tradytics/surpriver
Find big moving stocks before they move using machine learning and anomaly detection
Computes technical indicators (RSI, MACD, Bollinger Bands, etc.) and multi-timeframe price/volume features, then applies Isolation Forest anomaly detection to identify statistically unusual patterns that historically precede larger price moves. Integrates with yfinance for OHLCV data, scikit-learn for ML, and ta-lib for technical analysis; supports flexible backtest validation against future price action and Docker containerization for deployment.
1,851 stars. No commits in the last 6 months.
Stars
1,851
Forks
333
Language
Python
License
GPL-3.0
Category
Last pushed
Aug 13, 2021
Commits (30d)
0
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