9 Commits

Author SHA1 Message Date
21in7
5b3f6af13c feat: add symbol comparison and position sizing analysis tools
- Add payoff_ratio and max_consecutive_losses to backtester summary
- Add compare_symbols.py: per-symbol parameter sweep for candidate evaluation
- Add position_sizing_analysis.py: robust Monte Carlo position sizing
- Fetch historical data for SOL, LINK, AVAX candidates (365 days)
- Update existing symbol data (XRP, TRX, DOGE)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-18 23:35:42 +09:00
21in7
55c20012a3 feat: add per-symbol strategy params with sweep-optimized values
Support per-symbol strategy parameters (ATR_SL_MULT_XRPUSDT, etc.)
via env vars, falling back to global defaults. Sweep results:
- XRPUSDT: SL=1.5 TP=4.0 ADX=30 (PF 2.39, Sharpe 61.0)
- TRXUSDT: SL=1.0 TP=4.0 ADX=30 (PF 3.87, Sharpe 62.8)
- DOGEUSDT: SL=2.0 TP=2.0 ADX=30 (PF 1.80, Sharpe 44.1)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-17 17:28:14 +09:00
21in7
de27f85e6d chore: update .gitignore, modify log file pattern, and add package-lock.json
- 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.
2026-03-09 22:57:23 +09:00
21in7
c577019793 docs: update architecture and README for improved clarity and structure
- 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.
2026-03-07 02:12:48 +09:00
21in7
2a767c35d4 feat(weekly-report): implement weekly report generation with live trade data and performance tracking
- Added functionality to fetch live trade data from the dashboard API.
- Implemented weekly report generation that includes backtest results, live trade statistics, and performance trends.
- Enhanced error handling for API requests and improved logging for better traceability.
- Updated tests to cover new features and ensure reliability of the report generation process.
2026-03-07 01:13:03 +09:00
21in7
cd9d379bc2 feat: implement multi-symbol dashboard with updated data handling
- Added support for multi-symbol trading (XRP, TRX, DOGE) in the dashboard.
- Updated bot log messages to include [SYMBOL] prefix for better tracking.
- Enhanced log parser for multi-symbol state tracking and updated database schema.
- Introduced new API endpoints and UI components for symbol filtering and display.
- Added new model files and backtest results for multi-symbol strategies.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-06 23:43:41 +09:00
21in7
2b3f39b5d1 feat: enhance data handling and model training features
- Updated .gitignore to include .venv and .worktrees.
- Removed symlink for the virtual environment.
- Added new parquet files for dogeusdt, trxusdt, and xrpusdt datasets.
- Introduced model files and training logs for dogeusdt, trxusdt, and xrpusdt.
- Enhanced fetch_history.py to support caching of correlation symbols.
- Updated train_and_deploy.sh to manage correlation cache directory.
2026-03-05 23:57:44 +09:00
21in7
2bb2bf2896 feat: add per-symbol model/data directories and update .env.example
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-05 23:24:50 +09:00
21in7
7e4e9315c2 feat: implement ML filter with LightGBM for trading signal validation
- Added MLFilter class to load and evaluate LightGBM model for trading signals.
- Introduced retraining mechanism to update the model daily based on new data.
- Created feature engineering and label building utilities for model training.
- Updated bot logic to incorporate ML filter for signal validation.
- Added scripts for data fetching and model training.

Made-with: Cursor
2026-03-01 17:07:18 +09:00