nautilus_trader

Why we included this project

Quant developers and algorithmic trading teams who want one codebase for research and live execution will find this engine unusually practical. It pairs a Rust-native core with a Python control plane, so the Python side handles strategy logic and orchestration while the hot path runs in a compiled, type-safe runtime; configuration also stays in Python. The same deterministic event-driven time model drives backtesting and live trading, so a strategy validated on historical tick, bar, and order-book data can move to production without being rewritten. It is asset-class agnostic, with adapters spanning crypto exchanges, FX, equities, futures, options, and betting venues, and it is fast enough to train reinforcement-learning or evolutionary-strategy trading agents. For teams that need low-latency, multi-venue execution with research-to-live parity, this is a serious alternative to building a bespoke internal stack.