- 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.
- Updated active LGBM parameters with new timestamp, trial results, and model configurations to reflect recent training outcomes.
- Added new entries to the training log, capturing detailed metrics including AUC, precision, recall, and tuned parameters for the latest model iterations.
This update enhances the tracking of model performance and parameter tuning in the ML pipeline.
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>
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>
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.
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.
