Simon Moisy
0c82c4f366
- Added FastAPI backend with core API endpoints for strategies, backtests, and data management. - Introduced Vue 3 frontend with a dark theme, enabling users to run backtests, adjust parameters, and compare results. - Implemented Pydantic schemas for request/response validation and SQLAlchemy models for database interactions. - Enhanced project structure with dedicated modules for services, routers, and components. - Updated dependencies in `pyproject.toml` and `frontend/package.json` to include FastAPI, SQLAlchemy, and Vue-related packages. - Improved `.gitignore` to exclude unnecessary files and directories.
285 lines
9.7 KiB
Python
285 lines
9.7 KiB
Python
"""
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Live Regime Reversion Strategy.
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Adapts the backtest regime strategy for live trading.
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Uses a pre-trained ML model or trains on historical data.
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"""
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import logging
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import pickle
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from pathlib import Path
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from typing import Optional
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import numpy as np
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import pandas as pd
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from sklearn.ensemble import RandomForestClassifier
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from .config import TradingConfig, PathConfig
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logger = logging.getLogger(__name__)
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class LiveRegimeStrategy:
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"""
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Live trading implementation of the ML-based regime detection
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and mean reversion strategy.
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Logic:
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1. Calculates BTC/ETH spread Z-Score
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2. Uses Random Forest to predict reversion probability
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3. Applies funding rate filter
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4. Generates long/short signals on ETH perpetual
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"""
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def __init__(
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self,
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trading_config: TradingConfig,
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path_config: PathConfig
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):
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self.config = trading_config
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self.paths = path_config
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self.model: Optional[RandomForestClassifier] = None
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self.feature_cols: Optional[list] = None
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self._load_or_train_model()
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def _load_or_train_model(self) -> None:
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"""Load pre-trained model or train a new one."""
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if self.paths.model_path.exists():
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try:
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with open(self.paths.model_path, 'rb') as f:
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saved = pickle.load(f)
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self.model = saved['model']
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self.feature_cols = saved['feature_cols']
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logger.info(f"Loaded model from {self.paths.model_path}")
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return
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except Exception as e:
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logger.warning(f"Could not load model: {e}")
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logger.info("No pre-trained model found. Will train on first data batch.")
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def save_model(self) -> None:
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"""Save trained model to file."""
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if self.model is None:
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return
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try:
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with open(self.paths.model_path, 'wb') as f:
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pickle.dump({
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'model': self.model,
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'feature_cols': self.feature_cols,
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}, f)
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logger.info(f"Saved model to {self.paths.model_path}")
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except Exception as e:
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logger.error(f"Could not save model: {e}")
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def train_model(self, features: pd.DataFrame) -> None:
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"""
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Train the Random Forest model on historical data.
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Args:
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features: DataFrame with calculated features
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"""
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logger.info(f"Training model on {len(features)} samples...")
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z_thresh = self.config.z_entry_threshold
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horizon = 102 # Optimal horizon from research
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profit_target = 0.005 # 0.5% profit threshold
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# Define targets
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future_min = features['spread'].rolling(window=horizon).min().shift(-horizon)
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future_max = features['spread'].rolling(window=horizon).max().shift(-horizon)
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target_short = features['spread'] * (1 - profit_target)
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target_long = features['spread'] * (1 + profit_target)
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success_short = (features['z_score'] > z_thresh) & (future_min < target_short)
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success_long = (features['z_score'] < -z_thresh) & (future_max > target_long)
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targets = np.select([success_short, success_long], [1, 1], default=0)
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# Exclude non-feature columns
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exclude = ['spread', 'btc_close', 'eth_close', 'eth_volume']
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self.feature_cols = [c for c in features.columns if c not in exclude]
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# Clean features
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X = features[self.feature_cols].fillna(0)
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X = X.replace([np.inf, -np.inf], 0)
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# Remove rows with invalid targets
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valid_mask = ~np.isnan(targets) & future_min.notna().values & future_max.notna().values
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X_clean = X[valid_mask]
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y_clean = targets[valid_mask]
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if len(X_clean) < 100:
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logger.warning("Not enough data to train model")
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return
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# Train model
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self.model = RandomForestClassifier(
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n_estimators=300,
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max_depth=5,
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min_samples_leaf=30,
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class_weight={0: 1, 1: 3},
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random_state=42
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)
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self.model.fit(X_clean, y_clean)
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logger.info(f"Model trained on {len(X_clean)} samples")
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self.save_model()
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def generate_signal(
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self,
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features: pd.DataFrame,
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current_funding: dict
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) -> dict:
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"""
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Generate trading signal from latest features.
