Our Technology
A detailed look at the engine, infrastructure, and processes that power AlphaMachine's performance.
System Architecture
High-quality market data
Multiple timeframes & resolutions
Technical indicators (RSI, MACD, Bollinger)
Price patterns & momentum features
Volatility & correlation metrics
Time-series transformations
Accurate representation of market dynamics
State space: Engineered features
Action space: Trading decisions
Reward function: Portfolio optimization
Multi-agent training
Model checkpointing
Hyperparameter Optimization:
- Environment parameters
- DRL & algorithm-specific parameters
- Neural network (topology, learning rate, batch size)
Realistic market conditions
Transaction costs & slippage
Performance analytics
• Compound Annual Return
• Expectancy Score
• Sharpe Ratio
• Risk-adjusted metrics
Dynamic weight allocation
Ensemble model deployment
Risk management
Performance monitoring
Automated rebalancing
Efficient order execution
Low latency connections
Institutional liquidity
This comprehensive architecture demonstrates how our deep reinforcement learning models process market data through advanced feature engineering, rigorous backtesting, and intelligent ensemble selection to deliver superior trading performance.
Technology Infrastructure
Google for Startups
Participant in the Cloud Program, leveraging scalable infrastructure.
Institutional Engine
Proprietary integration with leading algorithmic trading platforms providing robust backtesting capabilities, data access, and deployment infrastructure.
Backtesting Core
Advanced algorithmic trading engine that powers sophisticated quantitative trading strategies with institutional-grade infrastructure.
Cloud Computing
Leveraging cloud infrastructure for scalable computing power, enabling intensive model training and real-time trading execution.