Simon Moisy
10bb371054
- Introduced `RegimeReversionStrategy` for ML-based regime detection and mean reversion trading. - Added feature engineering and model training logic within the new strategy. - Removed the deprecated `regime_detection.py` file to streamline the codebase. - Updated the strategy factory to include the new regime strategy configuration.
281 lines
12 KiB
Python
281 lines
12 KiB
Python
import pandas as pd
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import numpy as np
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import ta
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import vectorbt as vbt
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from sklearn.ensemble import RandomForestClassifier
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from strategies.base import BaseStrategy
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from engine.market import MarketType
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from engine.data_manager import DataManager
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from engine.logging_config import get_logger
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logger = get_logger(__name__)
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class RegimeReversionStrategy(BaseStrategy):
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"""
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ML-Based Regime Detection & Mean Reversion Strategy.
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Logic:
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1. Tracks the BTC/ETH Spread and its Z-Score (24h window).
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2. Uses a Random Forest model to predict if an extreme Z-Score will revert profitably.
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3. Features: Spread Technicals (RSI, ROC) + On-Chain Flows (Inflow, Funding).
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4. Entry: When Model Probability > 0.5.
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5. Exit: Z-Score reversion to 0 or SL/TP.
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Walk-Forward Training:
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- Trains on first `train_ratio` of data (default 70%)
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- Generates signals only for remaining test period (30%)
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- Eliminates look-ahead bias for realistic backtest results
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"""
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def __init__(self,
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model_path: str = "data/regime_model.pkl",
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horizon: int = 96, # 4 Days based on research
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z_window: int = 24,
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stop_loss: float = 0.06, # 6% to survive 2% avg MAE
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take_profit: float = 0.05, # Swing target
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train_ratio: float = 0.7 # Walk-forward: train on first 70%
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):
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super().__init__()
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self.model_path = model_path
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self.horizon = horizon
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self.z_window = z_window
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self.stop_loss = stop_loss
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self.take_profit = take_profit
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self.train_ratio = train_ratio
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# Default Strategy Config
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self.default_market_type = MarketType.PERPETUAL
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self.default_leverage = 1
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self.dm = DataManager()
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self.model = None
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self.feature_cols = None
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self.train_end_idx = None # Will store the training cutoff point
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def run(self, close, **kwargs):
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"""
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Execute the strategy logic.
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We assume this strategy is run on ETH-USDT (the active asset).
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We will fetch BTC-USDT internally to calculate the spread.
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"""
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# 1. Identify Context
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# We need BTC data aligned with the incoming ETH 'close' series
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start_date = close.index.min()
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end_date = close.index.max()
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logger.info("Fetching BTC context data...")
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try:
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# Load BTC data (Context) - Must match the timeframe of the backtest
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# Research was done on 1h candles, so strategy should be run on 1h
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df_btc = self.dm.load_data("okx", "BTC-USDT", "1h", MarketType.SPOT)
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# Align BTC to ETH (close)
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df_btc = df_btc.reindex(close.index, method='ffill')
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btc_close = df_btc['close']
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except Exception as e:
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logger.error(f"Failed to load BTC context: {e}")
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empty = self.create_empty_signals(close)
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return empty, empty, empty, empty
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# 2. Construct DataFrames for Feature Engineering
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# We need volume/high/low for features, but 'run' signature primarily gives 'close'.
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# kwargs might have high/low/volume if passed by Backtester.run_strategy
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eth_vol = kwargs.get('volume')
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if eth_vol is None:
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logger.warning("Volume data missing. Feature calculation might fail.")
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# Fallback or error handling
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eth_vol = pd.Series(0, index=close.index)
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# Construct dummy dfs for prepare_features
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# We only really need Close and Volume for the current feature set
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df_a = pd.DataFrame({'close': btc_close, 'volume': df_btc['volume']})
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df_b = pd.DataFrame({'close': close, 'volume': eth_vol})
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# 3. Load On-Chain Data (CryptoQuant)
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# We use the saved CSV for training/inference
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# In a live setting, this would query the API for recent data
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cq_df = None
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try:
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cq_path = "data/cq_training_data.csv"
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cq_df = pd.read_csv(cq_path, index_col='timestamp', parse_dates=True)
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if cq_df.index.tz is None:
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cq_df.index = cq_df.index.tz_localize('UTC')
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except Exception:
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logger.warning("CryptoQuant data not found. Running without on-chain features.")
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# 4. Calculate Features
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features = self.prepare_features(df_a, df_b, cq_df)
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# 5. Walk-Forward Split
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# Train on first `train_ratio` of data, test on remainder
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n_samples = len(features)
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train_size = int(n_samples * self.train_ratio)
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train_features = features.iloc[:train_size]
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test_features = features.iloc[train_size:]
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train_end_date = train_features.index[-1]
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test_start_date = test_features.index[0]
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logger.info(
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f"Walk-Forward Split: Train={len(train_features)} bars "
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f"(until {train_end_date.strftime('%Y-%m-%d')}), "
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f"Test={len(test_features)} bars "
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f"(from {test_start_date.strftime('%Y-%m-%d')})"
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)
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# 6. Train Model on Training Period ONLY
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if self.model is None:
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logger.info("Training Regime Model on training period only...")
