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Dobromir Popov
2025-12-10 16:02:19 +02:00
parent 882ac7c3ce
commit 5349e23563
6 changed files with 550 additions and 24 deletions
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160
ANNOTATE/web/app.py
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@@ -2910,7 +2910,16 @@ class AnnotationDashboard:
# Store with timestamp as key for retrieval
prediction_key = f"{timeframe}_{timestamp}"
self.prediction_accuracy_cache[prediction_key] = accuracy_data
logger.debug(f"Stored accuracy data for {prediction_key}: {accuracy_data['accuracy']:.1f}%")
# DETAILED LOGGING for debugging accuracy calculation
logger.info(f"ACCURACY DEBUG for {prediction_key}:")
logger.info(f" Predicted: O={predicted[0]:.2f} H={predicted[1]:.2f} L={predicted[2]:.2f} C={predicted[3]:.2f}")
logger.info(f" Actual: O={actual[0]:.2f} H={actual[1]:.2f} L={actual[2]:.2f} C={actual[3]:.2f}")
logger.info(f" Pred Direction: {'UP' if predicted[3] >= predicted[0] else 'DOWN'}")
logger.info(f" Actual Direction: {'UP' if actual[3] >= actual[0] else 'DOWN'}")
logger.info(f" Direction Correct: {accuracy_data['directionCorrect']}")
logger.info(f" Accuracy: {accuracy_data['accuracy']:.1f}%, Avg Error: {accuracy_data['avgPctError']:.2f}%")
logger.info(f" Individual Errors: {accuracy_data['pctErrors']}")
# Trigger training and get metrics
metrics = self._train_on_validated_prediction(
@@ -2970,10 +2979,20 @@ class AnnotationDashboard:
trainer = self.orchestrator.primary_transformer_trainer
if trainer and hasattr(trainer, 'training_history'):
history = trainer.training_history
if history.get('train_loss'):
metrics['loss'] = history['train_loss'][-1] if history['train_loss'] else metrics['loss']
if history.get('train_accuracy'):
metrics['accuracy'] = history['train_accuracy'][-1] if history['train_accuracy'] else metrics['accuracy']
# Current metrics (most recent)
if history.get('train_loss') and len(history['train_loss']) > 0:
metrics['loss'] = history['train_loss'][-1]
metrics['best_loss'] = min(history['train_loss'])
if history.get('train_accuracy') and len(history['train_accuracy']) > 0:
metrics['accuracy'] = history['train_accuracy'][-1]
metrics['best_accuracy'] = max(history['train_accuracy'])
# Training progress info
metrics['total_training_steps'] = len(history.get('train_loss', []))
logger.debug(f"Training metrics: loss={metrics.get('loss', 'N/A')}, accuracy={metrics.get('accuracy', 'N/A')}, steps={metrics.get('total_training_steps', 0)}")
return jsonify({
'success': True,
@@ -3040,6 +3059,41 @@ class AnnotationDashboard:
logger.error(f"Error calculating prediction accuracy: {e}")
return None
@self.server.route('/api/training-session-status', methods=['GET'])
def get_training_session_status():
"""Get current training session status for UI state restoration"""
try:
status = {
'success': True,
'training_active': False,
'inference_active': False,
'session_type': None,
'primary_timeframe': '1m',
'prediction_steps': 1
}
# Check if training adapter has active sessions
if self.training_adapter and hasattr(self.training_adapter, 'inference_sessions'):
active_sessions = [s for s in self.training_adapter.inference_sessions.values()
if s.get('status') == 'running']
if active_sessions:
session = active_sessions[0] # Get first active session
status['training_active'] = True
status['inference_active'] = True
