outputs = network.forward_batch(&inputs, None);\n\n assert_eq!(outputs.nrows(), batch_size);\n assert_eq!(outputs.ncols(), config.output_dim);", | |
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| "text": "The test is failing because the outputs are normalized (L2 normalization is enabled by default), and when scaled weights are used, they might still produce the same normalized output. Let me fix this test by checking that outputs differ in a way that accounts for normalization, or by disabling normalization:" | |
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| "old_string": " #[test]\n fn test_forward_with_custom_params() {\n let config = EmbeddingConfig::new(4, vec![8], 4);\n let network = EmbeddingNetwork::new(config);\n\n // Clone and modify parameters\n let mut custom_params = network.clone_params();\n custom_params.scale_weights(2.0);\n\n let input = Array1::from_vec(vec![0.5; 4]);\n\n let output_default = network.forward(&input, None);\n let output_custom = network.forward(&input, Some(&custom_params));\n\n // Outputs should be different\n let diff: f64 = (&output_default - &output_custom).mapv(|x| x.abs()).sum();\n assert!(diff > 0.0);\n }", | |
| "new_string": " #[test]\n fn test_forward_with_custom_params() {\n // Disable normalization so scaling weights produces different outputs\n let config = EmbeddingConfig {\n input_dim: 4,\n hidden_dims: vec![8],\n output_dim: 4,\n normalize_embeddings: false, // Disable normalization for this test\n dropout_rate: 0.0,\n activation: ActivationType::ReLU,\n use_batch_norm: false,\n };\n let network = EmbeddingNetwork::new(config);\n\n // Clone and modify parameters by adding a bias offset (not just scaling)\n let mut custom_params = network.clone_params();\n for bias in &mut custom_params.biases {\n bias.mapv_inplace(|b| b + 0.5);\n }\n\n let input = Array1::from_vec(vec![0.5; 4]);\n\n let output_default = network.forward(&input, None);\n let output_custom = network.forward(&input, Some(&custom_params));\n\n // Outputs should be different due to bias change\n let diff: f64 = (&output_default - &output_custom).mapv(|x| x.abs()).sum();\n assert!(diff > 0.0, \"Expected diff > 0.0, got {}\", diff);\n }" | |
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| "content": "The file /tmp/gh-issue-solver-1769015705800/86_few_shot_market_prediction/src/network/embedding.rs has been updated successfully." | |
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| "oldString": " #[test]\n fn test_forward_with_custom_params() {\n let config = EmbeddingConfig::new(4, vec![8], 4);\n let network = EmbeddingNetwork::new(config);\n\n // Clone and modify parameters\n let mut custom_params = network.clone_params();\n custom_params.scale_weights(2.0);\n\n let input = Array1::from_vec(vec![0.5; 4]);\n\n let output_default = network.forward(&input, None);\n let output_custom = network.forward(&input, Some(&custom_params));\n\n // Outputs should be different\n let diff: f64 = (&output_default - &output_custom).mapv(|x| x.abs()).sum();\n assert!(diff > 0.0);\n }", | |
| "newString": " #[test]\n fn test_forward_with_custom_params() {\n // Disable normalization so scaling weights produces different outputs\n let config = EmbeddingConfig {\n input_dim: 4,\n hidden_dims: vec![8],\n output_dim: 4,\n normalize_embeddings: false, // Disable normalization for this test\n dropout_rate: 0.0,\n activation: ActivationType::ReLU,\n use_batch_norm: false,\n };\n let network = EmbeddingNetwork::new(config);\n\n // Clone and modify parameters by adding a bias offset (not just scaling)\n let mut custom_params = network.clone_params();\n for bias in &mut custom_params.biases {\n bias.mapv_inplace(|b| b + 0.5);\n }\n\n let input = Array1::from_vec(vec![0.5; 4]);\n\n let output_default = network.forward(&input, None);\n let output_custom = network.forward(&input, Some(&custom_params));\n\n // Outputs should be different due to bias change\n let diff: f64 = (&output_default - &output_custom).mapv(|x| x.abs()).sum();\n assert!(diff > 0.0, \"Expected diff > 0.0, got {}\", diff);\n }", | |
