How to Keep an AI Quant Research Log That Can Be Reviewed
A practical framework for documenting AI quant experiments, data lineage, backtest assumptions, and risk decisions without turning research into trade advice.
Key Takeaways
- A research log turns AI quant work from isolated experiments into a reviewable process.
- Good logs record hypotheses, data lineage, feature timing, costs, validation windows, and rejected ideas.
- For public education sites, documentation also helps separate research methodology from investment advice.