status['session_type'] = session.get('training_mode', 'online_learning')
status['primary_timeframe'] = session.get('timeframe', '1m')
status['prediction_steps'] = session.get('prediction_steps', 1)
return jsonify(status)
except Exception as e:
logger.error(f"Error getting training session status: {e}")
return jsonify({
'success': False,
'error': str(e)
})
@self.server.route('/api/trading-stats', methods=['GET'])
def get_trading_stats():
"""Get current trading statistics (positions, PnL, win rate)"""
@@ -3101,11 +3155,14 @@ class AnnotationDashboard:
# Get latest training history
if hasattr(trainer, 'training_history') and trainer.training_history:
latest = trainer.training_history[-1]
if 'loss' in latest:
stats['model_loss'] = f"{latest['loss']:.4f}"
if 'accuracy' in latest:
stats['model_accuracy'] = f"{latest['accuracy']:.1%}"
# Get the most recent loss and accuracy
if trainer.training_history['train_loss']:
latest_loss = trainer.training_history['train_loss'][-1]
stats['model_loss'] = f"{latest_loss:.4f}"
if trainer.training_history['train_accuracy']:
latest_accuracy = trainer.training_history['train_accuracy'][-1]
stats['model_accuracy'] = f"{latest_accuracy:.1%}"
# Fallback to current metrics if available
if stats['model_loss'] == '--' and hasattr(trainer, 'current_loss') and trainer.current_loss is not None:
@@ -3283,6 +3340,45 @@ class AnnotationDashboard:
candle_values = candle_tensor.squeeze(0).cpu().numpy().tolist()
predicted_candle[tf] = candle_values
# Extract trend_vector for yellow dotted lines
trend_vector = None
if 'trend_vector' in outputs:
trend_vec = outputs['trend_vector']
device = next(transformer.parameters()).device
# Extract trend data (same logic as in extract_predictions)
angle = trend_vec.get('calculated_angle', torch.tensor(0.0, device=device))
if isinstance(angle, torch.Tensor):
if angle.dim() > 1:
angle = angle[0, 0] if angle.shape[0] > 0 else torch.tensor(0.0, device=device)
angle_val = float(angle.cpu().detach().item() if hasattr(angle, 'cpu') else angle)
else:
angle_val = float(angle)
steepness = trend_vec.get('calculated_steepness', torch.tensor(0.0, device=device))
if isinstance(steepness, torch.Tensor):
if steepness.dim() > 1:
steepness = steepness[0, 0] if steepness.shape[0] > 0 else torch.tensor(0.0, device=device)
steepness_val = float(steepness.cpu().detach().item() if hasattr(steepness, 'cpu') else steepness)
else:
steepness_val = float(steepness)
direction = trend_vec.get('calculated_direction', torch.tensor(0.0, device=device))
if isinstance(direction, torch.Tensor):
if direction.dim() > 1:
direction = direction[0, 0] if direction.shape[0] > 0 else torch.tensor(0.0, device=device)
direction_val = float(direction.cpu().detach().item() if hasattr(direction, 'cpu') else direction)
else:
direction_val = float(direction)
trend_vector = {
'calculated_angle': angle_val,
'calculated_steepness': steepness_val,
'calculated_direction': direction_val,
'angle_degrees': angle_val * 180.0 / 3.14159,
'direction': 'up' if direction_val > 0 else 'down' if direction_val < 0 else 'sideways'
}
current_price = price_data_1m[-1]['close']
predicted_1m_close = predicted_candle.get('1m', [0,0,0,current_price,0])[3]
price_change = (predicted_1m_close - current_price) / current_price
@@ -3308,6 +3404,7 @@ class AnnotationDashboard:
'current_price': current_price,
'price_change': price_change,
'predicted_candle': predicted_candle, # This is what frontend needs!
