The QuantLib C++ library

Portfolio Optimization and Quantitative Strategic Asset Allocation in Python

ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.

Open Source Risk Engine

A high-performance C++ orderbook engine with microsecond-level latency, supporting multiple ordertypes, price-time priority matching and real time data integration from Binance

Companion code for "Modern Computational Finance: AAD and Parallel Simulations" (Antoine Savine, Wiley, 2018)

Low latency Limit Order Book and Matching Engine created in C++, able to handle over 1.4 million transactions per second.

Hybrid Event-driven and Vectorized Strategy Backtesting Library

Sub-microsecond bare-metal execution engine with deterministic replay, lock-free order path, and hardware-timestamped latency measurement.

QuantLib ported to C++17 and with all Boost dependency removed

Personal Project that implements a variety of HFT strategies in C++

Exchange-grade CLOB matching engine + microstructure analytics in C++20

A zero-alloc, compile-time hardened FIX engine built for sub-100ns execution.

High-performance limit order book engine with C++ core and Python SDK. Processes 20M+ msgs/sec with µs latency. Supports real crypto/equity data replay, spread/imbalance/impact analytics, and backtesting of VWAP, TWAP, POV, and market-making strategies with reproducible PnL and risk metrics.

QuantLib with AAD

REST API for QuantLib. This project aims to simplify the development of microservices for risk management and pricing of various financial instruments in the distributed environment using QuantLib

开放信号聚合ensemble框架。

Use fuzzy logic control with PL/EL in MultiCharts

C++ implementation of rBergomi model

Calculate technical factors for stocks in an efficient, maintainable and correct way