- Updated timestamp and elapsed seconds in models/active_lgbm_params.json.
- Adjusted baseline AUC and fold AUCs to reflect new model performance.
- Added a new entry in models/training_log.json with detailed metrics from the latest training run, including tuned parameters and model path.
Made-with: Cursor
- tune_hyperparams.py: 탐색 완료 후 Best AUC > Baseline AUC 이면
models/active_lgbm_params.json 자동 갱신
- tune_hyperparams.py: 베이스라인을 active 파일 기준으로 측정
(active 없으면 코드 내 기본값 사용)
- train_model.py: _load_lgbm_params()에 active 파일 자동 탐색 추가
우선순위: --tuned-params > active_lgbm_params.json > 하드코딩 기본값
- models/active_lgbm_params.json: 현재 best 파라미터로 초기화
- .gitignore: tune_results_*.json 제외, active 파일은 git 추적 유지
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
바이낸스 OI 히스토리 API가 최근 30일치만 제공하는 제약을 우회하기 위해
upsert_parquet() 함수를 추가. 매일 실행 시 기존 parquet의 oi_change/funding_rate가
0.0인 구간만 신규 값으로 덮어써 점진적으로 과거 데이터를 채워나감.
--no-upsert 플래그로 기존 덮어쓰기 동작 유지 가능.
Made-with: Cursor
- _fetch_market_microstructure: oi_val > 0 체크 후에만 _calc_oi_change 호출하여
API 실패(None/Exception) 시 0.0으로 폴백하고 _prev_oi 상태 오염 방지
- README: ML 피처 수 오기재 수정 (25개 → 23개)
- tests: _calc_oi_change 첫 캔들 및 API 실패 시 상태 보존 유닛 테스트 추가
Made-with: Cursor
- Add asyncio import to bot.py
- Add _prev_oi state for OI change rate calculation
- Add _fetch_market_microstructure() for concurrent OI/funding rate fetch with exception fallback
- Add _calc_oi_change() for relative OI change calculation
- Always call build_features() before ML filter check in process_candle()
- Pass oi_change/funding_rate kwargs to build_features() in both process_candle() and _close_and_reenter()
- Update _close_and_reenter() signature to accept oi_change/funding_rate params
Made-with: Cursor
- Changed python-binance version requirement from 1.0.19 to >=1.0.28 for better compatibility and features.
- Modified exception handling in the cancel_all_orders method to catch all exceptions instead of just BinanceAPIException, enhancing robustness.
- Updated the cancel_all_orders method to also cancel all Algo open orders in addition to regular open orders.
- Added error handling to log warnings if the cancellation of Algo orders fails.
- Introduced support for Algo Order API, allowing automatic sending of STOP_MARKET and TAKE_PROFIT_MARKET orders.
- Updated README.md to include new features related to Algo Order API and real-time handling of ML features.
- Enhanced ML feature processing to fill missing OI and funding rate values with zeros for consistency in training data.
- Added new training log entries for the lgbm model with updated metrics.
- Updated README.md to reflect new features including dynamic margin ratio, model hot-reload, and multi-symbol streaming.
- Modified bot logic to ensure raw signals are passed to the `_close_and_reenter` method, even when the ML filter is loaded.
- Introduced a new script `run_tests.sh` for streamlined test execution.
- Improved test coverage for signal processing and re-entry logic, ensuring correct behavior under various conditions.
- 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.
- Added new training log entries for lgbm backend with AUC, precision, and recall metrics.
- Enhanced deploy_model.sh to manage ONNX and lgbm model files based on the selected backend.
- Adjusted output shape in mlx_filter.py for ONNX export to support dynamic batch sizes.
- Updated `fetch_history.py` to collect open interest (OI) and funding rate data from Binance, improving the dataset for model training.
- Modified `train_and_deploy.sh` to include options for OI and funding rate collection during data fetching.
- Enhanced `dataset_builder.py` to incorporate OI change and funding rate features with rolling z-score normalization.
- Updated training logs to reflect new metrics and features, ensuring comprehensive tracking of model performance.
- Adjusted feature columns in `ml_features.py` to include OI and funding rate for improved model robustness.
- 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 stage to the Jenkins pipeline to notify Discord when a build starts, succeeds, or fails, improving communication during the CI/CD process.
- Implemented model hot-reload functionality in the MLFilter class, allowing automatic reloading of models when file changes are detected, enhancing responsiveness to updates.
- Updated deployment scripts to provide clearer messaging regarding model loading and container status, improving user experience and debugging capabilities.
- Introduced a new function `_split_combined` to separate XRP, BTC, and ETH data from a combined DataFrame.
- Updated `train_mlx` to utilize the new function, improving data management and feature handling.
- Adjusted dataset generation to accommodate BTC and ETH features, with warnings for missing features.
- Changed default data path in `train_mlx` and `train_model` to point to the combined dataset for consistency.
- Increased `LOOKAHEAD` from 60 to 90 and adjusted `ATR_TP_MULT` for better model performance.
- Updated `train_model.py` and `train_mlx_model.py` to include a time weight decay parameter for improved sample weighting during training.
- Modified dataset generation to incorporate sample weights based on time decay, enhancing model performance.
- Adjusted deployment scripts to support new backend options and improved error handling for model file transfers.
- Added new entries to the training log for better tracking of model performance metrics over time.
- Included ONNX model export functionality in the MLX filter for compatibility with Linux servers.
