2 Commits

Include renames

Author	SHA1	Message	Date
cbazzaandClaude Sonnet 4.6	4e45db967b	fix: core module review fixes (signal scoring, ML, session filter, telegram, MT filter) ... enhanced_signal_scoring.py: - mt -> mt5, add logging module, replace print() with logger - Remove no-op df['tick_volume'] = df['tick_volume'] line - Fix RSI division-by-zero: loss.replace(0, nan) + fillna(100) ml_signal_predictor.py: - Remove global warnings.filterwarnings('ignore') suppression - Fix bare except -> except Exception with logger.warning - Add note: default data files excluded from git, need manual export - Add pickle security warning comment session_confidence_filter.py: - Move imports to file top, add logging + functools.wraps - Remove repeated AdaptiveRhythmManager() per-call instantiation - Add functools.wraps to preserve wrapped function metadata - Document that extended_top_down_v2_adaptive is notebook-only - Replace print() with logger, pass **kwargs through wrapper telegram_notifier.py: - Remove network call from __init__ -> explicit test_connection() method - Add logging module, replace all print() with logger calls - Narrow exception type: Exception -> requests.RequestException - Remove unused imports (timedelta) - notify_trade_entry/exit now return bool from send_message multi_timeframe_regime_filter.py: - Change debug default from True to False in wrapper to avoid verbose production output Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-05-12 10:12:25 +02:00
cbazzaandClaude Opus 4.5	7f2de4d227	feat: Add XGBoost ML Signal Quality Predictor ... - New ml_signal_predictor.py with XGBoost model - Feature extraction from historical trades (17 features) - 5-fold cross-validation for robust training - Integrated into enhanced_trading_check_wrapper as optional layer - Disabled by default until model improves (AUC: 0.508) - Key insight: Asian session is strongest predictor of success Features used: - Signal: confidence, threshold, regime_strength - Session: asian/london/ny/overlap (one-hot) - Time: hour (cyclical), day_of_week - Quality: signal_quality score - Direction: long/short Usage: - train_ml_model() to train - enable_ml_predictor() to activate - get_ml_status() for info Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>	2026-02-02 10:37:32 +01:00