data capture implemented - needed for training
This commit is contained in:
Dobromir Popov
@ -1540,6 +1540,9 @@ class CleanTradingDashboard:
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logger.warning("No current price available for manual trade")
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return
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# CAPTURE ALL MODEL INPUTS FOR COLD START TRAINING
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model_inputs = self._capture_comprehensive_model_inputs(symbol, action, current_price)
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# Create manual trading decision
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decision = {
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'timestamp': datetime.now().strftime('%H:%M:%S'),
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@ -1551,7 +1554,8 @@ class CleanTradingDashboard:
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'executed': False,
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'blocked': False,
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'manual': True,
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'reason': f'Manual {action} button'
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'reason': f'Manual {action} button',
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'model_inputs': model_inputs # Store for training
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}
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# Execute through trading executor
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@ -1561,7 +1565,7 @@ class CleanTradingDashboard:
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decision['executed'] = True
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logger.info(f"Manual {action} executed at ${current_price:.2f}")
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# Create a trade record for tracking
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# Create a trade record for tracking WITH model inputs
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trade_record = {
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'symbol': symbol,
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'side': action,
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@ -1572,12 +1576,21 @@ class CleanTradingDashboard:
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'exit_time': datetime.now(),
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'pnl': 0.0, # Manual test trades have 0 P&L initially
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'fees': 0.0,
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'confidence': 1.0
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'confidence': 1.0,
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'trade_type': 'manual',
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'model_inputs_at_entry': model_inputs, # CRITICAL: Store model inputs for training
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'entry_market_state': model_inputs.get('market_state', {}),
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'entry_features': model_inputs.get('features', {}),
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'entry_predictions': model_inputs.get('predictions', {}),
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'training_ready': True # Mark as ready for cold start training
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}
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# Add to closed trades for display
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self.closed_trades.append(trade_record)
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# Store for cold start training when trade closes
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self._store_trade_for_training(trade_record)
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# Update session metrics
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if action == 'BUY':
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self.session_pnl += 0.0 # No immediate P&L for entry
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@ -1587,6 +1600,9 @@ class CleanTradingDashboard:
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self.session_pnl += demo_pnl
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trade_record['pnl'] = demo_pnl
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# TRIGGER COLD START TRAINING on profitable demo trade
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self._trigger_cold_start_training(trade_record, demo_pnl)
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else:
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decision['executed'] = False
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decision['blocked'] = True
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@ -1609,6 +1625,411 @@ class CleanTradingDashboard:
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except Exception as e:
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logger.error(f"Error executing manual {action}: {e}")
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def _capture_comprehensive_model_inputs(self, symbol: str, action: str, current_price: float) -> Dict[str, Any]:
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"""Capture comprehensive model inputs for cold start training"""
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try:
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logger.info(f"Capturing model inputs for {action} trade on {symbol} at ${current_price:.2f}")
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model_inputs = {
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'timestamp': datetime.now().isoformat(),
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'symbol': symbol,
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'action': action,
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'price': current_price,
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'capture_type': 'trade_execution'
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}
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# 1. Market State Features
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try:
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market_state = self._get_comprehensive_market_state(symbol, current_price)
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model_inputs['market_state'] = market_state
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logger.debug(f"Captured market state: {len(market_state)} features")
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except Exception as e:
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logger.warning(f"Error capturing market state: {e}")
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model_inputs['market_state'] = {}
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# 2. CNN Features and Predictions
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try:
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cnn_data = self._get_cnn_features_and_predictions(symbol)
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model_inputs['cnn_features'] = cnn_data.get('features', {})
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model_inputs['cnn_predictions'] = cnn_data.get('predictions', {})
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logger.debug(f"Captured CNN data: {len(cnn_data)} items")
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except Exception as e:
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logger.warning(f"Error capturing CNN data: {e}")
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model_inputs['cnn_features'] = {}
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model_inputs['cnn_predictions'] = {}
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# 3. DQN/RL State Features
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try:
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dqn_state = self._get_dqn_state_features(symbol, current_price)
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model_inputs['dqn_state'] = dqn_state
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logger.debug(f"Captured DQN state: {len(dqn_state) if dqn_state else 0} features")
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except Exception as e:
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logger.warning(f"Error capturing DQN state: {e}")
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model_inputs['dqn_state'] = {}
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# 4. COB (Order Book) Features
