Diagnosing Quantile-Binning Errors in Simulated Trading

Summary

This brief troubleshooting post presents a ValueError raised while a BigQuant workflow runs an automatic labeling module. The traceback ends in pandas' binning routine and reports that the generated bin edges are not unique: after negative and positive infinity, the intermediate edges are NaN. The post frames the issue as a discrepancy between a successful backtest and a failing simulated-trading run.

The excerpt contains no diagnosis, fix, code changes, or follow-up evidence; it only supplies the question and traceback. The error indicates that the labeling step received data or computed boundaries that could not form valid bins, but the document does not establish why the backtest and simulation inputs differ. It therefore serves as a concrete example of an environment- or data-dependent failure to investigate, rather than a complete troubleshooting method. Readers would need to inspect the label expression and the data available during simulation to identify the cause.

Key ideas

• The traceback locates the failure in pandas binning invoked by an automatic labeling module.
• The reported bin edges contain repeated NaN values, which prevents valid unique boundaries from being formed.
• The post contrasts successful backtesting with failure during simulated trading but does not explain the difference.
• No fix or confirmed root cause is provided, so investigation must focus on the label expression and simulation inputs.

Tags

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