- Introduced a new trading dashboard consisting of a FastAPI backend (`dashboard-api`) for data retrieval and a React frontend (`dashboard-ui`) for visualization.
- Implemented a log parser to monitor and store bot logs in an SQLite database.
- Configured Docker setup for both API and UI, including necessary Dockerfiles and a docker-compose configuration.
- Added setup documentation for running the dashboard and accessing its features.
- Enhanced the Jenkins pipeline to build and push the new dashboard images.
- 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.
Add two new OI-derived features to improve ML model's market microstructure
understanding:
- oi_change_ma5: 5-candle moving average of OI change rate (short-term trend)
- oi_price_spread: z-scored OI minus z-scored price return (divergence signal)
Both features use 96-candle rolling z-score window. FEATURE_COLS expanded from
24 to 26. Existing tests updated to reflect new feature counts.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Migrate ADX from hard filter (ADX < 25 blocks entry) to ML feature so
the model can learn optimal ADX thresholds from data. Updates FEATURE_COLS,
build_features(), and corresponding tests from 23 to 24 features.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Log current price and unrealized PnL every 5 minutes while holding a position,
using the existing kline WebSocket's unclosed candle data for real-time price updates.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Added ML_THRESHOLD to .env.example and updated Config class to include ml_threshold with a default value of 0.55.
- Modified MLFilter initialization in bot.py to utilize the new ml_threshold configuration.
- Updated Jenkinsfile to change the registry URL for Docker image management.
These changes enhance the model's adaptability by allowing for a configurable machine learning threshold, improving overall performance.
The two HOLD negative tests (test_hold_negative_labels_are_all_zero,
test_signal_samples_preserved_after_sampling) were passing vacuously
because sample_df produces 0 signal candles (ADX ~18, below threshold
25). Added signal_producing_df fixture with higher volatility and volume
surges to reliably generate signals. Removed if-guards so assertions
are mandatory. Also restored the full docstring for
generate_dataset_vectorized() documenting btc_df/eth_df,
time_weight_decay, and negative_ratio parameters.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add negative_ratio parameter to generate_dataset_vectorized() that
samples HOLD candles as label=0 negatives alongside signal candles.
This increases training data from ~535 to ~3,200 samples when enabled.
- Split valid_rows into base_valid (shared) and sig_valid (signal-only)
- Add 'source' column ("signal" vs "hold_negative") for traceability
- HOLD samples get label=0 and random 50/50 side assignment
- Default negative_ratio=0 preserves backward compatibility
- Fix incorrect column count assertion in existing test
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
ADX < 25 now returns HOLD in get_signal(), preventing entries during
trendless (sideways) markets. NaN ADX values fall through to existing
weighted signal logic. Also syncs the vectorized dataset builder with
the same ADX filter to keep training data consistent.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add ADX (Average Directional Index) with period 14 to calculate_all()
for sideways market filtering. Includes test verifying the adx column
exists and contains non-negative values.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Added a new entry to the training log with detailed metrics for a LightGBM model, including AUC, precision, recall, and tuned parameters.
- Enhanced the MLFilter class to include a guard clause that prevents execution if the filter is disabled, improving robustness.
- 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.
- __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
- _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.
