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This commit is contained in:
Dobromir Popov
@@ -42,8 +42,26 @@ from dataclasses import asdict
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import math
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import subprocess
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# Conditional imports for optional dependencies
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try:
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import torch
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import torch.nn as nn
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HAS_TORCH = True
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except ImportError:
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torch = None
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nn = None
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HAS_TORCH = False
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try:
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import numpy as np
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HAS_NUMPY = True
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except ImportError:
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np = None
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HAS_NUMPY = False
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# Setup logger
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO) # Ensure we can see INFO messages for predictions
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# Reduce Werkzeug/Dash logging noise
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logging.getLogger('werkzeug').setLevel(logging.WARNING)
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@@ -117,6 +135,9 @@ class CleanTradingDashboard:
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# Initialize multi-timeframe prediction system
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self.multi_timeframe_predictor = None
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self._initialize_multi_timeframe_predictor()
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# Initialize 10-minute prediction storage
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self.current_10min_prediction = None
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# Initialize layout and component managers
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self.layout_manager = DashboardLayoutManager(
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@@ -1911,11 +1932,155 @@ class CleanTradingDashboard:
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self._add_dqn_predictions_to_chart(fig, symbol, df_main, row)
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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_iterative_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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except Exception as e:
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logger.warning(f"Error adding model predictions to chart: {e}")
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def _add_iterative_predictions_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
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"""Add 10-minute iterative predictions to the main chart with fading opacity"""
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try:
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if not hasattr(self, 'multi_timeframe_predictor') or not self.multi_timeframe_predictor:
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logger.debug("❌ Multi-timeframe predictor not available")
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return
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# Run iterative prediction every minute
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current_time = datetime.now()
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if not hasattr(self, '_last_prediction_time') or \
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(current_time - self._last_prediction_time).total_seconds() >= 60:
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try:
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prediction_result = self.run_iterative_prediction_10min(symbol)
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if prediction_result:
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self._last_prediction_time = current_time
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logger.info("✅ 10-minute iterative prediction completed")
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else:
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logger.warning("❌ 10-minute iterative prediction returned None")
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except Exception as e:
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logger.error(f"Error running iterative prediction: {e}")
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# Get current predictions from stored result
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if hasattr(self, 'current_10min_prediction') and self.current_10min_prediction:
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predictions = self.current_10min_prediction.get('predictions', [])
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logger.debug(f"🔍 Found {len(predictions)} predictions in current_10min_prediction")
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if predictions:
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logger.info(f"📊 Processing {len(predictions)} predictions for chart display")
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# Group predictions by age for fading effect
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prediction_groups = {}
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current_time = datetime.now()
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for pred in predictions[-50:]: # Last 50 predictions
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prediction_time = pred.get('timestamp')
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if not prediction_time:
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logger.debug(f"❌ Prediction missing timestamp: {pred}")
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continue
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if isinstance(prediction_time, str):
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try:
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prediction_time = pd.to_datetime(prediction_time)
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except Exception as e:
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logger.debug(f"❌ Could not parse timestamp '{prediction_time}': {e}")
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continue
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# Calculate age in minutes (how long ago this prediction was made)
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# For future predictions, use a small positive age to show them as current
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if prediction_time > current_time:
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age_minutes = 0.1 # Future predictions treated as very recent
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else:
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age_minutes = (current_time - prediction_time).total_seconds() / 60
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logger.debug(f"🔍 Prediction age: {age_minutes:.2f} min, timestamp: {prediction_time}, current: {current_time}")
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# Group by age ranges for fading
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if age_minutes <= 1:
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group = 'current' # Very recent, high opacity
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elif age_minutes <= 3:
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group = 'recent' # Recent, medium opacity
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elif age_minutes <= 5:
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group = 'old' # Older, low opacity
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else:
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continue # Too old, skip
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if group not in prediction_groups:
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prediction_groups[group] = []
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prediction_groups[group].append({
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'x': prediction_time,
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'y': pred.get('close', 0),
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'high': pred.get('high', 0),
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'low': pred.get('low', 0),
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'confidence': pred.get('confidence', 0),
