Building an AI Equity Strategy from Labels to Backtesting

Summary

This introductory guide describes a supervised machine-learning workflow for quantitative equity strategies. It begins by defining the stock universe and target, such as a future return, volatility, or ranking, then splitting historical observations by time into training and validation periods. Candidate predictors, or factors, are paired with labels, missing data are handled, and a model is trained before producing predictions for unseen observations.

The guide illustrates a visual workflow in which a stock-ranking model scores stocks and passes the rankings to a backtest under specified trading rules. Its central practical advice is to experiment with features and assess predictions on held-out data before simulating trades. The article is a high-level tutorial rather than a complete research protocol: it does not detail leakage controls, transaction costs, portfolio construction, evaluation metrics, or out-of-sample deployment. Its examples focus on stocks, though it also mentions other markets as possible data domains.

Key ideas

• Define the investment universe and prediction target before training a model.
• Use time-ordered training and validation data to assess predictions on later observations.
• Construct features and labels, align them by instrument and date, and address missing values.
• A ranking model can turn stock features into daily rankings for a rule-based backtest.
• Backtest results depend on details that the guide does not specify, including costs and leakage controls.

Tags

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