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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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Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
AKShare is an elegant and simple financial data interface library for Python, built for human beings! 开源财经数据接口库
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
Python training for business analysts and traders
Statsmodels: statistical modeling and econometrics in Python
Automatic extraction of relevant features from time series:
The backtesting engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.
Probabilistic time series modeling in Python
Visualizer for pandas data structures
150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data
Algorithmic Trading in Python with Machine Learning
ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.
Repository containing notebooks of my posts on Medium
Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.
fastquant — Backtest and optimize your ML trading strategies with only 3 lines of code!
Python AutoML for Trading Systems and Sports Betting
Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy
A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.
AI-powered SDK featuring algorithmic trading, backtesting, deployment on 100+ exchanges, and multiple optimization engines.
An open and opinionated trading platform using productive & familiar open source libraries and tools for strategy research, execution and operation.
Open-source investment analytics platform bridging academic research and retail finance. Features include portfolio risk decomposition [Fama-French Five Factor Model], retirement sustainability modeling [Block Bootstrap Monte Carlo], max drawdown/CVaR dashboards, and risk-return optimisation [Markowitz, Ledoit-Wolf] via an intuitive user interface.
Automatic discovery of non-trivial statistical truths from 500+ public time series — mutual information, Granger causality, FDR correction