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'age': age_minutes
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})
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# Add predictions with fading opacity
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opacity_levels = {
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'current': 0.8, # Bright for very recent
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'recent': 0.5, # Medium for recent
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'old': 0.3 # Dim for older
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}
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logger.info(f"📊 Adding {len(prediction_groups)} prediction groups to chart")
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for group, preds in prediction_groups.items():
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if not preds:
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continue
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opacity = opacity_levels[group]
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logger.info(f"📈 Adding {group} predictions: {len(preds)} points, opacity: {opacity}")
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# Add prediction line
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fig.add_trace(
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go.Scatter(
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x=[p['x'] for p in preds],
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y=[p['y'] for p in preds],
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mode='lines+markers',
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line=dict(
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color=f'rgba(255, 215, 0, {opacity})', # Gold color
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width=2,
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dash='dash'
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),
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marker=dict(
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symbol='diamond',
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size=6,
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color=f'rgba(255, 215, 0, {opacity})',
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line=dict(width=1, color='rgba(255, 140, 0, 0.8)')
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),
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name=f'🔮 10min Pred ({group})',
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showlegend=True,
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hovertemplate="<b>🔮 10-Minute Prediction</b><br>" +
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"Predicted Close: $%{y:.2f}<br>" +
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"Time: %{x}<br>" +
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"Age: %{customdata:.1f} min<br>" +
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"Confidence: %{text:.1%}<extra></extra>",
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customdata=[p['age'] for p in preds],
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text=[p['confidence'] for p in preds]
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),
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row=row, col=1
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)
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# Add confidence bands (high/low range)
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if len(preds) > 1:
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fig.add_trace(
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go.Scatter(
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x=[p['x'] for p in preds] + [p['x'] for p in reversed(preds)],
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y=[p['high'] for p in preds] + [p['low'] for p in reversed(preds)],
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fill='toself',
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fillcolor=f'rgba(255, 215, 0, {opacity * 0.2})',
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line=dict(width=0),
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mode='lines',
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name=f'Prediction Range ({group})',
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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 adding iterative predictions to chart: {e}")
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def _add_dqn_predictions_to_chart(self, fig: go.Figure, symbol: str, df_main: pd.DataFrame, row: int = 1):
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"""Add DQN action predictions as directional arrows"""
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try:
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@@ -5133,6 +5298,268 @@ class CleanTradingDashboard:
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filename = f"trades_export_{timestamp}.csv"
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return self.export_trade_history_csv(filename)
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def run_iterative_prediction_10min(self, symbol: str = "ETH/USDT") -> Optional[Dict]:
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"""Run 10-minute iterative prediction using the multi-timeframe predictor"""
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try:
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if not self.multi_timeframe_predictor:
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logger.warning("Multi-timeframe predictor not available")
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return None
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logger.info(f"🔮 Running 10-minute iterative prediction for {symbol}")
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# Get current price and market conditions
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current_price = self._get_current_price(symbol)
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if not current_price:
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logger.warning(f"Could not get current price for {symbol}")
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return None
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# Run iterative prediction for 10 minutes
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iterative_predictions = self.multi_timeframe_predictor._generate_iterative_predictions(
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symbol=symbol,
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base_data=self.multi_timeframe_predictor._get_sequence_data_for_horizon(
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symbol, self.multi_timeframe_predictor.horizons[PredictionHorizon.TEN_MINUTES]['sequence_length']
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),
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num_steps=10, # 10 steps for 10-minute prediction
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market_conditions={'confidence_multiplier': 1.0}
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)
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if iterative_predictions:
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# Analyze the 10-minute prediction
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config = self.multi_timeframe_predictor.horizons[PredictionHorizon.TEN_MINUTES]
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market_conditions = self.multi_timeframe_predictor._assess_market_conditions(symbol)
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horizon_prediction = self.multi_timeframe_predictor._analyze_horizon_prediction(
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iterative_predictions, config, market_conditions
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)
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if horizon_prediction:
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# Store the prediction for dashboard display
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self.current_10min_prediction = {
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'symbol': symbol,
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'timestamp': datetime.now(),
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'predictions': iterative_predictions,
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'horizon_analysis': horizon_prediction,
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'current_price': current_price
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}
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logger.info(f"✅ 10-minute iterative prediction completed for {symbol}")
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logger.info(f"📊 Generated {len(iterative_predictions)} candle predictions")
