Screening Stocks by Turnover, Reversal Pattern, and Prior-Day Rankings

Summary

This document describes a stock screen combining a turnover-rate band of 3% to 12%, a reversal or engulfing-style price pattern, and inclusion on the previous day’s Chinese market rankings list. The stated aim is to find active stocks associated with market attention. It includes screening expressions and a Python example that derives a reversal measure from daily high, low, and prior close, then joins that measure with ranking-list and turnover data.

The post warns that the selection relies heavily on technical signals and does not account for company fundamentals. It suggests adding trend or candlestick analysis, valuation measures, and potentially model-based parameter tuning. No backtest, return data, or evidence of predictive power is supplied. The sample code also warrants inspection before use: its stated reversal condition and ranking-list filter may not align cleanly with all of the implementation details, and the shown data joins and field availability need validation. The screen is best treated as a candidate research rule, with risk controls and out-of-sample testing still required.

Key ideas

• The proposed screen combines a turnover range, a reversal-style pattern, and prior-day inclusion on a market rankings list.
• The sample computes a range-based measure using daily prices and combines it with turnover and rankings data.
• The author notes that the screen omits company fundamentals and may overemphasize technical signals.
• Fundamental filters and additional trend analysis are suggested as possible refinements.
• No performance evidence is provided, and the sample implementation needs validation before use.

Tags

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