ai_quant_trade
股票AI操盘手:从学习、模拟到实盘,一站式平台。包含股票知识、策略实例、因子挖掘、传统策略、机器学习、深度学习、强化学习、图网络、高频交易、C++部署和聚宽实例代码等,可以方便学习、模拟及实盘交易
Stars: 1214
The ai_quant_trade repository is a comprehensive platform for stock AI trading, offering learning, simulation, and live trading capabilities. It includes features such as factor mining, traditional strategies, machine learning, deep learning, reinforcement learning, graph networks, and high-frequency trading. The repository provides tools for monitoring stocks, stock recommendations, and deployment tools for live trading. It also features new functionalities like sentiment analysis using StructBERT, reinforcement learning for multi-stock trading with a 53% annual return, automatic factor mining with 5000 factors, customized stock monitoring software, and local deep reinforcement learning strategies.
README:
AI炒股教程 | 本地策略 | 辅助操盘 | 因子挖掘 | 文本分析 | 数据处理 | 在线投研平台 | 使用文档
如果喜欢本项目,或希望随时关注动态,请给我点个赞吧 (页面右上角的小星星),欢迎分享到社区!
股票AI操盘手
- 一站式平台:从学习、模拟到实盘
- 炒股策略:因子挖掘、传统策略、机器学习、深度学习、强化学习、图网络、高频交易等
- 提供辅助操盘工具:辅助盯盘、股票推荐
- 实盘部署工具:C++/CPU/GPU等部署
时间 | 特性 | 代码路径 |
---|---|---|
2023.04.09 | StructBERT市场情绪分析 | egs_fin_nlp/emotion_analysis/01_StructBert_Binary_Class |
2023.03.28 | 强化学习多股票交易:年化收益53% | egs_trade/rl/a002_finRL/a01_Stock_NeurIPS2018 |
2023.02.28 | 机器学习自动挖掘5000个因子及股票趋势预测 | egs_alpha/auto_alpha/tsfresh |
2023.02.05 | 定制化看盘软件 | egs_aide/看盘神器/v1 |
2023.01.01 | 本地深度强化学习策略 | egs_trade/rl/a001_proto_sb3 |
2022.11.07 | Wind本地实盘模拟 | egs_trade/real_bid_simulate/wind |
2022.08.03 | 基础回测框架 + 双均线策略 | egs_trade/vanilla/double_ma |
- 1. 简介
- 2. 使用
- 3. 本地量化平台
- 4. 实盘
- 5. 辅助操盘
- 6. 因子挖掘
- 7. 数据获取
- 8. 文本分析
- 9. AI-实践指南
- 10. 在线投研平台
- 关注我
- 讨论
- 技术支持
- 常见问题
- 引用
-
本系统适合的人群:
- 机构
- 散户
- 有编程基础
- 无编程基础
-
本仓库代码结构和内容简介
ai_quant_trade ├── ai_wiki (AI全栈教学知识,以Markdown, Jupyter Notebook汇总知识体系) │ ├── 基础:操作系统、软件编程、数学基础 │ ├── 进阶:机器学习、深度学习、强化学习、图网络 │ ├── 实战:量化交易与投资、模型部署 ├── docs (本仓库使用说明文档) ├── egs_aide (辅助操盘工具) │ ├── 看盘神器 ├── egs_alpha (因子库) ├── egs_data (数据获取及处理) │ ├── wind (Wind万得数据处理) ├── egs_fin_nlp (文本分析) │ ├── emotion_analysis (情感分析) ├── egs_online_platform (在线投研平台策略) │ ├── 优矿_Uqer │ ├── 聚宽_JoinQuant ├── egs_trade (本地量化炒股策略) │ ├── paper_trade (实盘模拟) │ ├── wind万得实盘模拟 │ ├── rl (强化学习炒股) │ ├── vanilla (传统规则类策略) ├── quant_brain (核心算法库) ├── runtime (模型的部署和实际使用) ├── tools (辅助工具) ├── requirements.txt └── README.md
本仓库暂未进行封装成python包,拷贝整个项目源代码,
-
安装所需库
pip install -r requirements.txt
-
查看egs策略文件夹下文档, 并运行对应实例即可
代码详细参见目录:egs_trade
可在本地可构建一套独立的量化交易系统,包含的策略:
