added initial py analytics / rust core / ts orchestrator services
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apps/stock/TRADING_SYSTEM_README.md
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apps/stock/TRADING_SYSTEM_README.md
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# Unified Trading System Architecture
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A high-performance trading system that seamlessly handles backtesting, paper trading, and live trading using a three-tier architecture optimized for different performance requirements.
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## Architecture Overview
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### Three-Tier Design
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1. **Rust Core (Hot Path - Microseconds)**
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- Order book management
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- Order matching engine
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- Real-time risk checks
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- Position tracking
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- Live P&L calculations
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2. **Bun Orchestrator (Warm Path - Milliseconds)**
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- System coordination
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- Data routing and normalization
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- API gateway (REST + WebSocket)
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- Exchange connectivity
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- Strategy management
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3. **Python Analytics (Cold Path - Seconds+)**
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- Portfolio optimization
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- Complex risk analytics
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- ML model inference
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- Performance attribution
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- Market regime detection
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## Trading Modes
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### Backtest Mode
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- Processes historical data at maximum speed
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- Realistic fill simulation with market impact
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- Comprehensive performance metrics
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- Event-driven architecture for accuracy
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### Paper Trading Mode
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- Uses real-time market data
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- Simulates fills using actual order book
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- Tracks virtual portfolio with realistic constraints
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- Identical logic to live trading for validation
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### Live Trading Mode
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- Connects to real brokers/exchanges
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- Full risk management and compliance
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- Real-time position and P&L tracking
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- Audit trail for all activities
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## Key Features
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### Unified Strategy Interface
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Strategies work identically across all modes:
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```typescript
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class MyStrategy extends BaseStrategy {
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async onMarketData(data: MarketData) {
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// Same code works in backtest, paper, and live
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const signal = await this.generateSignal(data);
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if (signal.strength > 0.7) {
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await this.submitOrder(signal.toOrder());
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}
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}
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}
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```
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### Mode Transitions
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Seamlessly transition between modes:
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- Backtest → Paper: Validate strategy performance
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- Paper → Live: Deploy with confidence
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- Live → Paper: Test modifications safely
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### Performance Optimizations
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**Backtest Mode:**
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- Batch data loading
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- Parallel event processing
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- Memory-mapped large datasets
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- Columnar data storage
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**Paper/Live Mode:**
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- Lock-free data structures
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- Batched market data updates
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- Efficient cross-language communication
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- Minimal allocations in hot path
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## Getting Started
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### Prerequisites
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- Rust (latest stable)
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- Bun runtime
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- Python 3.10+
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- Docker (for dependencies)
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### Installation
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1. **Build Rust Core:**
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```bash
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cd apps/stock/core
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cargo build --release
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npm run build:napi
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```
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2. **Install Bun Orchestrator:**
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```bash
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cd apps/stock/orchestrator
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bun install
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```
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3. **Setup Python Analytics:**
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```bash
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cd apps/stock/analytics
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python -m venv venv
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source venv/bin/activate # or venv\Scripts\activate on Windows
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pip install -r requirements.txt
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```
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### Running the System
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1. **Start Analytics Service:**
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```bash
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cd apps/stock/analytics
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python main.py
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```
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2. **Start Orchestrator:**
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```bash
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cd apps/stock/orchestrator
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bun run dev
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```
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3. **Connect to UI:**
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Open WebSocket connection to `ws://localhost:3002`
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## API Examples
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### Submit Order (REST)
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```bash
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curl -X POST http://localhost:3002/api/orders \
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-H "Content-Type: application/json" \
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-d '{
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"symbol": "AAPL",
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"side": "buy",
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"quantity": 100,
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"orderType": "limit",
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"limitPrice": 150.00
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}'
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```
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### Subscribe to Market Data (WebSocket)
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```javascript
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const socket = io('ws://localhost:3002');
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socket.emit('subscribe', {
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symbols: ['AAPL', 'GOOGL'],
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dataTypes: ['quote', 'trade']
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});
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socket.on('marketData', (data) => {
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console.log('Market update:', data);
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});
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```
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### Run Backtest
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```bash
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curl -X POST http://localhost:3002/api/backtest/run \
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-H "Content-Type: application/json" \
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-d '{
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"mode": "backtest",
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"startDate": "2023-01-01T00:00:00Z",
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"endDate": "2023-12-31T23:59:59Z",
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"symbols": ["AAPL", "GOOGL", "MSFT"],
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"initialCapital": 100000,
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"strategies": [{
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"id": "mean_reversion_1",
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"name": "Mean Reversion Strategy",
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"enabled": true,
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"allocation": 1.0,
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"symbols": ["AAPL", "GOOGL", "MSFT"],
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"parameters": {
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"lookback": 20,
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"entryZScore": 2.0,
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"exitZScore": 0.5
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}
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}]
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}'
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```
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## Configuration
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### Environment Variables
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**Orchestrator (.env):**
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```env
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PORT=3002
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DATA_INGESTION_URL=http://localhost:3001
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ANALYTICS_SERVICE_URL=http://localhost:3003
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QUESTDB_HOST=localhost
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QUESTDB_PORT=9000
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POSTGRES_HOST=localhost
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POSTGRES_PORT=5432
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```
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**Analytics Service (.env):**
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```env
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ANALYTICS_PORT=3003
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REDIS_URL=redis://localhost:6379
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DATABASE_URL=postgresql://user:pass@localhost:5432/trading
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```
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### Risk Limits Configuration
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```json
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{
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"maxPositionSize": 100000,
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"maxOrderSize": 10000,
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"maxDailyLoss": 5000,
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"maxGrossExposure": 1000000,
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"maxSymbolExposure": 50000
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}
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```
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## Monitoring
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### Metrics Exposed
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- Order latency (submission to fill)
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- Market data latency
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- Strategy performance metrics
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- System resource usage
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- Risk limit utilization
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### Health Endpoints
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- Orchestrator: `GET http://localhost:3002/health`
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- Analytics: `GET http://localhost:3003/health`
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## Development
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### Adding a New Strategy
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1. Extend `BaseStrategy` class
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2. Implement required methods
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3. Register with `StrategyManager`
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4. Configure parameters
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### Adding a New Data Source
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1. Implement `MarketDataSource` trait in Rust
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2. Add connector in Bun orchestrator
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3. Configure data routing
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### Adding Analytics
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1. Create new endpoint in Python service
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2. Implement analysis logic
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3. Add caching if needed
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4. Update API documentation
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## Performance Benchmarks
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### Backtest Performance
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- 1M bars/second processing rate
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- 100K orders/second execution
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- Sub-millisecond strategy evaluation
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### Live Trading Latency
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- Market data to strategy: <100μs
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- Order submission: <1ms
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- Risk check: <50μs
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### Resource Usage
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- Rust Core: ~200MB RAM
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- Bun Orchestrator: ~500MB RAM
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- Python Analytics: ~1GB RAM
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## Troubleshooting
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### Common Issues
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**"Trading engine not initialized"**
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- Ensure mode is properly initialized
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- Check Rust build completed successfully
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**"No market data received"**
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- Verify data-ingestion service is running
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- Check symbol subscriptions
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- Confirm network connectivity
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**"Risk check failed"**
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- Review risk limits configuration
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- Check current positions
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- Verify daily P&L hasn't exceeded limits
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## License
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MIT License - See LICENSE file for details
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