feat: add 5 dashboard features — dark mode, trade history, backtests, model insights, alerts
- Dark mode: class-based theme toggle with localStorage persistence and flash prevention - Trade History (/trades): paginated table, stats cards, equity curve chart with DB API endpoints - Backtest Viewer (/backtests): log parser for 35 backtest results, sidebar + detail + comparison tabs - Model Insights: dashboard card + dialog showing feature importance, regime distribution, training history - Alert/Signal Log (/alerts): signal stats, filterable table with execution tracking - API: 8 new endpoints with psycopg2 DB connection pool - Dark mode sweep across books page, about dialog, and all dashboard components - Architecture docs rewritten with Mermaid diagrams (23 docs) - README and FEATURES.md rewritten bilingual (Indonesian + English) - main_live.py: write model_metrics.json on startup and retrain Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
GifariKemal
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
b2dc2dacd7
commit
e8355b3f62
+76
-9
@@ -54,7 +54,9 @@ from src.smc_polars import SMCAnalyzer, SMCSignal
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from src.feature_eng import FeatureEngineer
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from src.regime_detector import MarketRegimeDetector, FlashCrashDetector, MarketRegime, RegimeState
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from src.risk_engine import RiskEngine
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from src.ml_model import TradingModel, get_default_feature_columns
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from backtests.ml_v2.ml_v2_model import TradingModelV2
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from backtests.ml_v2.ml_v2_feature_eng import MLV2FeatureEngineer
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from src.ml_model import get_default_feature_columns # keep for fallback
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from src.position_manager import SmartPositionManager
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from src.session_filter import SessionFilter, create_wib_session_filter
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from src.auto_trainer import AutoTrainer, create_auto_trainer
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@@ -123,11 +125,13 @@ class TradingBot:
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# Initialize risk engine
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self.risk_engine = RiskEngine(self.config)
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# Initialize ML model (will load model)
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self.ml_model = TradingModel(
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# Initialize ML V2 Model D (76 features, AUC 0.7339)
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self.ml_model = TradingModelV2(
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confidence_threshold=self.config.ml.confidence_threshold,
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model_path="models/xgboost_model.pkl",
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model_path="models/xgboost_model_v2d.pkl",
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)
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self.fe_v2 = MLV2FeatureEngineer()
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self._h1_df_cached = None # Cache H1 DataFrame with indicators for V2 features
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# Initialize Smart Position Manager - ATR-ADAPTIVE (#24B)
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self.position_manager = SmartPositionManager(
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@@ -225,20 +229,26 @@ class TradingBot:
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logger.error(f"Failed to load HMM model: {e}")
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models_ok = False
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# Load XGBoost model
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# Load ML V2 Model D
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try:
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self.ml_model.load()
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if self.ml_model.fitted:
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logger.info("XGBoost model loaded successfully")
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logger.info("ML V2 Model D loaded successfully")
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logger.info(f" Features: {len(self.ml_model.feature_names)}")
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logger.info(f" Type: {self.ml_model.model_type.value}")
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else:
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logger.warning("XGBoost model not found or not fitted")
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logger.warning("ML V2 Model D not found or not fitted")
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models_ok = False
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except Exception as e:
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logger.error(f"Failed to load XGBoost model: {e}")
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logger.error(f"Failed to load ML V2 Model D: {e}")
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models_ok = False
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self._models_loaded = models_ok
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# Write model metrics for dashboard
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if models_ok:
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self._write_model_metrics()
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return models_ok
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def _dash_log(self, level: str, message: str):
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@@ -250,6 +260,50 @@ class TradingBot:
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"message": message,
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})
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def _write_model_metrics(self, retrain_results: dict = None):
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"""Write model metrics JSON for dashboard Model Insights feature."""
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try:
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import json as _json
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metrics = {
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"featureImportance": [],
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"trainAuc": 0,
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"testAuc": 0,
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"sampleCount": 0,
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"updatedAt": datetime.now(ZoneInfo("Asia/Jakarta")).isoformat(),
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}
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# Extract feature importance from XGBoost model
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if self.ml_model.fitted and hasattr(self.ml_model, 'model') and self.ml_model.model is not None:
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try:
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booster = self.ml_model.model
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importance = booster.get_score(importance_type='gain') if hasattr(booster, 'get_score') else {}
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if not importance and hasattr(booster, 'feature_importances_'):
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names = self.ml_model.feature_names if hasattr(self.ml_model, 'feature_names') else []
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importance = dict(zip(names, booster.feature_importances_))
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total = sum(importance.values()) if importance else 1
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sorted_features = sorted(importance.items(), key=lambda x: x[1], reverse=True)
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metrics["featureImportance"] = [
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{"name": name, "importance": round(val / total, 4)}
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for name, val in sorted_features[:20]
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]
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except Exception:
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pass
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# Use retrain results if available
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if retrain_results:
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metrics["trainAuc"] = retrain_results.get("xgb_train_auc", 0)
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metrics["testAuc"] = retrain_results.get("xgb_test_auc", 0)
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metrics["sampleCount"] = retrain_results.get("sample_count", 0)
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elif hasattr(self, 'auto_trainer') and hasattr(self.auto_trainer, 'last_auc'):
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metrics["testAuc"] = self.auto_trainer.last_auc or 0
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metrics_file = Path("data/model_metrics.json")
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metrics_file.parent.mkdir(parents=True, exist_ok=True)
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metrics_file.write_text(_json.dumps(metrics, indent=2))
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except Exception as e:
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logger.debug(f"Failed to write model metrics: {e}")
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def _write_dashboard_status(self):
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"""Write current bot state to JSON file for Docker dashboard API."""
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try:
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@@ -730,6 +784,11 @@ class TradingBot:
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if len(df_h1) < 20:
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return "NEUTRAL"
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# Calculate indicators + SMC on H1 and cache for V2 features
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df_h1 = self.features.calculate_all(df_h1, include_ml_features=False)
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df_h1 = self.smc.calculate_all(df_h1)
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self._h1_df_cached = df_h1 # Cache for V2 features
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# #31B: Price vs EMA20 method (backtested winner)
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import numpy as np
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closes = df_h1["close"].to_list()
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@@ -910,6 +969,8 @@ class TradingBot:
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if len(df) == 0:
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return
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df = self.features.calculate_all(df, include_ml_features=True)
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df = self.smc.calculate_all(df)
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df = self.fe_v2.add_all_v2_features(df, self._h1_df_cached)
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feature_cols = self._get_available_features(df)
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ml_prediction = self.ml_model.predict(df, feature_cols)
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await self._smart_position_management(
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@@ -943,7 +1004,10 @@ class TradingBot:
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# 3. Apply SMC analysis
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df = self.smc.calculate_all(df)
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# 3b. Add V2 features for Model D (23 extra features)
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df = self.fe_v2.add_all_v2_features(df, self._h1_df_cached)
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# 4. Detect regime
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try:
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df = self.regime_detector.predict(df)
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@@ -2365,6 +2429,9 @@ class TradingBot:
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logger.info(f" Train AUC: {results.get('xgb_train_auc', 0):.4f}")
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logger.info(f" Test AUC: {results.get('xgb_test_auc', 0):.4f}")
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# Write updated model metrics for dashboard
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self._write_model_metrics(retrain_results=results)
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# Check if new model is worse - rollback if needed
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# FIX: Increased minimum AUC from 0.52 to 0.60 (0.52 is barely better than random)
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if results.get("xgb_test_auc", 0) < 0.60:
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