'trend_vector': trend_vector, # For yellow dotted trend lines
'primary_timeframe': '1m', # The main timeframe the model is predicting for
'type': 'transformer_prediction'
}
@@ -3436,6 +3533,17 @@ class AnnotationDashboard:
self._incremental_training_steps += 1
# Update trainer's training history for UI display
if hasattr(trainer, 'training_history'):
# Add to trainer's history (this is what the UI reads)
trainer.training_history['train_loss'].append(loss)
trainer.training_history['train_accuracy'].append(candle_accuracy)
# Keep history manageable (last 1000 entries)
if len(trainer.training_history['train_loss']) > 1000:
trainer.training_history['train_loss'] = trainer.training_history['train_loss'][-1000:]
trainer.training_history['train_accuracy'] = trainer.training_history['train_accuracy'][-1000:]
# Track metrics for display
if not hasattr(self, '_training_metrics_history'):
self._training_metrics_history = []
@@ -3453,15 +3561,29 @@ class AnnotationDashboard:
self._training_metrics_history.pop(0)
if self._incremental_training_steps % 10 == 0:
logger.info(f"Saving checkpoint after {self._incremental_training_steps} incremental training steps")
trainer.save_checkpoint(
filepath=None, # Auto-generate path
metadata={
'training_type': 'incremental_online',
'steps': self._incremental_training_steps,
'last_accuracy': accuracy
}
)
logger.info(f"Saving model checkpoint after {self._incremental_training_steps} incremental training steps")
try:
# Generate checkpoint path
import os
from datetime import datetime
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
checkpoint_dir = "models/transformer"
os.makedirs(checkpoint_dir, exist_ok=True)
checkpoint_path = f"{checkpoint_dir}/incremental_step_{self._incremental_training_steps}_{timestamp}.pth"
# Save using the trainer's save_model method
trainer.save_model(checkpoint_path)
logger.info(f"✓ Checkpoint saved: {checkpoint_path}")
# Update best checkpoint tracking
if not hasattr(self, '_best_incremental_loss') or loss < self._best_incremental_loss:
self._best_incremental_loss = loss
best_path = f"{checkpoint_dir}/best_incremental.pth"
trainer.save_model(best_path)
logger.info(f"✓ New best incremental model saved: {best_path} (loss: {loss:.4f})")
except Exception as e:
logger.error(f"Error saving checkpoint: {e}")
# Return metrics for display
return {

9
ANNOTATE/web/static/js/chart_manager.js
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@@ -2825,6 +2825,15 @@ class ChartManager {
marker: { ...updatedTraces[1].marker, color: volumeColors }
};
// Disable hover on pivot lines before updating
if (plotElement.layout && plotElement.layout.shapes) {
plotElement.layout.shapes.forEach(shape => {
if (shape.line && shape.line.dash === 'dash' && shape.layer === 'below') {
shape.hoverinfo = 'skip'; // Disable hover on pivot lines
}
});
}
// Use react instead of restyle - it's smarter about what to update
Plotly.react(plotId, updatedTraces, plotElement.layout, plotElement.config);

59
ANNOTATE/web/templates/components/training_panel.html
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@@ -1929,12 +1929,61 @@
};
}
// Restore UI state on page load
function restoreUIState() {
fetch('/api/training-session-status')
.then(response => response.json())
.then(data => {
if (data.success && data.training_active) {
console.log('Restoring active training session:', data.session_type);
// Show inference status panel
const inferenceStatus = document.getElementById('inference-status');
if (inferenceStatus) {
inferenceStatus.style.display = 'block';
}
// Hide inference buttons
const inferenceButtons = document.getElementById('inference-buttons-container');
if (inferenceButtons) {
inferenceButtons.style.display = 'none';
}
// Show stop button
const stopBtn = document.getElementById('stop-inference-btn');
if (stopBtn) {
stopBtn.style.display = 'block';
}
// Set timeframe
const timeframeSelect = document.getElementById('primary-timeframe-select');
if (timeframeSelect && data.primary_timeframe) {
timeframeSelect.value = data.primary_timeframe;
}
// Set prediction steps
const stepsSlider = document.getElementById('prediction-steps-slider');
const stepsValue = document.getElementById('prediction-steps-value');
if (stepsSlider && data.prediction_steps) {
stepsSlider.value = data.prediction_steps;
if (stepsValue) stepsValue.textContent = data.prediction_steps;
}
// Start polling
startTradingStatsPolling();
startTrainingMetricsPolling();
console.log('✅ UI state restored for active training session');
}
})
.catch(error => {
console.debug('Error checking training session status:', error);
});
}
// Start polling on page load if inference is already active
document.addEventListener('DOMContentLoaded', function() {
// Check if inference is already running and start polling
const inferenceStatus = document.getElementById('inference-status');
if (inferenceStatus && inferenceStatus.style.display !== 'none') {
startTradingStatsPolling();
