Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
6 documents
This notebook demonstrates an Alphalens workflow for evaluating a daily stock factor based on the gap between the prior close and current open. It defines an example universe of large-cap equities with sector labels, calculates the gap, and aligns the factor…
Alphalens is a Python library for evaluating predictive stock factors. It turns a factor signal and pricing data into a structured dataset of forward returns, optionally assigning observations to quantiles and groups such as sectors. The resulting analysis…
This example adapts Alphalens return analysis to study a discrete stock event rather than rank a cross-section of securities. It defines an event when a stock’s opening price crosses below a specified dollar threshold after being at or above it the prior…
This notebook illustrates factor evaluation with Alphalens using a large-cap equity universe assigned to sectors. It compares a baseline factor based on each stock’s recent ten-day performance with a second factor constructed from future price changes. The…
This tutorial shows how to evaluate a stock factor with Alphalens and then examine a portfolio built from its strongest and weakest ranked groups with Pyfolio. Its example defines a mean-reversion signal from the negative five-day change in opening prices,…
This notebook constructs artificial price and factor data to demonstrate the input structure expected by Alphalens and to provide a controlled setting for factor analysis. It creates daily prices for six assets with different deterministic paths, assigns…