gogo2/ANNOTATE/IMPLEMENTATION_SUMMARY.md

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