Joining Multiple Model Predictions into an Averaged Forecast

Summary

This forum post presents a workflow for combining predictions from three model outputs. It merges the datasets on instrument and date, preserves columns that are not already present, renames each model’s prediction column, and computes their arithmetic mean as the combined prediction. It also stores the original row order before joining and sorts the merged result back into that order.

The post includes a backtest and trade-module error report involving an HDF5 file signature failure followed by an index error. It does not establish that the merge or averaging code caused those errors, nor does it provide a resolution. The sample includes a hash-based sampling helper, but gives no evidence about its reproducibility or suitability. The approach illustrates basic prediction ensembling and keyed data alignment; users still need to check for duplicate keys, missing predictions, mismatched rows, and whether the model outputs are comparable before averaging them.

Key ideas

• The example combines three prediction tables by instrument and date.
• It avoids bringing in columns already present before merging each additional model output.
• The combined prediction is the arithmetic average of the three model predictions.
• An added row-order field is used to restore the first input’s ordering after the joins.
• The reported HDF5 and index errors are not diagnosed or shown to be caused by the ensemble code.

Tags

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