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try:
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cob_data = self._get_cob_features_for_training(symbol)
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model_inputs['cob_features'] = cob_data
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logger.debug(f"Captured COB features: {len(cob_data) if cob_data else 0} features")
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except Exception as e:
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logger.warning(f"Error capturing COB features: {e}")
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model_inputs['cob_features'] = {}
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# 5. Technical Indicators
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try:
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technical_indicators = self._get_technical_indicators(symbol)
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model_inputs['technical_indicators'] = technical_indicators
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logger.debug(f"Captured technical indicators: {len(technical_indicators)} indicators")
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except Exception as e:
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logger.warning(f"Error capturing technical indicators: {e}")
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model_inputs['technical_indicators'] = {}
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# 6. Recent Price History (for context)
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try:
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price_history = self._get_recent_price_history(symbol, periods=50)
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model_inputs['price_history'] = price_history
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logger.debug(f"Captured price history: {len(price_history)} periods")
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except Exception as e:
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logger.warning(f"Error capturing price history: {e}")
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model_inputs['price_history'] = []
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total_features = sum(len(v) if isinstance(v, (dict, list)) else 1 for v in model_inputs.values())
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logger.info(f"✅ Captured {total_features} total features for cold start training")
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return model_inputs
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except Exception as e:
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logger.error(f"Error capturing model inputs: {e}")
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return {
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'timestamp': datetime.now().isoformat(),
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'symbol': symbol,
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'action': action,
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'price': current_price,
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'error': str(e)
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}
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def _get_comprehensive_market_state(self, symbol: str, current_price: float) -> Dict[str, float]:
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"""Get comprehensive market state features"""
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try:
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market_state = {}
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# Price-based features
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market_state['current_price'] = current_price
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# Get historical data for features
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df = self.data_provider.get_historical_data(symbol, '1m', limit=100)
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if df is not None and not df.empty:
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prices = df['close'].values
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volumes = df['volume'].values
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# Price features
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market_state['price_sma_5'] = float(prices[-5:].mean())
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market_state['price_sma_20'] = float(prices[-20:].mean())
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market_state['price_std_20'] = float(prices[-20:].std())
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market_state['price_rsi'] = self._calculate_rsi(prices, 14)
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# Volume features
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market_state['volume_current'] = float(volumes[-1])
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market_state['volume_sma_20'] = float(volumes[-20:].mean())
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market_state['volume_ratio'] = float(volumes[-1] / volumes[-20:].mean())
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# Trend features
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market_state['price_momentum_5'] = float((prices[-1] - prices[-5]) / prices[-5])
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market_state['price_momentum_20'] = float((prices[-1] - prices[-20]) / prices[-20])
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# Add timestamp features
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now = datetime.now()
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market_state['hour_of_day'] = now.hour
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market_state['minute_of_hour'] = now.minute
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market_state['day_of_week'] = now.weekday()
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return market_state
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except Exception as e:
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logger.warning(f"Error getting market state: {e}")
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return {'current_price': current_price}
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def _calculate_rsi(self, prices, period=14):
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"""Calculate RSI indicator"""
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try:
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deltas = np.diff(prices)
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gains = np.where(deltas > 0, deltas, 0)
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losses = np.where(deltas < 0, -deltas, 0)
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avg_gain = np.mean(gains[-period:])
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avg_loss = np.mean(losses[-period:])
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if avg_loss == 0:
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return 100.0
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rs = avg_gain / avg_loss
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rsi = 100 - (100 / (1 + rs))
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return float(rsi)
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except:
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return 50.0 # Neutral RSI
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def _get_cnn_features_and_predictions(self, symbol: str) -> Dict[str, Any]:
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"""Get CNN features and predictions from orchestrator"""
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try:
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cnn_data = {}
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# Get CNN features if available
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if hasattr(self.orchestrator, 'latest_cnn_features'):
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cnn_features = getattr(self.orchestrator, 'latest_cnn_features', {}).get(symbol)
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if cnn_features is not None:
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cnn_data['features'] = cnn_features.tolist() if hasattr(cnn_features, 'tolist') else cnn_features
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# Get CNN predictions if available
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if hasattr(self.orchestrator, 'latest_cnn_predictions'):