- AI策略
- 强化学习
- 图网络
- 深度学习
- 机器学习
- 高频交易
- 因子挖掘
- 传统规则类策略
代码详细参见目录:egs_trade/rl
自从2017年AlphaGo与柯洁围棋大战之后,深度强化学习大火。
相比于机器学习和深度学习, 强化学习是以最终目标为导向 (以交互作为目标) , 而很多其他方法是考虑孤立的子问题 (如“股价预测”,“大盘预测”,“交易决策”等) , 这并不能直接获得交互的动作, 比如“命令机器人炒股盈利”, 这个任务包含了“股价预测”,”大盘预测”等等, 而强化学习的目标则是“完成命令者的任务”, 可以直接得到“炒股盈利”的一连贯动作。
-
样例介绍:
序号 策略 代码路径 论文 1 原型 egs_trade/rl/a001_proto_sb3 2 FinRL教程0-NeurIPS2018 egs_trade/rl/a002_finRL_tutorial/a01_Stock_NeurIPS2018 Practical Deep Reinforcement Learning Approach for Stock Trading (https://arxiv.org/abs/1811.07522) -
样例回测详情
序号 策略 市场 年化收益 最大回撤 夏普率 1 原型 中国A股 2 FinRL教程0-NeurIPS2018 美股道儿琼斯30 53.1% -10.4% 2.17
图网络可以更好的构建股票和股票之间的关系,同时关联股票、新闻、情绪等各类信息,能更好的挖掘全局关系网。
(构建中,尽请期待。。。)
自从2012年AlexNet在图像分类任务上,性能碾压传统机器学习性能后,深度学习大火, 随机开启第一波人工智能热潮。其主要用于股价和大盘的预测等。
(构建中,尽请期待。。。)
机器学习以统计学为基础,以其坚实的数据基础,可解性,数据依赖少,资源占用低,训练速度快,在表格任务上, 仍然可以追平深度学习等优势,任有其应用价值。
(构建中,尽请期待。。。)
(构建中,尽请期待。。。)
传统策略虽然看似昨日黄花,但其可操作性更强,仍又一定使用价值。深度学习和机器学习,往往需要配合规则使用。
参见目录:egs_trade/paper_trade
代码详细参见目录:egs_aide
序号 | 工具 | 代码路径 |
---|---|---|
1 | 定制化看盘工具 | egs_aide/看盘神器/v1 |
代码详细参见目录:egs_alpha
序号 | 策略 | 代码路径 | 论文 |
---|---|---|---|
1 | 机器学习自动挖掘5000个因子及股票趋势预测 | egs_alpha/auto_alpha/tsfresh |
序号 | 因子库 |
---|---|
1 | alpha101 |
2 | stockstats |
3 | ta_lib |
- 各类常见数据源使用详解
- 统一数据源接口
序号 | 工具 | 代码路径 |
---|---|---|
1 | StructBERT市场情绪分析 | egs_fin_nlp/emotion_analysis/01_StructBert_Binary_Class |
代码参见:ai_wiki
本部分代码独立同步至仓库AI-实践指南-
这里汇总了各种量化相关的平台、开源资源和知识。这里是一个丰富的知识仓库和导航地图。
这里将汇总包括量化投资,windows, linux, shell, vim, markdown,python, c++,机器学习数学基础,
leetcode(c++, python),机器学习、 深度学习、强化学习、图神经网络,语音识别、NLP和图像识别等基础知识
-
代码结构和内容简介
ai_wiki (AI全栈教学知识,以Markdown, Jupyter Notebook汇总知识体系) ├── 01_系统平台 │ ├── 基础:常用网站、通用工具 │ ├── 系统:Windows/Linux ├── 02_程序代码 │ ├── 编程:python, c, c++, 数据库, LeetCode │ ├── 实战:常用工具、常见问题汇总 ├── 03_数学基础(程序员必备数学知识) ├── 04_算法原理(传统算法,优化算法,遗传算法) ├── 05_机器学习(资源+原理+实战) ├── 06_深度学习(资源+原理+实战) ├── 07_强化学习(资源+原理+实战) ├── 08_图网络(资源+原理+实战) ├── 09_模型部署(资源+原理+实战) ├── 10_实践应用 │ ├── 01_开源平台 │ ├── 02_音频 (语音识别、唤醒、声纹、语音合成、语音增强) │ ├── 03_文本处理 │ ├── 04_时间序列 │ ├── 05_图像识别 ├── 11_面试 ├── 12_量化交易与投资 └── README.md
-
量化相关资源
序号 工具 路径 1 全网量化资源汇总 ai_wiki/12_量化交易与投资/01_资源
国内量化平台,如聚宽、优矿、米筐、果仁和BigQuant等,如果感兴趣,也可以自行尝试。
投研平台是为量化爱好者(宽客)量身打造的云平台,提供免费股票数据获取、精准的回测功能、 高速实盘交易接口、易用的API文档、由易入难的策略库,便于快速实现和验证策略。( 注:如下策略仅在所述回测段有效,没有进行详细的调优和全周期验证。另外,没有策略能保证全周期有效的, 如果实盘使用如下策略,请慎重使用)
欢迎在聚宽平台关注我:量客攻城狮
- 具体策略详细介绍和源码请单击如下对应策略链接访问查看
- 聚宽使用介绍请查看: egs_online_platform/聚宽_JoinQuant
- 该部分代码仅能在 聚宽平台 运行
-
股票量化策略
