Atlas Trading System
Overview
Production algorithmic trading system managing real capital. Implements machine learning ensemble models for market prediction and automated trade execution.
Technical Architecture
System Design
- Event-driven architecture with real-time data processing
- Microservices approach for scalability and fault isolation
- Message queue system for component communication
- Redundant data feeds with automatic failover
Core Components
┌─────────────────────────────────────────────┐
│ Market Data Layer │
│ Real-time L2 • Options • Indices │
└─────────────┬───────────────────────────────┘
│
┌─────────────▼───────────────────────────────┐
│ Feature Engineering │
│ 20+ calculated features per tick │
│ Multi-timeframe analysis │
└─────────────┬───────────────────────────────┘
│
┌─────────────▼───────────────────────────────┐
│ ML Models Ensemble │
│ Multiple models with weighted voting │
└─────────────┬───────────────────────────────┘
│
┌─────────────▼───────────────────────────────┐
│ Risk Management Layer │
│ Position limits • Real-time monitoring │
└─────────────────────────────────────────────┘
Technical Implementation
Data Pipeline
- Ingestion Rate: Processing thousands of market events per second
- Latency: Sub-millisecond feature calculation
- Storage: Time-series optimized database with compression
- Reliability: 99.5% uptime with automatic recovery
Machine Learning Stack
Ensemble Approach:
- Multiple models trained on different timeframes
- Adaptive weighting based on recent performance
- Cross-validation with walk-forward analysis
- Feature importance tracking and selection
Training Pipeline:
# Example: Feature engineering approach
class FeatureEngineer:
def __init__(self):
self.feature_window = 100
self.features = []
def calculate_microstructure_features(self, market_data):
"""
Calculate market microstructure indicators
"""
features = {
'bid_ask_spread': self.calculate_spread(market_data),
'order_imbalance': self.calculate_imbalance(market_data),
'price_impact': self.estimate_impact(market_data),
'volume_profile': self.analyze_volume(market_data)
}
return features
Risk Management
Position Controls:
- Dynamic position sizing based on volatility
- Maximum drawdown limits
- Correlation-based portfolio constraints
- Real-time P&L tracking and alerts
Safety Features:
- Automated circuit breakers
- Order validation pipeline
- Market hour restrictions
- Emergency position flattening
Technical Achievements
- Data Processing: 2.48 million data points for training
- Model Training: Multiple algorithms with ensemble voting
- Prediction Accuracy: Validated through backtesting
- System Reliability: Fault-tolerant with automatic recovery
Engineering Challenges Solved
-
Market Data Handling
- Solved API rate limiting with intelligent rotation
- Implemented efficient data compression for storage
- Built redundant data pipeline for reliability
-
Feature Engineering
- Developed custom market microstructure indicators
- Created multi-timeframe feature aggregation
- Implemented real-time feature calculation
-
Model Deployment
- Containerized deployment for consistency
- A/B testing framework for model updates
- Monitoring and alerting infrastructure
Technology Stack
Languages & Frameworks:
- Python 3.11 (core system)
- NumPy/Pandas (data processing)
- Asyncio (concurrent processing)
Machine Learning:
- Scikit-learn (preprocessing)
- XGBoost (gradient boosting)
- TensorFlow (deep learning)
- Custom ensemble framework
Infrastructure:
- Docker (containerization)
- Redis (caching layer)
- Time-series database
- Cloud storage for backups
Monitoring:
- Custom dashboards
- Performance metrics tracking
- Automated alerting system
Development Practices
Code Quality
- Comprehensive test coverage
- Type hints throughout codebase
- Automated code review tools
- Performance profiling
DevOps
- CI/CD pipeline
- Automated testing
- Rolling deployments
- Version control for models
Results
- System Status: Production deployment with real capital
- Uptime: 99.5% availability
- Data Scale: Millions of market events processed
- Architecture: Scalable microservices design
Specific client details and proprietary algorithms have been omitted.