Building an AI Stock Selector with a 10-Stock, 15-Day Portfolio

Summary

The document outlines a beginner workflow for creating an AI-assisted stock selection strategy on a quantitative platform. It starts with defining a trading style, translating that idea into candidate features, expressing those features for model training, labeling historical outcomes, and setting filters for the stock universe. The workflow then covers holding periods, allocation, backtesting, and later tuning.

The accompanying example is described as a short-term strategy that equally weights ten stocks and holds positions for 15 days. The author reports an annualized return of 81%, but provides no backtest period, benchmark, costs, drawdown, or other supporting performance details. The source strategy is explicitly presented as a newly written example without multi-dimensional validation or live trading validation. The article also notes that AI-generated selections may be difficult to interpret, while custom-coded rules offer more control and tuning flexibility. Its guidance is introductory; it does not specify the actual trained features, labeling scheme, or validation procedure needed to assess robustness.

Key ideas

• Define the intended trading style before choosing features for an AI stock selection model.
• Translate candidate market or fundamental characteristics into model inputs and label historical outcomes for training.
• Set stock-universe filters, holding rules, and position allocation before evaluating a strategy through backtesting.
• The example holds ten equally weighted stocks for 15 days and reports an 81% annualized return.
• The reported result lacks validation details, and the author cautions that the strategy has not been validated in live trading.

Tags

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