5. Add daily PnL reset loop — UTC midnight auto-reset via
_daily_reset_loop in main.py, prevents stale daily_pnl accumulation
6. Fix set_base_balance race condition — call once in main.py before
spawning bots, instead of each bot calling independently
7. Remove realized_pnl != 0 from close detection — prevents entry
orders with small rp values being misclassified as closes
8. Rename xrp_btc_rs/xrp_eth_rs → primary_btc_rs/primary_eth_rs —
generic column names for multi-symbol support (dataset_builder,
ml_features, and tests updated consistently)
9. Replace asyncio.get_event_loop() → get_running_loop() — fixes
DeprecationWarning on Python 3.10+
10. Parallelize candle preload — asyncio.gather for all symbols
instead of sequential REST calls, ~3x faster startup
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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
