{"id":35665,"date":"2025-05-23T09:39:55","date_gmt":"2025-05-23T01:39:55","guid":{"rendered":"https:\/\/www.1ai.net\/?p=35665"},"modified":"2025-05-20T12:41:35","modified_gmt":"2025-05-20T04:41:35","slug":"%e8%82%a1%e7%a5%a8ai%e9%87%8f%e5%8c%96%e5%88%86%e6%9e%90%e5%b7%a5%e5%85%b7%e6%9c%89%e9%82%a3%e4%ba%9b%ef%bc%9f%e5%88%86%e4%ba%ab3%e4%b8%aaai%e8%82%a1%e7%a5%a8%e9%87%8f%e5%8c%96%e5%bc%80%e6%ba%90","status":"publish","type":"post","link":"https:\/\/www.1ai.net\/en\/35665.html","title":{"rendered":"What are those AI quantitative stock analysis tools? Share 3 AI Stock Quantitative Open Source Tools"},"content":{"rendered":"

Write an introduction AI Stock<\/a>quantitative trading<\/a>ofOpen Source Projects<\/a>This post will be GitHub quantitative trading related open source projects to search and organize, to share with you. They are:<\/p>\n

1. Equity AI Managers<\/strong><\/p>\n

2. Local quantitative trading solutions<\/strong><\/p>\n

3. Quantitative backtesting framework to support real-time trading<\/strong><\/p>\n

Stocks AI Trader<\/strong><\/p>\n

Number of GitHub Stars: 2,900<\/p>\n

The open source project called AI Quantitative Trading Trader is currently getting 3K Stars on GitHub.<\/p>\n

\"75376e35j00swjm8r0068d000u000fom\"<\/p>\n

He can be understood asAI to help you speculate in the stock market one-stop toolbox, from learning, simulation to the real market, this open source project have corresponding chapters to explain the equivalent of teaching white and stockholders to engage in an AI speculation assistant.<\/strong><\/p>\n

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Look at the above screenshot , is the core content of this open source project , comes with a stock knowledge base and real-world cases , like the stock market manual to teach you the basics .<\/strong><\/p>\n

Colleagues can also simulate training, including traditional methods: such as averaging strategies, KDJ indicators, but also AI quantitative strategies, through machine learning \/ deep learning algorithms to find hidden behind the law.<\/strong><\/p>\n

Open source address: https:\/\/github.com\/charliedream1\/ai_quant_trade<\/p>\n

Local quantitative trading programs<\/strong><\/p>\n

Number of GitHub Stars: 8,700<\/p>\n

QUANTAXIS is a local quantitative trading solution that supports task scheduling, distributed deployment, and is used by many investors and quantitative developers in China, and has received 8.7K Stars on GitHub.<\/p>\n

\"89d9c832j00swjm8r0056d000u000ffm\"<\/p>\n

This open source quantitative financial strategy framework, integrated data acquisition, cleaning and storage, analysis and backtesting, visualization and trading review of the whole process, support for multiple markets (stocks, futures, options, foreign exchange , etc.) and multi-language collaboration.<\/p>\n

A full-process localization solution for small and medium-sized teams with an active developer community and a continuously updated open source ecosystem.<\/p>\n

Open source address: https:\/\/github.com\/yutiansut\/QUANTAXIS<\/p>\n

Quantitative trading backtesting framework<\/strong><\/p>\n

Number of GitHub Stars: 17,100<\/p>\n

Backtrader is a Python-based open source quantitative trading backtesting framework that is feature-rich, flexible and easy to use.<\/p>\n

\"00a9a54ej00swjm8r004gd000u000ebm\"<\/p>\n

This framework focuses on strategy backtesting and live trading, using this framework you can interface CSV, database and other data sources or interface to some real-time trading interface (Interactive Brokers, Oanda, etc.) for strategy backtesting and live trading.<\/p>\n

This way you can focus on the trading strategy logic instead of the underlying infrastructure. Provides an intuitive API design that allows users to quickly implement trading logic by writing strategy classes.<\/p>\n

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It's very empowering.<\/p>\n

This open source project to work with the following study open source notes , this note is the use of Chinese detailed organization of the Backtrader textbook .<\/p>\n

\"fd02b1a2j00swjm8r004pd000u000epm\"<\/p>\n

Systematically introduces the characteristics of this framework, strategy construction, data structure, backtesting trading, etc., and thoroughly masters the use of quantitative artifacts.<\/p>\n

Open source address: https:\/\/github.com\/mementum\/backtrader
\nTextbook: https:\/\/www.backtrader.com\/home\/helloalgotrading\/
\nTutorial: https:\/\/github.com\/jrothschild33\/learn_backtrader<\/p>","protected":false},"excerpt":{"rendered":"

Write an introduction to the AI stock quantitative trading open source projects, this post will GitHub quantitative trading related open source projects to search and organize, to share with you. They are: 1. stock AI trader 2. local quantitative trading solutions 3. support for real-time trading quantitative backtesting framework stock AI trader GitHub received Star number: 2,900 This is called AI quantitative trading trader open source project, currently on GitHub received 3K Star. You can understand him as a one-stop toolkit to help you speculate on the stock market with AI, from learning, simulation to real trading, this open source project has corresponding chapters. This open source project has corresponding chapters for learning, simulation and real trading, which is equivalent to teaching white people and stockholders to engage in an AI stock speculation assistant. See<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[144],"tags":[2670,2284,6674],"collection":[],"class_list":["post-35665","post","type-post","status-publish","format-standard","hentry","category-baike","tag-ai","tag-2284","tag-6674"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/35665","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/comments?post=35665"}],"version-history":[{"count":0,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/35665\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/media?parent=35665"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/categories?post=35665"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/tags?post=35665"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/collection?post=35665"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}