Diagnosing a Missing DataFrame Column in BigQuant Position Conditions

Summary

This note diagnoses a backtest error in BigQuant position-condition functions. Both the opening and closing condition functions attempt to filter a DataFrame using a column named “fantanbili.” The author points out that the prediction output supplied by an upstream module does not contain that field, so selecting the column raises an error. The central lesson is to check that the columns expected by downstream logic are present in the data produced upstream.

The document does not provide a corrected implementation, explain what the missing field was intended to represent, or show a successful backtest after a fix. It also repeats the same diagnosis several times. The issue is a specific data-interface mismatch rather than a strategy concept: users would need to confirm the prediction schema and either supply the required field or revise the conditions to use an available field before the backtest can proceed.

Key ideas

• The opening and closing conditions both reference a field named fantanbili.
• The upstream prediction DataFrame is described as lacking that field.
• Filtering by a nonexistent DataFrame column causes the backtest logic to fail.
• The diagnosis implies that downstream field requirements should be checked against upstream output schemas.

Tags

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