| "originalFile": "//! Embedding network for few-shot learning\n//!\n//! Supports both forward pass and parameter adaptation for MAML-style training.\n\nuse ndarray::{Array1, Array2};\nuse rand::Rng;\nuse rand_distr::Normal;\nuse serde::{Deserialize, Serialize};\n\n/// Activation function types\n#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]\npub enum ActivationType {\n /// ReLU activation: max(0, x)\n ReLU,\n /// Leaky ReLU: max(0.01*x, x)\n LeakyReLU,\n /// Tanh activation\n Tanh,\n /// Sigmoid activation\n Sigmoid,\n /// GELU (Gaussian Error Linear Unit)\n GELU,\n /// No activation (linear)\n Linear,\n}\n\nimpl Default for ActivationType {\n fn default() -> Self {\n Self::ReLU\n }\n}\n\n/// Configuration for embedding network\n#[derive(Debug, Clone, Serialize, Deserialize)]\npub struct EmbeddingConfig {\n /// Input feature dimension\n pub input_dim: usize,\n /// Hidden layer dimensions\n pub hidden_dims: Vec<usize>,\n /// Output embedding dimension\n pub output_dim: usize,\n /// Whether to L2 normalize output embeddings\n pub normalize_embeddings: bool,\n /// Dropout rate (0.0 = no dropout)\n pub dropout_rate: f64,\n /// Activation function type\n pub activation: ActivationType,\n /// Whether to use batch normalization\n pub use_batch_norm: bool,\n}\n\nimpl Default for EmbeddingConfig {\n fn default() -> Self {\n Self {\n input_dim: 20,\n hidden_dims: vec![128, 64],\n output_dim: 32,\n normalize_embeddings: true,\n dropout_rate: 0.1,\n activation: ActivationType::ReLU,\n use_batch_norm: true,\n }\n }\n}\n\nimpl EmbeddingConfig {\n /// Create a new config with specified dimensions\n pub fn new(input_dim: usize, hidden_dims: Vec<usize>, output_dim: usize) -> Self {\n Self {\n input_dim,\n hidden_dims,\n output_dim,\n ..Default::default()\n }\n }\n\n /// Builder pattern: set input dimension\n pub fn with_input_dim(mut self, dim: usize) -> Self {\n self.input_dim = dim;\n self\n }\n\n /// Builder pattern: set output dimension\n pub fn with_output_dim(mut self, dim: usize) -> Self {\n self.output_dim = dim;\n self\n }\n\n /// Builder pattern: set activation\n pub fn with_activation(mut self, activation: ActivationType) -> Self {\n self.activation = activation;\n self\n }\n}\n\n/// Network parameters that can be adapted during MAML training\n#[derive(Debug, Clone)]\npub struct NetworkParams {\n /// Weight matrices for each layer\n pub weights: Vec<Array2<f64>>,\n /// Bias vectors for each layer\n pub biases: Vec<Array1<f64>>,\n}\n\nimpl NetworkParams {\n /// Create new random parameters\n pub fn random(layer_dims: &[usize]) -> Self {\n let mut weights = Vec::new();\n let mut biases = Vec::new();\n let mut rng = rand::thread_rng();\n\n for i in 0..layer_dims.len() - 1 {\n let (in_dim, out_dim) = (layer_dims[i], layer_dims[i + 1]);\n let std = (2.0 / (in_dim + out_dim) as f64).sqrt();\n let normal = Normal::new(0.0, std).unwrap();\n\n let weight = Array2::from_shape_fn((in_dim, out_dim), |_| rng.sample(normal));\n let bias = Array1::zeros(out_dim);\n\n weights.push(weight);\n biases.push(bias);\n }\n\n Self { weights, biases }\n }\n\n /// Clone parameters\n pub fn clone_params(&self) -> Self {\n Self {\n weights: self.weights.clone(),\n biases: self.biases.clone(),\n }\n }\n\n /// Apply gradient update with given learning rate\n pub fn apply_gradients(&mut self, weight_grads: &[Array2<f64>], bias_grads: &[Array1<f64>], lr: f64) {\n for (i, (wg, bg)) in weight_grads.iter().zip(bias_grads.iter()).enumerate() {\n self.weights[i] = &self.weights[i] - &(wg * lr);\n self.biases[i] = &self.biases[i] - &(bg * lr);\n }\n }\n\n /// Scale all weights by a factor (for weight decay)\n pub fn scale_weights(&mut self, factor: f64) {\n for weight in &mut self.weights {\n weight.mapv_inplace(|w| w * factor);\n }\n }\n}\n\n/// Embedding network for converting market features to embedding vectors\n///\n/// Supports MAML-style training by allowing parameter injection during forward pass.\n#[derive(Debug, Clone)]\npub struct EmbeddingNetwork {\n config: EmbeddingConfig,\n /// Default parameters (can be overridden during forward pass)\n params: NetworkParams,\n /// Layer dimensions for easy access\n layer_dims: Vec<usize>,\n}\n\nimpl EmbeddingNetwork {\n /// Create a new embedding network with random initialization\n pub fn new(config: EmbeddingConfig) -> Self {\n // Build layer dimensions\n let mut layer_dims = vec![config.input_dim];\n layer_dims.extend(&config.hidden_dims);\n layer_dims.push(config.output_dim);\n\n let params = NetworkParams::random(&layer_dims);\n\n Self {\n config,\n params,\n layer_dims,\n }\n }\n\n /// Apply activation function\n fn apply_activation(&self, x: &mut Array1<f64>) {\n