Files
gogo2/ORCHESTRATOR_STREAMLINING_PLAN.md
2025-06-25 15:16:49 +03:00

229 lines
6.3 KiB
Markdown

# Orchestrator Architecture Streamlining Plan
## Current State Analysis
### Basic TradingOrchestrator (`core/orchestrator.py`)
- **Size**: 880 lines
- **Purpose**: Core trading decisions, model coordination
- **Features**:
- Model registry and weight management
- CNN and RL prediction combination
- Decision callbacks
- Performance tracking
- Basic RL state building
### Enhanced TradingOrchestrator (`core/enhanced_orchestrator.py`)
- **Size**: 5,743 lines (6.5x larger!)
- **Inherits from**: TradingOrchestrator
- **Additional Features**:
- Universal Data Adapter (5 timeseries)
- COB Integration
- Neural Decision Fusion
- Multi-timeframe analysis
- Market regime detection
- Sensitivity learning
- Pivot point analysis
- Extrema detection
- Context data management
- Williams market structure
- Microstructure analysis
- Order flow analysis
- Cross-asset correlation
- PnL-aware features
- Trade flow features
- Market impact estimation
- Retrospective CNN training
- Cold start predictions
## Problems Identified
### 1. **Massive Feature Bloat**
- Enhanced orchestrator has become a "god object" with too many responsibilities
- Single class doing: trading, analysis, training, data processing, market structure, etc.
- Violates Single Responsibility Principle
### 2. **Code Duplication**
- Many features reimplemented instead of extending base functionality
- Similar RL state building in both classes
- Overlapping market analysis
### 3. **Maintenance Nightmare**
- 5,743 lines in single file is unmaintainable
- Complex interdependencies
- Hard to test individual components
- Performance issues due to size
### 4. **Resource Inefficiency**
- Loading entire enhanced orchestrator even if only basic features needed
- Memory overhead from unused features
- Slower initialization
## Proposed Solution: Modular Architecture
### 1. **Keep Streamlined Base Orchestrator**
```
TradingOrchestrator (core/orchestrator.py)
├── Basic decision making
├── Model coordination
├── Performance tracking
└── Core RL state building
```
### 2. **Create Modular Extensions**
```
core/
├── orchestrator.py (Basic - 880 lines)
├── modules/
│ ├── cob_module.py # COB integration
│ ├── market_analysis_module.py # Market regime, volatility
│ ├── multi_timeframe_module.py # Multi-TF analysis
│ ├── neural_fusion_module.py # Neural decision fusion
│ ├── pivot_analysis_module.py # Williams/pivot points
│ ├── extrema_module.py # Extrema detection
│ ├── microstructure_module.py # Order flow analysis
│ ├── correlation_module.py # Cross-asset correlation
│ └── training_module.py # Advanced training features
```
### 3. **Configurable Enhanced Orchestrator**
```python
class ConfigurableOrchestrator(TradingOrchestrator):
def __init__(self, data_provider, modules=None):
super().__init__(data_provider)
self.modules = {}
# Load only requested modules
if modules:
for module_name in modules:
self.load_module(module_name)
def load_module(self, module_name):
# Dynamically load and initialize module
pass
```
### 4. **Module Interface**
```python
class OrchestratorModule:
def __init__(self, orchestrator):
self.orchestrator = orchestrator
def initialize(self):
pass
def get_features(self, symbol):
pass
def get_predictions(self, symbol):
pass
```
## Implementation Plan
### Phase 1: Extract Core Modules (Week 1)
1. Extract COB integration to `cob_module.py`
2. Extract market analysis to `market_analysis_module.py`
3. Extract neural fusion to `neural_fusion_module.py`
4. Test basic functionality
### Phase 2: Refactor Enhanced Features (Week 2)
1. Move pivot analysis to `pivot_analysis_module.py`
2. Move extrema detection to `extrema_module.py`
3. Move microstructure analysis to `microstructure_module.py`
4. Update imports and dependencies
### Phase 3: Create Configurable System (Week 3)
1. Implement `ConfigurableOrchestrator`
2. Create module loading system
3. Add configuration file support
4. Test different module combinations
### Phase 4: Clean Dashboard Integration (Week 4)
1. Update dashboard to work with both Basic and Configurable
2. Add module status display
3. Dynamic feature enabling/disabling
4. Performance optimization
## Benefits
### 1. **Maintainability**
- Each module ~200-400 lines (manageable)
- Clear separation of concerns
- Individual module testing
- Easier debugging
### 2. **Performance**
- Load only needed features
- Reduced memory footprint
- Faster initialization
- Better resource utilization
### 3. **Flexibility**
- Mix and match features
- Easy to add new modules
- Configuration-driven setup
- Development environment vs production
### 4. **Development**
- Teams can work on individual modules
- Clear interfaces reduce conflicts
- Easier to add new features
- Better code reuse
## Configuration Examples
### Minimal Setup (Basic Trading)
```yaml
orchestrator:
type: basic
modules: []
```
### Full Enhanced Setup
```yaml
orchestrator:
type: configurable
modules:
- cob_module
- neural_fusion_module
- market_analysis_module
- pivot_analysis_module
```
### Custom Setup (Research)
```yaml
orchestrator:
type: configurable
modules:
- market_analysis_module
- extrema_module
- training_module
```
## Migration Strategy
### 1. **Backward Compatibility**
- Keep current Enhanced orchestrator as deprecated
- Gradually migrate features to modules
- Provide compatibility layer
### 2. **Gradual Migration**
- Start with dashboard using Basic orchestrator
- Add modules one by one
- Test each integration
### 3. **Performance Testing**
- Compare Basic vs Enhanced vs Modular
- Memory usage analysis
- Initialization time comparison
- Decision-making speed tests
## Success Metrics
1. **Code Size**: Enhanced orchestrator < 1,000 lines
2. **Memory**: 50% reduction in memory usage for basic setup
3. **Speed**: 3x faster initialization for basic setup
4. **Maintainability**: Each module < 500 lines
5. **Testing**: 90%+ test coverage per module
This plan will transform the current monolithic enhanced orchestrator into a clean, modular, maintainable system while preserving all functionality and improving performance.