AI Hedge Fund Technical Documentation
1. Overview and Purpose
The AI Hedge Fund project is an advanced algorithmic trading system that combines multiple AI agents, sophisticated risk management, and portfolio optimization to make data-driven investment decisions in cryptocurrency markets.
Key Objectives
- Automate trading decisions using multiple specialized AI agents
- Implement robust risk management controls
- Optimize portfolio allocation across multiple assets
- Provide real-time monitoring and performance analytics
Value Proposition
- Multi-Agent Architecture: Combines insights from technical, fundamental, sentiment, and value analysis
- Risk-First Approach: Implements multiple layers of risk controls and position sizing
- Portfolio Optimization: Uses modern portfolio theory and adaptive rebalancing
- Extensible Framework: Modular design allows easy addition of new strategies and data sources
2. System Architecture
High-Level Architecture
flowchart TB
subgraph Data Layer
MD[Market Data]
FD[Fundamental Data]
SD[Sentiment Data]
TD[Technical Data]
end
subgraph Agent Layer
TA[Technical Agent]
FA[Fundamental Agent]
SA[Sentiment Agent]
VA[Value Agent]
AA[Activist Agent]
end
subgraph Risk Layer
RM[Risk Manager]
PS[Position Sizing]
PE[Portfolio Exposure]
SL[Stop Loss]
end
subgraph Portfolio Layer
PM[Portfolio Manager]
PO[Portfolio Optimizer]
RB[Rebalancer]
end
subgraph Execution Layer
EB[Execution Broker]
MT[Monitor & Track]
end
MD --> TA
FD --> FA
SD --> SA
TD --> VA
FD --> AA
TA & FA & SA & VA & AA --> RM
RM --> PS
RM --> PE
RM --> SL
PS & PE & SL --> PM
PM --> PO
PM --> RB
PO & RB --> EB
EB --> MT
Component Interactions
sequenceDiagram
participant DM as Data Manager
participant AM as Agent Manager
participant RM as Risk Manager
participant PM as Portfolio Manager
participant EB as Execution Broker
DM->>AM: Market Data Updates
activate AM
AM->>AM: Generate Signals
AM->>RM: Trading Signals
deactivate AM
activate RM
RM->>RM: Evaluate Risk
RM->>RM: Size Positions
RM->>PM: Valid Trades
deactivate RM
activate PM
PM->>PM: Optimize Portfolio
PM->>PM: Check Rebalancing
PM->>EB: Execute Orders
deactivate PM
activate EB
EB->>EB: Place Orders
EB->>PM: Execution Results
deactivate EB
3. Code Structure
Project Organization
alpha_pulse/
├── agents/ # Trading agents implementation
├── api/ # API endpoints and routing
├── backtesting/ # Backtesting framework
├── config/ # Configuration files
├── data_pipeline/ # Data ingestion and processing
├── examples/ # Example scripts and demos
├── execution/ # Order execution and broker interfaces
├── features/ # Feature engineering
├── hedging/ # Hedging strategies
├── models/ # ML models
├── monitoring/ # Performance monitoring
├── portfolio/ # Portfolio management
├── risk_management/ # Risk controls
└── tests/ # Unit and integration tests
Core Classes Relationship
classDiagram
class AgentManager {
+List~Agent~ agents
+initialize()
+generate_signals()
}
class RiskManager {
+PositionSizer sizer
+RiskAnalyzer analyzer
+evaluate_trade()
+calculate_position_size()
}
class PortfolioManager {
+Optimizer optimizer
+Strategy strategy
+rebalance_portfolio()
+get_portfolio_data()
}
class DataManager {
+List~Provider~ providers
+get_market_data()
+process_data()
}
AgentManager --> RiskManager : Signals
RiskManager --> PortfolioManager : Valid Trades
DataManager --> AgentManager : Market Data
PortfolioManager --> Broker : Orders
4. Core Logic and Algorithms
Technical Agent Signal Generation
def generate_technical_signal(self, data: pd.DataFrame) -> Signal:
# Calculate technical indicators
trend_score = self._calculate_trend_score(data)
momentum_score = self._calculate_momentum_score(data)
volatility_score = self._calculate_volatility_score(data)
volume_score = self._calculate_volume_score(data)