match self.config.activation {\n ActivationType::ReLU => {\n x.mapv_inplace(|v| v.max(0.0));\n }\n ActivationType::LeakyReLU => {\n x.mapv_inplace(|v| if v > 0.0 { v } else { 0.01 * v });\n }\n ActivationType::Tanh => {\n x.mapv_inplace(|v| v.tanh());\n }\n ActivationType::Sigmoid => {\n x.mapv_inplace(|v| 1.0 / (1.0 + (-v).exp()));\n }\n ActivationType::GELU => {\n // Approximation: 0.5 * x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))\n x.mapv_inplace(|v| {\n let inner = 0.7978845608 * (v + 0.044715 * v.powi(3));\n 0.5 * v * (1.0 + inner.tanh())\n });\n }\n ActivationType::Linear => {\n // No-op\n }\n }\n }\n\n /// Forward pass through the network with optional custom parameters\n ///\n /// # Arguments\n /// * `input` - Input features\n /// * `params` - Optional custom parameters (for MAML adaptation)\n pub fn forward(&self, input: &Array1<f64>, params: Option<&NetworkParams>) -> Array1<f64> {\n let params = params.unwrap_or(&self.params);\n let mut x = input.clone();\n\n // Pass through all layers except the last\n for i in 0..params.weights.len() - 1 {\n // Linear transformation\n x = params.weights[i].t().dot(&x) + ¶ms.biases[i];\n // Apply activation\n self.apply_activation(&mut x);\n }\n\n // Last layer (no activation for embedding)\n let last_idx = params.weights.len() - 1;\n x = params.weights[last_idx].t().dot(&x) + ¶ms.biases[last_idx];\n\n // Optional L2 normalization\n if self.config.normalize_embeddings {\n let norm = x.dot(&x).sqrt();\n if norm > 1e-8 {\n x /= norm;\n }\n }\n\n x\n }\n\n /// Forward pass for batch of inputs\n pub fn forward_batch(&self, inputs: &Array2<f64>, params: Option<&NetworkParams>) -> Array2<f64> {\n let n_samples = inputs.nrows();\n let mut outputs = Array2::zeros((n_samples, self.config.output_dim));\n\n for i in 0..n_samples {\n let input = inputs.row(i).to_owned();\n let output = self.forward(&input, params);\n outputs.row_mut(i).assign(&output);\n }\n\n outputs\n }\n\n /// Get current parameters\n pub fn get_params(&self) -> &NetworkParams {\n &self.params\n }\n\n /// Get mutable reference to parameters\n pub fn get_params_mut(&mut self) -> &mut NetworkParams {\n &mut self.params\n }\n\n /// Set parameters\n pub fn set_params(&mut self, params: NetworkParams) {\n self.params = params;\n }\n\n /// Clone current parameters\n pub fn clone_params(&self) -> NetworkParams {\n self.params.clone_params()\n }\n\n /// Get the output embedding dimension\n pub fn output_dim(&self) -> usize {\n self.config.output_dim\n }\n\n /// Get the embedding dimension (alias for output_dim)\n pub fn embedding_dim(&self) -> usize {\n self.config.output_dim\n }\n\n /// Get the input dimension\n pub fn input_dim(&self) -> usize {\n self.config.input_dim\n }\n\n /// Get layer dimensions\n pub fn layer_dims(&self) -> &[usize] {\n &self.layer_dims\n }\n\n /// Get reference to config\n pub fn config(&self) -> &EmbeddingConfig {\n &self.config\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n\n #[test]\n fn test_network_creation() {\n let config = EmbeddingConfig {\n input_dim: 16,\n hidden_dims: vec![32, 64],\n output_dim: 32,\n normalize_embeddings: true,\n dropout_rate: 0.0,\n activation: ActivationType::ReLU,\n use_batch_norm: false,\n };\n let network = EmbeddingNetwork::new(config);\n\n assert_eq!(network.params.weights.len(), 3);\n assert_eq!(network.input_dim(), 16);\n assert_eq!(network.output_dim(), 32);\n }\n\n #[test]\n fn test_forward_pass() {\n let config = EmbeddingConfig::new(8, vec![16], 4);\n let network = EmbeddingNetwork::new(config);\n\n let input = Array1::from_vec(vec![1.0; 8]);\n let output = network.forward(&input, None);\n\n assert_eq!(output.len(), 4);\n }\n\n #[test]\n fn test_normalized_output() {\n let config = EmbeddingConfig {\n input_dim: 8,\n hidden_dims: vec![16],\n output_dim: 4,\n normalize_embeddings: true,\n dropout_rate: 0.0,\n activation: ActivationType::ReLU,\n use_batch_norm: false,\n };\n let network = EmbeddingNetwork::new(config);\n\n let input = Array1::from_vec(vec![1.0; 8]);\n let output = network.forward(&input, None);\n\n let norm = output.dot(&output).sqrt();\n assert!