Generating Single-Factor Analysis Reports with BigQuant FactorLens

Summary

This guide outlines a workflow for preparing a factor research report for a quantitative trading competition. It recommends generating factor data, then using BigQuant’s FactorLens v4 to calculate single-factor results. The platform returns ranking metrics such as mean information coefficient, ICIR, Sharpe ratio, and turnover, along with detailed data for grouped performance and IC analysis. The article describes examining long, short, and long-short portfolios, yearly performance, time-series IC measures, and cumulative group returns.

Because the platform’s charts cannot be exported directly as standard image files, the guide proposes Python plotting and formatting templates for factor distributions, cumulative returns, performance summaries, and IC metrics. These outputs can be assembled into a Markdown report or converted to PDF. The example uses separate training and test datasets, while noting that only a specified portion of the test data counts for public scoring. The templates are illustrative rather than exhaustive, and the guide encourages additional analysis. It provides a reporting workflow, not evidence that any particular factor is profitable or robust.

Key ideas

• Use FactorLens v4 to produce single-factor statistics and detailed analysis data.
• Review grouped long, short, and long-short performance alongside IC time series and turnover.
• Export platform results through custom plots and Markdown summaries when native charts cannot be saved as images.
• Keep training and test periods distinct, and observe which test dates are included in scoring.
• Treat the provided report framework as a starting point rather than a complete evaluation standard.

Tags

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