updated design

.kiro/specs/multi-modal-trading-system/design.md

@@ -58,9 +58,11 @@ The DataProvider class will:
 - Identify pivot points
 - Normalize data
 - Distribute data to subscribers
+- Calculate any other algoritmic manipulations/calculations on the data
+- Cache up to 3x the model inputs (300 ticks OHLCV, etc) data so we can do a proper backtesting in up to 2x time in the future
 
 Based on the existing implementation in `core/data_provider.py`, we'll enhance it to:
-- Improve pivot point calculation using Williams Market Structure
+- Improve pivot point calculation using reccursive Williams Market Structure 
 - Optimize data caching for better performance
 - Enhance real-time data streaming
 - Implement better error handling and fallback mechanisms
@@ -129,33 +131,141 @@ Training:
 
 ### 4. Orchestrator
 
-The Orchestrator is responsible for making final trading decisions based on inputs from both CNN and RL models.
+The Orchestrator serves as the central coordination hub of the multi-modal trading system, responsible for data subscription management, model inference coordination, output storage, and training pipeline orchestration.
 
 #### Key Classes and Interfaces
 
 - **Orchestrator**: Main class for the orchestrator.
+- **DataSubscriptionManager**: Manages subscriptions to multiple data streams with different refresh rates.
+- **ModelInferenceCoordinator**: Coordinates inference across all models.
+- **ModelOutputStore**: Stores and manages model outputs for cross-model feeding.
+- **TrainingPipelineManager**: Manages training pipelines for all models.
 - **DecisionMaker**: Interface for making trading decisions.
 - **MoEGateway**: Mixture of Experts gateway for model integration.
 
+#### Core Responsibilities
+
+##### 1. Data Subscription and Management
+
+The Orchestrator subscribes to the Data Provider and manages multiple data streams with varying refresh rates:
+
+- **10Hz COB (Cumulative Order Book) Data**: High-frequency order book updates for real-time market depth analysis
+- **OHLCV Data**: Traditional candlestick data at multiple timeframes (1s, 1m, 1h, 1d)
+- **Market Tick Data**: Individual trade executions and price movements
+- **Technical Indicators**: Calculated indicators that update at different frequencies
+- **Pivot Points**: Market structure analysis data
+
+**Data Stream Management**:
+- Maintains separate buffers for each data type with appropriate retention policies
+- Ensures thread-safe access to data streams from multiple models
+- Implements intelligent caching to serve "last updated" data efficiently
+- Maintains full base dataframe that stays current for any model requesting data
+- Handles data synchronization across different refresh rates
+
+##### 2. Model Inference Coordination
+
+The Orchestrator coordinates inference across all models in the system:
+
+**Inference Pipeline**:
+- Triggers model inference when relevant data updates occur
+- Manages inference scheduling based on data availability and model requirements
+- Coordinates parallel inference execution for independent models
+- Handles model dependencies (e.g., RL model waiting for CNN hidden states)
+
+**Model Input Management**:
+- Assembles appropriate input data for each model based on their requirements
+- Ensures models receive the most current data available at inference time
+- Manages feature engineering and data preprocessing for each model
+- Handles different input formats and requirements across models
+
+##### 3. Model Output Storage and Cross-Feeding
+
+The Orchestrator maintains a centralized store for all model outputs and manages cross-model data feeding:
+
+**Output Storage**:
+- Stores CNN predictions, confidence scores, and hidden layer states
+- Stores RL action recommendations and value estimates
+- Maintains historical output sequences for temporal analysis
+- Implements efficient retrieval mechanisms for real-time access
+
+**Cross-Model Feeding**:
+- Feeds CNN hidden layer states into RL model inputs
+- Provides CNN predictions as context for RL decision-making
+- Stores model outputs that become inputs for subsequent inference cycles
+- Manages circular dependencies and feedback loops between models
+
+##### 4. Training Pipeline Management
+
+The Orchestrator coordinates training for all models by managing the prediction-result feedback loop:
+
+**Training Coordination**:
+- Calls each model's training pipeline when new inference results are available
+- Provides previous predictions alongside new results for supervised learning
+- Manages training data collection and labeling
+- Coordinates online learning updates based on real-time performance
+
+**Training Data Management**:
+- Maintains training datasets with prediction-result pairs
+- Implements data quality checks and filtering
+- Manages training data retention and archival policies
+- Provides training data statistics and monitoring
+
+**Performance Tracking**:
+- Tracks prediction accuracy for each model over time
+- Monitors model performance degradation and triggers retraining
+- Maintains performance metrics for model comparison and selection
+
+##### 5. Decision Making and Trading Actions
+
+Beyond coordination, the Orchestrator makes final trading decisions:
+
+**Decision Integration**:
+- Combines outputs from CNN and RL models using Mixture of Experts approach
+- Applies confidence-based filtering to avoid uncertain trades
+- Implements configurable thresholds for buy/sell decisions
+- Considers market conditions and risk parameters
+
 #### Implementation Details
 
-The Orchestrator will:
-- Accept inputs from both CNN and RL models
-- Output final trading actions (buy/sell)
-- Consider confidence levels of both models
-- Learn to avoid entering positions when uncertain
-- Allow for configurable thresholds for entering and exiting positions
+**Architecture**:
+```python
+class Orchestrator:
+    def __init__(self):
+        self.data_subscription_manager = DataSubscriptionManager()
+        self.model_inference_coordinator = ModelInferenceCoordinator()
+        self.model_output_store = ModelOutputStore()
+        self.training_pipeline_manager = TrainingPipelineManager()
+        self.decision_maker = DecisionMaker()
+        self.moe_gateway = MoEGateway()
+    
+    async def run(self):
+        # Subscribe to data streams
+        await self.data_subscription_manager.subscribe_to_data_provider()
+        
+        # Start inference coordination loop
+        await self.model_inference_coordinator.start()
+        
+        # Start training pipeline management
+        await self.training_pipeline_manager.start()
+```
 
-Architecture:
-- Mixture of Experts (MoE) approach
-- Gating network: Determine which expert to trust
-- Expert models: CNN, RL, and potentially others
-- Decision network: Combine expert outputs
+**Data Flow Management**:
+- Implements event-driven architecture for data updates
+- Uses async/await patterns for non-blocking operations
+- Maintains data freshness timestamps for each stream
+- Implements backpressure handling for high-frequency data
 
-Training:
-- Train on historical data
-- Update model based on trading outcomes
-- Use reinforcement learning to optimize decision-making
+**Model Coordination**:
+- Manages model lifecycle (loading, inference, training, updating)
+- Implements model versioning and rollback capabilities
+- Handles model failures and fallback mechanisms
+- Provides model performance monitoring and alerting
+
+**Training Integration**:
+- Implements incremental learning strategies
+- Manages training batch composition and scheduling
+- Provides training progress monitoring and control
+- Handles training failures and recovery
 
 ### 5. Trading Executor