main cleanup
This commit is contained in:
Dobromir Popov
536
core/multi_horizon_trainer.py
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536
core/multi_horizon_trainer.py
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#!/usr/bin/env python3
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"""
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Multi-Horizon Trainer
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This module trains models using stored prediction snapshots when outcomes are known.
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It handles training for different time horizons and model types.
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"""
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import logging
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import threading
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import time
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from datetime import datetime, timedelta
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from typing import Dict, List, Any, Optional, Tuple
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import numpy as np
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import torch
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from collections import defaultdict
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from .prediction_snapshot_storage import PredictionSnapshotStorage
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from .multi_horizon_prediction_manager import PredictionSnapshot
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logger = logging.getLogger(__name__)
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class MultiHorizonTrainer:
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"""Trainer for multi-horizon predictions using stored snapshots"""
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def __init__(self, orchestrator=None, snapshot_storage: Optional[PredictionSnapshotStorage] = None):
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"""Initialize the multi-horizon trainer"""
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self.orchestrator = orchestrator
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self.snapshot_storage = snapshot_storage or PredictionSnapshotStorage()
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# Training configuration
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self.batch_size = 32
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self.min_batch_size = 10
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self.training_interval_seconds = 300 # 5 minutes
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self.max_training_age_hours = 24 # Don't train on predictions older than 24 hours
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# Model training settings
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self.learning_rate = 0.001
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self.epochs_per_batch = 5
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self.validation_split = 0.2
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# Training state
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self.training_active = False
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self.training_thread = None
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self.last_training_time = 0.0
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# Performance tracking
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self.training_stats = {
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'total_training_sessions': 0,
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'models_trained': defaultdict(int),
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'training_accuracy': defaultdict(list),
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'loss_history': defaultdict(list),
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'last_training_time': None
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}
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logger.info("MultiHorizonTrainer initialized")
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def start(self):
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"""Start the training system"""
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if self.training_active:
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logger.warning("Training system already active")
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return
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self.training_active = True
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self.training_thread = threading.Thread(
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target=self._training_loop,
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daemon=True,
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name="MultiHorizonTrainer"
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)
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self.training_thread.start()
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logger.info("MultiHorizonTrainer started")
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def stop(self):
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"""Stop the training system"""
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self.training_active = False
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if self.training_thread and self.training_thread.is_alive():
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self.training_thread.join(timeout=10)
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logger.info("MultiHorizonTrainer stopped")
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def _training_loop(self):
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"""Main training loop"""
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while self.training_active:
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try:
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current_time = time.time()
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# Check if it's time for training
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if current_time - self.last_training_time >= self.training_interval_seconds:
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self._run_training_session()
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self.last_training_time = current_time
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# Sleep before next check
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time.sleep(60) # Check every minute
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except Exception as e:
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logger.error(f"Error in training loop: {e}")
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time.sleep(300) # Longer sleep on error
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def _run_training_session(self):
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"""Run a complete training session"""
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try:
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logger.info("Starting multi-horizon training session")
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training_results = {}
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# Train each horizon separately
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horizons = [1, 5, 15, 60]
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symbols = ['ETH/USDT', 'BTC/USDT']
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for horizon in horizons:
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for symbol in symbols:
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try:
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horizon_results = self._train_horizon_models(horizon, symbol)
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if horizon_results:
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training_results[f"{horizon}m_{symbol}"] = horizon_results
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except Exception as e:
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logger.error(f"Error training {horizon}m models for {symbol}: {e}")
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# Update statistics
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self.training_stats['total_training_sessions'] += 1
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self.training_stats['last_training_time'] = datetime.now()
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if training_results:
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logger.info(f"Training session completed: {len(training_results)} model updates")
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for key, results in training_results.items():
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logger.info(f" {key}: {results}")
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else:
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logger.debug("No models were trained in this session")
