gogo2/reports/UNIFIED_ORCHESTRATOR_ARCHITECTURE.md

Unified Orchestrator Architecture Summary

Overview

Implemented a unified orchestrator architecture that eliminates the need for multiple orchestrator types. The system now uses a single, comprehensive orchestrator with a specialized decision-making model.

Architecture Components

1. Unified Data Bus

• Real-time Market Data: Live prices, volume, order book data
• COB Integration: Market microstructure data from multiple exchanges
• Technical Indicators: Williams market structure, momentum, volatility
• Multi-timeframe Data: 1s ticks, 1m, 1h, 1d candles for ETH/USDT and BTC/USDT

2. Model Pipeline (Data Bus Consumers)

All models consume from the unified data bus but serve different purposes:

A. DQN Agent (5M parameters)

• Purpose: Q-value estimation and action-value learning
• Input: Market state features from data bus
• Output: Action values (not direct trading decisions)
• Training: Continuous RL training on market states

B. CNN Model (50M parameters)

• Purpose: Pattern recognition in market structure
• Input: Multi-timeframe price/volume data
• Output: Pattern predictions and confidence scores
• Training: Williams market structure analysis

C. COB RL Model (400M parameters)

• Purpose: Market microstructure analysis
• Input: Order book changes, bid/ask dynamics
• Output: Microstructure predictions
• Training: Real-time order flow learning

3. Decision-Making Model (10M parameters)

• Purpose: FINAL TRADING DECISIONS ONLY
• Input: Data bus + ALL model outputs (DQN values + CNN patterns + COB analysis)
• Output: BUY/SELL signals with confidence
• Training: Trained ONLY on actual trading signals and their outcomes
• Key Difference: Does NOT predict prices - only makes trading decisions

Signal Generation Flow

Data Bus → [DQN, CNN, COB_RL] → Decision Model → Trading Signal

1. Data Collection: Unified data bus aggregates all market data
2. Model Processing: Each model processes relevant data and generates predictions
3. Decision Fusion: Decision model takes all model outputs + raw data bus
4. Signal Generation: Decision model outputs final BUY/SELL signal
5. Execution: Trading executor processes the signal

Key Implementation Changes

Removed Orchestrator Type Branching

• ❌ No more "Enhanced" vs "Basic" orchestrator checks
• ❌ No more ENHANCED_ORCHESTRATOR_AVAILABLE flags
• ❌ No more conditional logic based on orchestrator type
• ✅ Single unified orchestrator for all functionality

Unified Model Status Display

• DQN: Shows as "Data Bus Input" model
• CNN: Shows as "Data Bus Input" model
• COB_RL: Shows as "Data Bus Input" model
• DECISION: Shows as "Final Decision Model (Trained on Signals Only)"

Training Architecture

• Input Models: Train on market data patterns
• Decision Model: Trains ONLY on signal outcomes
• No Price Predictions: Decision model doesn't predict prices, only makes trading decisions
• Signal-Based Learning: Decision model learns from actual trade results

Benefits

1. Cleaner Architecture: Single orchestrator, no branching logic
2. Specialized Decision Making: Dedicated model for trading decisions
3. Better Training: Decision model learns specifically from trading outcomes
4. Scalable: Easy to add new input models to the data bus
5. Maintainable: No complex orchestrator type management

Model Training Strategy

Input Models (DQN, CNN, COB_RL)

• Train continuously on market data patterns
• Focus on prediction accuracy for their domain
• Feed predictions into decision model

Decision Model

• Training Data: Actual trading signals and their P&L outcomes
• Learning Goal: Maximize profitable signals, minimize losses
• Input Features: Raw data bus + all model predictions
• No Price Targets: Only learns BUY/SELL decision making

Status

✅ Unified orchestrator implemented
✅ Decision-making model architecture defined
✅ All branching logic removed
✅ Dashboard updated for unified display
✅ Main.py updated for unified orchestrator
🎯 Ready for production with clean, maintainable architecture