Screening Stocks by Turnover, Seven-Day Declines, and Bank Classification

Summary

This post describes an equity screen that selects stocks with turnover between 3% and 12%, a falling closing price across seven consecutive days, and a bank classification. It provides both a formula-style specification and a Python example that group price histories by stock, check the latest turnover and classification, and test the recent price sequence. The examples communicate the intended screening conditions, though implementations may differ in how they define consecutive daily declines and which data fields their platform supplies.

The screen is presented as a starting point rather than a validated strategy. The post identifies the use of enterprise classification as a source of labeling risk and notes that a long losing run can be followed by a sharp rebound, making stop-loss and risk controls relevant. It suggests adding fundamental and technical measures, but supplies no backtest, sample, performance statistics, or evidence that the filter predicts returns. Data definitions and platform-specific field names may need adjustment before the rule can be evaluated or used.

Key ideas

• The screen combines turnover between 3% and 12% with seven consecutive declining closes and a bank classification.
• The post offers both a formula-style rule and a Python example for applying the filters to stock histories.
• Enterprise classification can introduce labeling risk, and stocks that have fallen repeatedly may rebound sharply.
• Additional fundamental or technical filters are suggested, but no performance evidence or backtest is reported.
• Data field names and the precise meaning of consecutive declines may vary by platform.

Tags

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