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cnn_predictions = getattr(self.orchestrator, 'latest_cnn_predictions', {}).get(symbol)
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if cnn_predictions is not None:
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cnn_data['predictions'] = cnn_predictions.tolist() if hasattr(cnn_predictions, 'tolist') else cnn_predictions
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return cnn_data
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except Exception as e:
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logger.debug(f"Error getting CNN data: {e}")
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return {}
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def _get_dqn_state_features(self, symbol: str, current_price: float) -> Dict[str, Any]:
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"""Get DQN state features from orchestrator"""
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try:
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# Get DQN state from orchestrator if available
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if hasattr(self.orchestrator, 'build_comprehensive_rl_state'):
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rl_state = self.orchestrator.build_comprehensive_rl_state(symbol)
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if rl_state is not None:
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return {
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'state_vector': rl_state.tolist() if hasattr(rl_state, 'tolist') else rl_state,
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'state_size': len(rl_state) if hasattr(rl_state, '__len__') else 0
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}
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return {}
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except Exception as e:
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logger.debug(f"Error getting DQN state: {e}")
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return {}
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def _get_cob_features_for_training(self, symbol: str) -> Dict[str, Any]:
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"""Get COB features for training"""
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try:
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cob_data = {}
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# Get COB features from orchestrator
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if hasattr(self.orchestrator, 'latest_cob_features'):
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cob_features = getattr(self.orchestrator, 'latest_cob_features', {}).get(symbol)
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if cob_features is not None:
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cob_data['features'] = cob_features.tolist() if hasattr(cob_features, 'tolist') else cob_features
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# Get COB snapshot
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cob_snapshot = self._get_cob_snapshot(symbol)
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if cob_snapshot:
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cob_data['snapshot_available'] = True
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cob_data['bid_levels'] = len(getattr(cob_snapshot, 'consolidated_bids', []))
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cob_data['ask_levels'] = len(getattr(cob_snapshot, 'consolidated_asks', []))
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else:
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cob_data['snapshot_available'] = False
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return cob_data
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except Exception as e:
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logger.debug(f"Error getting COB features: {e}")
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return {}
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def _get_technical_indicators(self, symbol: str) -> Dict[str, float]:
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"""Get technical indicators"""
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try:
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indicators = {}
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# Get recent price data
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df = self.data_provider.get_historical_data(symbol, '1m', limit=50)
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if df is not None and not df.empty:
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closes = df['close'].values
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highs = df['high'].values
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lows = df['low'].values
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volumes = df['volume'].values
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# Moving averages
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indicators['sma_10'] = float(closes[-10:].mean())
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indicators['sma_20'] = float(closes[-20:].mean())
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# Bollinger Bands
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sma_20 = closes[-20:].mean()
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std_20 = closes[-20:].std()
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indicators['bb_upper'] = float(sma_20 + 2 * std_20)
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indicators['bb_lower'] = float(sma_20 - 2 * std_20)
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indicators['bb_position'] = float((closes[-1] - indicators['bb_lower']) / (indicators['bb_upper'] - indicators['bb_lower']))
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# MACD
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ema_12 = closes[-12:].mean() # Simplified
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ema_26 = closes[-26:].mean() # Simplified
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indicators['macd'] = float(ema_12 - ema_26)
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# Volatility
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indicators['volatility'] = float(std_20 / sma_20)
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return indicators
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except Exception as e:
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logger.debug(f"Error calculating technical indicators: {e}")
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return {}
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def _get_recent_price_history(self, symbol: str, periods: int = 50) -> List[float]:
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"""Get recent price history"""
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try:
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df = self.data_provider.get_historical_data(symbol, '1m', limit=periods)
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if df is not None and not df.empty:
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return df['close'].tolist()
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return []
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except Exception as e:
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logger.debug(f"Error getting price history: {e}")
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return []
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def _store_trade_for_training(self, trade_record: Dict[str, Any]):
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"""Store trade for future cold start training"""
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try:
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# Create training data storage directory
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import os
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training_dir = "training_data"
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os.makedirs(training_dir, exist_ok=True)
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# Store trade data with timestamp
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"trade_{trade_record['symbol'].replace('/', '')}_{timestamp}.json"
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filepath = os.path.join(training_dir, filename)
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import json
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with open(filepath, 'w') as f:
