predictions candles tooltips work
This commit is contained in:
Dobromir Popov
@@ -765,6 +765,10 @@ class AnnotationDashboard:
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self.annotation_manager = AnnotationManager()
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# Use REAL training adapter - NO SIMULATION!
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self.training_adapter = RealTrainingAdapter(None, self.data_provider)
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# Store prediction accuracy data for hover tooltips
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self.prediction_accuracy_cache = {} # {prediction_id: accuracy_data}
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# Initialize training strategy manager (controls training decisions)
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self.training_strategy = TrainingStrategyManager(self.data_provider, self.training_adapter)
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self.training_strategy.dashboard = self
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@@ -2656,6 +2660,47 @@ class AnnotationDashboard:
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predictions['transformer'] = self._serialize_prediction(transformer_pred)
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# Add accuracy data if available
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prediction_id = transformer_pred.get('prediction_id')
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if prediction_id and prediction_id in self.prediction_accuracy_cache:
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predictions['transformer']['accuracy'] = self.prediction_accuracy_cache[prediction_id]
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# Try to calculate accuracy on-the-fly if we have both predicted and actual candles
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elif 'predicted_candle' in transformer_pred and timeframe in transformer_pred['predicted_candle']:
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predicted_candle = transformer_pred['predicted_candle'][timeframe]
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# Try to get the actual candle for the same timestamp
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prediction_timestamp = transformer_pred.get('timestamp')
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if prediction_timestamp and response.get('chart_update'):
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chart_timestamp = response['chart_update']['candle']['timestamp']
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# If timestamps match (or close), calculate accuracy
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try:
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from datetime import datetime, timezone
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pred_time = datetime.fromisoformat(prediction_timestamp.replace('Z', '+00:00'))
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chart_time = datetime.fromisoformat(chart_timestamp.replace('Z', '+00:00'))
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# Allow 1 minute tolerance for timestamp matching
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time_diff = abs((pred_time - chart_time).total_seconds())
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if time_diff <= 60: # Within 1 minute
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actual_candle = [
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response['chart_update']['candle']['open'],
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response['chart_update']['candle']['high'],
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response['chart_update']['candle']['low'],
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response['chart_update']['candle']['close'],
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response['chart_update']['candle']['volume']
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]
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accuracy = self._calculate_prediction_accuracy(predicted_candle, actual_candle)
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if accuracy:
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predictions['transformer']['accuracy'] = accuracy
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# Cache for future use
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if prediction_id:
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self.prediction_accuracy_cache[prediction_id] = accuracy
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except Exception as e:
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logger.debug(f"Error calculating real-time accuracy: {e}")
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# Verify predicted_candle is preserved after serialization
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if 'predicted_candle' not in predictions['transformer'] and 'predicted_candle' in transformer_pred:
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logger.warning("predicted_candle was lost during serialization!")
