""" Regime Detection Research Script with Walk-Forward Training. Tests multiple holding horizons to find optimal parameters without look-ahead bias. """ import sys import os sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) import pandas as pd import numpy as np import ta from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report, f1_score from engine.data_manager import DataManager from engine.market import MarketType from engine.logging_config import get_logger logger = get_logger(__name__) # Configuration TRAIN_RATIO = 0.7 # 70% train, 30% test PROFIT_THRESHOLD = 0.005 # 0.5% profit target Z_WINDOW = 24 FEE_RATE = 0.001 # 0.1% round-trip fee def load_data(): """Load and align BTC/ETH data.""" dm = DataManager() df_btc = dm.load_data("okx", "BTC-USDT", "1h", MarketType.SPOT) df_eth = dm.load_data("okx", "ETH-USDT", "1h", MarketType.SPOT) # Filter to Oct-Dec 2025 start = pd.Timestamp("2025-10-01", tz="UTC") end = pd.Timestamp("2025-12-31", tz="UTC") df_btc = df_btc[(df_btc.index >= start) & (df_btc.index <= end)] df_eth = df_eth[(df_eth.index >= start) & (df_eth.index <= end)] # Align indices common = df_btc.index.intersection(df_eth.index) df_btc = df_btc.loc[common] df_eth = df_eth.loc[common] logger.info(f"Loaded {len(common)} aligned hourly bars") return df_btc, df_eth def load_cryptoquant_data(): """Load CryptoQuant on-chain data if available.""" try: cq_path = "data/cq_training_data.csv" cq_df = pd.read_csv(cq_path, index_col='timestamp', parse_dates=True) if cq_df.index.tz is None: cq_df.index = cq_df.index.tz_localize('UTC') logger.info(f"Loaded CryptoQuant data: {len(cq_df)} rows") return cq_df except Exception as e: logger.warning(f"CryptoQuant data not available: {e}") return None def calculate_features(df_btc, df_eth, cq_df=None): """Calculate all features for the model.""" spread = df_eth['close'] / df_btc['close'] # Z-Score rolling_mean = spread.rolling(window=Z_WINDOW).mean() rolling_std = spread.rolling(window=Z_WINDOW).std() z_score = (spread - rolling_mean) / rolling_std # Technicals spread_rsi = ta.momentum.RSIIndicator(spread, window=14).rsi() spread_roc = spread.pct_change(periods=5) * 100 spread_change_1h = spread.pct_change(periods=1) # Volume vol_ratio = df_eth['volume'] / df_btc['volume'] vol_ratio_ma = vol_ratio.rolling(window=12).mean() # Volatility ret_btc = df_btc['close'].pct_change() ret_eth = df_eth['close'].pct_change() vol_btc = ret_btc.rolling(window=Z_WINDOW).std() vol_eth = ret_eth.rolling(window=Z_WINDOW).std() vol_spread_ratio = vol_eth / vol_btc features = pd.DataFrame(index=spread.index) features['spread'] = spread features['z_score'] = z_score features['spread_rsi'] = spread_rsi features['spread_roc'] = spread_roc features['spread_change_1h'] = spread_change_1h features['vol_ratio'] = vol_ratio features['vol_ratio_rel'] = vol_ratio / vol_ratio_ma features['vol_diff_ratio'] = vol_spread_ratio # Add CQ features if available if cq_df is not None: cq_aligned = cq_df.reindex(features.index, method='ffill') if 'btc_funding' in cq_aligned.columns and 'eth_funding' in cq_aligned.columns: cq_aligned['funding_diff'] = cq_aligned['eth_funding'] - cq_aligned['btc_funding'] if 'btc_inflow' in cq_aligned.columns and 'eth_inflow' in cq_aligned.columns: cq_aligned['inflow_ratio'] = cq_aligned['eth_inflow'] / (cq_aligned['btc_inflow'] + 1) features = features.join(cq_aligned) return features.dropna() def calculate_targets(features, horizon): """Calculate target labels for a given horizon.""" spread = features['spread'] z_score = features['z_score'] # For Short (Z > 1): Did spread drop below target? future_min = spread.rolling(window=horizon).min().shift(-horizon) target_short = spread * (1 - PROFIT_THRESHOLD) success_short = (z_score > 1.0) & (future_min < target_short) # For Long (Z < -1): Did spread rise above target? future_max = spread.rolling(window=horizon).max().shift(-horizon) target_long = spread * (1 + PROFIT_THRESHOLD) success_long = (z_score < -1.0) & (future_max > target_long) targets = np.select([success_short, success_long], [1, 1], default=0) # Create valid mask (rows with complete future data) valid_mask = future_min.notna() & future_max.notna() return targets, valid_mask, future_min, future_max def calculate_mae(features, predictions, test_idx, horizon): """Calculate Maximum Adverse Excursion for predicted trades.""" test_features = features.loc[test_idx] spread = test_features['spread'] z_score = test_features['z_score'] mae_values = [] for i, (idx, pred) in enumerate(zip(test_idx, predictions)): if pred != 1: continue entry_spread = spread.loc[idx] z = z_score.loc[idx] # Get future spread values future_idx = features.index.get_loc(idx) future_end = min(future_idx + horizon, len(features)) future_spreads = features['spread'].iloc[future_idx:future_end] if len(future_spreads) < 2: continue if z > 1.0: # Short trade max_adverse = (future_spreads.max() - entry_spread) / entry_spread else: # Long