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Awesome List
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
GitHub stars and default-branch commits for wilsonfreitas/awesome-quant.
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Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
基于Python的开源量化交易平台开发框架
FinceptTerminal is a modern finance application offering advanced market analytics, investment research, and economic data tools, designed for interactive exploration and data-driven decision-making in a user-friendly environment.
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Portfolio Optimization in Python
Algorithmic Trading in Python with Machine Learning
A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
🚀 💸 Easily build, backtest and deploy your algo in just a few lines of code. Trade stocks, cryptos, and forex across exchanges w/ one package.
A program for financial portfolio management, analysis and optimisation.
An Open Source Portfolio Backtesting Engine for Everyone | 面向所有人的开源投资组合回测引擎
Python-based framework for backtesting trading strategies & analyzing financial markets [GUI :neckbeard:]
A composable, real time, market data and trade execution toolkit. Built with Elixir, runs on the Erlang virtual machine
A fixed income library for pricing bonds and bond futures, and derivatives such as interest rate swaps (IRS), cross-currency swaps (XCS) and FX swaps. Contains tools for full curveset construction with market standard optimisers and automatic differentiation (AD) and risk sensitivity calculations including delta and cross-gamma.
Entropy Pooling views and stress testing combined with Conditional Value-at-Risk (CVaR) portfolio optimization in Python.
Terminal ETF research & portfolio analytics via SEC EDGAR and IBKR