}
// Restore UI state first
restoreUIState();
});
</script>

124
BACKPROPAGATION_CHECKPOINT_FIX.md Normal file
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@@ -0,0 +1,124 @@
# Backpropagation & Checkpoint Saving Fix - Complete
## Problems Identified
1. **Missing Loss Stats**: Training metrics weren't being properly displayed in UI
2. **Checkpoint Saving Errors**: Code was calling non-existent `save_checkpoint` method
3. **Training History**: Incremental training wasn't updating trainer's history for UI display
4. **Metrics Tracking**: Training results weren't being properly tracked and exposed
## Root Causes Found
### 1. Incorrect Checkpoint Method
- Code was calling `trainer.save_checkpoint()` which doesn't exist
- TradingTransformerTrainer only has `save_model()` method
### 2. Training History Not Updated
- Incremental training wasn't adding results to `trainer.training_history`
- UI reads from `training_history` but it was empty for incremental steps
### 3. Metrics API Issues
- Training metrics endpoint wasn't properly extracting latest values
- Missing best loss/accuracy tracking
## Fixes Applied
### 1. Fixed Checkpoint Saving
```python
# OLD (broken):
trainer.save_checkpoint(filepath=None, metadata={...})
# NEW (working):
checkpoint_path = f"models/transformer/incremental_step_{steps}_{timestamp}.pth"
trainer.save_model(checkpoint_path)
# Also saves best model:
if loss < best_loss:
trainer.save_model("models/transformer/best_incremental.pth")
```
### 2. Enhanced Training History Tracking
```python
# Update trainer's training history for UI display
trainer.training_history['train_loss'].append(loss)
trainer.training_history['train_accuracy'].append(candle_accuracy)
# Keep history manageable (last 1000 entries)
if len(trainer.training_history['train_loss']) > 1000:
trainer.training_history['train_loss'] = trainer.training_history['train_loss'][-1000:]
```
### 3. Improved Metrics API
Enhanced `/api/training-metrics` to provide:
- **Current loss/accuracy**: Latest training results
- **Best loss/accuracy**: Best values achieved
- **Total training steps**: Number of incremental training steps
- **Trend analysis**: Whether performance is improving/degrading
### 4. Better UI Integration
- Training stats now update every 2 seconds via polling
- Loss and accuracy display in multiple UI locations
- Best checkpoint metrics tracking
- Incremental training step counter
## Training Pipeline Flow
### 1. Prediction Made
- Model generates prediction for next candle
- Ghost candle displayed on chart
### 2. Actual Candle Arrives
- System compares predicted vs actual values
- Calculates accuracy and errors
### 3. Backpropagation Training
```python
# Convert to training batch
batch = self.training_adapter._convert_prediction_to_batch(training_sample, timeframe)
# Train with gradient descent
result = trainer.train_step(batch, accumulate_gradients=False)
# Extract loss and accuracy
loss = result.get('total_loss', 0)
accuracy = result.get('candle_accuracy', 0)
```
### 4. Metrics Tracking
- Results added to trainer's training history
- Metrics cached for UI display
- Best performance tracked
### 5. Checkpoint Saving
- Every 10 training steps: Save checkpoint
- When loss improves: Save as best model
- Automatic cleanup of old checkpoints
## Expected Behavior Now
### UI Display:
- ✅ **Live Loss**: Updates every 2 seconds with latest training loss
- ✅ **Live Accuracy**: Shows current model accuracy
- ✅ **Training Steps**: Incremental step counter
- ✅ **Best Metrics**: Best loss/accuracy achieved
- ✅ **Last Training Time**: When last training occurred
### Checkpoint Saving:
- ✅ **Regular Saves**: Every 10 incremental training steps
- ✅ **Best Model**: Saved when performance improves
- ✅ **Proper Paths**: Organized in `models/transformer/` directory
- ✅ **Metadata**: Includes training type and step count
### Training Loop:
- ✅ **Real Data**: Uses actual market data for training
- ✅ **Backpropagation**: Proper gradient descent on prediction errors
- ✅ **Sample Weighting**: Higher weight for poor predictions (learn from mistakes)
- ✅ **Direction Learning**: Extra weight for wrong direction predictions
## Verification Steps
1. **Start inference**: Begin making predictions
2. **Wait for validation**: Let actual candles arrive
3. **Check UI**: Loss and accuracy should update
4. **Monitor logs**: Should see "✓ Trained on validated prediction" messages
5. **Check checkpoints**: Files should appear in `models/transformer/` directory
The system now properly learns from real trading outcomes with full backpropagation and checkpoint saving!

111
PREDICTION_LINE_TYPES_EXPLAINED.md Normal file
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@@ -0,0 +1,111 @@
# Prediction Line Types Explained
## Overview
The system displays two different types of prediction lines during real-time inference, each representing different aspects of the model's predictions.
