AI Hedge Fund System Architecture

System Overview

The AI Hedge Fund system implements a multi-agent trading architecture with four main layers:

Input Layer (Specialized Agents)
Risk Management Layer
Portfolio Management Layer
Output Layer (Trading Actions)

Additionally, the system includes advanced features:

Market Regime Detection (HMM-based, currently 10% integrated)
Explainable AI (SHAP, LIME, counterfactual explanations)
Distributed Computing (Ray & Dask for parallel processing)
High-Performance Caching (Multi-tier Redis architecture)
Database Optimization (Connection pooling, query optimization)

Component Architecture

graph TD
    subgraph "Input Layer"
        BA[Bill Ackman Agent]
        WB[Warren Buffett Agent]
        FA[Fundamentals Agent]
        SA[Sentiment Agent]
        TA[Technical Agent]
        VA[Valuation Agent]
        AM[Agent Manager]
        BA & WB & FA & SA & TA & VA --> AM
    end

    subgraph "Risk Management Layer"
        RM[Risk Manager]
        PS[Position Sizer]
        RA[Risk Analyzer]
        AM --> RM
        RM --> PS
        RM --> RA
    end

    subgraph "Portfolio Management Layer"
        PM[Portfolio Manager]
        MPT[MPT Strategy]
        HRP[HRP Strategy]
        BL[Black-Litterman]
        LLM[LLM Strategy]
        RM --> PM
        PM --> MPT & HRP & BL & LLM
    end

    subgraph "Output Layer"
        TR[Trade Router]
        PM --> TR
        TR --> Buy & Cover & Sell & Short & Hold
    end

Component Interactions

1. Input Layer - Signal Generation

Implementation: src/alpha_pulse/agents/manager.py
Key Components:
- Agent Factory (AgentFactory)
- Individual Agents (6 specialized agents)
- Agent Manager (coordinates signals)

Signal Flow:

# Agent signal generation
signals = await agent.generate_signals(market_data)
  
# Signal aggregation with weights
weighted_strength = signal.confidence * agent_weight
  
# Consensus threshold check
if dominant_direction[1] >= 0.6:  # Minimum consensus
    # Generate aggregated signal

2. Risk Management Layer - Signal Processing

Implementation: src/alpha_pulse/risk_management/manager.py
Key Components:
- Risk Manager
- Position Sizer
- Risk Analyzer

Risk Controls:

# Position size limits
if position_size > self.config.max_position_size:
    return False
      
# Portfolio leverage check
if total_exposure / portfolio_value > self.config.max_portfolio_leverage:
    return False
      
# Drawdown monitoring
if abs(self.state.risk_metrics.max_drawdown) > self.config.max_drawdown:
    return False

3. Portfolio Management Layer - Decision Making

Implementation: src/alpha_pulse/portfolio/portfolio_manager.py
Key Components:
- Portfolio Manager
- Multiple Strategy Support
- Rebalancing Logic

Portfolio Operations:

# Strategy selection and execution
target_allocation = self.strategy.compute_target_allocation(
    current_allocation,
    historical_data,
    self.risk_constraints
)
  
# Trade computation
trades = self.compute_rebalancing_trades(
    current_allocation,
    target_allocation,
    total_value
)

4. Output Layer - Trade Execution

Implementation: src/alpha_pulse/execution/
Key Components:
- Trade Router
- Execution Handlers
- Order Management

Trade Actions:

# Trade execution with rollback support
result = await self._retry_with_timeout(
    exchange.execute_trade(
        asset=trade['asset'],
        amount=abs(trade['value']),
        side=trade['type'],
        order_type=self.trading_config.get('execution_style', 'market')
    )
)

Data Flow

Market Data Flow
- Real-time data from exchanges
- Fundamental data from providers
- Sentiment analysis from news/social
- Technical indicators computation
Signal Flow
- Individual agent analysis
- Signal generation with confidence
- Signal aggregation with weights
- Consensus-based final signals
Risk Processing Flow
- Signal validation
- Risk metric computation
- Position size calculation
- Portfolio constraint checking
Portfolio Management Flow
- Strategy selection
- Portfolio optimization
- Rebalancing decisions
- Trade generation
Execution Flow
- Order routing
- Trade execution
- Position tracking
- Performance monitoring

System Features

1. Market Regime Detection (Currently 10% Integrated)

Implemented: HMM-based regime detection with 5 states (Bull, Bear, Sideways, Crisis, Recovery)
Missing: Service not started, agents not integrated, no dashboard
Impact: System operates without market context awareness
Files: src/alpha_pulse/models/market_regime_hmm.py, src/alpha_pulse/services/regime_detection_service.py

2. Adaptive Learning

Performance-based agent weight adjustment
Strategy adaptation based on market conditions
Risk parameter optimization

3. Risk Management

Multi-level risk controls
Dynamic position sizing
Portfolio-level risk monitoring
Drawdown protection

4. Portfolio Optimization

Multiple strategy support
Adaptive rebalancing
Transaction cost consideration
LLM-assisted analysis

5. Execution Management

Smart order routing
Transaction cost analysis
Trade impact monitoring
Error handling and rollback

6. High-Performance Caching

Multi-tier Redis architecture (L1 Memory, L2 Local, L3 Distributed)
Intelligent invalidation and warming strategies
Performance monitoring and optimization
90%+ cache hit rates for frequently accessed data

7. Database Optimization

Advanced connection pooling with pgbouncer
Query optimization and intelligent routing
Read/write splitting across replicas
Automated index management

Monitoring and Control

1. Performance Metrics

Risk-adjusted returns
Sharpe/Sortino ratios
Maximum drawdown
Win/loss ratio

2. Risk Metrics

Value at Risk (VaR)
Expected Shortfall
Beta exposure
Correlation analysis

3. Operational Metrics

Signal accuracy by agent
Execution efficiency
System latency
Decision accuracy

Implementation Notes

Scalability
- Async operations throughout
- Efficient data caching
- Parallel signal processing
- Modular component design
Reliability
- Comprehensive error handling
- Automatic retry mechanisms
- Trade rollback capability
- State management
Maintainability
- Clear component boundaries
- Standardized interfaces
- Comprehensive logging
- Configuration-driven
Extensibility
- Plugin architecture for agents
- Strategy framework
- Provider interfaces
- Custom risk controls