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⚡ Optimize O(N^2) aggregation in _mergecftb - #206
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Replaced O(N^2) manual aggregation of fund cash flows with efficient Pandas-based operations using pd.concat and groupby().sum(). This change significantly improves performance when merging transaction histories from multiple funds by avoiding nested loops and row-wise iteration. - Reduced complexity from O(N*M) to O(N log N) - Added safety check for empty fund lists - Preserved functional equivalence and output schema Co-authored-by: refraction-ray <35157286+refraction-ray@users.noreply.github.com>
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💡 What: The optimization implemented
Optimized the
_mergecftbmethod inxalpha/multiple.pyby replacing a manual, row-by-row aggregation process with vectorized Pandas operations.🎯 Why: The performance problem it solves
The original implementation used an$O(N^2)$ approach where it first iterated over all transaction rows from all funds using
iterrows()to build a list, and then for every unique date, it re-filtered that entire list to calculate the sum. This led to severe performance degradation as the number of funds or the length of the transaction history increased.📊 Measured Improvement
Due to the environment lacking$O(N \times M)$ (where $N$ is total transactions and $M$ is unique dates) to $O(N \log N)$ (primarily due to the grouping and sorting operations). In practice, using
pandasand network access, direct benchmarking was not possible. However, the theoretical complexity has been reduced frompd.concatandgroupbyutilizes optimized C-level implementations, which are orders of magnitude faster than Python-level loops and list comprehensions for this type of data manipulation.Verified the change via
python3 -m py_compileto ensure no syntax errors were introduced and performed a manual code review to ensure the logic remains functionally identical.PR created automatically by Jules for task 167193237784393486 started by @refraction-ray