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return self.current_10min_prediction
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logger.warning("Failed to generate 10-minute iterative prediction")
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return None
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except Exception as e:
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logger.error(f"Error running 10-minute iterative prediction: {e}")
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return None
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def create_10min_prediction_chart(self, opacity: float = 0.4) -> Dict[str, Any]:
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"""DEPRECATED: Create a chart visualizing the 10-minute iterative predictions with opacity
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Note: Predictions are now integrated directly into the main 1-minute chart"""
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try:
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if not self.current_10min_prediction or not self.current_10min_prediction.get('predictions'):
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# Return empty chart if no predictions available
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return {
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'data': [],
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'layout': {
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'title': '10-Minute Iterative Predictions - No Data Available',
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'template': 'plotly_dark',
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'height': 400,
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'annotations': [{
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'text': 'Run iterative prediction to see forecast',
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'xref': 'paper', 'yref': 'paper',
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'x': 0.5, 'y': 0.5,
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'showarrow': False,
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'font': {'size': 16, 'color': 'gray'}
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}]
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}
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}
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predictions = self.current_10min_prediction['predictions']
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current_price = self.current_10min_prediction['current_price']
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horizon_analysis = self.current_10min_prediction['horizon_analysis']
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# Create time points for the next 10 minutes
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base_time = self.current_10min_prediction['timestamp']
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time_points = [base_time + timedelta(minutes=i) for i in range(11)] # 0 to 10 minutes
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# Extract predicted prices
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predicted_prices = [current_price] # Start with current price
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confidence_levels = [1.0] # Current price has full confidence
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for i, pred in enumerate(predictions[:10]): # Limit to 10 predictions
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if 'ohlcv_prediction' in pred:
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close_price = pred['ohlcv_prediction']['close']
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predicted_prices.append(close_price)
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# Get confidence for this prediction
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confidence = pred.get('action_confidence', 0.5)
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confidence_levels.append(confidence)
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# Create the main prediction line
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prediction_trace = go.Scatter(
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x=time_points[:len(predicted_prices)],
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y=predicted_prices,
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mode='lines+markers',
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name='Predicted Price',
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line=dict(color='cyan', width=3),
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marker=dict(size=6, color='cyan'),
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opacity=opacity
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)
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# Create confidence bands
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upper_bound = []
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lower_bound = []
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for i, price in enumerate(predicted_prices):
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if i == 0: # Current price has no uncertainty
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upper_bound.append(price)
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lower_bound.append(price)
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else:
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# Create confidence bands based on prediction confidence
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confidence = confidence_levels[i]
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uncertainty = (1 - confidence) * price * 0.02 # 2% max uncertainty
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upper_bound.append(price + uncertainty)
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lower_bound.append(price - uncertainty)
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# Confidence band fill
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confidence_fill = go.Scatter(
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x=time_points[:len(predicted_prices)] + time_points[:len(predicted_prices)][::-1],
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y=upper_bound + lower_bound[::-1],
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fill='toself',
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fillcolor=f'rgba(0, 255, 255, {opacity * 0.3})', # Cyan with reduced opacity
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line=dict(color='rgba(255,255,255,0)'),
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name='Confidence Band',
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showlegend=True
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)
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# Individual candle predictions as scatter points
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candle_traces = []
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for i, pred in enumerate(predictions[:10]):
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if 'ohlcv_prediction' in pred:
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ohlcv = pred['ohlcv_prediction']
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pred_time = base_time + timedelta(minutes=i+1)
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confidence = pred.get('action_confidence', 0.5)
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# Color based on price movement
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if ohlcv['close'] > ohlcv['open']:
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color = f'rgba(0, 255, 0, {opacity})' # Green for bullish
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else:
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color = f'rgba(255, 0, 0, {opacity})' # Red for bearish
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candle_trace = go.Scatter(
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x=[pred_time],
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y=[ohlcv['close']],
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mode='markers',
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marker=dict(
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size=max(8, int(confidence * 20)), # Size based on confidence
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color=color,
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symbol='diamond',
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line=dict(width=2, color='white')
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),
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name=f'Candle {i+1}',
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showlegend=False,
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hovertemplate=f'Candle {i+1}<br>Time: {pred_time.strftime("%H:%M")}<br>Close: ${ohlcv["close"]:.2f}<br>Confidence: {confidence:.2f}<extra></extra>'
