Stock Screening with a 20-Day Trend Filter, Range, and Trading Heat

Summary

The article describes a stock screen combining three criteria: daily amplitude above one percent, ranking by a measure of individual-stock heat, and a 20-day moving average above the 120-day moving average. It presents the moving-average relationship as a trend filter and treats amplitude and heat as signs of activity or market attention. A Python example illustrates filtering historical prices and ranking selected names using a proxy based on current trading volume and price.

The article cautions that historical-data selection can favor inflated past performance, that the rules omit many relevant factors, and that trend-based selection may encourage chasing price moves. It recommends adding company fundamentals and other indicators, alongside risk controls such as reducing exposure or using stops. The example does not establish that the screen is profitable: it includes additional filters and data handling beyond the stated core conditions, and supplies no backtest results or validation of its heat proxy.

Key ideas

• The core screen requires amplitude above one percent and the 20-day average above the 120-day average.
• Selected stocks are ranked by a heat measure, illustrated with volume multiplied by price.
• The article warns that the rules may overfit past data or encourage chasing trends.
• It recommends adding fundamental information and explicit risk controls.
• The examples provide no evidence of profitability and include implementation choices beyond the core screen.

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