Machine Learning Trading System

System Architecture

This project implements a production-grade autonomous trading system using machine learning models, real-time market data processing, and sophisticated risk management strategies.

Core Components

1. Data Pipeline

class MarketDataPipeline:
    def __init__(self, config: Dict[str, Any]):
        self.data_sources = {
            'primary': AlpacaMarketDataAPI(config['alpaca_key']),
            'secondary': YFinanceAPI(),
            'sentiment': NewsAPIClient(config['news_api_key'])
        }
        self.preprocessor = DataPreprocessor(
            feature_config=config['features'],
            normalization=config['normalization']
        )
        
    async def fetch_market_data(self, symbols: List[str]) -> pd.DataFrame:
        tasks = []
        for source in self.data_sources.values():
            tasks.append(source.fetch_data(symbols))
        
        raw_data = await asyncio.gather(*tasks)
        return self.preprocessor.process(self.merge_data(raw_data))
        
    def merge_data(self, data_list: List[pd.DataFrame]) -> pd.DataFrame:
        # Custom logic to merge data from different sources
        pass

2. Feature Engineering

class FeatureEngineer:
    def __init__(self):
        self.technical_indicators = {
            'momentum': self._calculate_momentum,
            'volatility': self._calculate_volatility,
            'trend': self._calculate_trend
        }
        self.market_features = MarketFeatureExtractor()
        self.sentiment_analyzer = SentimentAnalyzer()

    def generate_features(self, data: pd.DataFrame) -> pd.DataFrame:
        features = data.copy()
        
        # Technical indicators
        for indicator in self.technical_indicators.values():
            features = indicator(features)
            
        # Market microstructure features
        features = self.market_features.extract(features)
        
        # Sentiment features
        features = self.sentiment_analyzer.add_sentiment_features(features)
        
        return features

    def _calculate_momentum(self, data: pd.DataFrame) -> pd.DataFrame:
        return ta.momentum.rsi(data['close'], window=14)

3. Model Architecture

class MLTradingModel(nn.Module):
    def __init__(self, input_dim: int, hidden_dim: int):
        super().__init__()
        self.lstm = nn.LSTM(
            input_size=input_dim,
            hidden_size=hidden_dim,
            num_layers=2,
            dropout=0.2,
            batch_first=True
        )
        self.attention = MultiHeadAttention(hidden_dim, 4)
        self.fc = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(hidden_dim // 2, 3)  # Buy, Sell, Hold
        )
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        lstm_out, _ = self.lstm(x)
        attention_out = self.attention(lstm_out)
        return self.fc(attention_out[:, -1, :])

4. Risk Management

class RiskManager:
    def __init__(self, config: Dict[str, Any]):
        self.position_limits = config['position_limits']
        self.var_calculator = ValueAtRiskCalculator(
            confidence_level=0.99,
            time_horizon='1d'
        )
        self.portfolio_manager = PortfolioManager(
            max_leverage=config['max_leverage'],
            margin_requirements=config['margin_reqs']
        )

    def validate_trade(self, trade: Trade) -> bool:
        checks = [
            self._check_position_limits(trade),
            self._check_var_limits(trade),
            self._check_portfolio_constraints(trade)
        ]
        return all(checks)

    def _check_var_limits(self, trade: Trade) -> bool:
        portfolio_var = self.var_calculator.compute_var(
            self.portfolio_manager.get_portfolio()
        )
        new_var = self.var_calculator.compute_var_with_trade(
            self.portfolio_manager.get_portfolio(), trade
        )
        return new_var <= self.position_limits['var_limit']

5. Execution Engine

class ExecutionEngine:
    def __init__(self, broker_api: BrokerAPI):
        self.broker = broker_api
        self.order_manager = OrderManager()
        self.execution_optimizer = ExecutionOptimizer(
            strategy='twap',  # Time-Weighted Average Price
            max_participation_rate=0.1
        )
        
    async def execute_trade(self, trade: Trade) -> OrderStatus:
        optimized_orders = self.execution_optimizer.split_order(trade)
        
        execution_tasks = []
        for order in optimized_orders:
            execution_tasks.append(self._execute_single_order(order))
            
        results = await asyncio.gather(*execution_tasks)
        return self.order_manager.aggregate_results(results)
        
    async def _execute_single_order(self, order: Order) -> OrderResult:
        try:
            return await self.broker.place_order(order)
        except BrokerAPIError as e:
            self.order_manager.handle_error(e)
            raise

Performance Monitoring

class PerformanceMonitor:
    def __init__(self):
        self.metrics = {
            'returns': self._calculate_returns,
            'sharpe': self._calculate_sharpe,
            'drawdown': self._calculate_drawdown,
            'win_rate': self._calculate_win_rate
        }
        self.risk_metrics = RiskMetricsCalculator()
        
    def generate_report(self, portfolio_history: pd.DataFrame) -> Dict[str, float]:
        report = {}
        for metric_name, metric_func in self.metrics.items():
            report[metric_name] = metric_func(portfolio_history)
        
        report.update(self.risk_metrics.calculate_all(portfolio_history))
        return report

Backtesting Framework

class Backtester:
    def __init__(self, 
                 model: MLTradingModel,
                 data_pipeline: MarketDataPipeline,
                 risk_manager: RiskManager):
        self.model = model
        self.data_pipeline = data_pipeline
        self.risk_manager = risk_manager
        self.performance_monitor = PerformanceMonitor()
        
    def run_backtest(self, 
                     start_date: datetime,
                     end_date: datetime,
                     initial_capital: float) -> BacktestResults:
        portfolio = Portfolio(initial_capital)
        trades = []
        
        for date in self._date_range(start_date, end_date):
            data = self.data_pipeline.get_historical_data(date)
            signals = self.model.predict(data)
            
            for signal in signals:
                if self.risk_manager.validate_trade(signal):
                    trade = self._execute_trade(signal, portfolio)
                    trades.append(trade)
                    
            portfolio.update(date)
            
        return BacktestResults(
            trades=trades,
            portfolio_history=portfolio.history,
            metrics=self.performance_monitor.generate_report(portfolio.history)
        )

Production Deployment

1. Infrastructure Setup

   • AWS infrastructure using Terraform
   • Kubernetes cluster for scalability
   • Monitoring with Prometheus/Grafana
   • Log aggregation with ELK stack

2. CI/CD Pipeline

   name: ML Trading System CI/CD
   
   on:
     push:
       branches: [ main ]
     pull_request:
       branches: [ main ]
   
   jobs:
     test:
       runs-on: ubuntu-latest
       steps:
         - uses: actions/checkout@v2
         - name: Run tests
           run: |
             python -m pytest tests/
             python -m pylint src/
   
     deploy:
       needs: test
       runs-on: ubuntu-latest
       steps:
         - name: Deploy to Kubernetes
           run: |
             kubectl apply -f k8s/

System Requirements

1. Hardware

   • Minimum 32GB RAM
   • GPU for model training
   • Low-latency network connection
   • SSD storage for data

2. Software

   • Python 3.8+
   • PyTorch/NumPy/Pandas
   • Redis for caching
   • PostgreSQL for storage

Getting Started

1. Clone the repository
2. Install dependencies: pip install -r requirements.txt
3. Configure API keys in .env
4. Run tests: pytest tests/
5. Start the system: python src/main.py

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