Awesome GitHub RepositoriesBacktesting Workload Distribution

Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies across global equities, futures, and cryptocurrencies. It integrates an event-driven backtesting engine, a multi-market execution gateway for order routing, and a quantitative data pipeline for ingesting and storing multi-asset market data. The system features a Rust-accelerated financial library that utilizes Apache Arrow for high-performance technical indicator calculation and zero-copy data processing. It provides a containerized infrastructure model designed for orchestrati

backtesting.py is a Python trading backtesting framework used to simulate trading strategies against historical price data to evaluate performance and risk. It includes a technical trade simulator, a quantitative performance analyzer, and a financial strategy optimizer. The framework features a parallel strategy simulator that distributes execution across multiple processor cores to reduce computation time. It also provides tools for strategy parameter optimization, allowing the identification of performant settings through the use of heatmaps and metrics. The system covers trade execution m

pybroker is a Python algorithmic trading framework and quantitative technical analysis library designed for developing, testing, and optimizing trading strategies using historical market data. It functions as a trading strategy backtester and a financial performance evaluator, providing a structured environment to simulate trading rules and analyze their statistical reliability. The framework distinguishes itself through a market data integration layer that handles the fetching and caching of historical price data from external providers. It incorporates an event-driven backtesting engine and