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except Exception as e:
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logger.error(f"Error in training session: {e}")
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def _train_horizon_models(self, horizon_minutes: int, symbol: str) -> Dict[str, Any]:
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"""Train models for a specific horizon and symbol"""
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results = {}
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# Get training batch
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snapshots = self.snapshot_storage.get_training_batch(
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horizon_minutes=horizon_minutes,
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symbol=symbol,
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batch_size=self.batch_size,
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min_confidence=0.3
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)
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if len(snapshots) < self.min_batch_size:
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logger.debug(f"Insufficient training data for {horizon_minutes}m {symbol}: {len(snapshots)} snapshots")
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return results
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logger.info(f"Training {horizon_minutes}m models for {symbol} with {len(snapshots)} snapshots")
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# Train CNN model
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if self.orchestrator and hasattr(self.orchestrator, 'cnn_model'):
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try:
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cnn_results = self._train_cnn_model(snapshots, horizon_minutes, symbol)
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if cnn_results:
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results['cnn'] = cnn_results
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self.training_stats['models_trained']['cnn'] += 1
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except Exception as e:
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logger.error(f"CNN training failed for {horizon_minutes}m {symbol}: {e}")
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# Train RL model
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if self.orchestrator and hasattr(self.orchestrator, 'rl_agent'):
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try:
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rl_results = self._train_rl_model(snapshots, horizon_minutes, symbol)
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if rl_results:
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results['rl'] = rl_results
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self.training_stats['models_trained']['rl'] += 1
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except Exception as e:
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logger.error(f"RL training failed for {horizon_minutes}m {symbol}: {e}")
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return results
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def _train_cnn_model(self, snapshots: List[PredictionSnapshot],
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horizon_minutes: int, symbol: str) -> Dict[str, Any]:
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"""Train CNN model using prediction snapshots"""
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try:
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if not self.orchestrator or not hasattr(self.orchestrator, 'cnn_model'):
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return None
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cnn_model = self.orchestrator.cnn_model
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# Prepare training data
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features_list = []
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targets_list = []
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for snapshot in snapshots:
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# Extract CNN features
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features = snapshot.model_inputs.get('cnn_features')
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if features is None:
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continue
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# Create target based on prediction accuracy
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if snapshot.actual_min_price is not None and snapshot.actual_max_price is not None:
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# Calculate prediction error
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pred_range = snapshot.predicted_max_price - snapshot.predicted_min_price
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actual_range = snapshot.actual_max_price - snapshot.actual_min_price
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# Simple target: 1 if prediction was reasonably accurate, 0 otherwise
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range_overlap = self._calculate_range_overlap(
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(snapshot.predicted_min_price, snapshot.predicted_max_price),
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(snapshot.actual_min_price, snapshot.actual_max_price)
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)
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target = 1 if range_overlap > 0.3 else 0 # 30% overlap threshold
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features_list.append(features)
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targets_list.append(target)
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if len(features_list) < self.min_batch_size:
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return {'error': 'Insufficient training data'}
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# Convert to tensors
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features_array = np.array(features_list, dtype=np.float32)
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targets_array = np.array(targets_list, dtype=np.float32)
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# Split into train/validation
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split_idx = int(len(features_array) * (1 - self.validation_split))
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train_features = features_array[:split_idx]
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train_targets = targets_array[:split_idx]
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val_features = features_array[split_idx:]
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val_targets = targets_array[split_idx:]
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# Training loop
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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cnn_model.to(device)
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if not hasattr(cnn_model, 'optimizer'):
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cnn_model.optimizer = torch.optim.Adam(cnn_model.parameters(), lr=self.learning_rate)
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criterion = torch.nn.BCELoss() # Binary classification
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train_losses = []
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val_accuracies = []
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for epoch in range(self.epochs_per_batch):
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# Training step
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cnn_model.train()
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cnn_model.optimizer.zero_grad()
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# Forward pass
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inputs = torch.FloatTensor(train_features).to(device)
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targets = torch.FloatTensor(train_targets).to(device)
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# Handle different model outputs
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outputs = cnn_model(inputs)
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if isinstance(outputs, dict):
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if 'main_output' in outputs:
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logits = outputs['main_output']
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else:
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logits = list(outputs.values())[0]
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else:
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logits = outputs
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# Apply sigmoid for binary classification
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predictions = torch.sigmoid(logits.squeeze())
