- Fix LightGBM predict_proba ValueError by filtering FEATURE_COLS and casting to float64
- Extract BTC/ETH correlation data from embedded parquet columns instead of missing separate files
- Disable ONNX priority in ML filter tests to use mocked LightGBM correctly
- Add NO_ML_FILTER=true to .env.example (ML adds no value with current signal thresholds)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Updated the change detection script to compare the current commit with the previous successful build, falling back to HEAD~5 if no previous commit exists.
- Enhanced logging to indicate the base commit used for comparison.
- 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>
- Add [SYMBOL] prefix to all bot/user_data_stream log messages
- Rewrite log_parser.py with multi-symbol regex, per-symbol state tracking, symbol columns in DB schema
- Rewrite dashboard_api.py with /api/symbols endpoint, symbol query params on all endpoints, SQL injection fix
- Update App.jsx with symbol filter tabs, multi-position display, dynamic header
- Add tests for log parser (8 tests) and dashboard API (7 tests)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- 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.
- Added design document outlining the architecture for multi-symbol trading, including independent TradingBot instances and shared RiskManager.
- Created implementation plan detailing tasks for configuration, risk management, and execution structure for multi-symbol support.
- Updated configuration to support multiple trading symbols and correlation symbols, ensuring backward compatibility.
- Introduced shared RiskManager with constraints on position limits and direction to manage global risk effectively.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Include RSI, MACD_H, ATR in bot entry log so the log parser can
extract and store them in the trades DB for dashboard display
- Update log parser regex and _handle_entry() to persist indicator values
- Add dashboard section to README (tech stack, screens, API endpoints)
- Add dashboard/ directory to project structure in README
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The previous /proc-based process kill attempted to read cmdline from
non-PID entries like /proc/fb, causing NotADirectoryError in Docker.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Replaced subprocess-based termination of the log parser with a Python-native approach using os and signal modules.
- Enhanced process handling to ensure proper termination of existing log parser instances before restarting.
This change improves reliability and compatibility across different environments.
- POST /api/reset endpoint: clears all tables and restarts log parser
- UI: Reset DB button in footer with confirmation dialog
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Log parser: fix entry dedup to check direction instead of price tolerance,
preventing duplicate OPEN trades for the same position
- Log parser: close all OPEN trades on position close, delete stale duplicates
- Jenkinsfile: detect changed files and only build/deploy affected services,
allowing dashboard-only changes without restarting the bot
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Changed image references for cointrader, dashboard-api, and dashboard-ui services to use the new GitLab registry URL.
- Ensured consistency in image source for better maintainability and deployment.
- 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.
- Added support for demo trading on Binance Futures with a new configuration for 1-minute candles and 125x leverage.
- Updated various components including Config, Exchange, DataStream, UserDataStream, and Bot to handle demo and testnet flags.
- Enhanced the training pipeline to collect 1-minute data and adjusted the lookahead for model training.
- Updated environment variables in .env and .env.example to include demo settings.
This commit lays the groundwork for testing ML-based automated trading strategies in a controlled environment.
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>
- Added `ML_THRESHOLD` parameter to README, specifying its role in ML filter predictions.
- Included a new entry in the training log with detailed metrics from a recent model training session, enhancing performance tracking and documentation.
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.
- 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.
