
Python Quant Finance: 280+ Algorithms
280 runnable Python implementations for quant finance
What it does
A complete Python quantitative finance library — 280 standalone runnablepy files covering options pricing (Black-Scholes, Heston, SABR), portfolio optimization (Markowitz, HRP, Black-Litterman), ML/AI trading (LSTM, XGBoost, RL), risk management (VaR, CVaR), fixed income, DeFi/crypto, and more. Each algorithm has a companion DOCX tutorial with formulas and charts. All code is in English, globally applicable.
Does the same job
all alternatives →- QIQuantra.io, a quantitative finance API made with Quantlib2017 · quantra.io · ▲90
- PAProphet – A financial micro-framework in Python2014 · blog.michaelsu.io · ▲152
- MOMove over Yfinance – Quantel, the Most Powerful Finance Library Python2021 · github.com · ▲15


- PAPyBroker – Algotrading in Python with Machine Learning2023 · github.com · ▲70
Hello, I am excited to share PyBroker with you, a free and open-source Python framework that I developed for creating algorithmic trading strategies, including those that utilize machine learning. With PyBroker, you can easily develop and fine-tune trading rules, build powerful ML models, and gain valuable insights into your strategy's performance. Some of the key features of PyBroker include: - A super-fast backtesting engine built using NumPy and accelerated with Numba. - The ability to create and execute trading rules and models across multiple instruments with ease. - Access to…
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