Defining Forward-Return Labels for a Chinese Stock Training Set

Summary

This forum excerpt concerns constructing training labels for a Chinese stock dataset and asks why label extraction produces an empty training set. The displayed expression derives a future return from a later close divided by a later open, assigns a stock-list field named price_change_2d as the return in an alternate expression, clips the chosen return at its lower and upper one-percent quantiles, and bins the clipped values into twenty categories. It also shows exclusion of date and instrument fields from the input columns.

The excerpt provides no reply or resolution, so it does not establish why the extracted dataset is empty or which label definition should be used. It raises a practical data-pipeline issue: label creation depends on valid joined fields, available forward observations, and compatible expressions. The two shown return expressions are distinct, and the post’s formatting does not clarify whether they are alternatives or part of one pipeline. No dataset statistics, validation results, or model performance are included, so the material is best read as a question about label construction rather than a validated labeling recipe.

Key ideas

• The excerpt shows a forward-return expression based on a later close and open, alongside an alternate stock-list return field.
• It describes clipping returns at the one-percent quantiles and converting them into twenty bins.
• The author reports that extracting a training set with the stock-list price-change field yields no rows.
• No answer is provided, so the cause of the empty set and a working fix remain unknown.
• The displayed expressions need clarification before they can be treated as one consistent labeling pipeline.

Tags

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