C5: Remove duplicate entry_fee deduction in backtester (balance and net_pnl)
C1: Add SL/TP retry (3x) with emergency market close on final failure
C3: Add _close_lock to prevent PnL double recording between callback and monitor
C8: Add SIGTERM/SIGINT handler with per-symbol order cancellation before exit
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Added entries to .gitignore for node_modules and dist directories in the dashboard UI.
- Updated log file pattern in log_parser.py to match 'bot*.log' instead of 'bot_*.log'.
- Introduced package-lock.json for the dashboard UI to manage dependencies.
- Updated CLAUDE.md to reflect the status of code review improvements.
- Added new weekly report files in HTML and JSON formats for 2026-03-07.
- Updated binary parquet files for dogeusdt, trxusdt, and xrpusdt with new data.
- Revised the architecture document to enhance clarity on system overview, trading decision process, and technical stack.
- Updated the README to emphasize the bot's operational guidelines and risk management features.
- Added new sections in the architecture document detailing the trading decision gates and data pipeline flow.
- Improved the table of contents for better navigation and understanding of the bot's architecture.
8-task plan covering: data fetch, WF backtest, log parsing, trend tracking,
ML re-trigger check, degradation sweep, Discord formatting, CLI orchestration.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add [SYMBOL] prefix to all bot/user_data_stream log messages
- Rewrite log_parser.py with multi-symbol regex, per-symbol state tracking, symbol columns in DB schema
- Rewrite dashboard_api.py with /api/symbols endpoint, symbol query params on all endpoints, SQL injection fix
- Update App.jsx with symbol filter tabs, multi-position display, dynamic header
- Add tests for log parser (8 tests) and dashboard API (7 tests)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Added .worktrees/ to .gitignore to prevent tracking of worktree files.
- Marked `optuna-precision-objective-plan` as completed in CLAUDE.md.
- Added new training log entry for a LightGBM model with updated parameters and performance metrics in training_log.json.
- Updated error handling in ml_filter.py to return False on prediction errors instead of True, improving the robustness of the ML filter.
- Introduced a new plan to modify the Optuna objective function to prioritize precision under a recall constraint of 0.35, improving model performance in scenarios where false positives are costly.
- Updated training scripts to implement precision-based metrics and adjusted the walk-forward cross-validation process to incorporate precision and recall calculations.
- Enhanced the active LGBM parameters and training log to reflect the new metrics and model configurations.
- Added a new design document outlining the implementation steps for the precision-focused optimization.
This update aims to refine the model's decision-making process by emphasizing precision, thereby reducing potential losses from false positives.
Introduced CLAUDE.md to provide comprehensive guidance on the CoinTrader project, including architecture, common commands, testing, and deployment details. Added settings.json to enable the superpowers plugin for Claude. This enhances the project's documentation and configuration management.
