Implementing Alpha101 Cross-Sectional Trading Signals

Summary

This Python class assembles a subset of Alpha101-style equity signals from price, volume, VWAP, returns, and precomputed factor series. Its methods apply operations such as cross-sectional ranking, rolling correlation, covariance, time-series ranking, decay, and threshold comparisons. The signals cover price and volume relationships, turnover and average daily volume, and other technical inputs; a calculation method collects them into a panel for multiple securities and dates.

The example data preparation routine queries daily stock data for a selected Chinese equity universe, reshapes it by date and symbol, and passes it to the signal class. The code supplies formulas and an execution outline, but no measured performance or validation results. Some inputs are assumed to have been calculated elsewhere, and the excerpt contains apparent inconsistencies, including repeated mappings and a truncated calculation section. Comments also flag formulas that returned all zeros in the author's tests. The implementation therefore documents signal construction rather than establishing predictive value; its database setup and dependencies may also need adaptation.

Key ideas

• The class builds a collection of equity signals from price, volume, VWAP, and precomputed factor inputs.
• Cross-sectional ranks and rolling time-series operators are central to many of the formulas.
• Some signals combine price-volume correlations with turnover or average-volume measures.
• The example organizes outputs across dates and stock symbols for a Chinese equity universe.
• The code reports unresolved formula behavior and provides no evidence of out-of-sample performance.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.