# Event-Driven Inference Training System - Implementation Summary

## Architecture Decisions

### Where Components Fit

1. **InferenceTrainingCoordinator** → **TradingOrchestrator**
   - **Rationale**: Orchestrator already manages models, training, and predictions
   - **Benefits**: 
     - Reduces duplication (orchestrator has model access)
     - Centralizes coordination logic
     - Reuses existing prediction storage
   - **Location**: `core/orchestrator.py` - initialized in `__init__`

2. **DataProvider Subscription Methods** → **DataProvider**
   - **Rationale**: Data layer responsibility - emits events when data changes
   - **Methods Added**:
     - `subscribe_candle_completion()` - Subscribe to candle completion events
     - `subscribe_pivot_events()` - Subscribe to pivot events
     - `_emit_candle_completion()` - Emit event when candle closes
     - `_emit_pivot_event()` - Emit event when pivot detected
   - **Location**: `core/data_provider.py`

3. **TrainingEventSubscriber Interface** → **RealTrainingAdapter**
   - **Rationale**: Training layer implements subscriber interface
   - **Methods Implemented**:
     - `on_candle_completion()` - Train on candle completion
     - `on_pivot_event()` - Train on pivot detection
   - **Location**: `ANNOTATE/core/real_training_adapter.py`

## Code Duplication Reduction

### Before (Duplicated Logic)

1. **Data Retrieval**: 
   - `_get_realtime_market_data()` in RealTrainingAdapter
   - Similar logic in orchestrator
   - Similar logic in data_provider

2. **Prediction Storage**:
   - `store_transformer_prediction()` in orchestrator
   - `inference_input_cache` in RealTrainingAdapter session
   - `prediction_cache` in app.py

3. **Training Coordination**:
   - Training logic in RealTrainingAdapter
   - Training logic in orchestrator
   - Training logic in enhanced_realtime_training

### After (Centralized)

1. **Data Retrieval**: 
   - Single source: `data_provider.get_historical_data()` queries DuckDB
   - Coordinator retrieves data on-demand using references
   - No copying - just timestamp ranges

2. **Prediction Storage**:
   - Orchestrator's `inference_training_coordinator` manages references
   - References stored in coordinator (not copied)
   - Data retrieved from DuckDB when needed

3. **Training Coordination**:
   - Orchestrator's coordinator handles event distribution
   - RealTrainingAdapter implements subscriber interface
   - Single training lock in RealTrainingAdapter

## Implementation Status

### ✅ Completed

1. **InferenceTrainingCoordinator** (`inference_training_system.py`)
   - Reference-based storage
   - Event subscription system
   - Data retrieval from DuckDB

2. **DataProvider Extensions** (`data_provider.py`)
   - `subscribe_candle_completion()` method
   - `subscribe_pivot_events()` method
   - `_emit_candle_completion()` method
   - `_emit_pivot_event()` method
   - Event emission in `_update_candle()`

3. **Orchestrator Integration** (`orchestrator.py`)
   - Coordinator initialized in `__init__`
   - Accessible via `orchestrator.inference_training_coordinator`

4. **RealTrainingAdapter Integration** (`real_training_adapter.py`)
   - Uses orchestrator's coordinator
   - Implements `TrainingEventSubscriber` interface
   - `on_candle_completion()` method
   - `on_pivot_event()` method
   - `_register_inference_frame()` method
   - Helper methods for batch creation

### ⚠️ Needs Completion

1. **Pivot Event Emission**
   - DataProvider needs to detect pivots and emit events
   - Currently pivots are calculated but not emitted as events
   - Need to integrate with WilliamsMarketStructure pivot detection

2. **Norm Params Storage**
   - Currently norm_params are calculated on retrieval
   - Could be stored in reference during registration for efficiency
   - Need to pass norm_params from `_get_realtime_market_data()` to `_register_inference_frame()`

3. **Device Handling**
   - Ensure tensors are on correct device when retrieved from DuckDB
   - May need to store device info in reference

4. **Testing**
   - Test candle completion events
   - Test pivot events
   - Test data retrieval from DuckDB
   - Test training on inference frames

## Key Benefits

1. **Memory Efficient**: No copying 600 candles every second
2. **Event-Driven**: Clean separation of concerns
3. **Flexible**: Supports time-based (candles) and event-based (pivots)
4. **Centralized**: Coordinator in orchestrator reduces duplication
5. **Extensible**: Easy to add new training methods or event types

## Next Steps

1. **Complete Pivot Event Emission**
   - Add pivot detection in DataProvider
   - Emit events when L2L, L2H, etc. detected

2. **Store Norm Params During Registration**
   - Pass norm_params from prediction to registration
   - Store in reference for faster retrieval

3. **Add Device Info to References**
   - Store device in InferenceFrameReference
   - Use when creating tensors

4. **Remove Old Caching Code**
   - Remove `inference_input_cache` from session
   - Remove `_make_realtime_prediction_with_cache()` (deprecated)
   - Clean up duplicate code

5. **Extend DuckDB Schema**
   - Add MA indicators to ohlcv_data
   - Create pivot_points table
   - Store technical indicators