Qlib Alpha158
A classic stock-selection factor set built into Microsoft Qlib, containing approximately 158 features extracted from market data. It covers categories such as candlestick patterns, price momentum and moving averages, volatility, and trading volume, providing machine learning models with a standardized, reproducible feature engineering baseline.
Timeline (last 90 days)
Lumina's second layer is the Qlib Alpha158 factor library implemented in pure Python, fully rewritten in vectorized form, with LightGBM used as the meta decision filter.
Alpha158 is a classic set of stock-selection factors built into Microsoft Qlib, containing about 158 features extracted from market data, divided into K-line pattern category, price momentum and moving average category, volatility category, and volume category.
All Facts (2)
Lumina's second layer is the Qlib Alpha158 factor library implemented in pure Python, fully rewritten in vectorized form, with LightGBM used as the meta decision filter.
50%UnverifiedAlpha158 is a classic set of stock-selection factors built into Microsoft Qlib, containing about 158 features extracted from market data, divided into K-line pattern category, price momentum and moving average category, volatility category, and volume category.
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