- 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.
Added HOLD candles as negative samples to increase training data from ~535 to ~3,200 samples. Introduced a negative_ratio parameter in generate_dataset_vectorized() for sampling HOLD candles alongside signal candles. Implemented stratified undersampling to ensure signal samples are preserved during training. Updated relevant tests to validate new functionality and maintain compatibility with existing tests.
- Modified dataset_builder.py to include HOLD negative sampling logic
- Updated train_model.py to apply stratified undersampling
- Added tests for new sampling methods
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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.
- Introduced User Data Stream to detect TP/SL executions in real-time.
- Added a new class `UserDataStream` for managing the stream and handling events.
- Updated `bot.py` to initialize and run the User Data Stream in parallel with the candle stream.
- Enhanced `notifier.py` to send detailed Discord notifications including estimated vs actual PnL.
- Added methods in `exchange.py` for managing listenKey lifecycle (create, keepalive, delete).
- Refactored PnL recording and notification logic to streamline handling of position closures.
Made-with: Cursor
- scripts/tune_hyperparams.py: Optuna + Walk-Forward 5폴드 AUC 목적 함수
- 데이터셋 1회 캐싱으로 모든 trial 공유 (속도 최적화)
- num_leaves <= 2^max_depth - 1 제약 강제 (소규모 데이터 과적합 방지)
- MedianPruner로 저성능 trial 조기 종료
- 결과: 콘솔 리포트 + models/tune_results_YYYYMMDD_HHMMSS.json
- requirements.txt: optuna>=3.6.0 추가
- README.md: 하이퍼파라미터 자동 튜닝 사용법 섹션 추가
- docs/plans/: 설계 문서 및 구현 플랜 추가
Made-with: Cursor
- Added `--upsert` flag to `fetch_history.py` for merging new data into existing parquet files.
- Implemented `upsert_parquet()` function to update existing rows with new values where `oi_change` and `funding_rate` are 0.0, while appending new rows.
- Created tests in `tests/test_fetch_history.py` to validate upsert behavior.
- Updated `.gitignore` to include `.cursor/` directory.
Made-with: Cursor
- Added `_close_and_reenter` method to handle immediate re-entry after closing a position when a reverse signal is detected, contingent on passing the ML filter.
- Updated `process_candle` to call `_close_and_reenter` instead of `_close_position` for reverse signals.
- Enhanced test coverage for the new functionality, ensuring correct behavior under various conditions, including ML filter checks and position limits.
- Introduced a new markdown document detailing the plan to transition the entire pipeline from a 1-minute to a 15-minute timeframe, aiming to improve model AUC from 0.49-0.50 to over 0.53.
- Updated key parameters across multiple scripts, including `LOOKAHEAD` adjustments and default data paths to reflect the new 15-minute interval.
- Modified data fetching and training scripts to ensure compatibility with the new timeframe, including changes in `fetch_history.py`, `train_model.py`, and `train_and_deploy.sh`.
- Enhanced the bot's data stream configuration to operate on a 15-minute interval, ensuring real-time data processing aligns with the new model training strategy.
- Updated training logs to capture new model performance metrics under the revised timeframe.
- Added a new markdown document outlining the plan to enhance the LightGBM model's AUC from 0.54 to 0.57+ through feature normalization, strong time weighting, and walk-forward validation.
- Implemented rolling z-score normalization for absolute value features in `src/dataset_builder.py` to improve model robustness against regime changes.
- Introduced a walk-forward validation function in `scripts/train_model.py` to accurately measure future prediction performance.
- Updated training log to include new model performance metrics and added ONNX model export functionality for compatibility.
- Adjusted model training parameters for better performance and included detailed validation results in the training log.
- Added a new design document outlining the integration of BTC/ETH candle data as additional features in the XRP ML filter, enhancing prediction accuracy.
- Introduced `MultiSymbolStream` for combined WebSocket data retrieval of XRP, BTC, and ETH.
- Expanded feature set from 13 to 21 by including 8 new BTC/ETH-related features.
- Updated various scripts and modules to support the new feature set and data handling.
- Enhanced training and deployment scripts to accommodate the new dataset structure.
This commit lays the groundwork for improved model performance by leveraging the correlation between BTC and ETH with XRP.
- Added comprehensive plans for training a LightGBM model on M4 Mac Mini and deploying it to an LXC container.
- Created scripts for model training, deployment, and a full pipeline execution.
- Enhanced model transfer with error handling and logging for better tracking.
- Introduced profiling for training time analysis and dataset generation optimization.
Made-with: Cursor
- 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