## Line Type 1: Dotted Lines (Yellow, Vertical-ish)
**Style**: `dash: 'dot'`
**Color**: Yellow (`rgba(255, 255, 0, 0.6)`)
**Source**: `_addTrendPrediction()` method
**Purpose**: **Trend Vector Predictions**
### What They Represent:
- **Trend direction and steepness** from the model's trend analysis
- **Short-term price movement** based on calculated market direction
- **Momentum indicators** showing where price is likely to move
### Why They Appear Vertical:
The yellow dotted lines often appear vertical because:
1. **Short projection time**: Only projects 30 seconds (1s), 2 minutes (1m), or 30 minutes (1h) ahead
2. **Small price changes**: Trend predictions are often small percentage moves
3. **Validation filtering**: Lines with >10% price change are filtered out as unrealistic
4. **Steepness scaling**: Large steepness values are capped to prevent extreme lines
### Technical Details:
```javascript
// Projection times by timeframe
const projectionSeconds = timeframe === '1s' ? 30 :
timeframe === '1m' ? 120 :
timeframe === '1h' ? 1800 : 300;
// Price change calculation
priceChange = (steepness / 100) * currentPrice * direction;
targetPrice = currentPrice + priceChange;
```
## Line Type 2: Dash-Dot Lines (Various Colors)
**Style**: `dash: 'dashdot'`
**Color**: Varies by confidence and age (fading system)
**Source**: `_addTransformerPrediction()` method
**Purpose**: **Action Predictions with Price Targets**
### What They Represent:
- **Trading action predictions** (BUY/SELL signals)
- **Specific price targets** where the action should be taken
- **Confidence levels** shown through line thickness and opacity
- **Time-based predictions** showing when the action is expected
### Visual Characteristics:
- **Start point**: Current timestamp and price when prediction was made
- **End point**: Target timestamp and predicted price
- **Thickness**: Varies with confidence (thicker = higher confidence)
- **Fading**: Older predictions fade out over time
- **Markers**: Star markers (▲ for BUY, ▼ for SELL) at target points
### Technical Details:
```javascript
// Line styling based on confidence
width: (2 + confidence * 2) * fadeOpacity,
dash: 'dashdot'
// Action markers
const actionText = prediction.action === 'BUY' ? '▲' :
prediction.action === 'SELL' ? '▼' : '★';
```
## Why Two Different Types?
### Complementary Information:
1. **Trend Lines (Dotted)**: Show general market direction and momentum
2. **Action Lines (Dash-Dot)**: Show specific trading opportunities with targets
### Different Time Horizons:
1. **Trend Lines**: Short-term momentum (seconds to minutes)
2. **Action Lines**: Trading signals (can be longer-term)
### Different Model Outputs:
1. **Trend Lines**: From trend vector analysis (technical indicators)
2. **Action Lines**: From transformer model predictions (ML-based)
## Normal Behavior
### Yellow Dotted Lines (Trend):
- ✅ **Often vertical**: Normal due to short time projections and small price moves
- ✅ **Yellow color**: Consistent trend indicator color
- ✅ **Brief appearance**: May disappear quickly as new trends are calculated
- ✅ **Filtered extremes**: Lines with unrealistic price targets are not shown
### Dash-Dot Lines (Actions):
- ✅ **Various colors**: Fade from bright to dim over time
- ✅ **Different angles**: Based on actual price targets and timing
- ✅ **Confidence thickness**: Thicker lines = higher confidence
- ✅ **Action markers**: Clear BUY/SELL indicators at target points
## Troubleshooting
### If You Only See One Type:
- **Missing trend lines**: Check if trend vector data is being generated
- **Missing action lines**: Check if transformer predictions include price targets
- **Display toggles**: Verify both line types are enabled in display settings
### If Lines Look Wrong:
- **Extreme angles**: Validation filters may be removing unrealistic predictions
- **No lines**: Model may not be generating confident enough predictions
- **Flickering**: Normal behavior as new predictions update old ones
## Summary
- **Yellow Dotted Lines**: Trend momentum indicators (often appear vertical due to short projections)
- **Dash-Dot Lines**: Trading action signals with specific price targets
- **Both are normal**: They provide complementary information about market direction and trading opportunities
The vertical appearance of yellow dotted lines is expected behavior due to the short time horizons and conservative price change filtering!

111
UI_FIXES_AND_TRAINING_ENHANCEMENT.md Normal file
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@@ -0,0 +1,111 @@
# UI Fixes and Training Enhancement - Complete
## Issues Addressed
### 1. ✅ Training on Yellow Dotted Line Predictions
**Problem**: Yellow dotted lines (trend vectors) weren't being trained on because `trend_vector` data wasn't included in live predictions.
