Remove deprecated training scripts and Systemd service files
- Deleted `install_cron.sh`, `setup_schedule.sh`, and `train_daily.sh` as part of the transition to a new scheduling mechanism. - Removed associated Systemd service and timer files for daily model training. - Updated `live_regime_strategy.py` and `main.py` to reflect changes in model training and scheduling logic. - Adjusted `regime_strategy.py` to align with new target calculation methods and updated optimal parameters. - Enhanced `regime_detection.py` to incorporate path-dependent labeling for target calculations.
This commit is contained in:
@@ -6,6 +6,7 @@ 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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import time
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from pathlib import Path
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from typing import Optional
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@@ -39,8 +40,9 @@ class LiveRegimeStrategy:
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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.horizon: int = 102 # Default horizon
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self.horizon: int = 54 # Default horizon
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self._last_model_load_time: float = 0.0
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self._last_train_time: float = 0.0
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self._load_or_train_model()
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def reload_model_if_changed(self) -> None:
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@@ -72,6 +74,13 @@ class LiveRegimeStrategy:
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logger.info(f"Loaded model from {self.paths.model_path} (horizon={self.horizon})")
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else:
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logger.info(f"Loaded model from {self.paths.model_path} (default horizon={self.horizon})")
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# Load timestamp if available
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if 'timestamp' in saved:
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self._last_train_time = saved['timestamp']
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else:
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self._last_train_time = self._last_model_load_time
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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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@@ -88,12 +97,20 @@ class LiveRegimeStrategy:
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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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'metrics': {'horizon': self.horizon} # Save horizon
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'metrics': {'horizon': self.horizon}, # Save horizon
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'timestamp': time.time()
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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 check_retrain(self, features: pd.DataFrame) -> None:
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"""Check if model needs retraining (older than 24h)."""
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if time.time() - self._last_train_time > 24 * 3600:
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logger.info("Model is older than 24h. Retraining...")
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self.train_model(features)
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self._last_train_time = time.time()
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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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@@ -106,18 +123,61 @@ class LiveRegimeStrategy:
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z_thresh = self.config.z_entry_threshold
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horizon = self.horizon
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profit_target = 0.005 # 0.5% profit threshold
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stop_loss_pct = self.config.stop_loss_pct
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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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# Calculate targets path-dependently
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spread = features['spread'].values
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z_score = features['z_score'].values
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n = len(spread)
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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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targets = np.zeros(n, dtype=int)
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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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candidates = np.where((z_score > z_thresh) | (z_score < -z_thresh))[0]
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targets = np.select([success_short, success_long], [1, 1], default=0)
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for i in candidates:
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if i + horizon >= n:
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continue
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entry_price = spread[i]
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future_prices = spread[i+1 : i+1+horizon]
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if z_score[i] > z_thresh: # Short
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target_price = entry_price * (1 - profit_target)
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stop_price = entry_price * (1 + stop_loss_pct)
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hit_tp = future_prices <= target_price
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hit_sl = future_prices >= stop_price
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if not np.any(hit_tp):
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targets[i] = 0
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elif not np.any(hit_sl):
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targets[i] = 1
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else:
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first_tp_idx = np.argmax(hit_tp)
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first_sl_idx = np.argmax(hit_sl)
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if first_tp_idx < first_sl_idx:
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targets[i] = 1
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else:
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targets[i] = 0
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else: # Long
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target_price = entry_price * (1 + profit_target)
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stop_price = entry_price * (1 - stop_loss_pct)
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hit_tp = future_prices >= target_price
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hit_sl = future_prices <= stop_price
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if not np.any(hit_tp):
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targets[i] = 0
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elif not np.any(hit_sl):
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targets[i] = 1
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else:
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first_tp_idx = np.argmax(hit_tp)
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first_sl_idx = np.argmax(hit_sl)
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if first_tp_idx < first_sl_idx:
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targets[i] = 1
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else:
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targets[i] = 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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@@ -127,8 +187,10 @@ class LiveRegimeStrategy:
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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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# Use rows where we had enough data to look ahead
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valid_mask = np.zeros(n, dtype=bool)
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valid_mask[:n-horizon] = True
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X_clean = X[valid_mask]
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y_clean = targets[valid_mask]
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@@ -152,7 +214,8 @@ class LiveRegimeStrategy:
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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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current_funding: dict,
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position_side: Optional[str] = None
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) -> dict:
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"""
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Generate trading signal from latest features.
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@@ -160,10 +223,14 @@ class LiveRegimeStrategy:
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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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position_side: Current position side ('long', 'short', or None)
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Returns:
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Signal dictionary with action, side, confidence, etc.
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"""
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# Check if retraining is needed
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self.check_retrain(features)
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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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@@ -233,12 +300,17 @@ class LiveRegimeStrategy:
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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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# Check for exit conditions (Overshoot Logic)
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if signal['action'] == 'hold' and position_side:
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# Overshoot Logic
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# If Long, exit if Z > 0.5 (Reverted past 0 to +0.5)
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if position_side == 'long' and z_score > 0.5:
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signal['action'] = 'check_exit'
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signal['reason'] = f'overshoot_exit_long (z={z_score:.2f} > 0.5)'
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# If Short, exit if Z < -0.5 (Reverted past 0 to -0.5)
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elif position_side == 'short' and z_score < -0.5:
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signal['action'] = 'check_exit'
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signal['reason'] = f'overshoot_exit_short (z={z_score:.2f} < -0.5)'
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logger.info(
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f"Signal: {signal['action']} {signal['side'] or ''} "
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