Preparing AlphaLab Data for Factor Research and Backtesting
Summary
This guide explains how to organize data for VeighNa’s AlphaLab research workflow. It describes the roles of its directories, daily and minute bar files, index constituent records, and contract settings, then shows how preparation notebooks supply data consumed by an AlphaDataset and backtest. Daily bars are stored as Parquet; constituent membership is kept by date, making it possible to identify both the symbols needed for a study and the dates when each belonged to an index.
The guide also distinguishes raw bar-object loading from DataFrame loading. The latter can extend the requested date range, mark all-zero rows as missing, and rescale prices within each symbol’s window. These transformations affect how the data should be interpreted and compared. The article’s readiness checklist calls for checking membership coverage, symbol files, contract parameters, and sufficient history. It explains the intended interfaces and common omissions, but does not benchmark the workflow or validate any particular data source; users still need to ensure that their downloaded data and symbol conventions are consistent.
Key ideas
- AlphaLab organizes research inputs and outputs under a shared root directory.
- Daily and minute bars are stored separately, while index membership and contract settings use their own storage formats.
- DataFrame loading extends the requested window and transforms prices, so its output differs from raw bars.
- Membership filters can restrict factor data to the dates when each symbol belonged to an index.
- Before backtesting, check constituent coverage, bar files, contract entries, and historical lookback depth.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.