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Args:
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features: DataFrame with calculated features
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current_funding: Dictionary with funding rate data
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Returns:
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Signal dictionary with action, side, confidence, etc.
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"""
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if self.model is None:
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# Train model if not available
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if len(features) >= 200:
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self.train_model(features)
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else:
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return {'action': 'hold', 'reason': 'model_not_trained'}
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if self.model is None:
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return {'action': 'hold', 'reason': 'insufficient_data_for_training'}
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# Get latest row
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latest = features.iloc[-1]
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z_score = latest['z_score']
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eth_price = latest['eth_close']
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btc_price = latest['btc_close']
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# Prepare features for prediction
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X = features[self.feature_cols].iloc[[-1]].fillna(0)
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X = X.replace([np.inf, -np.inf], 0)
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# Get prediction probability
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prob = self.model.predict_proba(X)[0, 1]
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# Apply thresholds
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z_thresh = self.config.z_entry_threshold
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prob_thresh = self.config.model_prob_threshold
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# Determine signal direction
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signal = {
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'action': 'hold',
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'side': None,
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'probability': prob,
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'z_score': z_score,
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'eth_price': eth_price,
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'btc_price': btc_price,
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'reason': '',
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}
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# Check for entry conditions
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if prob > prob_thresh:
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if z_score > z_thresh:
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# Spread high (ETH expensive relative to BTC) -> Short ETH
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signal['action'] = 'entry'
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signal['side'] = 'short'
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signal['reason'] = f'z_score={z_score:.2f}>threshold, prob={prob:.2f}'
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elif z_score < -z_thresh:
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# Spread low (ETH cheap relative to BTC) -> Long ETH
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signal['action'] = 'entry'
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signal['side'] = 'long'
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signal['reason'] = f'z_score={z_score:.2f}<-threshold, prob={prob:.2f}'
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else:
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signal['reason'] = f'z_score={z_score:.2f} within threshold'
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else:
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signal['reason'] = f'prob={prob:.2f}<threshold'
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# Apply funding rate filter
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if signal['action'] == 'entry':
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btc_funding = current_funding.get('btc_funding', 0)
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funding_thresh = self.config.funding_threshold
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if signal['side'] == 'long' and btc_funding > funding_thresh:
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# High positive funding = overheated, don't go long
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signal['action'] = 'hold'
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signal['reason'] = f'funding_filter_blocked_long (funding={btc_funding:.4f})'
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elif signal['side'] == 'short' and btc_funding < -funding_thresh:
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# High negative funding = oversold, don't go short
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signal['action'] = 'hold'
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signal['reason'] = f'funding_filter_blocked_short (funding={btc_funding:.4f})'
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# Check for exit conditions (mean reversion complete)
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if signal['action'] == 'hold':
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# Z-score crossed back through 0
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if abs(z_score) < 0.3:
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signal['action'] = 'check_exit'
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signal['reason'] = f'z_score_reverted_to_mean ({z_score:.2f})'
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logger.info(
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f"Signal: {signal['action']} {signal['side'] or ''} "
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f"(prob={prob:.2f}, z={z_score:.2f}, reason={signal['reason']})"
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)
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return signal
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def calculate_position_size(
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self,
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signal: dict,
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available_usdt: float
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) -> float:
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"""
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Calculate position size based on signal confidence.
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Args:
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signal: Signal dictionary with probability
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available_usdt: Available USDT balance
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Returns:
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Position size in USDT
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"""
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prob = signal.get('probability', 0.5)
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# Base size is max_position_usdt
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base_size = min(available_usdt, self.config.max_position_usdt)
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# Scale by probability (1.0x at 0.5 prob, up to 1.6x at 0.8 prob)
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scale = 1.0 + (prob - 0.5) * 2.0
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scale = max(1.0, min(scale, 2.0)) # Clamp between 1x and 2x
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size = base_size * scale
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# Ensure minimum position size
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if size < self.config.min_position_usdt:
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return 0.0
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return min(size, available_usdt * 0.95) # Leave 5% buffer
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def calculate_sl_tp(
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self,
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entry_price: float,
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side: str
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) -> tuple[float, float]:
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"""
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Calculate stop-loss and take-profit prices.
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Args:
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entry_price: Entry price
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side: "long" or "short"
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Returns:
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Tuple of (stop_loss_price, take_profit_price)
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"""
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sl_pct = self.config.stop_loss_pct
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tp_pct = self.config.take_profit_pct
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if side == "long":
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stop_loss = entry_price * (1 - sl_pct)
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take_profit = entry_price * (1 + tp_pct)
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else: # short
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stop_loss = entry_price * (1 + sl_pct)
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take_profit = entry_price * (1 - tp_pct)
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return stop_loss, take_profit
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