pattern_score = self._calculate_pattern_score(data)
# Weight and combine scores
technical_score = (
trend_score * 0.30 + # Trend following
momentum_score * 0.20 + # Price momentum
volatility_score * 0.20 + # Volatility regime
volume_score * 0.15 + # Volume analysis
pattern_score * 0.15 # Chart patterns
)
# Generate signal based on score
if abs(technical_score) > self.min_threshold:
direction = SignalDirection.BUY if technical_score > 0 else SignalDirection.SELL
confidence = min(abs(technical_score), self.max_confidence)
return Signal(direction=direction, confidence=confidence)
return None
Position Sizing Algorithm
def calculate_position_size(self, portfolio_value: float, signal: Signal) -> float:
# Kelly Criterion for base position size
win_rate = self._calculate_win_rate(signal.symbol)
avg_win = self._calculate_avg_win(signal.symbol)
avg_loss = self._calculate_avg_loss(signal.symbol)
kelly_fraction = (win_rate * avg_win - (1 - win_rate) * avg_loss) / avg_win
kelly_fraction = min(kelly_fraction, self.max_size_pct)
# Adjust by signal confidence and volatility
confidence_adj = signal.confidence / self.max_confidence
vol_adj = self._calculate_volatility_adjustment(signal.symbol)
# Final position size
position_size = portfolio_value * kelly_fraction * confidence_adj * vol_adj
return min(position_size, portfolio_value * self.max_size_pct)
5. Workflow Examples
Trading Decision Flow
flowchart TD
A[Market Data] --> B[Data Processing]
B --> C[Feature Engineering]
C --> D[Agent Analysis]
D --> E{Signal Generation}
E -->|No Signal| F[Hold Position]
E -->|Generate Signal| G[Risk Evaluation]
G -->|Pass| H[Position Sizing]
G -->|Fail| F
H --> I[Portfolio Check]
I -->|Within Limits| J[Execute Trade]
I -->|Exceeds Limits| K[Adjust Size]
K --> J
J --> L[Monitor Position]
6. Deployment and Usage
Environment Setup
- Clone repository and install dependencies:
git clone https://github.com/your-org/alpha-pulse.git cd alpha-pulse pip install -r requirements.txt - Configure environment variables:
export ALPHA_PULSE_ENV=production export ALPHA_PULSE_CONFIG=/path/to/config.yaml - Initialize database:
python scripts/init_db.py
Running the System
- Start the data pipeline:
python -m alpha_pulse.data_pipeline - Launch the trading engine:
python -m alpha_pulse.main - Monitor performance:
python -m alpha_pulse.monitoring
7. Risk Management and Validation
Risk Controls
- Position Size Limits: Maximum 20% of portfolio per position
- Portfolio Leverage: Maximum 1.5x total exposure
- Stop Loss: Dynamic ATR-based stops with 2% maximum loss per trade
- Drawdown Protection: Reduce exposure when approaching maximum drawdown limit
Validation Process
- Historical Backtesting
- Paper Trading Validation
- Small-Scale Live Testing
- Gradual Capital Allocation
8. Further Development
Planned Enhancements
- Additional Data Sources
- On-chain metrics
- Order book data
- Social media sentiment
- Advanced Analytics
- Deep learning models
- Reinforcement learning
- Natural language processing
- Infrastructure Improvements
- Real-time processing
- Distributed computing
- Cloud deployment
Performance Optimizations
- Implement data caching
- Parallelize agent computations
- Optimize database queries
- Use GPU acceleration for ML models
9. Appendices
Risk Metrics Calculation
flowchart LR
subgraph Portfolio Metrics
PV[Portfolio Value]
DD[Drawdown]
SR[Sharpe Ratio]
end
subgraph Position Metrics
PS[Position Size]
PE[Position Exposure]
PL[P&L]
end
subgraph Risk Limits
MP[Max Position]
ML[Max Leverage]
MD[Max Drawdown]
end
PS --> PE
PE --> PV
PL --> DD
DD --> MD
PS --> MP
PE --> ML
PL --> SR
References
- Modern Portfolio Theory
- Kelly Criterion
- Risk-Adjusted Position Sizing
- Technical Analysis
- Machine Learning in Finance