{"repo":"lucasinglese/oryon","free":true,"listed":false,"github":"https://github.com/lucasinglese/oryon","clone":"git clone https://github.com/lucasinglese/oryon.git","description":"Production-grade feature and target engineering for quantitative research. Rust core. Python API.","language":"Rust","stars":50,"topics":["python","quantitative-finance","quantitative-trading","rust","time-series"],"license":"MIT","category":"trading","readme_excerpt":"Production-grade feature and forward target engineering for quantitative research. Rust core. Python API. Streaming and batch, same object. --- The problem Most feature engineering libraries take a full DataFrame and return a DataFrame. That works in research. In live trading, it forces you to keep a growing history in memory and recompute every feature on every new bar. This doesn't scale and isn't how production systems work. A second, quieter problem: research code and live code diverge. Any inconsistency between them is a bug waiting to surface in production. Oryon solves both. Every feature is a stateful object with a fixed memory footprint. You feed it one bar at a time in live trading, or pass the full dataset in research. Same object, same Rust code, same output. --- Install No Rust toolchain required. Pre-built wheels for Linux, macOS, and Windows. --- Quick start Live trading, one bar at a time: Research, full dataset at once: The same feature pipeline ( fp ) defined above builds your training dataset. See the full quickstart for details. --- Benchmarks Rust core, Apple M-series. Python adds a constant 150 ns per call on top. Features: per update() call Feature w=20 w=200 --- --- --- Ema , SimpleReturn , LogReturn Developed by Lucas Inglese","default_branch":null,"files":null,"tree":[],"storefront":"/r/lucasinglese","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/lucasinglese/oryon/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}