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
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"""
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ML V2 Training Pipeline
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========================
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Training pipeline with purged walk-forward CV and experiment configs.
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Experiment Configs:
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- Baseline: 1-bar target + 37 base features + XGBoost (V1 reproduction)
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- A: 3-bar target + 37 base features + XGBoost
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- B: 3-bar target + 45 features (37 + 8 H1) + XGBoost
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- C: 3-bar target + 52 features (45 + 7 SMC) + XGBoost
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- D: 3-bar target + 60 features (52 + 8 regime/PA) + XGBoost
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- E: 3-bar target + 60 features + Ensemble (XGB + LGBM)
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"""
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import polars as pl
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import numpy as np
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from typing import List, Dict, Tuple, Optional
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from dataclasses import dataclass
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from enum import Enum
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from loguru import logger
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from .ml_v2_target import TargetBuilder
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from .ml_v2_feature_eng import MLV2FeatureEngineer
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from .ml_v2_model import TradingModelV2, ModelType
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@dataclass
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class ExperimentConfig:
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"""Configuration for a training experiment."""
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name: str
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target_col: str # Which target to use
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feature_cols: List[str] # Which features to use
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model_type: ModelType
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lookahead: int = 3 # For target creation
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threshold_atr_mult: float = 0.3 # For target creation
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class MLV2Trainer:
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"""
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ML V2 Training Pipeline.
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Features:
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- Purged walk-forward CV (5 folds)
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- Gap between folds to prevent temporal leakage
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- Experiment comparison
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- Optional Boruta feature selection
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"""
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def __init__(
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self,
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train_size: int = 5000,
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test_size: int = 1000,
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gap_size: int = 50,
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n_folds: int = 5,
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):
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"""
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Initialize trainer.
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Args:
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train_size: Samples per training fold
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test_size: Samples per test fold
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gap_size: Gap between train and test (prevent leakage)
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n_folds: Number of CV folds
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"""
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self.train_size = train_size
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self.test_size = test_size
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self.gap_size = gap_size
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self.n_folds = n_folds
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def purged_walk_forward_cv(
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self,
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df: pl.DataFrame,
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feature_cols: List[str],
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target_col: str,
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model_type: ModelType,
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) -> Dict[str, float]:
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"""
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Purged walk-forward cross-validation.
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Splits data into `n_folds` sequential folds with:
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- `train_size` samples for training
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- `gap_size` samples skipped (purge)
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- `test_size` samples for testing
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Args:
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df: DataFrame with features and target
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feature_cols: Feature columns
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target_col: Target column
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model_type: Model type to train
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Returns:
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Dict with mean/std of train/test AUC and overfitting ratio
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"""
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logger.info(f"Starting purged walk-forward CV ({self.n_folds} folds)...")
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# Drop nulls
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df_clean = df.select(feature_cols + [target_col]).drop_nulls()
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if len(df_clean) < self.train_size + self.gap_size + self.test_size:
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logger.error(f"Insufficient data for CV: {len(df_clean)} samples")
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return {}
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train_scores = []
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test_scores = []
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fold_step = self.train_size + self.gap_size + self.test_size
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for fold in range(self.n_folds):
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start_idx = fold * fold_step
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if start_idx + fold_step > len(df_clean):
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logger.warning(f"Fold {fold+1}: Not enough data, skipping")
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break
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train_end = start_idx + self.train_size
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test_start = train_end + self.gap_size
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test_end = test_start + self.test_size
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# Extract fold data
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df_train = df_clean.slice(start_idx, self.train_size)
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df_test = df_clean.slice(test_start, self.test_size)
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logger.info(f" Fold {fold+1}/{self.n_folds}: Train [{start_idx}:{train_end}], Test [{test_start}:{test_end}]")
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# Train model
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model = TradingModelV2(model_type=model_type)
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model.fit(
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df_train,
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feature_cols,
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target_col,
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train_ratio=1.0, # Use all training data
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num_boost_round=100,
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early_stopping_rounds=None, # No early stopping in CV
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)
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if not model.fitted:
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logger.warning(f" Fold {fold+1}: Model failed to fit")
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continue
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# Extract features and targets
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X_train = df_train.select(feature_cols).to_numpy()
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y_train = df_train.select(target_col).to_numpy().ravel()
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X_test = df_test.select(feature_cols).to_numpy()
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y_test = df_test.select(target_col).to_numpy().ravel()
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X_train = np.nan_to_num(X_train, nan=0.0)
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X_test = np.nan_to_num(X_test, nan=0.0)
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# Evaluate
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train_score = self._evaluate_binary(model, X_train, y_train)
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test_score = self._evaluate_binary(model, X_test, y_test)
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train_scores.append(train_score)
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test_scores.append(test_score)
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logger.info(f" Fold {fold+1}: Train AUC={train_score:.4f}, Test AUC={test_score:.4f}")
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if not train_scores:
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logger.error("No folds completed successfully")
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return {}
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# Compute statistics
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mean_train = np.mean(train_scores)
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std_train = np.std(train_scores)
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mean_test = np.mean(test_scores)
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std_test = np.std(test_scores)
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overfitting_ratio = mean_train / mean_test if mean_test > 0 else 999.0
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results = {
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"mean_train_auc": mean_train,
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"std_train_auc": std_train,
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"mean_test_auc": mean_test,
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"std_test_auc": std_test,
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"overfitting_ratio": overfitting_ratio,
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"n_folds": len(train_scores),
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}
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logger.info(f"CV Results: Train AUC={mean_train:.4f}±{std_train:.4f}, "
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f"Test AUC={mean_test:.4f}±{std_test:.4f}, "
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f"Overfit Ratio={overfitting_ratio:.2f}")
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return results
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def _evaluate_binary(self, model: TradingModelV2, X, y) -> float:
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"""Evaluate binary classification model."""