策略 收益 最大回撤 机器学习-动态因子选择策略 12.3% 38.93% 小市值+多均线量化炒股 58.4% 46.61% 龙虎榜-看长做短 41.82% 26.89% 强势股+趋势线判断+止损止盈 10.09% 21.449% -
股票分析研究
欢迎在 Github Discussions 中发起讨论。
欢迎在 Github Issues 中提交问题。
请查看文档常见问题
@misc{ai_quant_trade,
author={Yi Li},
title={ai_quant_trade},
year={2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/charliedream1/ai_quant_trade}},
}
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for ai_quant_trade
Similar Open Source Tools
ai_quant_trade
The ai_quant_trade repository is a comprehensive platform for stock AI trading, offering learning, simulation, and live trading capabilities. It includes features such as factor mining, traditional strategies, machine learning, deep learning, reinforcement learning, graph networks, and high-frequency trading. The repository provides tools for monitoring stocks, stock recommendations, and deployment tools for live trading. It also features new functionalities like sentiment analysis using StructBERT, reinforcement learning for multi-stock trading with a 53% annual return, automatic factor mining with 5000 factors, customized stock monitoring software, and local deep reinforcement learning strategies.
ChatGPT-Next-Web-Pro
ChatGPT-Next-Web-Pro is a tool that provides an enhanced version of ChatGPT-Next-Web with additional features and functionalities. It offers complete ChatGPT-Next-Web functionality, file uploading and storage capabilities, drawing and video support, multi-modal support, reverse model support, knowledge base integration, translation, customizations, and more. The tool can be deployed with or without a backend, allowing users to interact with AI models, manage accounts, create models, manage API keys, handle orders, manage memberships, and more. It supports various cloud services like Aliyun OSS, Tencent COS, and Minio for file storage, and integrates with external APIs like Azure, Google Gemini Pro, and Luma. The tool also provides options for customizing website titles, subtitles, icons, and plugin buttons, and offers features like voice input, file uploading, real-time token count display, and more.
llm-action
This repository provides a comprehensive guide to large language models (LLMs), covering various aspects such as training, fine-tuning, compression, and applications. It includes detailed tutorials, code examples, and explanations of key concepts and techniques. The repository is maintained by Liguo Dong, an AI researcher and engineer with expertise in LLM research and development.