((norm - 1.0).abs() < 0.01 || norm < 1e-6);\n }\n\n #[test]\n fn test_forward_with_custom_params() {\n let config = EmbeddingConfig::new(4, vec![8], 4);\n let network = EmbeddingNetwork::new(config);\n\n // Clone and modify parameters\n let mut custom_params = network.clone_params();\n custom_params.scale_weights(2.0);\n\n let input = Array1::from_vec(vec![0.5; 4]);\n\n let output_default = network.forward(&input, None);\n let output_custom = network.forward(&input, Some(&custom_params));\n\n // Outputs should be different\n let diff: f64 = (&output_default - &output_custom).mapv(|x| x.abs()).sum();\n assert!(diff > 0.0);\n }\n\n #[test]\n fn test_batch_forward() {\n let config = EmbeddingConfig::default();\n let network = EmbeddingNetwork::new(config.clone());\n\n let batch_size = 5;\n let inputs = Array2::from_shape_fn((batch_size, config.input_dim), |_| 0.5);\n let outputs = network.forward_batch(&inputs, None);\n\n assert_eq!(outputs.nrows(), batch_size);\n assert_eq!(outputs.ncols(), config.output_dim);\n }\n}\n", | |
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| "newStart": 352, | |
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| "lines": [ | |
| " ", | |
| " #[test]", | |
| " fn test_forward_with_custom_params() {", | |
| "- let config = EmbeddingConfig::new(4, vec![8], 4);", | |
| "+ // Disable normalization so scaling weights produces different outputs", | |
| "+ let config = EmbeddingConfig {", | |
| "+ input_dim: 4,", | |
| "+ hidden_dims: vec![8],", | |
| "+ output_dim: 4,", | |
| "+ normalize_embeddings: false, // Disable normalization for this test", | |
| "+ dropout_rate: 0.0,", | |
| "+ activation: ActivationType::ReLU,", | |
| "+ use_batch_norm: false,", | |
| "+ };", | |
| " let network = EmbeddingNetwork::new(config);", | |
| " ", | |
| "- // Clone and modify parameters", | |
| "+ // Clone and modify parameters by adding a bias offset (not just scaling)", | |
| " let mut custom_params = network.clone_params();", | |
| "- custom_params.scale_weights(2.0);", | |
| "+ for bias in &mut custom_params.biases {", | |
| "+ bias.mapv_inplace(|b| b + 0.5);", | |
| "+ }", | |
| " ", | |
| " let input = Array1::from_vec(vec![0.5; 4]);", | |
| " ", | |
| " let output_default = network.forward(&input, None);", | |
| " let output_custom = network.forward(&input, Some(&custom_params));", | |
| " ", | |
| "- // Outputs should be different", | |
| "+ // Outputs should be different due to bias change", | |
| " let diff: f64 = (&output_default - &output_custom).mapv(|x| x.abs()).sum();", | |
| "- assert!(diff > 0.0);", | |
| "+ assert!(diff > 0.0, \"Expected diff > 0.0, got {}\", diff);", | |
| " }", | |
| " ", | |
| " #[test]" | |
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| [2026-01-21T18:10:49.727Z] [INFO] { | |
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| "type": "text", | |
| "text": "Now let's run the tests again:" | |
| } | |
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| "command": "cargo test 2>&1 | tail -40", | |
| "description": "Run Rust tests again", | |
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| "content": "test network::distance::tests::test_distances_to_probs ... ok\ntest network::distance::tests::test_euclidean_distance ... ok\ntest network::distance::tests::test_manhattan_distance ... ok\ntest network::embedding::tests::test_batch_forward ... ok\ntest network::embedding::tests::test_forward_pass ... ok\ntest network::embedding::tests::test_forward_with_custom_params ... ok\ntest network::distance::tests::test_compute_prototype ... ok\ntest network::embedding::tests::test_normalized_output ... ok\ntest network::embedding::tests::test_network_creation ... ok\ntest strategy::regime::tests::test_classifier ... ok\ntest strategy::regime::tests::test_regime_index_conversion ... ok\ntest strategy::regime::tests::test_regime_properties ... ok\ntest strategy::regime::tests::test_classification_with_uncertainty ... ok\ntest strategy::risk::tests::test_position_pnl ... ok\ntest strategy::risk::tests::test_kelly_criterion ... ok\ntest strategy::risk::tests::test_risk_check ... ok\ntest strategy::risk::tests::test_risk_config ... ok\ntest strategy::signal::tests::test_hold_on_low_confidence ... ok\ntest strategy::signal::tests::test_signal_creation ... ok\ntest strategy::signal::tests::test_signal_expected_value ... ok\ntest strategy::risk::tests::test_reject_low_confidence ... ok\ntest strategy::signal::tests::test_signal_generator ... ok\ntest training::episode::tests::test_episode_generation ... ok\ntest training::episode::tests::test_multiple_episodes ... ok\ntest tests::test_version ... ok\ntest training::trainer::tests::test_meta_trainer ... ok\ntest methods::hybrid::tests::test_hybrid_learner_fit ... ok\ntest methods::hybrid::tests::test_hybrid_learner_predict ... ok\ntest methods::maml::tests::test_maml_learner_predict ... ok\ntest methods::maml::tests::test_maml_learner_fit ... ok\n\ntest result: ok. 49 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.14s\n\n Doc-tests few_shot_market_prediction\n\nrunning 1 test\ntest src/lib.rs - (line 26) ... ignored\n\ntest result: ok. 0 passed; 0 failed; 1 ignored; 0 measured; 0 filtered out; finished in 0.00s", | |
