Building an AI Quantitative Strategy: Labels, Features, Training, and Backtesting
Summary
This introductory guide explains how machine learning can be applied to quantitative investing through a staged research workflow. Using a fruit-selection analogy, it describes gathering historical data, defining the prediction target, labeling observations, selecting potentially informative features, joining and cleaning datasets, training a model, making predictions on later data, and evaluating those predictions through backtesting.
The example workflow uses stock returns as a possible target and mentions features such as turnover, valuation measures, and technical indicators. It also describes a ranking model that produces daily stock rankings for a simulated trading process. The article offers a conceptual overview rather than a complete empirical study: it reports no strategy performance or validation results, and gives limited detail on leakage prevention, portfolio construction, trading costs, or model selection. Its train and validation split is described chronologically, but robust research would still need careful out-of-sample design and realistic execution assumptions.
Key ideas
- A quantitative machine learning project starts by defining its market universe and prediction target.
- Labels represent the outcome to be predicted, while features provide candidate explanatory inputs.
- Training data and later validation data serve different roles in model development.
- Predictions can be converted into stock rankings and assessed through a backtest.
- Data cleaning and careful evaluation are necessary, but the guide does not specify a full validation protocol.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.