Awesome-LLM-RAG-Application
Awesome-LLM-RAG-Application is a repository that provides resources and information about applications based on Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) pattern. It includes a survey paper, GitHub repo, and guides on advanced RAG techniques. The repository covers various aspects of RAG, including academic papers, evaluation benchmarks, downstream tasks, tools, and technologies. It also explores different frameworks, preprocessing tools, routing mechanisms, evaluation frameworks, embeddings, security guardrails, prompting tools, SQL enhancements, LLM deployment, observability tools, and more. The repository aims to offer comprehensive knowledge on RAG for readers interested in exploring and implementing LLM-based systems and products.
BlueLM
BlueLM is a large-scale pre-trained language model developed by vivo AI Global Research Institute, featuring 7B base and chat models. It includes high-quality training data with a token scale of 26 trillion, supporting both Chinese and English languages. BlueLM-7B-Chat excels in C-Eval and CMMLU evaluations, providing strong competition among open-source models of similar size. The models support 32K long texts for better context understanding while maintaining base capabilities. BlueLM welcomes developers for academic research and commercial applications.
Qbot
Qbot is an AI-oriented automated quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It provides a full closed-loop process from data acquisition, strategy development, backtesting, simulation trading to live trading. The platform emphasizes AI strategies such as machine learning, reinforcement learning, and deep learning, combined with multi-factor models to enhance returns. Users with some Python knowledge and trading experience can easily utilize the platform to address trading pain points and gaps in the market.
HaE
HaE is a framework project in the field of network security (data security) that combines artificial intelligence (AI) large models to achieve highlighting and information extraction of HTTP messages (including WebSocket). It aims to reduce testing time, focus on valuable and meaningful messages, and improve vulnerability discovery efficiency. The project provides a clear and visual interface design, simple interface interaction, and centralized data panel for querying and extracting information. It also features built-in color upgrade algorithm, one-click export/import of data, and integration of AI large models API for optimized data processing.
DeepLearing-Interview-Awesome-2024
DeepLearning-Interview-Awesome-2024 is a repository that covers various topics related to deep learning, computer vision, big models (LLMs), autonomous driving, smart healthcare, and more. It provides a collection of interview questions with detailed explanations sourced from recent academic papers and industry developments. The repository is aimed at assisting individuals in academic research, work innovation, and job interviews. It includes six major modules covering topics such as large language models (LLMs), computer vision models, common problems in computer vision and perception algorithms, deep learning basics and frameworks, as well as specific tasks like 3D object detection, medical image segmentation, and more.
Speech-AI-Forge
Speech-AI-Forge is a project developed around TTS generation models, implementing an API Server and a WebUI based on Gradio. The project offers various ways to experience and deploy Speech-AI-Forge, including online experience on HuggingFace Spaces, one-click launch on Colab, container deployment with Docker, and local deployment. The WebUI features include TTS model functionality, speaker switch for changing voices, style control, long text support with automatic text segmentation, refiner for ChatTTS native text refinement, various tools for voice control and enhancement, support for multiple TTS models, SSML synthesis control, podcast creation tools, voice creation, voice testing, ASR tools, and post-processing tools. The API Server can be launched separately for higher API throughput. The project roadmap includes support for various TTS models, ASR models, voice clone models, and enhancer models. Model downloads can be manually initiated using provided scripts. The project aims to provide inference services and may include training-related functionalities in the future.
Awesome-ChatTTS
Awesome-ChatTTS is an official recommended guide for ChatTTS beginners, compiling common questions and related resources. It provides a comprehensive overview of the project, including official introduction, quick experience options, popular branches, parameter explanations, voice seed details, installation guides, FAQs, and error troubleshooting. The repository also includes video tutorials, discussion community links, and project trends analysis. Users can explore various branches for different functionalities and enhancements related to ChatTTS.