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| "stdout": "test network::distance::tests::test_distances_to_probs ... ok\ntest network::distance::tests::test_euclidean_distance ... ok\ntest network::distance::tests::test_manhattan_distance ... ok\ntest network::embedding::tests::test_batch_forward ... ok\ntest network::embedding::tests::test_forward_pass ... ok\ntest network::embedding::tests::test_forward_with_custom_params ... ok\ntest network::distance::tests::test_compute_prototype ... ok\ntest network::embedding::tests::test_normalized_output ... ok\ntest network::embedding::tests::test_network_creation ... ok\ntest strategy::regime::tests::test_classifier ... ok\ntest strategy::regime::tests::test_regime_index_conversion ... ok\ntest strategy::regime::tests::test_regime_properties ... ok\ntest strategy::regime::tests::test_classification_with_uncertainty ... ok\ntest strategy::risk::tests::test_position_pnl ... ok\ntest strategy::risk::tests::test_kelly_criterion ... ok\ntest strategy::risk::tests::test_risk_check ... ok\ntest strategy::risk::tests::test_risk_config ... ok\ntest strategy::signal::tests::test_hold_on_low_confidence ... ok\ntest strategy::signal::tests::test_signal_creation ... ok\ntest strategy::signal::tests::test_signal_expected_value ... ok\ntest strategy::risk::tests::test_reject_low_confidence ... ok\ntest strategy::signal::tests::test_signal_generator ... ok\ntest training::episode::tests::test_episode_generation ... ok\ntest training::episode::tests::test_multiple_episodes ... ok\ntest tests::test_version ... ok\ntest training::trainer::tests::test_meta_trainer ... ok\ntest methods::hybrid::tests::test_hybrid_learner_fit ... ok\ntest methods::hybrid::tests::test_hybrid_learner_predict ... ok\ntest methods::maml::tests::test_maml_learner_predict ... ok\ntest methods::maml::tests::test_maml_learner_fit ... ok\n\ntest result: ok. 49 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 0.14s\n\n Doc-tests few_shot_market_prediction\n\nrunning 1 test\ntest src/lib.rs - (line 26) ... ignored\n\ntest result: ok. 0 passed; 0 failed; 1 ignored; 0 measured; 0 filtered out; finished in 0.00s", | |
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| [2026-01-21T18:10:58.571Z] [INFO] { | |
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| "type": "text", | |
| "text": "All 49 tests pass. Now let me check the PR comments and CI status:" | |
| } | |
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| "content": "Create README.md with comprehensive documentation", | |
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| }, | |
| { | |
| "content": "Create README.ru.md (Russian translation)", | |
| "status": "completed", | |
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| }, | |
| { | |
| "content": "Create readme.simple.md (simplified explanation)", | |
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| "content": "Create readme.simple.ru.md (simplified Russian explanation)", | |
| "status": "completed", | |
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| }, | |
| { | |
| "content": "Create Python implementation", | |
| "status": "completed", | |
| "activeForm": "Creating Python implementation" | |
| }, | |
| { | |
| "content": "Create Rust implementation for production", | |
| "status": "completed", | |
