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A curated list of insanely awesome libraries, packages, and resources for Quantitative Finance (Quants).
An elegant and simple Python library for fetching financial data from various sources, designed for quantitative research.
An open-source Python quantitative trading system supporting stocks, options, futures, and cryptocurrencies with integrated machine learning.
A curated list of practical financial machine learning tools, applications, and research repositories.
A curated list of awesome resources for applying LLMs and deep learning to financial market analysis and algorithmic trading.
A modular quantitative finance framework for data collection, analysis, strategy backtesting, and machine learning across multiple markets.
A Python live trading framework with a zipline-compatible API for executing algorithms via broker APIs.
A .NET library for computing technical indicators, building trading strategies, and backtesting automated stock trading systems.
A modern, high-performance technical analysis library built in Rust with Python and WebAssembly bindings.
A high-performance Rust library for simulating stochastic processes, with applications in quantitative finance, statistical modeling, and synthetic data generation.
A collection of quantitative trading research experiments exploring uncommon strategies and techniques through Jupyter notebooks.
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