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@@ -2859,6 +2904,14 @@ class AnnotationDashboard:
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logger.info(f"[ONLINE LEARNING] Received validation for {timeframe}: accuracy={accuracy:.1f}%, direction={'✓' if direction_correct else '✗'}")
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# Calculate and store accuracy data for hover tooltips
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accuracy_data = self._calculate_prediction_accuracy(predicted, actual)
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if accuracy_data:
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# Store with timestamp as key for retrieval
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prediction_key = f"{timeframe}_{timestamp}"
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self.prediction_accuracy_cache[prediction_key] = accuracy_data
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logger.debug(f"Stored accuracy data for {prediction_key}: {accuracy_data['accuracy']:.1f}%")
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# Trigger training and get metrics
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metrics = self._train_on_validated_prediction(
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timeframe, timestamp, predicted, actual,
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@@ -2934,6 +2987,59 @@ class AnnotationDashboard:
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'error': str(e)
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}), 500
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def _calculate_prediction_accuracy(self, predicted_candle, actual_candle):
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"""Calculate accuracy metrics for a prediction vs actual candle"""
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try:
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if not predicted_candle or not actual_candle:
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return None
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# Ensure both are lists/arrays with at least 4 values (OHLC)
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pred = list(predicted_candle) if hasattr(predicted_candle, '__iter__') else [predicted_candle]
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actual = list(actual_candle) if hasattr(actual_candle, '__iter__') else [actual_candle]
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if len(pred) < 4 or len(actual) < 4:
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return None
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# Calculate percentage errors for each field
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pct_errors = {}
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field_names = ['open', 'high', 'low', 'close']
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if len(pred) >= 5 and len(actual) >= 5:
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field_names.append('volume')
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total_error = 0
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valid_fields = 0
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for i, field in enumerate(field_names):
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if i < len(pred) and i < len(actual) and actual[i] != 0:
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error = abs((pred[i] - actual[i]) / actual[i]) * 100
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pct_errors[field] = error
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total_error += error
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valid_fields += 1
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if valid_fields == 0:
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return None
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# Calculate overall accuracy (100% - average error)
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avg_error = total_error / valid_fields
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accuracy_pct = max(0, 100 - avg_error)
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# Check direction correctness (close vs open)
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pred_direction = pred[3] >= pred[0] # close >= open
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actual_direction = actual[3] >= actual[0]
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direction_correct = pred_direction == actual_direction
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return {
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'accuracy': accuracy_pct,
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'directionCorrect': direction_correct,
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'avgPctError': avg_error,
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'actualCandle': actual,
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'pctErrors': pct_errors
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}
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except Exception as e:
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logger.error(f"Error calculating prediction accuracy: {e}")
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return None
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@self.server.route('/api/trading-stats', methods=['GET'])
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def get_trading_stats():
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"""Get current trading statistics (positions, PnL, win rate)"""
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@@ -2834,6 +2834,26 @@ class ChartManager {
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this._checkPredictionAccuracy(timeframe, data);
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}
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/**
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* Update ghost candle with accuracy data
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*/
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updateGhostCandleAccuracy(timeframe, timestamp, accuracyData) {
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if (!this.ghostCandleHistory || !this.ghostCandleHistory[timeframe]) return;
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// Find ghost candle by timestamp and update accuracy
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for (let ghost of this.ghostCandleHistory[timeframe]) {
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if (ghost.timestamp === timestamp ||
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(ghost.targetTime && Math.abs(new Date(ghost.targetTime) - new Date(timestamp)) < 60000)) {
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ghost.accuracy = accuracyData;
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console.log(`Updated ghost candle accuracy for ${timestamp}: ${accuracyData.accuracy.toFixed(1)}%`);
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// Refresh the display to show updated tooltips
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this._refreshPredictionDisplay(timeframe);
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break;
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}
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}
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}
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/**
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* Calculate prediction accuracy by comparing ghost predictions with actual candles
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*/
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@@ -3359,6 +3379,7 @@ class ChartManager {
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// Handle Predicted Candles (ghost candles) - only for the most recent prediction
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if (predictions.transformer && predictions.transformer.predicted_candle) {
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console.log(`[updatePredictions] predicted_candle data:`, predictions.transformer.predicted_candle);
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console.log(`[updatePredictions] accuracy data:`, predictions.transformer.accuracy);
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const candleData = predictions.transformer.predicted_candle[timeframe];
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console.log(`[updatePredictions] candleData for ${timeframe}:`, candleData);
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if (candleData) {
