Chinese Stock Selection by Recent Declines and Average Turnover

Summary

This BigQuant example describes a stock-selection workflow that calculates each stock’s 15-day price change and 20-day average turnover from local CSV files. It ranks stocks by 15-day change, keeps the lower half, joins those names to a current stock list, then filters by price, listing category, special treatment status, and positive market capitalization. The remaining stocks are ranked by average turnover, with market capitalization used as a secondary ordering, and up to 20 are exported.

The code illustrates data loading, field checks, merges, filters, ranking, and output generation; it does not provide a rationale for interpreting recent losses as an investment signal or report backtest results. Its output depends on data quality, date sorting, consistent identifiers, and the correctness of the supplied market data. The selection rules also exclude some securities and impose a minimum price, so results reflect those implementation choices rather than a broadly validated strategy.

Key ideas

• The workflow ranks stocks by 15-day price change and retains the lower half of the list.
• It calculates average turnover over 20 observations and uses it to rank the filtered candidates.
• Price, market capitalization, listing category, and special treatment status are additional screens.
• The method exports a capped selection, but supplies no performance evidence or investment justification for the ranking.

Tags

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