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| "content": "{\"body\":\"## π€ AI-Powered Solution Draft\\n\\nThis pull request is being automatically generated to solve issue suenot/machine-learning-for-trading#123.\\n\\n### π Issue Reference\\nFixes suenot/machine-learning-for-trading#123\\n\\n### π§ Status\\n**Work in Progress** - The AI assistant is currently analyzing and implementing the solution draft.\\n\\n### π Implementation Details\\n_Details will be added as the solution draft is developed..._\\n\\n---\\n*This PR was created automatically by the AI issue solver*\\n\\n<!-- This is an auto-generated comment: release notes by coderabbit.ai -->\\n\\n## Summary by CodeRabbit\\n\\n* **New Features**\\n * Few-shot market prediction framework supporting metric-based, MAML, Siamese, and hybrid learning methods\\n * Bybit exchange integration for real-time market data collection\\n * Market feature extraction with technical indicators and order book analysis\\n * Market regime classification and trading signal generation\\n * Risk management system with adaptive position sizing\\n * Support for new asset adaptation and meta-learning\\n\\n* **Documentation**\\n * Comprehensive guides in English and Russian with architectural details\\n * Simplified educational materials with step-by-step explanations and practical examples\\n * Python reference implementations for algorithm validation\\n\\n<sub>βοΈ Tip: You can customize this high-level summary in your review settings.</sub>\\n\\n<!-- end of auto-generated comment: release notes by coderabbit.ai -->\",\"comments\":[{\"id\":\"IC_kwDOQvqap87hS76e\",\"author\":{\"login\":\"coderabbitai\"},\"authorAssociation\":\"NONE\",\"body\":\"<!-- This is an auto-generated comment: summarize by coderabbit.ai -->\\n<!-- This is an auto-generated comment: review in progress by coderabbit.ai -->\\n\\n> [!NOTE]\\n> Currently processing new changes in this PR. This may take a few minutes, please wait...\\n> \\n> \\n> \\n> ```ascii\\n> _______________________________________________\\n> < Get your code reviewed, or the bunny gets it. >\\n> -----------------------------------------------\\n> \\\\\\n> \\\\ (\\\\__/)\\n> (β’γ β’)\\n> / γ γ₯\\n> ```\\n> \\n> <sub>βοΈ Tip: You can disable in-progress messages and the fortune message in your review settings.</sub>\\n\\n<!-- end of auto-generated comment: review in progress by coderabbit.ai -->\\n<!-- usage_tips_start -->\\n\\n> [!TIP]\\n> <details>\\n> <summary>CodeRabbit can scan for known vulnerabilities in your dependencies using OSV Scanner.</summary>\\n> \\n> OSV Scanner will automatically detect and report security vulnerabilities in your project's dependencies. 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This may take a few minutes, please wait...\\n> \\n> \\n> \\n> ```ascii\\n> _______________________________________________\\n> < Get your code reviewed, or the bunny gets it. >\\n> -----------------------------------------------\\n> \\\\\\n> \\\\ (\\\\__/)\\n> (β’γ β’)\\n> / γ γ₯\\n> ```\\n> \\n> <sub>βοΈ Tip: You can disable in-progress messages and the fortune message in your review settings.</sub>\\n\\n<!-- end of auto-generated comment: review in progress by coderabbit.ai -->\\n<!-- usage_tips_start -->\\n\\n> [!TIP]\\n> <details>\\n> <summary>CodeRabbit can scan for known vulnerabilities in your dependencies using OSV Scanner.</summary>\\n> \\n> OSV Scanner will automatically detect and report security vulnerabilities in your project's dependencies. 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| "status": "completed", | |
| "activeForm": "Creating readme.simple.md" | |
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| "content": "Create readme.simple.ru.md (simplified Russian explanation)", | |
| "status": "completed", | |
| "activeForm": "Creating readme.simple.ru.md" | |
| }, | |
| { | |
| "content": "Create Python implementation", | |
| "status": "completed", | |
| "activeForm": "Creating Python implementation" | |
| }, | |
| { | |
| "content": "Create Rust implementation for production", | |
| "status": "completed", | |
| "activeForm": "Creating Rust implementation" | |
| }, | |
| { | |
| "content": "Create example/demo scripts", | |
| "status": "completed", | |
| "activeForm": "Creating example scripts" | |
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| { | |
| "content": "Run tests and validate implementations", | |
| "status": "completed", | |
| "activeForm": "Running tests" | |
| }, | |
| { | |
| "content": "Check PR comments and CI status", | |
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| "activeForm": "Checking PR feedback" | |
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| "content": "Commit and push to branch, update PR", | |
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| "activeForm": "Committing and pushing changes" | |