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loss = criterion(predictions, targets)
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loss.backward()
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cnn_model.optimizer.step()
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train_losses.append(loss.item())
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# Validation step
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if len(val_features) > 0:
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cnn_model.eval()
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with torch.no_grad():
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val_inputs = torch.FloatTensor(val_features).to(device)
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val_targets_tensor = torch.FloatTensor(val_targets).to(device)
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val_outputs = cnn_model(val_inputs)
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if isinstance(val_outputs, dict):
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if 'main_output' in val_outputs:
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val_logits = val_outputs['main_output']
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else:
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val_logits = list(val_outputs.values())[0]
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else:
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val_logits = val_outputs
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val_predictions = torch.sigmoid(val_logits.squeeze())
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val_binary_preds = (val_predictions > 0.5).float()
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val_accuracy = (val_binary_preds == val_targets_tensor).float().mean().item()
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val_accuracies.append(val_accuracy)
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# Calculate final metrics
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avg_train_loss = np.mean(train_losses)
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final_val_accuracy = val_accuracies[-1] if val_accuracies else 0.0
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self.training_stats['loss_history']['cnn'].append(avg_train_loss)
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self.training_stats['training_accuracy']['cnn'].append(final_val_accuracy)
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results = {
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'epochs': self.epochs_per_batch,
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'final_loss': avg_train_loss,
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'validation_accuracy': final_val_accuracy,
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'samples_used': len(features_list)
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}
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logger.info(f"CNN training completed: loss={avg_train_loss:.4f}, val_acc={final_val_accuracy:.2f}")
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return results
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except Exception as e:
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logger.error(f"Error training CNN model: {e}")
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return {'error': str(e)}
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def _train_rl_model(self, snapshots: List[PredictionSnapshot],
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horizon_minutes: int, symbol: str) -> Dict[str, Any]:
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"""Train RL model using prediction snapshots"""
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try:
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if not self.orchestrator or not hasattr(self.orchestrator, 'rl_agent'):
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return None
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rl_agent = self.orchestrator.rl_agent
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# Prepare RL training data
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experiences = []
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for snapshot in snapshots:
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# Extract RL state
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state = snapshot.model_inputs.get('rl_state')
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if state is None:
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continue
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# Determine action from prediction
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# For min/max prediction, we can derive action from predicted direction
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predicted_range = snapshot.predicted_max_price - snapshot.predicted_min_price
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current_price = snapshot.current_price
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# Simple action derivation: if predicted range is mostly above current price, BUY
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# if mostly below, SELL, else HOLD
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range_center = (snapshot.predicted_min_price + snapshot.predicted_max_price) / 2
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if range_center > current_price * 1.002: # 0.2% threshold
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action = 0 # BUY
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elif range_center < current_price * 0.998:
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action = 1 # SELL
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else:
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action = 2 # HOLD
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# Calculate reward based on prediction accuracy
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if snapshot.actual_min_price is not None and snapshot.actual_max_price is not None:
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actual_center = (snapshot.actual_min_price + snapshot.actual_max_price) / 2
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# Reward based on how well we predicted the price movement direction
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predicted_direction = 1 if range_center > current_price else -1 if range_center < current_price else 0
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actual_direction = 1 if actual_center > current_price else -1 if actual_center < current_price else 0
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if predicted_direction == actual_direction:
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reward = snapshot.confidence # Positive reward scaled by confidence
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else:
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reward = -snapshot.confidence # Negative reward scaled by confidence
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# Additional reward based on range accuracy
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range_overlap = self._calculate_range_overlap(
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(snapshot.predicted_min_price, snapshot.predicted_max_price),
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(snapshot.actual_min_price, snapshot.actual_max_price)
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)
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reward += range_overlap * 0.5 # Bonus for accurate range prediction
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# Create next state (simplified)
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next_state = state.copy()
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experiences.append((state, action, reward, next_state, True)) # done=True
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if len(experiences) < self.min_batch_size:
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return {'error': 'Insufficient training data'}
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# Add experiences to RL agent memory
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experiences_added = 0
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for state, action, reward, next_state, done in experiences:
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try:
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if hasattr(rl_agent, 'store_experience'):
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rl_agent.store_experience(
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state=np.array(state),
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action=action,
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reward=reward,
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next_state=np.array(next_state),
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done=done
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)
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experiences_added += 1
