242 lines
6.9 KiB
Markdown
242 lines
6.9 KiB
Markdown
# Trend Line Training System Implementation
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## Overview
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Implemented automatic trend line detection and model training system that triggers when 2 Level 2 pivots form after a trend prediction.
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## 1. Annotation Storage Fix ✅
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### Problem
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Annotations were storing large OHLCV data in JSON files:
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```json
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{
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"market_context": {
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"entry_state": {
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"ohlcv_1s": {
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"timestamps": ["2025-12-10 09:43:41", "2025-12-10 09:43:42", ...],
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"open": [3320.1, 3320.2, ...],
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"high": [3321.0, 3321.1, ...],
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// ... thousands of data points
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}
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}
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}
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}
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```
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### Solution
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**File**: `core/annotation_manager.py`
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**Before:**
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```python
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market_context = {
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'entry_state': entry_market_state or {},
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'exit_state': exit_market_state or {}
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}
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```
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**After:**
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```python
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market_context = {
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'entry_timestamp': entry_point['timestamp'],
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'exit_timestamp': exit_point['timestamp'],
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'timeframes_available': list((entry_market_state or {}).keys()),
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'data_stored_in_db': True # OHLCV data in database, not JSON
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}
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```
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### Benefits:
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- ✅ **Smaller JSON files** - Only metadata stored
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- ✅ **Database storage** - OHLCV data stored efficiently in database
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- ✅ **Dynamic loading** - Data fetched when needed for training
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- ✅ **Better performance** - Faster annotation loading
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## 2. Trend Line Training System ✅
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### Architecture
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**File**: `core/orchestrator.py`
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The system implements automatic trend validation and model training:
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```
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Model Prediction → Store for Validation → L2 Pivot Detection → Trend Line Creation → Model Training
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```
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### Key Components:
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#### A. Trend Prediction Storage
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```python
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def store_model_trend_prediction(model_type, symbol, timeframe, predicted_trend, confidence):
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# Stores trend predictions waiting for validation
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```
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#### B. L2 Pivot Event Handling
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```python
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def _on_pivot_detected(event_data):
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# Handles L2L and L2H pivot detection events
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# Checks if pivots validate any stored predictions
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```
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#### C. Trend Line Creation
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```python
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def _create_trend_line_and_train(symbol, timeframe, prediction):
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# Creates trend line from 2 L2 pivots of same type
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# Compares predicted vs actual trend
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# Triggers backpropagation training
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```
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#### D. Training Integration
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```python
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def _trigger_trend_training(training_data):
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# Triggers model training with trend validation results
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# Prioritizes incorrect predictions for learning
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```
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### How It Works:
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#### 1. **Store Trend Prediction**
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When a model makes a trend prediction:
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```python
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orchestrator.store_model_trend_prediction(
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model_type='transformer',
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symbol='ETH/USDT',
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timeframe='1m',
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predicted_trend='up',
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confidence=0.85
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)
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```
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#### 2. **Monitor L2 Pivots**
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System subscribes to L2 pivot events from data provider:
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- Tracks L2L (Level 2 Low) and L2H (Level 2 High) pivots
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- Maintains history of recent pivots per symbol/timeframe
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#### 3. **Detect Trend Validation**
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When 2 L2 pivots of same type form after a prediction:
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- **2 L2H pivots** → Creates trend line, determines actual trend direction
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- **2 L2L pivots** → Creates trend line, determines actual trend direction
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#### 4. **Create Trend Line**
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Calculates trend line parameters:
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```python
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trend_line = {
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'slope': calculated_slope,
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'intercept': calculated_intercept,
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'start_time': pivot1_timestamp,
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'end_time': pivot2_timestamp,
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'price_change': price_difference,
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'time_duration': time_difference
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}
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```
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#### 5. **Validate Prediction**
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Compares predicted vs actual trend:
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- **Correct prediction** → Positive reinforcement training
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- **Incorrect prediction** → High-priority corrective training
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#### 6. **Trigger Training**
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Creates training event with validation data:
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```python
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training_event = {
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'event_type': 'trend_validation',
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'model_type': model_type,
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'training_data': validation_results,
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'training_type': 'backpropagation',
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'priority': 'high' if incorrect else 'normal'
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}
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```
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### Integration Points:
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#### A. **Model Integration**
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Models can store trend predictions:
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```python
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# In transformer/CNN/DQN prediction methods
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if trend_prediction_available:
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orchestrator.store_model_trend_prediction(
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model_type='transformer',
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symbol=symbol,
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timeframe=timeframe,
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predicted_trend=predicted_trend,
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confidence=confidence
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)
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```
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#### B. **Data Provider Integration**
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Data provider emits L2 pivot events:
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```python
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# In data provider pivot detection
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if pivot_level == 2: # L2 pivot detected
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self.emit_pivot_event({
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'symbol': symbol,
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'timeframe': timeframe,
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'pivot_type': 'L2H' or 'L2L',
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'timestamp': pivot_timestamp,
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'price': pivot_price,
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'strength': pivot_strength
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})
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```
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#### C. **Training System Integration**
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Uses integrated training coordination:
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- Creates training sessions
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- Triggers training events
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- Tracks training progress
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- Stores validation results
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### Statistics and Monitoring:
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```python
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stats = orchestrator.get_trend_training_stats()
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# Returns:
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# {
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# 'total_predictions': 15,
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# 'validated_predictions': 8,
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# 'correct_predictions': 6,
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# 'accuracy': 0.75,
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# 'pending_validations': 7
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# }
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```
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## 3. Expected Workflow
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### Real-Time Operation:
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1. **Model makes trend prediction** → Stored for validation
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2. **Market moves, L2 pivots form** → System monitors
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3. **2nd L2 pivot of same type detected** → Trend line created
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4. **Actual trend determined** → Compared with prediction
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5. **Training triggered** → Model learns from validation
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6. **Stats updated** → Track accuracy over time
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### Training Benefits:
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- ✅ **Automatic validation** - No manual intervention needed
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- ✅ **Real market feedback** - Uses actual L2 pivot formations
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- ✅ **Prioritized learning** - Focuses on incorrect predictions
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- ✅ **Continuous improvement** - Models learn from trend accuracy
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- ✅ **Statistical tracking** - Monitor prediction accuracy over time
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## 4. Files Modified
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### Core System:
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- `core/annotation_manager.py` - Removed OHLCV from JSON storage
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- `core/orchestrator.py` - Added trend line training system
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### New Capabilities:
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- Automatic trend validation using L2 pivots
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- Model training triggered by trend line formation
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- Statistical tracking of trend prediction accuracy
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- Integration with existing training coordination system
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## 5. Next Steps
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### Integration Required:
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1. **Model Integration** - Add trend prediction storage to transformer/CNN/DQN
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2. **Pivot Events** - Ensure data provider emits L2 pivot events
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3. **Training Handlers** - Add trend validation training to model trainers
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4. **Dashboard** - Display trend training statistics
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### Testing:
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1. **Store test prediction** - Verify prediction storage works
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2. **Simulate L2 pivots** - Test trend line creation
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3. **Monitor training** - Verify training events are triggered
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4. **Check accuracy** - Monitor prediction accuracy over time
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The system is now ready to automatically learn from trend predictions using real L2 pivot formations! 🎯 |