AlphaPulse Feature Integration Map

Overview

This document maps all Sprint 3-4 features and their current integration status as of v1.17.0.0.

Integration Status Legend

✅ Implemented: Core functionality exists
🔌 Integrated: Connected to trading flow
🖥️ UI: Accessible through user interface
📡 API: Exposed through REST API
📊 Monitored: Performance metrics tracked
❌ Missing: Not implemented or integrated

Sprint 3: Risk Management Features

1. Tail Risk Hedging

✅ Implementation: /src/alpha_pulse/hedging/
- GridHedgeBot: Main hedging bot
- HedgeManager: Orchestration
- LLMHedgeAnalyzer: AI-powered analysis
📡 API: /api/v1/hedging/ endpoints
❌ UI: No dashboard integration
❌ Trading Flow: Not auto-triggered by portfolio optimizer
❌ Monitoring: No business impact tracking

2. Correlation Analysis

✅ Implementation: /src/alpha_pulse/risk/correlation_analyzer.py
- Multiple correlation methods
- Regime-based analysis
- Tail dependency using copulas
🔌 Integration: Used by DynamicRiskBudgetManager
❌ API: No dedicated endpoints
❌ UI: No correlation visualization
❌ Monitoring: No real-time tracking

3. Dynamic Risk Budgeting

✅ Implementation: /src/alpha_pulse/risk/dynamic_budgeting.py
✅ Service: /src/alpha_pulse/services/risk_budgeting_service.py
- Regime-based allocation
- Volatility targeting
- Auto-rebalancing
🔌 Integration: Background monitoring loops
❌ API: No exposure endpoints
❌ UI: No budget visualization
❌ Trading Flow: Not connected to execution engine

4. Liquidity Management

✅ Implementation: /src/alpha_pulse/services/liquidity_risk_service.py
- Liquidity risk assessment
- Slippage estimation
- Execution planning
❌ API: No liquidity endpoints
❌ UI: No liquidity monitoring
❌ Trading Flow: Not connected to order router
❌ Monitoring: No cost tracking

5. Monte Carlo Simulation

✅ Implementation: /src/alpha_pulse/risk/monte_carlo_engine.py
✅ Service: /src/alpha_pulse/services/simulation_service.py
- Multiple stochastic processes
- GPU acceleration support
- Variance reduction
❌ API: No simulation endpoints
❌ UI: No scenario analysis tools
❌ Trading Flow: Not used in decision making
❌ Monitoring: No simulation metrics

Sprint 4: ML Enhancement Features

1. Ensemble Methods

✅ Implementation: /src/alpha_pulse/ml/ensemble/
- Voting, stacking, boosting
- Signal aggregation
- Performance tracking
✅ Service: /src/alpha_pulse/services/ensemble_service.py
✅ Database: Complete models for persistence
❌ API: No ensemble endpoints
❌ UI: No ensemble management
❌ Trading Flow: Not connected to signal routing
❌ Monitoring: Not integrated with metrics

2. Online Learning

✅ Implementation: /src/alpha_pulse/ml/online/
- Incremental models
- Concept drift detection
- Streaming validation
✅ Service: /src/alpha_pulse/ml/online/online_learning_service.py
❌ API: No online learning endpoints
❌ UI: No adaptation monitoring
❌ Trading Flow: Not connected to model serving
❌ Monitoring: No effectiveness tracking

3. GPU Acceleration

✅ Implementation: /src/alpha_pulse/ml/gpu/
- Resource management
- GPU-optimized models
- Batch processing
✅ Service: /src/alpha_pulse/ml/gpu/gpu_service.py
✅ Integration: Monte Carlo, portfolio optimization
❌ API: No GPU management endpoints
❌ UI: No GPU monitoring
❌ Trading Flow: Not used by trading agents
❌ Monitoring: No ROI tracking

4. Explainable AI

✅ Implementation: /src/alpha_pulse/ml/explainability/
- SHAP, LIME explanations
- Feature importance
- Counterfactuals
✅ Service: /src/alpha_pulse/services/explainability_service.py
✅ Database: Explanation persistence
❌ API: No explainability endpoints
❌ UI: No explanation visualization
❌ Trading Flow: Not connected to decision display
❌ Monitoring: No compliance metrics

Critical Integration Gaps

1. API Layer

Missing routers for all ML features
Limited risk management endpoints
No service initialization in main API

2. Trading Flow

Features operate in isolation
No connection to signal generation
Portfolio optimizer doesn’t use advanced risk features
Execution engine ignores liquidity analysis

3. User Interface

No risk management dashboard
No ML feature controls
No performance visualization
Features not discoverable

4. Business Impact

No metrics tracking feature contributions
No A/B testing framework
No cost-benefit analysis
No user adoption tracking

Integration Priority Matrix

Feature	Implementation	API	UI	Trading Flow	Business Impact	Priority
Tail Risk Hedging	✅	📡	❌	❌	❌	HIGH
Correlation Analysis	✅	❌	❌	🔌	❌	HIGH
Dynamic Risk Budgeting	✅	❌	❌	🔌	❌	CRITICAL
Liquidity Management	✅	❌	❌	❌	❌	CRITICAL
Monte Carlo	✅	❌	❌	❌	❌	MEDIUM
Ensemble Methods	✅	❌	❌	❌	❌	CRITICAL
Online Learning	✅	❌	❌	❌	❌	HIGH
GPU Acceleration	✅	❌	❌	🔌	❌	MEDIUM
Explainable AI	✅	❌	❌	❌	❌	HIGH

Recommendations

Immediate Actions (Phase 3):
- Wire ensemble methods into signal aggregator
- Connect liquidity management to order router
- Integrate dynamic risk budgeting with execution
API Development (Phase 4):
- Create dedicated routers for each feature
- Add service initialization in API startup
- Implement proper error handling
UI Development (Phase 4):
- Build risk management dashboard
- Add ML feature controls
- Create explanation viewers
Business Metrics (Phase 5):
- Implement feature contribution tracking
- Add performance impact metrics
- Create ROI dashboards

Integration Statistics

By Implementation Status

Fully Implemented: 9/9 features (100%)
API Integration: 1/9 features (11%) - Only tail risk hedging
UI Integration: 0/9 features (0%)
Trading Flow Integration: 3/9 features (33%)
Business Metrics: 0/9 features (0%)

By Sprint

Sprint 3 (Risk Management): 5 features, 20% integrated
Sprint 4 (ML Enhancement): 4 features, 0% integrated

Conclusion

While the core implementations are solid and feature-complete, approximately 80% of Sprint 3-4 features are not integrated into the trading flow or accessible to users. This represents significant untapped potential in the system, similar to the regime detection issue discovered in v1.17.0.0.