Boost Python Performance with PyO3 and Rust

Have you tried using PyO3? It is a library that helps integrate Rust with Python. It allows you to either expose Rust code as a Python extension module or embed the Python interpreter directly into a Rust application. A common usecase is when you have built your application in Python and start running into bottlenecks. Because Python being a dynamically typed interpreted language running on the GIL, has inherent performance limitations. I am sure some of you out there have already ran into it. Rust can give you a significant uplift here. Rather than rewriting the entire program, you profile your Python application, rewrite the hot paths in Rust, compile it as a native extension, and let the Python interpreter consume it seamlessly. Libraries like Polars and Pydantic V2 are great real-world examples of this pattern. In HFT systems built in Rust, the usecase flips. You want the strategies and algorithms written in Python because they are easier to iterate on, backtest, and hand off to quants. But the execution layer needs to be fast and deterministic which is happening in microseconds. Here you embed the Python interpreter into the Rust binary, load your core algorithm logic at runtime, and let the Rust runtime drive execution while calling into Python only when needed. It's fun to do this! PyO3 paired with Maturin (for build tooling) makes both of these workflows surprisingly ergonomic. How effectively have you used PyO3 in your projects or organizations? . . . . #rust #python #pyo3 #HFT #programming

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