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self.model, self.feature_cols = self.train_model(train_features)
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# 7. Predict on TEST Period ONLY
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# Use valid columns only
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X_test = test_features[self.feature_cols].fillna(0)
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X_test = X_test.replace([np.inf, -np.inf], 0)
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# Predict Probabilities for test period
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probs = self.model.predict_proba(X_test)[:, 1]
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# 8. Generate Entry Signals (TEST period only)
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# If Z > 1 (Spread High, ETH Expensive) -> Short ETH
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# If Z < -1 (Spread Low, ETH Cheap) -> Long ETH
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short_signal_test = (probs > 0.5) & (test_features['z_score'].values > 1.0)
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long_signal_test = (probs > 0.5) & (test_features['z_score'].values < -1.0)
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# Create full-length signal series (False for training period)
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long_entries = pd.Series(False, index=close.index)
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short_entries = pd.Series(False, index=close.index)
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# Map test signals to their correct indices
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test_idx = test_features.index
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for i, idx in enumerate(test_idx):
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if idx in close.index:
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long_entries.loc[idx] = bool(long_signal_test[i])
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short_entries.loc[idx] = bool(short_signal_test[i])
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# 9. Generate Exits
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# Exit when Z-Score crosses back through 0 (mean reversion complete)
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z_reindexed = features['z_score'].reindex(close.index, fill_value=0)
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# Exit Long when Z > 0, Exit Short when Z < 0
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long_exits = z_reindexed > 0
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short_exits = z_reindexed < 0
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# Log signal counts for verification
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n_long = long_entries.sum()
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n_short = short_entries.sum()
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logger.info(f"Generated {n_long} long signals, {n_short} short signals (test period only)")
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return long_entries, long_exits, short_entries, short_exits
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def prepare_features(self, df_btc, df_eth, cq_df=None):
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"""Replicate research feature engineering"""
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# Align
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common = df_btc.index.intersection(df_eth.index)
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df_a = df_btc.loc[common].copy()
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df_b = df_eth.loc[common].copy()
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# Spread
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spread = df_b['close'] / df_a['close']
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# Z-Score
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rolling_mean = spread.rolling(window=self.z_window).mean()
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rolling_std = spread.rolling(window=self.z_window).std()
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z_score = (spread - rolling_mean) / rolling_std
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# Technicals
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spread_rsi = ta.momentum.RSIIndicator(spread, window=14).rsi()
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spread_roc = spread.pct_change(periods=5) * 100
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spread_change_1h = spread.pct_change(periods=1)
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# Volume
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vol_ratio = df_b['volume'] / df_a['volume']
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vol_ratio_ma = vol_ratio.rolling(window=12).mean()
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# Volatility
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ret_a = df_a['close'].pct_change()
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ret_b = df_b['close'].pct_change()
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vol_a = ret_a.rolling(window=self.z_window).std()
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vol_b = ret_b.rolling(window=self.z_window).std()
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vol_spread_ratio = vol_b / vol_a
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features = pd.DataFrame(index=spread.index)
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features['spread'] = spread
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features['z_score'] = z_score
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features['spread_rsi'] = spread_rsi
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features['spread_roc'] = spread_roc
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features['spread_change_1h'] = spread_change_1h
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features['vol_ratio'] = vol_ratio
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features['vol_ratio_rel'] = vol_ratio / vol_ratio_ma
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features['vol_diff_ratio'] = vol_spread_ratio
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# CQ Merge
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if cq_df is not None:
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cq_aligned = cq_df.reindex(features.index, method='ffill')
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if 'btc_funding' in cq_aligned.columns and 'eth_funding' in cq_aligned.columns:
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cq_aligned['funding_diff'] = cq_aligned['eth_funding'] - cq_aligned['btc_funding']
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if 'btc_inflow' in cq_aligned.columns and 'eth_inflow' in cq_aligned.columns:
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cq_aligned['inflow_ratio'] = cq_aligned['eth_inflow'] / (cq_aligned['btc_inflow'] + 1)
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features = features.join(cq_aligned)
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return features.dropna()
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def train_model(self, train_features):
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"""
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Train Random Forest on training data only.
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This method receives ONLY the training subset of features,
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ensuring no look-ahead bias. The model learns from historical
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patterns and is then applied to unseen test data.
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Args:
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train_features: DataFrame containing features for training period only
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"""
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threshold = 0.005
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horizon = self.horizon
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# Define targets using ONLY training data
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# For Short Spread (Z > 1): Did spread drop below target within horizon?
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future_min = train_features['spread'].rolling(window=horizon).min().shift(-horizon)
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target_short = train_features['spread'] * (1 - threshold)
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success_short = (train_features['z_score'] > 1.0) & (future_min < target_short)
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# For Long Spread (Z < -1): Did spread rise above target within horizon?
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future_max = train_features['spread'].rolling(window=horizon).max().shift(-horizon)
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target_long = train_features['spread'] * (1 + threshold)
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success_long = (train_features['z_score'] < -1.0) & (future_max > target_long)
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targets = np.select([success_short, success_long], [1, 1], default=0)
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# Build model
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model = RandomForestClassifier(
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n_estimators=300, max_depth=5, min_samples_leaf=30,
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class_weight={0: 1, 1: 3}, random_state=42
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)
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# Exclude non-feature columns
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exclude = ['spread']
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cols = [c for c in train_features.columns if c not in exclude]
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# Clean features
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X_train = train_features[cols].fillna(0)
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X_train = X_train.replace([np.inf, -np.inf], 0)
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# Remove rows with NaN targets (from rolling window at end of training period)
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valid_mask = ~np.isnan(targets) & ~np.isinf(targets)
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# Also check for rows where future data doesn't exist (shift created NaNs)
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valid_mask = valid_mask & (future_min.notna().values) & (future_max.notna().values)
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X_train_clean = X_train[valid_mask]
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targets_clean = targets[valid_mask]
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logger.info(f"Training on {len(X_train_clean)} valid samples (removed {len(X_train) - len(X_train_clean)} with incomplete future data)")
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model.fit(X_train_clean, targets_clean)
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return model, cols
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