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>

backtests/ml_v2/__init__.py

@@ -0,0 +1,26 @@
+"""
+ML V2 Package
+==============
+Full ML overhaul with better target variables, enhanced features, and ensemble models.
+
+Components:
+- ml_v2_target.py: Improved target variables (multi-bar + ATR threshold)
+- ml_v2_feature_eng.py: 23 new features (H1 MTF, continuous SMC, regime, price action)
+- ml_v2_model.py: Multi-model support (XGBoost, LightGBM, ensemble)
+- ml_v2_train.py: Training pipeline with purged walk-forward CV
+- backtest_36_ml_v2.py: Main backtest (configs A/B/C/D/E)
+"""
+
+from .ml_v2_target import TargetBuilder
+from .ml_v2_feature_eng import MLV2FeatureEngineer
+from .ml_v2_model import TradingModelV2, ModelType
+from .ml_v2_train import ExperimentConfig, MLV2Trainer
+
+__all__ = [
+    "TargetBuilder",
+    "MLV2FeatureEngineer",
+    "TradingModelV2",
+    "ModelType",
+    "ExperimentConfig",
+    "MLV2Trainer",
+]