Rust-first L3 limit order book backtesting engine with Python bindings for market microstructure research.
L3 limit order book replay beats OHLC backtesting, but only matters if you're serious quant.
Quantitative traders, market microstructure researchers, algorithmic trading teams
Backtrader · VectorBT · QuantConnect
I’ve been building a Rust-first backtesting engine for limit order book strategies and just open sourced the core engine.
Repo: https://github.com/chasemetoyer/Backtesting-Engine
The goal was to build something closer to how exchanges actually behave than typical OHLC-based backtesting frameworks.
Key features:
• L3 limit order book replay • deterministic event-driven matching engine • FIFO queue position modeling • Python strategy bindings for research workflows • parquet ingestion for high-volume datasets • replay trace tools for debugging strategy behavior
The core engine is written in Rust and exposed to Python via maturin. The idea is to combine Rust performance with Python-based research workflows.
Typical workflow:
1) Convert raw exchange data (ex: CoinAPI LIMITBOOK files) into canonical engine parquet 2) Run deterministic replay through the Rust engine 3) Execute strategies through Python bindings 4) analyze fills, equity curves, and risk metrics
The repo currently includes several example microstructure strategies such as:
• queue imbalance scalper • flow microprice scalper • cumulative flow momentum
I built this mainly to experiment with order book strategies where queue position and microstructure actually matter.
Would love feedback from people working on:
• market microstructure research • HFT simulation • Rust systems engineering • trading infrastructure
Especially interested in ideas for improving:
• event replay throughput • strategy interface design • multi-asset simulation
Thanks!
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