- Introduced a comprehensive architecture document detailing the CoinTrader system, including an overview, core layer architecture, MLOps pipeline, and key operational scenarios.
- Updated README to reference the new architecture document and added a configuration option to disable the ML filter.
- Enhanced the ML filter to allow for complete signal acceptance when the NO_ML_FILTER environment variable is set.
- Updated the README to clarify the listenKey auto-renewal mechanism, including the use of `stream.recv()` for message reception.
- Added information on immediate reconnection upon detecting internal error payloads to prevent zombie connections.
- 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
- __init__에 _entry_price, _entry_quantity 상태 변수 추가 (None 초기화)
- _open_position()에서 current_trade_side 저장 직후 진입가/수량 저장
- _calc_estimated_pnl() 헬퍼: LONG/SHORT 방향별 예상 PnL 계산
- _on_position_closed() 콜백: UDS 청산 감지 시 PnL 기록·알림·상태 초기화
Made-with: Cursor
- 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.
