price vector predictions
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Dobromir Popov
@ -2201,6 +2201,9 @@ class CleanTradingDashboard:
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self._add_cnn_predictions_to_chart(fig, symbol, df_main, row)
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self._add_cob_rl_predictions_to_chart(fig, symbol, df_main, row)
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self._add_prediction_accuracy_feedback(fig, symbol, df_main, row)
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# 3. Add price vector predictions as directional lines
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self._add_price_vector_predictions_to_chart(fig, symbol, df_main, row)
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except Exception as e:
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logger.warning(f"Error adding model predictions to chart: {e}")
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@ -2590,6 +2593,142 @@ class CleanTradingDashboard:
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except Exception as e:
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logger.debug(f"Error adding prediction accuracy feedback to chart: {e}")
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def _add_price_vector_predictions_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
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"""Add price vector predictions as thin directional lines on the chart"""
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try:
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# Get recent predictions with price vectors from orchestrator
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vector_predictions = self._get_recent_vector_predictions(symbol)
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if not vector_predictions:
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return
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for pred in vector_predictions[-20:]: # Last 20 vector predictions
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try:
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timestamp = pred.get('timestamp')
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price = pred.get('price', 0)
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vector = pred.get('price_direction', {})
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confidence = pred.get('confidence', 0)
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model_name = pred.get('model_name', 'unknown')
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if not vector or price <= 0:
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continue
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direction = vector.get('direction', 0.0)
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vector_confidence = vector.get('confidence', 0.0)
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# Skip weak predictions
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if abs(direction) < 0.1 or vector_confidence < 0.3:
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continue
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# Calculate vector endpoint
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# Scale magnitude based on direction and confidence
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predicted_magnitude = abs(direction) * vector_confidence * 2.0 # Scale to ~2% max
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price_change = predicted_magnitude if direction > 0 else -predicted_magnitude
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end_price = price * (1 + price_change / 100.0)
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# Create time projection (5-minute forward projection)
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if isinstance(timestamp, str):
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timestamp = pd.to_datetime(timestamp)
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end_time = timestamp + timedelta(minutes=5)
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# Color based on direction and confidence
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if direction > 0:
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# Upward prediction - green shades
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color = f'rgba(0, 255, 0, {vector_confidence:.2f})'
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else:
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# Downward prediction - red shades
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color = f'rgba(255, 0, 0, {vector_confidence:.2f})'
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# Draw vector line
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fig.add_trace(
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go.Scatter(
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x=[timestamp, end_time],
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y=[price, end_price],
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mode='lines',
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line=dict(
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color=color,
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width=2,
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dash='dot' if vector_confidence < 0.6 else 'solid'
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),
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name=f'{model_name.upper()} Vector',
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showlegend=False,
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hovertemplate=f"<b>{model_name.upper()} PRICE VECTOR</b><br>" +
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"Start: $%{y[0]:.2f}<br>" +
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"Target: $%{y[1]:.2f}<br>" +
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f"Direction: {direction:+.3f}<br>" +
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f"V.Confidence: {vector_confidence:.1%}<br>" +
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f"Magnitude: {predicted_magnitude:.2f}%<br>" +
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f"Model Confidence: {confidence:.1%}<extra></extra>"
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),
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row=row, col=1
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)
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# Add small marker at vector start
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marker_color = 'green' if direction > 0 else 'red'
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fig.add_trace(
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go.Scatter(
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x=[timestamp],
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y=[price],
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mode='markers',
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marker=dict(
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symbol='circle',
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size=4,
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color=marker_color,
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opacity=vector_confidence
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),
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name=f'{model_name} Vector Start',
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showlegend=False,
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hoverinfo='skip'
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),
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row=row, col=1
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)
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except Exception as e:
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logger.debug(f"Error drawing vector for prediction: {e}")
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continue
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except Exception as e:
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logger.debug(f"Error adding price vector predictions to chart: {e}")
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def _get_recent_vector_predictions(self, symbol: str) -> List[Dict]:
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"""Get recent predictions that include price vector data"""
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try:
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vector_predictions = []
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# Get from orchestrator's recent predictions
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if hasattr(self.trading_executor, 'orchestrator') and self.trading_executor.orchestrator:
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orchestrator = self.trading_executor.orchestrator
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# Check last inference data for each model
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for model_name, inference_data in getattr(orchestrator, 'last_inference', {}).items():
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if not inference_data:
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continue
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prediction = inference_data.get('prediction', {})
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metadata = inference_data.get('metadata', {})
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# Look for price direction in prediction or metadata
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price_direction = None
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if 'price_direction' in prediction:
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price_direction = prediction['price_direction']
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elif 'price_direction' in metadata:
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price_direction = metadata['price_direction']
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if price_direction:
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vector_predictions.append({
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'timestamp': inference_data.get('timestamp', datetime.now()),
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'price': inference_data.get('inference_price', 0),
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'price_direction': price_direction,
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'confidence': prediction.get('confidence', 0),
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'model_name': model_name
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})
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return vector_predictions
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except Exception as e:
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logger.debug(f"Error getting recent vector predictions: {e}")
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return []
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def _get_real_cob_rl_predictions(self, symbol: str) -> List[Dict]:
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"""Get real COB RL predictions from the model"""
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try:
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