Building an AI Stock-Ranking Strategy from Labels to Backtests

Summary

This overview explains the main stages of a machine-learning workflow for quantitative investing, using a fruit-selection analogy to introduce training data, labels, features, prediction, and validation. It recommends defining the market and stock universe, splitting historical data by time, choosing a target such as future returns or volatility, and pairing labeled observations with candidate factors such as turnover, valuation measures, and technical indicators. After cleaning missing data, a model is trained on one period and applied to a later period.

The example describes a visual workflow in BigStudio: extract training features and labels, fit a StockRanker model, rank stocks using later-period features, and pass the rankings to a backtest under specified trading rules. The document teaches the process rather than a tested investment strategy. It provides no performance results or detailed safeguards against leakage, overfitting, transaction costs, or changing market conditions, so readers would need to address those issues before evaluating a strategy in practice.

Key ideas

• Split historical observations by time so model fitting and evaluation use separate periods.
• Define the prediction target explicitly, such as future returns, volatility, or a return ranking.
• Construct features that may relate to the target, then align them with labels and handle missing values.
• Use the trained model to rank stocks on later data and evaluate the rankings through a backtest.
• The workflow is a general template and does not establish that any factor combination is profitable.

Tags

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