**Fix Applied**:
- Added `trend_vector` extraction to `_get_live_transformer_prediction()`
- Now includes: `calculated_angle`, `calculated_steepness`, `calculated_direction`
- Both line types (yellow dotted + dash-dot) can now be trained on
**Result**: Model can learn from both trend predictions and action predictions.
### 2. ✅ UI State Persistence After Page Reload
**Problem**: If you started online learning and reloaded the page, UI would reset and not show active training session.
**Fix Applied**:
- Added `/api/training-session-status` endpoint
- Added `restoreUIState()` JavaScript function
- Automatically restores on page load:
- Shows inference status panel
- Hides start buttons, shows stop button
- Restores timeframe and prediction steps
- Resumes polling for metrics
**Result**: UI now reflects actual server state after page reload.
### 3. ✅ Pivot Point Hover Fix
**Problem**: Pivot tooltips triggered when hovering over vertical lines, not just the pivot dots.
**Fix Applied**:
- Added `hoverinfo: 'skip'` to all pivot lines (dashed horizontal lines)
- Only pivot dots (triangular markers) now trigger hover tooltips
- Applied to all chart updates to ensure consistency
**Result**: Hover tooltips only appear when hovering directly over pivot points.
### 4. ✅ Enhanced Loss Calculation Debugging
**Problem**: Candle loss calculation seemed wrong - showing <1% difference when actual vs predicted directions were opposite.
**Fix Applied**:
- Added detailed logging for accuracy calculations
- Logs predicted vs actual OHLC values
- Shows direction predictions and correctness
- Displays individual field errors and overall accuracy
**Debug Output Example**:
```
ACCURACY DEBUG for 1m_2025-12-10T14:30:00Z:
Predicted: O=3320.15 H=3325.42 L=3318.23 C=3322.61
Actual: O=3320.89 H=3326.12 L=3318.45 C=3318.18
Pred Direction: UP
Actual Direction: DOWN
Direction Correct: False
Accuracy: 87.5%, Avg Error: 1.25%
Individual Errors: {'open': 0.8, 'high': 1.2, 'low': 0.5, 'close': 0.9}
```
**Result**: Can now debug exactly why accuracy calculations appear incorrect.
## Training Enhancement
### Both Line Types Now Trainable
1. **Yellow Dotted Lines (Trend Vectors)**:
- Source: `trend_vector` from transformer model
- Data: Angle, steepness, direction predictions
- Training: Can validate against actual price movement direction
2. **Dash-Dot Lines (Action Predictions)**:
- Source: `action`, `confidence`, `predicted_price` from transformer
- Data: Specific trading signals with price targets
- Training: Can validate against actual price targets and timing
### Training Data Flow
1. **Prediction Made**: Both trend and action data generated
2. **Actual Candle Arrives**: Compare predicted vs actual
3. **Accuracy Calculated**: Detailed error analysis for each component
4. **Backpropagation**: Train model on prediction errors
5. **Checkpoint Saved**: Best models preserved
## UI State Management
### Session Restoration
- ✅ **Training Status**: Shows if online learning is active
- ✅ **Button States**: Correct start/stop button visibility
- ✅ **Settings**: Timeframe and prediction steps restored
- ✅ **Polling**: Metrics and trading stats resume automatically
### Hover Behavior
- ✅ **Pivot Points**: Only dots trigger tooltips, not lines
- ✅ **Ghost Candles**: Rich accuracy information on hover
- ✅ **Prediction Lines**: Action markers show confidence levels
## Debugging Capabilities
### Accuracy Calculation
The detailed logging will help identify if the issue is:
1. **Wrong candle comparison**: Comparing prediction at time T with candle at time T instead of T+1
2. **Direction calculation error**: Logic error in up/down determination
3. **Percentage calculation**: Error in how percentage differences are computed
4. **Data timing**: Mismatch between prediction timestamp and validation timestamp
### Next Steps for Loss Investigation
With the enhanced logging, you can now:
1. **Check timestamps**: Verify prediction and actual candle timestamps align correctly
2. **Verify direction logic**: Confirm up/down calculations are correct
3. **Validate percentages**: Ensure error calculations match expected values
4. **Debug timing**: Identify if predictions are being compared to the right candles
The system now provides complete visibility into the training process and should help identify the root cause of any accuracy calculation discrepancies!