AI-Assisted Crypto Factor Discovery and Validation Workflow

Summary

The document outlines an automated process for turning a natural-language crypto factor idea into a calculated signal and evaluation report. A language model identifies the factor’s direction and data needs, generates a JavaScript function, and the workflow applies it to daily market data across a watchlist. The described checks include information and rank correlations with next-day returns, significance statistics, quantile monotonicity, long-short balance, signal decay, market-cap consistency, and simulated fees and slippage. A second model summarizes the findings and suggests potential improvements and risks.

This is a system description, not evidence that any particular factor predicts returns. The document provides no sample evaluation results or independent validation of the generated factors. Its data window is capped at 365 daily periods, and results depend on the chosen assets, data cleaning, implementation, and transaction-cost assumptions. Automated interpretation may help screen ideas, but the report should be treated as an assessment to verify, not proof of profitability.

Key ideas

• Natural-language descriptions are translated into directional factor calculations for crypto assets.
• The workflow evaluates signals using correlation, quantile, decay, market-cap, and cost analyses.
• Generated factors are assessed against next-day returns across a preconfigured asset list.
• AI-generated grades and recommendations summarize results but do not establish predictive validity.
• The described historical dataset is limited to at most 365 daily periods.

Tags

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