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elif hasattr(rl_agent, 'remember'):
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rl_agent.remember(np.array(state), action, reward, np.array(next_state), done)
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experiences_added += 1
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except Exception as e:
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logger.debug(f"Error adding RL experience: {e}")
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# Perform training steps
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training_losses = []
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if hasattr(rl_agent, 'replay') and experiences_added > 0:
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try:
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for _ in range(min(5, experiences_added // 8)): # Conservative training
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loss = rl_agent.replay(batch_size=min(32, experiences_added))
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if loss is not None:
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training_losses.append(loss)
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except Exception as e:
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logger.debug(f"RL training step failed: {e}")
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avg_loss = np.mean(training_losses) if training_losses else 0.0
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results = {
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'experiences_added': experiences_added,
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'training_steps': len(training_losses),
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'avg_loss': avg_loss,
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'samples_used': len(experiences)
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}
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logger.info(f"RL training completed: {experiences_added} experiences, avg_loss={avg_loss:.4f}")
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return results
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except Exception as e:
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logger.error(f"Error training RL model: {e}")
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return {'error': str(e)}
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def _calculate_range_overlap(self, range1: Tuple[float, float], range2: Tuple[float, float]) -> float:
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"""Calculate overlap between two price ranges (0.0 to 1.0)"""
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try:
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min1, max1 = range1
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min2, max2 = range2
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# Find overlap
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overlap_min = max(min1, min2)
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overlap_max = min(max1, max2)
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if overlap_max <= overlap_min:
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return 0.0
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overlap_size = overlap_max - overlap_min
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union_size = max(max1, max2) - min(min1, min2)
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return overlap_size / union_size if union_size > 0 else 0.0
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except Exception:
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return 0.0
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def force_training_session(self, horizon_minutes: Optional[int] = None,
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symbol: Optional[str] = None) -> Dict[str, Any]:
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"""Force a training session for specific parameters"""
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try:
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logger.info(f"Forcing training session: horizon={horizon_minutes}, symbol={symbol}")
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results = {}
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horizons = [horizon_minutes] if horizon_minutes else [1, 5, 15, 60]
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symbols = [symbol] if symbol else ['ETH/USDT', 'BTC/USDT']
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for h in horizons:
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for s in symbols:
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try:
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horizon_results = self._train_horizon_models(h, s)
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if horizon_results:
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results[f"{h}m_{s}"] = horizon_results
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except Exception as e:
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logger.error(f"Error in forced training for {h}m {s}: {e}")
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return results
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except Exception as e:
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logger.error(f"Error in forced training session: {e}")
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return {'error': str(e)}
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def get_training_stats(self) -> Dict[str, Any]:
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"""Get training statistics"""
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stats = dict(self.training_stats)
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stats['is_training_active'] = self.training_active
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# Calculate averages
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for model_type in ['cnn', 'rl']:
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if stats['training_accuracy'][model_type]:
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stats[f'{model_type}_avg_accuracy'] = np.mean(stats['training_accuracy'][model_type])
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else:
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stats[f'{model_type}_avg_accuracy'] = 0.0
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if stats['loss_history'][model_type]:
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stats[f'{model_type}_avg_loss'] = np.mean(stats['loss_history'][model_type])
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else:
|
||||
stats[f'{model_type}_avg_loss'] = 0.0
|
||||
|
||||
return stats
|
||||
|
||||
def validate_recent_predictions(self):
|
||||
"""Validate predictions that should have outcomes available"""
|
||||
try:
|
||||
# Get pending snapshots
|
||||
pending_snapshots = self.snapshot_storage.get_pending_validation_snapshots()
|
||||
|
||||
if not pending_snapshots:
|
||||
return
|
||||
|
||||
logger.info(f"Validating {len(pending_snapshots)} pending predictions")
|
||||
|
||||
# Group by symbol for efficient data access
|
||||
by_symbol = defaultdict(list)
|
||||
for snapshot in pending_snapshots:
|
||||
by_symbol[snapshot.symbol].append(snapshot)
|
||||
|
||||
# Validate each symbol
|
||||
for symbol, snapshots in by_symbol.items():
|
||||
try:
|
||||
self._validate_symbol_predictions(symbol, snapshots)
|
||||
except Exception as e:
|
||||
logger.error(f"Error validating predictions for {symbol}: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error validating recent predictions: {e}")
|
||||
|
||||
def _validate_symbol_predictions(self, symbol: str, snapshots: List[PredictionSnapshot]):
|
||||
"""Validate predictions for a specific symbol"""
|
||||
try:
|
||||
# Get historical data for the validation period
|
||||
# This is a simplified approach - in practice you'd need to get the price range
|
||||
# during the prediction horizon
|
||||
|
||||
for snapshot in snapshots:
|
||||
try:
|
||||
# For now, use a simple validation approach
|
||||
# In a real implementation, you'd query historical data for the exact time range
|
||||
# and calculate actual min/max prices during the prediction horizon
|
||||
|
||||
# Simplified: assume current price as both min and max (not accurate but functional)
|
||||
current_time = datetime.now()
|
||||
current_price = snapshot.current_price # Placeholder
|
||||
|
||||
# Update snapshot with "outcome"
|
||||
self.snapshot_storage.update_snapshot_outcome(
|
||||
snapshot.prediction_id,
|
||||
current_price, # actual_min
|
||||
current_price, # actual_max
|
||||
current_time
|
||||
)
|
||||
|
||||
logger.debug(f"Validated prediction {snapshot.prediction_id}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error validating snapshot {snapshot.prediction_id}: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error validating symbol predictions for {symbol}: {e}")
|
||||
Reference in New Issue
Block a user