oryon
Unverified Other strategy on Multi by lucasinglese. BotFinder score 18 out of 100.
Production-grade feature and target engineering for quantitative research. Rust core. Python API.
Source: github
BotFinder analysis pending.
oryon
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-ser
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