trade max_adverse = (entry_spread - future_spreads.min()) / entry_spread mae_values.append(max_adverse * 100) # As percentage return np.mean(mae_values) if mae_values else 0.0 def calculate_net_profit(features, predictions, test_idx, horizon): """Calculate estimated net profit including fees.""" test_features = features.loc[test_idx] spread = test_features['spread'] z_score = test_features['z_score'] total_pnl = 0.0 n_trades = 0 for i, (idx, pred) in enumerate(zip(test_idx, predictions)): if pred != 1: continue entry_spread = spread.loc[idx] z = z_score.loc[idx] # Get future spread values future_idx = features.index.get_loc(idx) future_end = min(future_idx + horizon, len(features)) future_spreads = features['spread'].iloc[future_idx:future_end] if len(future_spreads) < 2: continue # Calculate PnL based on direction if z > 1.0: # Short trade - profit if spread drops exit_spread = future_spreads.iloc[-1] # Exit at horizon pnl = (entry_spread - exit_spread) / entry_spread else: # Long trade - profit if spread rises exit_spread = future_spreads.iloc[-1] pnl = (exit_spread - entry_spread) / entry_spread # Subtract fees net_pnl = pnl - FEE_RATE total_pnl += net_pnl n_trades += 1 return total_pnl, n_trades def test_horizon(features, horizon): """Test a single horizon with walk-forward training.""" # Calculate targets targets, valid_mask, _, _ = calculate_targets(features, horizon) # Walk-forward split n_samples = len(features) train_size = int(n_samples * TRAIN_RATIO) train_features = features.iloc[:train_size] test_features = features.iloc[train_size:] train_targets = targets[:train_size] test_targets = targets[train_size:] train_valid = valid_mask.iloc[:train_size] test_valid = valid_mask.iloc[train_size:] # Prepare training data (only valid rows) exclude = ['spread'] cols = [c for c in features.columns if c not in exclude] X_train = train_features[cols].fillna(0).replace([np.inf, -np.inf], 0) X_train_valid = X_train[train_valid] y_train_valid = train_targets[train_valid] if len(X_train_valid) < 50: return None # Not enough training data # Train model model = RandomForestClassifier( n_estimators=300, max_depth=5, min_samples_leaf=30, class_weight={0: 1, 1: 3}, random_state=42 ) model.fit(X_train_valid, y_train_valid) # Predict on test set X_test = test_features[cols].fillna(0).replace([np.inf, -np.inf], 0) predictions = model.predict(X_test) # Only evaluate on valid test rows (those with complete future data) test_valid_mask = test_valid.values y_test_valid = test_targets[test_valid_mask] pred_valid = predictions[test_valid_mask] if len(y_test_valid) < 10: return None # Calculate metrics f1 = f1_score(y_test_valid, pred_valid, zero_division=0) # Calculate MAE and Net Profit on ALL test predictions (not just valid targets) test_idx = test_features.index avg_mae = calculate_mae(features, predictions, test_idx, horizon) net_pnl, n_trades = calculate_net_profit(features, predictions, test_idx, horizon) return { 'horizon': horizon, 'f1_score': f1, 'avg_mae': avg_mae, 'net_pnl': net_pnl, 'n_trades': n_trades, 'train_samples': len(X_train_valid), 'test_samples': len(X_test) } def test_horizons(features, horizons): """Test multiple horizons and return comparison.""" results = [] print("\n" + "=" * 80) print("WALK-FORWARD HORIZON OPTIMIZATION") print(f"Train Ratio: {TRAIN_RATIO*100:.0f}% | Profit Target: {PROFIT_THRESHOLD*100:.1f}% | Fee Rate: {FEE_RATE*100:.2f}%") print("=" * 80) for h in horizons: result = test_horizon(features, h) if result: results.append(result) print(f"Horizon {h:3d}h: F1={result['f1_score']:.3f}, " f"MAE={result['avg_mae']:.2f}%, " f"Net PnL={result['net_pnl']*100:.2f}%, " f"Trades={result['n_trades']}") return results def main(): """Main research function.""" # Load data df_btc, df_eth = load_data() cq_df = load_cryptoquant_data() # Calculate features features = calculate_features(df_btc, df_eth, cq_df) logger.info(f"Calculated {len(features)} feature rows with {len(features.columns)} columns") # Test horizons from 6h to 150h horizons = list(range(6, 151, 6)) # 6, 12, 18, ..., 150 results = test_horizons(features, horizons) if not results: print("No valid results!") return # Find best by different metrics results_df = pd.DataFrame(results) print("\n" + "=" * 80) print("BEST HORIZONS BY METRIC") print("=" * 80) best_f1 = results_df.loc[results_df['f1_score'].idxmax()] print(f"Best F1 Score: {best_f1['horizon']:.0f}h (F1={best_f1['f1_score']:.3f})") best_pnl = results_df.loc[results_df['net_pnl'].idxmax()] print(f"Best Net PnL: {best_pnl['horizon']:.0f}h (PnL={best_pnl['net_pnl']*100:.2f}%)") lowest_mae = results_df.loc[results_df['avg_mae'].idxmin()] print(f"Lowest MAE: {lowest_mae['horizon']:.0f}h (MAE={lowest_mae['avg_mae']:.2f}%)") # Save results output_path = "research/horizon_optimization_results.csv" results_df.to_csv(output_path, index=False) print(f"\nResults saved to {output_path}") return results_df if __name__ == "__main__": main()