Automating Quantitative Factor Screening with AI

Summary

This article outlines an automated workflow for turning a written trading hypothesis into a preliminary factor test. A user submits an idea through messaging; an AI model translates it into JavaScript, the workflow retrieves cryptocurrency data, checks data sufficiency, computes the factor, and produces analyses that include information coefficient, monotonicity, signal decay, and trading costs. The model then explains the report in plain language and suggests possible revisions.

The worked example tests whether a small prior-day price range predicts a rise the following day. The report gives negative returns after costs, weak monotonicity, and a short signal half-life, and recommends abandoning or modifying the idea. These figures are presented as one workflow’s example, not independently verified research. The article frames automation as a way to screen ideas quickly, while acknowledging that model interpretation can be wrong, historical data is limited, and past relationships may not persist. A promising screen still needs careful methodology, robustness checks, and further testing before trading.

Key ideas

• A written hypothesis can be converted into a factor calculation and historical analysis through an automated workflow.
• The described report evaluates predictive association, monotonicity, decay, returns, risk, and estimated trading costs.
• The example’s low-range hypothesis receives weak results and negative returns after costs.
• AI-generated code and explanations may misunderstand the hypothesis and should be reviewed.
• Historical screening can prioritize ideas for research but does not establish future performance.

Tags

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