基于Python的开源量化交易平台开发框架

Investment Research for Everyone, Everywhere.

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

A curated list of practical financial machine learning tools and applications.

🔎 📈 🐍 💰 Backtest trading strategies in Python.

基金投资管理回测引擎

Find big moving stocks before they move using machine learning and anomaly detection

🚀 💸 Easily build, backtest and deploy your algo in just a few lines of code. Trade stocks, cryptos, and forex across exchanges w/ one package.

Algorithmic Trading in Python with Machine Learning

Python-based framework for backtesting trading strategies & analyzing financial markets [GUI ]

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

Invest Alchemy is a trading assistant focused on ETF portfolios.

Basic Discounted Cash Flow library written in Python. Automatically fetches relevant financial documents for chosen company and calculates DCF based on specified parameters.

Qlib-Server is the data server system for Qlib. It enable Qlib to run in online mode. Under online mode, the data will be deployed as a shared data service. The data and their cache will be shared by all the clients. The data retrieval performance is expected to be improved due to a higher rate of cache hits. It will consume less disk space, too.

FinHack®，一个易于拓展的量化金融框架，它在当前版本中集成了数据采集、因子计算、因子挖掘、因子分析、机器学习、策略编写、量化回测、实盘接入等全流程的量化投研工作。

Crawler for Fundamental analysis platform for BOVESPA stocks, generating a score for each share according to the selected criteria on the indicators.

Python-based portfolio / stock widget which sources data from Yahoo Finance and calculates different types of Value-at-Risk (VaR) metrics and many other (ex-post) risk/return characteristics both on an individual stock and portfolio-basis, stand-alone and vs. a benchmark of choice (constructed with wxPython)

Python client library to download historical data from finam.ru

📈 PatternPy: A Python package revolutionizing trading analysis with high-speed pattern recognition, leveraging Pandas & Numpy. Effortlessly spot Head & Shoulders, Tops & Bottoms, Supports & Resistances. For experts & beginners. #TradingMadeEasy 🔥