DISC-LawLLM
DISC-LawLLM is a legal domain large model that aims to provide professional, intelligent, and comprehensive **legal services** to users. It is developed and open-sourced by the Data Intelligence and Social Computing Lab (Fudan-DISC) at Fudan University.
AI-Competition-Collections
AI-Competition-Collections is a repository that collects and curates various experiences and tips from AI competitions. It includes posts on competition experiences in computer vision, NLP, speech, and other AI-related fields. The repository aims to provide valuable insights and techniques for individuals participating in AI competitions, covering topics such as image classification, object detection, OCR, adversarial attacks, and more.
END-TO-END-GENERATIVE-AI-PROJECTS
The 'END TO END GENERATIVE AI PROJECTS' repository is a collection of awesome industry projects utilizing Large Language Models (LLM) for various tasks such as chat applications with PDFs, image to speech generation, video transcribing and summarizing, resume tracking, text to SQL conversion, invoice extraction, medical chatbot, financial stock analysis, and more. The projects showcase the deployment of LLM models like Google Gemini Pro, HuggingFace Models, OpenAI GPT, and technologies such as Langchain, Streamlit, LLaMA2, LLaMAindex, and more. The repository aims to provide end-to-end solutions for different AI applications.
Nocode-Wep
Nocode/WEP is a forward-looking office visualization platform that includes modules for document building, web application creation, presentation design, and AI capabilities for office scenarios. It supports features such as configuring bullet comments, global article comments, multimedia content, custom drawing boards, flowchart editor, form designer, keyword annotations, article statistics, custom appreciation settings, JSON import/export, content block copying, and unlimited hierarchical directories. The platform is compatible with major browsers and aims to deliver content value, iterate products, share technology, and promote open-source collaboration.
ChuanhuChatGPT
Chuanhu Chat is a user-friendly web graphical interface that provides various additional features for ChatGPT and other language models. It supports GPT-4, file-based question answering, local deployment of language models, online search, agent assistant, and fine-tuning. The tool offers a range of functionalities including auto-solving questions, online searching with network support, knowledge base for quick reading, local deployment of language models, GPT 3.5 fine-tuning, and custom model integration. It also features system prompts for effective role-playing, basic conversation capabilities with options to regenerate or delete dialogues, conversation history management with auto-saving and search functionalities, and a visually appealing user experience with themes, dark mode, LaTeX rendering, and PWA application support.
AI0x0.com
AI 0x0 is a versatile AI query generation desktop floating assistant application that supports MacOS and Windows. It allows users to utilize AI capabilities in any desktop software to query and generate text, images, audio, and video data, helping them work more efficiently. The application features a dynamic desktop floating ball, floating dialogue bubbles, customizable presets, conversation bookmarking, preset packages, network acceleration, query mode, input mode, mouse navigation, deep customization of ChatGPT Next Web, support for full-format libraries, online search, voice broadcasting, voice recognition, voice assistant, application plugins, multi-model support, online text and image generation, image recognition, frosted glass interface, light and dark theme adaptation for each language model, and free access to all language models except Chat0x0 with a key.
For similar tasks
ai_quant_trade
The ai_quant_trade repository is a comprehensive platform for stock AI trading, offering learning, simulation, and live trading capabilities. It includes features such as factor mining, traditional strategies, machine learning, deep learning, reinforcement learning, graph networks, and high-frequency trading. The repository provides tools for monitoring stocks, stock recommendations, and deployment tools for live trading. It also features new functionalities like sentiment analysis using StructBERT, reinforcement learning for multi-stock trading with a 53% annual return, automatic factor mining with 5000 factors, customized stock monitoring software, and local deep reinforcement learning strategies.
moon-dev-ai-agents-for-trading
Moon Dev AI Agents for Trading is an experimental project exploring the potential of artificial financial intelligence for trading and investing research. The project aims to develop AI agents to complement and potentially replace human trading operations by addressing common trading challenges such as emotional reactions, ego-driven decisions, inconsistent execution, fatigue effects, impatience, and fear & greed cycles. The project focuses on research areas like risk control, exit timing, entry strategies, sentiment collection, and strategy execution. It is important to note that this project is not a profitable trading solution and involves substantial risk of loss.
