Stock Screening with Price Range, Auction Volume, and Recent Returns

Summary

This post describes a stock screen that combines daily price range with a measure based on yesterday’s turnover and today’s auction volume relative to the prior day’s volume. It also filters for a positive but limited ten-day return, aiming to identify shares with recent movement and trading activity. A Python-style example sketches how to query market data, calculate a rolling return, apply the conditions, and rank qualifying stocks by closing price. The post does not provide backtest results or evidence that the selected conditions predict future returns.

The author cautions that the screen uses price and activity data without company fundamentals or financial statements. The short return window may reflect temporary fluctuations rather than durable investment value. The example also relies on fields and calculations that are not fully specified, so implementation details would need to be checked against the data source. Suggested extensions include adding fundamental variables and using additional indicators or longer return periods.

Key ideas

• The screen combines daily price range, turnover adjusted by auction volume, and a ten-day return filter.
• The example ranks qualifying shares by closing price but supplies no performance evidence.
• Price and activity filters alone omit financial fundamentals and other sources of risk.
• A short return window can capture temporary movement rather than long-term value.
• The sample implementation leaves some data fields and calculations insufficiently specified.

Tags

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