AI-Powered ETF Portfolio Technology
An in-depth look at the machine learning models and algorithms that power our ETF portfolio generator
System Architecture
Our ETF Portfolio Generator is powered by a sophisticated ensemble of deep learning models and algorithmic components working together to deliver market-beating performance.
Core Components
Model Stack
- Combined LSTM & GCN Network for stock selection
- Advanced MLP for options pricing and strategy
- Market Regime Classifier for adaptive portfolio management
- Portfolio Optimizer for position sizing and risk control
Technology Framework
- TensorFlow/Keras for deep learning model implementation
- NumPy/Pandas for data processing and manipulation
- SQLite for efficient data storage and retrieval
- Technical Analysis Library for feature engineering
System Flow Diagram
Architecture Diagram
(Placeholder for system architecture visualization)
End-to-End Process
- 1
Data Acquisition & Processing
Historical equity data (OHLCV), options data, and treasury rates are collected and processed, with technical indicators calculated and features normalized.
- 2
Market Regime Detection
The system identifies the current market state (bull, bear, neutral, high volatility) to optimize parameters across all models and strategies.
- 3
Stock Selection
The LSTM-GCN combined model generates predictions and confidence scores for potential stocks, which are filtered based on momentum, volatility, and other factors.
- 4
Portfolio Optimization
Selected stocks are weighted to achieve target risk/return characteristics, with constraints for sector diversification and position sizing.
- 5
Options Strategy (Optional)
For enhanced income, the options pricing model identifies opportunities for covered calls or other strategies to augment portfolio returns.
- 6
Risk Management
Portfolio is continuously monitored with stop-loss levels, trailing stops, and maximum drawdown limits adapted to current market conditions.