improved data structure
This commit is contained in:
Dobromir Popov
@@ -10651,16 +10651,118 @@ class CleanTradingDashboard:
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price_change = (next_price - current_price) / current_price if current_price > 0 else 0
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cumulative_imbalance = current_data.get('cumulative_imbalance', {})
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# TODO(Guideline: no synthetic data) Replace random feature vectors with real market-derived inputs.
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features = np.random.randn(32) # Decision fusion expects 32 features
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features[0] = current_price / 10000
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# Build real feature vector from market data (128 features as per config)
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# Decision fusion network expects 128 features (configurable in models.yml)
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features = np.zeros(128, dtype=np.float32)
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# Price features (0-9)
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features[0] = current_price / 10000.0 if current_price > 0 else 0.0
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features[1] = price_change
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features[2] = current_data.get('volume', 0) / 1000000
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# Add cumulative imbalance features
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features[3] = cumulative_imbalance.get('1s', 0.0)
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features[4] = cumulative_imbalance.get('5s', 0.0)
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features[5] = cumulative_imbalance.get('15s', 0.0)
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features[6] = cumulative_imbalance.get('60s', 0.0)
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features[2] = current_data.get('volume', 0) / 1000000.0 if current_data.get('volume', 0) > 0 else 0.0
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features[3] = (next_price - current_price) / current_price if current_price > 0 else 0.0
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features[4] = current_data.get('high', current_price) / 10000.0 if current_data.get('high', 0) > 0 else 0.0
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features[5] = current_data.get('low', current_price) / 10000.0 if current_data.get('low', 0) > 0 else 0.0
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features[6] = current_data.get('open', current_price) / 10000.0 if current_data.get('open', 0) > 0 else 0.0
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features[7] = current_data.get('close', current_price) / 10000.0 if current_data.get('close', 0) > 0 else 0.0
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features[8] = abs(price_change) if price_change != 0 else 0.0 # Absolute price change
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features[9] = (current_data.get('high', current_price) - current_data.get('low', current_price)) / current_price if current_price > 0 else 0.0 # Price range
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# Cumulative imbalance features (10-13)
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features[10] = cumulative_imbalance.get('1s', 0.0)
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features[11] = cumulative_imbalance.get('5s', 0.0)
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features[12] = cumulative_imbalance.get('15s', 0.0)
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features[13] = cumulative_imbalance.get('60s', 0.0)
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# Technical indicators from market data (14-30)
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if 'indicators' in current_data:
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indicators = current_data['indicators']
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feature_idx = 14
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for key in ['rsi', 'macd', 'ema', 'sma', 'bb_upper', 'bb_lower', 'atr', 'adx', 'stoch', 'williams_r', 'cci', 'roc', 'momentum', 'ad', 'obv', 'vwap']:
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if feature_idx < 30 and key in indicators:
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features[feature_idx] = float(indicators[key]) if indicators[key] is not None else 0.0
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feature_idx += 1
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# Model prediction features (if available from orchestrator) (31-50)
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if self.orchestrator:
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if hasattr(self.orchestrator, 'recent_cnn_predictions') and self.symbol in self.orchestrator.recent_cnn_predictions:
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cnn_preds = self.orchestrator.recent_cnn_predictions[self.symbol]
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if cnn_preds:
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last_cnn = cnn_preds[-1]
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feature_idx = 31
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if feature_idx < 50:
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features[feature_idx] = last_cnn.get('confidence', 0.0)
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feature_idx += 1
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features[feature_idx] = last_cnn.get('buy_probability', 0.0)
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feature_idx += 1
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features[feature_idx] = last_cnn.get('sell_probability', 0.0)
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feature_idx += 1
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features[feature_idx] = last_cnn.get('hold_probability', 0.0)
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feature_idx += 1
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if hasattr(self.orchestrator, 'recent_dqn_predictions') and self.symbol in self.orchestrator.recent_dqn_predictions:
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dqn_preds = self.orchestrator.recent_dqn_predictions[self.symbol]
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if dqn_preds:
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last_dqn = dqn_preds[-1]
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feature_idx = 36
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if feature_idx < 50:
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features[feature_idx] = last_dqn.get('confidence', 0.0)
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feature_idx += 1
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features[feature_idx] = last_dqn.get('q_values', {}).get('BUY', 0.0) if isinstance(last_dqn.get('q_values'), dict) else 0.0
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feature_idx += 1
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features[feature_idx] = last_dqn.get('q_values', {}).get('SELL', 0.0) if isinstance(last_dqn.get('q_values'), dict) else 0.0
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feature_idx += 1
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# Market microstructure features (51-80)
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feature_idx = 51
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if 'market_microstructure' in current_data:
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micro = current_data['market_microstructure']
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for key in ['spread', 'bid_volume', 'ask_volume', 'imbalance_ratio', 'order_flow', 'liquidity', 'volatility', 'tick_size', 'depth_imbalance', 'momentum', 'acceleration', 'volume_profile', 'price_velocity', 'volume_velocity', 'order_book_pressure', 'trade_intensity', 'spread_ratio', 'depth_ratio', 'imbalance_momentum', 'liquidity_imbalance']:
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if feature_idx < 80 and key in micro:
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val = micro[key]
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features[feature_idx] = float(val) if val is not None and not np.isnan(val) else 0.0
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feature_idx += 1
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# Historical price features (81-100)
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if len(market_data) > 1:
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feature_idx = 81
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# Price momentum (last 5 periods)
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for i in range(min(5, len(market_data) - 1)):
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if i + 1 < len(market_data):
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prev_data = market_data[len(market_data) - 2 - i]
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prev_price = prev_data.get('price', 0)
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if prev_price > 0 and feature_idx < 100:
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features[feature_idx] = (current_price - prev_price) / prev_price
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feature_idx += 1
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# Volume features (101-110)
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feature_idx = 101
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if len(market_data) > 1:
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volumes = [d.get('volume', 0) for d in market_data[-10:] if d.get('volume', 0) > 0]
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if volumes:
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avg_volume = sum(volumes) / len(volumes)
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current_vol = current_data.get('volume', 0)
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if avg_volume > 0 and feature_idx < 110:
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features[feature_idx] = current_vol / avg_volume # Volume ratio
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feature_idx += 1
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features[feature_idx] = max(volumes) / avg_volume if max(volumes) > 0 else 0.0 # Max volume ratio
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feature_idx += 1
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# Position features (if available) (111-115)
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if self.orchestrator and hasattr(self.orchestrator, 'positions') and self.symbol in self.orchestrator.positions:
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position = self.orchestrator.positions[self.symbol]
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feature_idx = 111
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if feature_idx < 115:
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features[feature_idx] = 1.0 if position.get('size', 0) != 0 else 0.0
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feature_idx += 1
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features[feature_idx] = position.get('pnl', 0.0) / 1000.0 # Normalized PnL
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feature_idx += 1
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features[feature_idx] = abs(position.get('size', 0.0)) / 100.0 # Normalized size
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feature_idx += 1
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features[feature_idx] = position.get('entry_price', 0.0) / 10000.0 if position.get('entry_price', 0) > 0 else 0.0
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feature_idx += 1
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# Fill remaining features with zeros (116-127) - padding for future features
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# Features 116-127 are reserved for future expansion
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# Determine action target based on price change
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if price_change > 0.001: action_target = 0 # BUY
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