A Stock Screen Combining Intraday Activity and Moving Averages

Summary

The proposed screen combines a daily amplitude threshold, a ratio based on prior turnover and current auction volume relative to prior volume, and an opening price near the ten-day moving average. The stated turnover-volume ratio should lie between 0.5 and 2. The accompanying Python example also applies turnover and moving-average conditions, including a comparison between the ten-day and thirty-day averages, so the implementation is more elaborate than the short description. The indicator example references a moving-average crossover as well.

The author frames the inputs as measures of price volatility, trading activity, and technical context. The document gives no performance analysis or backtest results, and it acknowledges that these filters omit fundamentals and other relevant technical information. It suggests adding relative strength and volume measures and combining market data with company financials; machine learning is mentioned as a possible optimization, without a specified method or evidence of benefit.

Key ideas

• The proposed screen uses price amplitude, an auction-volume and turnover ratio, and proximity to the ten-day average.
• The stated ratio range is 0.5 to 2, while the code adds further turnover and moving-average rules.
• The article provides no performance evidence for the screening conditions.
• The author recommends combining technical signals with financial data and other factors.

Tags

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