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)
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candle_traces.append(candle_trace)
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# Current price marker
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current_price_trace = go.Scatter(
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x=[base_time],
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y=[current_price],
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mode='markers',
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marker=dict(
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size=12,
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color='yellow',
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symbol='star',
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line=dict(width=2, color='white')
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),
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name='Current Price',
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hovertemplate=f'Current Price<br>${current_price:.2f}<extra></extra>'
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)
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# Create the figure
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fig = go.Figure()
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# Add traces in order (confidence band first, then prediction line, then candles)
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fig.add_trace(confidence_fill)
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fig.add_trace(prediction_trace)
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fig.add_trace(current_price_trace)
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# Add individual candle traces
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for trace in candle_traces:
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fig.add_trace(trace)
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# Calculate overall trend
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if len(predicted_prices) > 1:
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start_price = predicted_prices[0]
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end_price = predicted_prices[-1]
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total_change_pct = ((end_price - start_price) / start_price) * 100
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trend_color = 'green' if total_change_pct > 0 else 'red'
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trend_text = f"Overall Trend: {'↗️ BULLISH' if total_change_pct > 0 else '↘️ BEARISH'} {abs(total_change_pct):.2f}%"
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else:
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trend_text = "No trend data available"
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trend_color = 'gray'
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# Update layout
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fig.update_layout(
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title={
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'text': f'🔮 10-Minute Iterative Price Prediction - {trend_text}',
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'y': 0.95,
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'x': 0.5,
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'xanchor': 'center',
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'yanchor': 'top',
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'font': dict(size=16, color=trend_color)
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},
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template='plotly_dark',
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height=500,
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xaxis=dict(
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title='Time',
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tickformat='%H:%M',
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showgrid=True,
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gridcolor='rgba(128,128,128,0.2)'
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),
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yaxis=dict(
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title='Price ($)',
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tickformat='.2f',
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showgrid=True,
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gridcolor='rgba(128,128,128,0.2)'
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),
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hovermode='x unified',
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legend=dict(
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yanchor="top",
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y=0.99,
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xanchor="left",
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x=0.01
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),
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annotations=[
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dict(
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text="💡 Predictions are iterative - each candle builds on the previous prediction",
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x=0.5,
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y=-0.15,
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xref="paper",
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yref="paper",
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showarrow=False,
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font=dict(size=10, color='gray')
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)
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]
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)
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return fig
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except Exception as e:
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logger.error(f"Error creating 10-minute prediction chart: {e}")
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return {
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'data': [],
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'layout': {
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'title': f'Error creating prediction chart: {str(e)[:50]}...',
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'template': 'plotly_dark',
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'height': 400
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}
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}
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def _train_dqn_on_signal(self, signal: Dict, trade_outcome: Dict):
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"""Train DQN agent on executed signal with trade outcome"""
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try:
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||||
@@ -7771,6 +8198,7 @@ class CleanTradingDashboard:
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||||
elif hasattr(network_output, 'dim'):
|
||||
# Single tensor output - assume it's action logits
|
||||
action_logits = network_output
|
||||
device = action_logits.device if hasattr(action_logits, 'device') else torch.device('cpu')
|
||||
predicted_confidence = torch.tensor(0.5, device=device) # Default confidence
|
||||
else:
|
||||
logger.debug(f"Unexpected network output format: {type(network_output)}")
|
||||
@@ -7779,6 +8207,7 @@ class CleanTradingDashboard:
|
||||
# Ensure predicted_confidence is a tensor with proper dimensions
|
||||
if not hasattr(predicted_confidence, 'dim'):
|
||||
# If it's not a tensor, convert it
|
||||
device = predicted_confidence.device if hasattr(predicted_confidence, 'device') else torch.device('cpu')
|
||||
predicted_confidence = torch.tensor(float(predicted_confidence), device=device)
|
||||
|
||||
if predicted_confidence.dim() == 0:
|
||||
@@ -8415,13 +8844,15 @@ class CleanTradingDashboard:
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
def create_clean_dashboard(data_provider: Optional[DataProvider] = None, orchestrator: Optional[TradingOrchestrator] = None, trading_executor: Optional[TradingExecutor] = None):
|
||||
"""Factory function to create a CleanTradingDashboard instance"""
|
||||
return CleanTradingDashboard(
|
||||
data_provider=data_provider,
|
||||
orchestrator=orchestrator,
|
||||
trading_executor=trading_executor
|
||||
)
|
||||
|
||||
|
||||
# test edit
|
||||
)
|
||||
|
||||
|
||||
# test edit
|
||||
@@ -455,5 +455,6 @@ class DashboardLayoutManager:
|
||||
], className="card-body p-2")
|
||||
], className="card", style={"width": "30%", "marginLeft": "2%"})
|
||||
], className="d-flex")
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user