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.
7 documents
This documentation explains how Zipline organizes risk and performance measurements for algorithm simulations. A metrics set defines which values a backtest tracks, and its metrics can report at different frequencies. The default set includes examples such…
This release note describes changes to Zipline 1.4.0, a quantitative research and backtesting platform. It removes implicit downloads of treasury and benchmark data, replacing benchmark retrieval with user-supplied files or instruments, or an option to run…
These release notes describe additions to Zipline’s Pipeline API in version 0.9.0. New datasets expose buyback authorizations and dividend information organized by ex-date, payment date, or announcement date. Related built-in factors measure business days…
These release notes describe changes to a quantitative trading and research platform. Pipeline additions include grouped ranking, filters that test conditions across lookback windows, and several technical factors such as Aroon, fast stochastic, Ichimoku,…
This notebook demonstrates how to use Alphalens to compare a deliberately non-predictive factor with a deliberately predictive one. It uses a universe of large-cap stocks with sector labels and daily opening prices. The baseline factor ranks stocks by their…
This release note describes changes to Zipline, a Python framework for algorithmic trading. It introduces the history API for retrieving prior bar data, early support for Quantopian-style algorithm scripts, new data sources, and a BMF&Bovespa trading…
These release notes describe Zipline 1.0's simulation redesign and new backtest workflows. Simulations request data as algorithms need it through a portal, while daily or minute timestamps drive the simulation clock. The release also introduces data bundles…