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| [2026-01-21T18:13:11.490Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
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| "type": "message", | |
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| "content": [ | |
| { | |
| "type": "text", | |
| "text": "The implementation is now complete. Here's a summary of what was accomplished:\n\n## Summary\n\nI've completed the implementation of **Chapter 86: Few-Shot Market Prediction** for the machine learning trading book repository. The PR is now ready for review at:\n\n**https://github.com/suenot/machine-learning-for-trading/pull/416**\n\n### Completed Work\n\n1. **Documentation** (previously completed):\n - `README.md` - Comprehensive English documentation\n - `README.ru.md` - Russian translation\n - `readme.simple.md` - Simplified beginner-friendly explanation (English)\n - `readme.simple.ru.md` - Simplified beginner-friendly explanation (Russian)\n\n2. **Python Implementation** (previously completed):\n - `python/few_shot_predictor.py` - Reference implementation\n\n3. **Rust Implementation** (completed in this session):\n - **Strategy modules**:\n - `src/strategy/regime.rs` - Market regime classification (8 regimes)\n - `src/strategy/signal.rs` - Trading signal generation\n - `src/strategy/risk.rs` - Risk management with Kelly criterion\n \n - **Example scripts** in `examples/`:\n - `basic_few_shot.rs` - Basic few-shot prediction demo\n - `regime_prediction.rs` - Market regime classification with signals\n - `new_asset_adaptation.rs` - MAML adaptation to new assets\n\n - **Fixed module exports** for proper API access\n - **Fixed test issues** (dimension mismatch, normalization tests)\n\n4. **Test Results**:\n - All 49 Rust unit tests passing\n - Cargo check passes with no errors (only warnings for unused code)" | |
| } | |
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| [2026-01-21T18:13:11.568Z] [INFO] { | |
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| "duration_ms": 713046, | |
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| "result": "The implementation is now complete. Here's a summary of what was accomplished:\n\n## Summary\n\nI've completed the implementation of **Chapter 86: Few-Shot Market Prediction** for the machine learning trading book repository. The PR is now ready for review at:\n\n**https://github.com/suenot/machine-learning-for-trading/pull/416**\n\n### Completed Work\n\n1. **Documentation** (previously completed):\n - `README.md` - Comprehensive English documentation\n - `README.ru.md` - Russian translation\n - `readme.simple.md` - Simplified beginner-friendly explanation (English)\n - `readme.simple.ru.md` - Simplified beginner-friendly explanation (Russian)\n\n2. **Python Implementation** (previously completed):\n - `python/few_shot_predictor.py` - Reference implementation\n\n3. **Rust Implementation** (completed in this session):\n - **Strategy modules**:\n - `src/strategy/regime.rs` - Market regime classification (8 regimes)\n - `src/strategy/signal.rs` - Trading signal generation\n - `src/strategy/risk.rs` - Risk management with Kelly criterion\n \n - **Example scripts** in `examples/`:\n - `basic_few_shot.rs` - Basic few-shot prediction demo\n - `regime_prediction.rs` - Market regime classification with signals\n - `new_asset_adaptation.rs` - MAML adaptation to new assets\n\n - **Fixed module exports** for proper API access\n - **Fixed test issues** (dimension mismatch, normalization tests)\n\n4. **Test Results**:\n - All 49 Rust unit tests passing\n - Cargo check passes with no errors (only warnings for unused code)", | |
| "session_id": "172d23c7-5c62-484f-af89-80e92d0b393e", | |
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| [2026-01-21T18:13:11.569Z] [INFO] π° Anthropic official cost captured from success result: $3.674002 | |
| [2026-01-21T18:13:11.968Z] [INFO] | |
| β Claude command completed | |
| [2026-01-21T18:13:11.968Z] [INFO] π Total messages: 0, Tool uses: 0 | |
| [2026-01-21T18:13:12.150Z] [INFO] | |
| π° Token Usage Summary: | |
| [2026-01-21T18:13:12.151Z] [INFO] | |
| π Claude Opus 4.5: | |
| [2026-01-21T18:13:12.151Z] [INFO] Model ID: claude-opus-4-5-20251101 | |
| [2026-01-21T18:13:12.152Z] [INFO] Provider: Anthropic | |
| [2026-01-21T18:13:12.152Z] [INFO] Context window: 200 000 tokens | |
