perf(factors): optimize rolling operators and bench path#376
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Closes #339 Original implementation from #342 by @shadowinlife. Maintainer integration keeps the bottleneck and numpy operator fast paths, vectorized base equity calculation, and bench parallelism while initializing large bench inputs once per worker process, preserving custom registry injection by using sequential mode for injected registries, removing the unused Hypothesis dev dependency, and quieting all-NaN ts_rank warnings.
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Summary
bottleneck/NumPy fast paths for hot factor operators:ts_rank,ts_argmax,ts_argmin, anddecay_linear.registry=injection by using sequential mode for injected registries, handle invalid worker env values, suppress all-NaNts_rankruntime warnings, and remove the unusedhypothesisdev dependency.Credit
Original performance implementation from #342 by @shadowinlife. This maintainer integration keeps the contribution and tightens the parallel execution and dependency edges before merge.
Closes #339
Refs #342
Validation
python -m pip install --target /tmp/vibe-pr342-bn-only --no-deps "bottleneck>=1.3.7" -qPYTHONPATH=/tmp/vibe-pr342-bn-only:agent pytest agent/tests/test_factor_operators.py agent/tests/test_bench_parallel.py agent/tests/test_equity_regression.py -q-> 50 passedVIBE_TRADING_DISABLE_BOTTLENECK=1 PYTHONPATH=/tmp/vibe-pr342-bn-only:agent pytest agent/tests/test_factor_operators.py -q-> 38 passedpython -m compileall -q agent/backtest/engines/base.py agent/scripts/bench_performance.py agent/src/factors/_backend.py agent/src/factors/base.py agent/src/factors/bench_runner.pygit diff --checkPYTHONPATH=/tmp/vibe-pr342-bn-only:agent python agent/scripts/bench_performance.py