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try:
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from sklearn.metrics import roc_auc_score
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import xgboost as xgb
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if model.xgb_model is not None:
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dmatrix = xgb.DMatrix(X, feature_names=model.feature_names)
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preds = model.xgb_model.predict(dmatrix)
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return roc_auc_score(y, preds)
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elif model.lgb_model is not None:
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preds = model.lgb_model.predict(X)
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return roc_auc_score(y, preds)
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else:
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return 0.5
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except Exception as e:
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logger.warning(f"Evaluation error: {e}")
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return 0.5
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def train_experiment(
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self,
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config: ExperimentConfig,
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df: pl.DataFrame,
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save_path: Optional[str] = None,
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run_cv: bool = True,
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) -> Tuple[TradingModelV2, Dict]:
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"""
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Train a single experiment config.
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Args:
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config: Experiment configuration
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df: DataFrame with all features and targets
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save_path: Path to save trained model
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run_cv: Whether to run cross-validation
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Returns:
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Tuple of (trained model, CV results dict)
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"""
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logger.info(f"\n{'='*60}")
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logger.info(f"Training Experiment: {config.name}")
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logger.info(f" Target: {config.target_col}")
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logger.info(f" Features: {len(config.feature_cols)}")
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logger.info(f" Model: {config.model_type.value}")
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logger.info(f"{'='*60}")
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# Cross-validation
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cv_results = {}
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if run_cv:
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cv_results = self.purged_walk_forward_cv(
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df,
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config.feature_cols,
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config.target_col,
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config.model_type,
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)
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# Train final model on all data
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logger.info("Training final model on all data...")
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model = TradingModelV2(
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model_type=config.model_type,
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model_path=save_path,
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)
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model.fit(
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df,
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config.feature_cols,
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config.target_col,
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train_ratio=0.8,
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num_boost_round=100,
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early_stopping_rounds=10,
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)
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logger.info(f"Experiment {config.name} complete!")
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return model, cv_results
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def get_baseline_config(base_features: List[str]) -> ExperimentConfig:
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"""Get baseline (V1) experiment config."""
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return ExperimentConfig(
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name="Baseline (V1)",
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target_col="baseline_target",
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feature_cols=base_features,
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model_type=ModelType.XGBOOST_BINARY,
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lookahead=1,
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threshold_atr_mult=0.0,
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)
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def get_config_a(base_features: List[str]) -> ExperimentConfig:
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"""Config A: Better target, same features."""
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return ExperimentConfig(
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name="A: Better Target",
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target_col="multi_bar_target",
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feature_cols=base_features,
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model_type=ModelType.XGBOOST_BINARY,
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lookahead=3,
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threshold_atr_mult=0.3,
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)
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def get_config_b(base_features: List[str], h1_features: List[str]) -> ExperimentConfig:
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"""Config B: Better target + H1 features."""
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features = base_features + h1_features
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return ExperimentConfig(
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name="B: +H1 Features",
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target_col="multi_bar_target",
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feature_cols=features,
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model_type=ModelType.XGBOOST_BINARY,
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lookahead=3,
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threshold_atr_mult=0.3,
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)
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def get_config_c(base_features: List[str], h1_features: List[str], smc_features: List[str]) -> ExperimentConfig:
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"""Config C: Better target + H1 + continuous SMC."""
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features = base_features + h1_features + smc_features
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return ExperimentConfig(
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name="C: +Continuous SMC",
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target_col="multi_bar_target",
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feature_cols=features,
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model_type=ModelType.XGBOOST_BINARY,
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lookahead=3,
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threshold_atr_mult=0.3,
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)
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def get_config_d(
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base_features: List[str],
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h1_features: List[str],
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smc_features: List[str],
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regime_features: List[str],
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pa_features: List[str],
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) -> ExperimentConfig:
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"""Config D: All features."""
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features = base_features + h1_features + smc_features + regime_features + pa_features
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return ExperimentConfig(
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name="D: All 60 Features",
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target_col="multi_bar_target",
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feature_cols=features,
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model_type=ModelType.XGBOOST_BINARY,
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lookahead=3,
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threshold_atr_mult=0.3,
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)
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def get_config_e(
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base_features: List[str],
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h1_features: List[str],
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smc_features: List[str],
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regime_features: List[str],
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pa_features: List[str],
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) -> ExperimentConfig:
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"""Config E: All features + ensemble."""
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features = base_features + h1_features + smc_features + regime_features + pa_features
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return ExperimentConfig(
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name="E: Ensemble",
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target_col="multi_bar_target",
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feature_cols=features,
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model_type=ModelType.ENSEMBLE,
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lookahead=3,
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threshold_atr_mult=0.3,
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)
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if __name__ == "__main__":
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# Test training pipeline
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import numpy as np
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np.random.seed(42)
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n = 10000
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# Synthetic data with 40 features
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feature_data = {}
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for i in range(40):
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feature_data[f"feat_{i}"] = np.random.randn(n)
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df = pl.DataFrame(feature_data)
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# Add target
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target = (df["feat_0"].to_numpy() + df["feat_1"].to_numpy() > 0).astype(int)
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df = df.with_columns([pl.Series("multi_bar_target", target)])
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# Test config
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config = ExperimentConfig(
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name="Test",
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target_col="multi_bar_target",
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feature_cols=[f"feat_{i}" for i in range(10)],
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model_type=ModelType.XGBOOST_BINARY,
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)
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# Train
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trainer = MLV2Trainer(train_size=1000, test_size=200, gap_size=50, n_folds=3)
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model, cv_results = trainer.train_experiment(config, df, run_cv=True)
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print(f"\nCV Results: {cv_results}")
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print(f"Model fitted: {model.fitted}")
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