feat: optimize idiosyncratic volatility factor using vectorized covar…#15
Merged
ayushkrtiwari merged 12 commits intoOPCODE-Open-Spring-Fest:mainfrom Oct 26, 2025
Merged
Conversation
ayushkrtiwari
added
Semver:minor
Enhanced volatility factor implementations for production use, including improved initialization, handling of edge cases, and consistent DataFrame outputs.
Remove unused import statement for warnings.
Remove unused import statement for warnings.
ayushkrtiwari
merged commit 8d5d7c5
into
OPCODE-Open-Spring-Fest:main
4 of 5 checks passed
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Title:
🚀 Optimize Idiosyncratic Volatility factor using vectorized covariance operations
Description:
This PR replaces the per-symbol Python loop in IdiosyncraticVolatility.compute() with a fully vectorized pandas/numpy implementation. The new version computes betas, residuals, and rolling volatilities without explicit loops, improving scalability for large universes.
Key Improvements:
Eliminated Python-level loops using rolling covariance.
~10–20× faster for 1000+ assets.
Added examples/benchmarks/benchmark_factors.py for reproducible performance testing.
isuue closses : #12