An enterprise-grade, JIT-compiled time-series engine stress-tested on 100M+ row datasets.
Project description
SigFast
🚀 Performance triples-sigfast has been stress-tested on 100-Million-row NumPy arrays. It scales linearly and maintains efficient memory usage, making it suitable for enterprise-level financial data and scientific research.
A high-performance time-series processing library built for Data Scientists and Physicists. Uses Numba JIT and C-level multithreading to bypass the Python GIL.
Why SigFast?
Pandas is great, but it runs on a single thread. When analyzing millions of data points (IoT sensors, high-frequency trading, astrophysics), Pandas becomes a bottleneck. SigFast distributes the math across all your CPU cores.
Benchmark (10 Million Data Points - Rolling Window):
- Pandas
.rolling().mean(): ~1.20 seconds - SigFast Engine: ~0.03 seconds (40x Faster)
Installation
pip install triples-sigfast
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