AlphaStream is a machine learning system for generating trading signals from technical indicators. It processes market data through 200+ indicators, trains multiple ML models, and outputs actionable signals.
The project demonstrates the intersection of quantitative finance and machine learning — using real market data to build predictive models that can inform trading decisions.
The Challenge
Manual technical analysis is subjective and time-consuming. Different traders interpret the same indicators differently, leading to inconsistent signals.
The goal was to build a systematic approach: compute indicators programmatically, train ML models on historical data, and generate consistent signals that can be backtested.
ML Pipeline Design
Built a feature engineering pipeline that computes 200+ technical indicators from OHLCV data. Features include moving averages, oscillators, volume indicators, and volatility measures.
Trained 5 different model architectures (Random Forest, XGBoost, LSTM, etc.) and used cross-validation to prevent overfitting. The best performers are ensembled for production signals.
Technical Implementation
Feature Engineering
Used pandas-ta and custom functions to compute 200+ indicators. Feature selection reduced dimensionality while preserving predictive power.
Model Training
Walk-forward validation prevents look-ahead bias. Models are retrained weekly on rolling windows of historical data.
Problems & Solutions
Overfitting Prevention
Results
AlphaStream is the most-starred project in my portfolio, demonstrating interest from the quant trading community.
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