Enhancement and bugfix in report plots #1413
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@you-n-g would you review please? |
| # Long-Average | ||
| t_df["long-average"] = t_df["Group1"] - pred_label.groupby(level="datetime")["label"].mean() | ||
| # Long-benchmark | ||
| benchmark = kwargs.get("benchmark", "average") |
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I think it would be better to have the benchmark as an explicit input parameter, and we can also add type hints.
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changed as suggested.
| benchmark = pred_label.groupby(level="datetime")["label"].mean() | ||
| elif isinstance(benchmark, pd.Series): | ||
| benchmark_name = benchmark.name if bool(benchmark.name) else "benchmark" | ||
| benchmark = benchmark.reindex(t_df.index) |
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There is a high possibility that the user-provided benchmark may not align with t_df.index. Perhaps we can add some common warnings in this case if the DataFrame doesn't have warning messages.
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I think it might not be necessary as it should be a common case especially for a global portfolio. Think about using CSI300 to benchmarking a portfolio which invests both A and H shares, for those mainland holidays which are not HK holidays.
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any further comments? @Fivele-Li |
Description
len(x) // N * i : len(x) // N * (i + 1)misseslen(x) % Nnames at end.Motivation and Context
How Has This Been Tested?
Generated plot locally.
Screenshots of Test Results (if appropriate):
Types of changes