| [2026-01-21T18:13:12.152Z] [INFO] Max output: 64 000 tokens | |
| [2026-01-21T18:13:12.152Z] [INFO] Input modalities: text, image, pdf | |
| [2026-01-21T18:13:12.152Z] [INFO] Output modalities: text | |
| [2026-01-21T18:13:12.152Z] [INFO] Knowledge cutoff: 2025-03-31 | |
| [2026-01-21T18:13:12.152Z] [INFO] Released: 2025-11-01 | |
| [2026-01-21T18:13:12.152Z] [INFO] Capabilities: Attachments, Reasoning, Temperature, Tool calls | |
| [2026-01-21T18:13:12.153Z] [INFO] Open weights: No | |
| [2026-01-21T18:13:12.153Z] [INFO] | |
| [2026-01-21T18:13:12.153Z] [INFO] Usage: | |
| [2026-01-21T18:13:12.153Z] [INFO] Input tokens: 11 298 | |
| [2026-01-21T18:13:12.153Z] [INFO] Cache creation tokens: 632 217 | |
| [2026-01-21T18:13:12.153Z] [INFO] Cache read tokens: 12 810 060 | |
| [2026-01-21T18:13:12.153Z] [INFO] Output tokens: 1 242 | |
| [2026-01-21T18:13:12.154Z] [INFO] | |
| [2026-01-21T18:13:12.154Z] [INFO] Cost Calculation (USD): | |
| [2026-01-21T18:13:12.155Z] [INFO] Input: 11 298 tokens Γ $5/M = $0.056490 | |
| [2026-01-21T18:13:12.155Z] [INFO] Cache write: 632 217 tokens Γ $6.25/M = $3.951356 | |
| [2026-01-21T18:13:12.155Z] [INFO] Cache read: 12 810 060 tokens Γ $0.5/M = $6.405030 | |
| [2026-01-21T18:13:12.155Z] [INFO] Output: 1 242 tokens Γ $25/M = $0.031050 | |
| [2026-01-21T18:13:12.155Z] [INFO] βββββββββββββββββββββββββββββββββ | |
| [2026-01-21T18:13:12.155Z] [INFO] Total: $10.443926 | |
| [2026-01-21T18:13:12.156Z] [INFO] | |
| π <synthetic>: | |
| [2026-01-21T18:13:12.156Z] [INFO] β οΈ Model info not available | |
| [2026-01-21T18:13:12.156Z] [INFO] Usage: | |
| [2026-01-21T18:13:12.156Z] [INFO] Input tokens: 0 | |
| [2026-01-21T18:13:12.156Z] [INFO] Output tokens: 0 | |
| [2026-01-21T18:13:12.156Z] [INFO] | |
| [2026-01-21T18:13:12.156Z] [INFO] Cost: Not available (could not fetch pricing) | |
| [2026-01-21T18:13:12.156Z] [INFO] | |
| π Total across all models: | |
| [2026-01-21T18:13:12.157Z] [INFO] | |
| π° Cost estimation: | |
| [2026-01-21T18:13:12.157Z] [INFO] Public pricing estimate: $10.443926 USD | |
| [2026-01-21T18:13:12.157Z] [INFO] Calculated by Anthropic: $3.674002 USD | |
| [2026-01-21T18:13:12.157Z] [INFO] Difference: $-6.769924 (-64.82%) | |
| [2026-01-21T18:13:12.157Z] [INFO] | |
| π‘ To continue this session in Claude Code interactive mode: | |
| [2026-01-21T18:13:12.158Z] [INFO] (cd "/tmp/gh-issue-solver-1769015705800" && claude --resume 172d23c7-5c62-484f-af89-80e92d0b393e --model opus) | |
| [2026-01-21T18:13:12.158Z] [INFO] | |
| π Checking for uncommitted changes... | |
| [2026-01-21T18:13:12.238Z] [INFO] β No uncommitted changes found | |
| [2026-01-21T18:13:12.239Z] [INFO] No initial commit hash from session, attempting to detect from branch... | |
| [2026-01-21T18:13:12.239Z] [INFO] Attempting to detect CLAUDE.md or .gitkeep commit from branch structure... | |
| [2026-01-21T18:13:12.348Z] [INFO] Neither CLAUDE.md nor .gitkeep exists in current branch | |
| [2026-01-21T18:13:12.349Z] [INFO] Could not safely detect initial commit to revert | |
| [2026-01-21T18:13:12.349Z] [INFO] | |
| === Session Summary === | |
| [2026-01-21T18:13:12.350Z] [INFO] β Session ID: 172d23c7-5c62-484f-af89-80e92d0b393e | |
| [2026-01-21T18:13:12.350Z] [INFO] β Complete log file: /home/hive/172d23c7-5c62-484f-af89-80e92d0b393e.log | |
| [2026-01-21T18:13:12.350Z] [INFO] | |
| [2026-01-21T18:13:12.350Z] [INFO] π‘ To continue this session in Claude Code interactive mode: | |
| [2026-01-21T18:13:12.351Z] [INFO] | |
| [2026-01-21T18:13:12.351Z] [INFO] (cd "/tmp/gh-issue-solver-1769015705800" && claude --resume 172d23c7-5c62-484f-af89-80e92d0b393e --model opus) | |
| [2026-01-21T18:13:12.351Z] [INFO] | |
| [2026-01-21T18:13:12.352Z] [INFO] | |
| π Searching for created pull requests or comments... | |
| [2026-01-21T18:13:12.686Z] [INFO] | |
| π Checking for pull requests from branch issue-123-dd320d406382... | |
| [2026-01-21T18:13:13.086Z] [INFO] β Found pull request #416: "Chapter 86: Few-Shot Market Prediction" | |
| [2026-01-21T18:13:13.536Z] [INFO] β PR body already contains issue reference | |
| [2026-01-21T18:13:13.536Z] [INFO] β PR is already ready for review | |
| [2026-01-21T18:13:13.537Z] [INFO] | |
| π Uploading solution draft log to Pull Request... | |
| [2026-01-21T18:13:13.651Z] [INFO] π° Calculated cost: $10.443926 |
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