Oversold Stabilization Stock Selection with Custom Rules

Summary

This Chinese-language article introduces a customizable stock-selection strategy on a quantitative trading platform. It describes combining five selection rules, mainly intended to find stocks that have fallen sharply and begun to stabilize, with some rules aimed at possible strong upward moves. The strategy also specifies holding a position for two days and concentrating heavily in one stock, while inviting users to adjust factors, rule combinations, and trading settings.

The article proposes an iterative research workflow: define factors and selection conditions, backtest individual rules, inspect daily holdings and returns, analyze both winners and losers, then repeat over different date ranges. It reports annualized and cumulative historical returns, but the supplied text gives no detailed performance tables, benchmark comparison, transaction costs, drawdown, or out-of-sample evidence. The underlying source code is referenced rather than reproduced here, so the five selection rules cannot be independently assessed from this document alone. The reported results should therefore be treated as unverified backtest claims, not evidence of future performance.

Key ideas

• The strategy combines five stock-selection rules, emphasizing sharp declines followed by signs of stabilization.
• The article distinguishes automatically trained AI strategies from manually coded strategies that are easier to inspect and adjust.
• It recommends testing selection rules individually and reviewing daily holdings and returns to guide revisions.
• It suggests checking revised rules across different historical periods, though this alone does not establish out-of-sample robustness.
• The described portfolio holds for two days and concentrates heavily in one stock, creating substantial concentration risk.

Tags

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