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>
- Added a new design document outlining the integration of BTC/ETH candle data as additional features in the XRP ML filter, enhancing prediction accuracy.
- Introduced `MultiSymbolStream` for combined WebSocket data retrieval of XRP, BTC, and ETH.
- Expanded feature set from 13 to 21 by including 8 new BTC/ETH-related features.
- Updated various scripts and modules to support the new feature set and data handling.
- Enhanced training and deployment scripts to accommodate the new dataset structure.
This commit lays the groundwork for improved model performance by leveraging the correlation between BTC and ETH with XRP.
- Added MLFilter class to load and evaluate LightGBM model for trading signals.
- Introduced retraining mechanism to update the model daily based on new data.
- Created feature engineering and label building utilities for model training.
- Updated bot logic to incorporate ML filter for signal validation.
- Added scripts for data fetching and model training.
Made-with: Cursor