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@@ -3427,7 +3448,8 @@ class ChartManager {
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this.ghostCandleHistory[timeframe].push({
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timestamp: formattedTimestamp,
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candle: candleData,
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targetTime: targetTimestamp
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targetTime: targetTimestamp,
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accuracy: predictions.transformer.accuracy || null // Include accuracy data if available
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});
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// 3. Keep only last 10 ghost candles
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98
HOVER_INFO_RESTORATION_FIX.md
Normal file
98
HOVER_INFO_RESTORATION_FIX.md
Normal file
@@ -0,0 +1,98 @@
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# Hover Info Restoration Fix - Complete
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## Problem Identified
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The candle prediction hover info was missing, which previously showed:
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- Prediction accuracy percentage
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- Direction correctness (✓ or ✗)
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- Predicted vs Actual OHLCV values with percentage errors
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- Overall validation status
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## Root Cause Analysis
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1. **Backend**: Accuracy data wasn't being calculated and stored for predictions
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2. **API**: Live updates endpoint wasn't providing accuracy data with predictions
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3. **Frontend**: Ghost candles weren't receiving accuracy data for tooltips
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## Fixes Applied
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### 1. Backend Accuracy Calculation
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Added `_calculate_prediction_accuracy()` method that computes:
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```python
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{
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'accuracy': 87.5, # Overall accuracy percentage
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'directionCorrect': True, # Direction prediction correctness
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'avgPctError': 12.5, # Average percentage error
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'actualCandle': [3320.1, 3325.4, 3318.2, 3322.8, 1250.5], # Actual OHLCV
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'pctErrors': { # Individual field errors
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'open': 0.8, 'high': 1.2, 'low': 0.5, 'close': 0.9, 'volume': 15.2
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}
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}
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```
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### 2. Accuracy Data Storage
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- Added `prediction_accuracy_cache` to store accuracy data by prediction ID
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- Accuracy is calculated when predictions are validated
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- Cache is used to provide accuracy data for hover tooltips
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### 3. Enhanced Live Updates API
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Updated `/api/live-updates` to include accuracy data:
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- Calculates accuracy on-the-fly when predicted and actual candles match
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- Includes accuracy data in transformer predictions
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- Caches accuracy for future retrieval
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### 4. Frontend Ghost Candle Enhancement
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- Ghost candles now store accuracy data when created
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- Added `updateGhostCandleAccuracy()` method to update existing ghost candles
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- Enhanced tooltip creation to show rich accuracy information
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### 5. Validation Integration
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Updated `/api/train-validated-prediction` to:
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- Calculate accuracy when predictions are validated
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- Store accuracy data in cache for hover display
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- Link accuracy to specific predictions by timestamp
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## Expected Hover Info Display
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### For Unvalidated Predictions:
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```
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PREDICTED CANDLE
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O: 3320.15 H: 3325.42
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L: 3318.23 C: 3322.61
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Direction: UP
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Status: AWAITING VALIDATION...
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```
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### For Validated Predictions:
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```
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PREDICTED CANDLE
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O: 3320.15 H: 3325.42
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L: 3318.23 C: 3322.61
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Direction: UP
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--- VALIDATION ---
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Accuracy: 87.5%
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Direction: CORRECT ✓
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Avg Error: 1.25%
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ACTUAL vs PREDICTED:
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Open: 3320.89 vs 3320.15 (0.8%)
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High: 3326.12 vs 3325.42 (1.2%)
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Low: 3318.45 vs 3318.23 (0.5%)
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Close: 3323.18 vs 3322.61 (0.9%)
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Volume: 1285.2 vs 1250.5 (15.2%)
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```
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## Data Flow
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1. **Prediction Made**: Model generates prediction with candle data
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2. **Ghost Candle Created**: Added to chart with "AWAITING VALIDATION" status
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3. **Actual Candle Arrives**: System compares predicted vs actual
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4. **Accuracy Calculated**: Percentage errors and direction correctness computed
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5. **Tooltip Updated**: Hover info shows detailed validation results
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## Verification Steps
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1. **Start inference**: Make predictions and see ghost candles
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2. **Hover over ghost candles**: Should show prediction details
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3. **Wait for validation**: Tooltips should update with accuracy data
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4. **Check accuracy**: Hover should show predicted vs actual comparison
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The hover info system now provides complete visibility into prediction accuracy and validation results, helping you understand model performance at the individual prediction level!
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