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json.dump(trade_record, f, indent=2, default=str)
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logger.info(f"✅ Stored trade data for training: {filepath}")
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# Also store in memory for immediate access
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if not hasattr(self, 'stored_trades'):
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self.stored_trades = []
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self.stored_trades.append(trade_record)
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# Keep only last 100 trades in memory
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if len(self.stored_trades) > 100:
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self.stored_trades = self.stored_trades[-100:]
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except Exception as e:
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logger.error(f"Error storing trade for training: {e}")
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def _trigger_cold_start_training(self, trade_record: Dict[str, Any], pnl: float):
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"""Trigger cold start training when we have trade outcome"""
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try:
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logger.info(f"🔥 TRIGGERING COLD START TRAINING")
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logger.info(f"Trade: {trade_record['side']} {trade_record['symbol']} @ ${trade_record['entry_price']:.2f}")
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logger.info(f"P&L: ${pnl:.4f} ({'PROFIT' if pnl > 0 else 'LOSS'})")
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# Calculate reward based on P&L
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reward = self._calculate_training_reward(pnl, trade_record)
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# Send to DQN agent if available
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if hasattr(self.orchestrator, 'sensitivity_dqn_agent') and self.orchestrator.sensitivity_dqn_agent:
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self._train_dqn_on_trade_outcome(trade_record, reward)
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# Send to CNN if available
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if hasattr(self.orchestrator, 'williams_structure') and self.orchestrator.williams_structure:
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self._train_cnn_on_trade_outcome(trade_record, reward)
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# Send to COB RL if available
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if hasattr(self.orchestrator, 'cob_integration') and self.orchestrator.cob_integration:
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self._train_cob_rl_on_trade_outcome(trade_record, reward)
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logger.info(f"✅ Cold start training triggered with reward: {reward:.4f}")
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except Exception as e:
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logger.error(f"Error triggering cold start training: {e}")
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def _calculate_training_reward(self, pnl: float, trade_record: Dict[str, Any]) -> float:
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"""Calculate training reward based on trade outcome"""
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try:
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# Base reward from P&L
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base_reward = pnl * 100 # Scale up for training
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# Confidence adjustment (higher confidence wrong predictions get bigger penalties)
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confidence = trade_record.get('confidence', 0.5)
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if pnl < 0: # Loss
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confidence_penalty = confidence * 2 # Higher confidence losses hurt more
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base_reward *= (1 + confidence_penalty)
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else: # Profit
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confidence_bonus = confidence * 0.5 # Higher confidence wins get small bonus
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base_reward *= (1 + confidence_bonus)
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# Time-based adjustment (faster profits are better)
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# For demo trades, just use a small bonus
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time_bonus = 0.1 if pnl > 0 else 0
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final_reward = base_reward + time_bonus
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logger.debug(f"Reward calculation: P&L={pnl:.4f}, Confidence={confidence:.2f}, Final={final_reward:.4f}")
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return final_reward
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except Exception as e:
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logger.warning(f"Error calculating reward: {e}")
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return pnl # Fallback to simple P&L
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def _train_dqn_on_trade_outcome(self, trade_record: Dict[str, Any], reward: float):
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"""Train DQN agent on trade outcome"""
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try:
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dqn_agent = self.orchestrator.sensitivity_dqn_agent
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# Get the state that was used for the decision
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model_inputs = trade_record.get('model_inputs_at_entry', {})
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dqn_state = model_inputs.get('dqn_state', {}).get('state_vector', [])
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if not dqn_state:
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logger.debug("No DQN state available for training")
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return
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# Convert to numpy array
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state = np.array(dqn_state, dtype=np.float32)
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# Map action to DQN action space
|
||||
action = 1 if trade_record['side'] == 'BUY' else 0
|
||||
|
||||
# Create next state (current market state after trade)
|
||||
current_state = self._get_dqn_state_features(trade_record['symbol'], trade_record['entry_price'])
|
||||
next_state = np.array(current_state.get('state_vector', state), dtype=np.float32)
|
||||
|
||||
# Add experience to DQN memory
|
||||
dqn_agent.remember(state, action, reward, next_state, True) # done=True for completed trade
|
||||
|
||||
# Trigger training if enough experiences
|
||||
if len(dqn_agent.memory) >= dqn_agent.batch_size:
|
||||
loss = dqn_agent.replay()
|
||||
if loss:
|
||||
logger.info(f"🧠 DQN trained on trade outcome - Loss: {loss:.6f}, Reward: {reward:.4f}")
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error training DQN on trade outcome: {e}")
|
||||
|
||||
def _train_cnn_on_trade_outcome(self, trade_record: Dict[str, Any], reward: float):
|
||||
"""Train CNN on trade outcome (simplified for now)"""
|
||||
try:
|
||||
# CNN training requires more complex setup - log for now
|
||||
logger.info(f"📊 CNN training opportunity: {trade_record['side']} with reward {reward:.4f}")
|
||||
|
||||
# In future: extract CNN features from model_inputs and create training sample
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error training CNN on trade outcome: {e}")
|
||||
|
||||
def _train_cob_rl_on_trade_outcome(self, trade_record: Dict[str, Any], reward: float):
|
||||
"""Train COB RL on trade outcome (simplified for now)"""
|
||||
try:
|
||||
# COB RL training requires accessing the 400M parameter model - log for now
|
||||
logger.info(f"📈 COB RL training opportunity: {trade_record['side']} with reward {reward:.4f}")
|
||||
|
||||
# In future: access COB RL model and create training sample
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Error training COB RL on trade outcome: {e}")
|
||||
|
||||
def _clear_session(self):
|
||||
"""Clear session data"""
|
||||
try:
|
||||
|
||||
Reference in New Issue
Block a user