For similar jobs
ai_quant_trade
The ai_quant_trade repository is a comprehensive platform for stock AI trading, offering learning, simulation, and live trading capabilities. It includes features such as factor mining, traditional strategies, machine learning, deep learning, reinforcement learning, graph networks, and high-frequency trading. The repository provides tools for monitoring stocks, stock recommendations, and deployment tools for live trading. It also features new functionalities like sentiment analysis using StructBERT, reinforcement learning for multi-stock trading with a 53% annual return, automatic factor mining with 5000 factors, customized stock monitoring software, and local deep reinforcement learning strategies.
qlib
Qlib is an open-source, AI-oriented quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It covers the entire chain of quantitative investment, from alpha seeking to order execution. The platform empowers researchers to explore ideas and implement productions using AI technologies in quantitative investment. Qlib collaboratively solves key challenges in quantitative investment by releasing state-of-the-art research works in various paradigms. It provides a full ML pipeline for data processing, model training, and back-testing, enabling users to perform tasks such as forecasting market patterns, adapting to market dynamics, and modeling continuous investment decisions.
jupyter-quant
Jupyter Quant is a dockerized environment tailored for quantitative research, equipped with essential tools like statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, numpy, pandas, sci-py, scikit-learn, yellowbricks, shap, optuna, ib_insync, Cython, Numba, bottleneck, numexpr, jedi language server, jupyterlab-lsp, black, isort, and more. It does not include conda/mamba and relies on pip for package installation. The image is optimized for size, includes common command line utilities, supports apt cache, and allows for the installation of additional packages. It is designed for ephemeral containers, ensuring data persistence, and offers volumes for data, configuration, and notebooks. Common tasks include setting up the server, managing configurations, setting passwords, listing installed packages, passing parameters to jupyter-lab, running commands in the container, building wheels outside the container, installing dotfiles and SSH keys, and creating SSH tunnels.
FinRobot
FinRobot is an open-source AI agent platform designed for financial applications using large language models. It transcends the scope of FinGPT, offering a comprehensive solution that integrates a diverse array of AI technologies. The platform's versatility and adaptability cater to the multifaceted needs of the financial industry. FinRobot's ecosystem is organized into four layers, including Financial AI Agents Layer, Financial LLMs Algorithms Layer, LLMOps and DataOps Layers, and Multi-source LLM Foundation Models Layer. The platform's agent workflow involves Perception, Brain, and Action modules to capture, process, and execute financial data and insights. The Smart Scheduler optimizes model diversity and selection for tasks, managed by components like Director Agent, Agent Registration, Agent Adaptor, and Task Manager. The tool provides a structured file organization with subfolders for agents, data sources, and functional modules, along with installation instructions and hands-on tutorials.
hands-on-lab-neo4j-and-vertex-ai
This repository provides a hands-on lab for learning about Neo4j and Google Cloud Vertex AI. It is intended for data scientists and data engineers to deploy Neo4j and Vertex AI in a Google Cloud account, work with real-world datasets, apply generative AI, build a chatbot over a knowledge graph, and use vector search and index functionality for semantic search. The lab focuses on analyzing quarterly filings of asset managers with $100m+ assets under management, exploring relationships using Neo4j Browser and Cypher query language, and discussing potential applications in capital markets such as algorithmic trading and securities master data management.
jupyter-quant
Jupyter Quant is a dockerized environment tailored for quantitative research, equipped with essential tools like statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, numpy, pandas, sci-py, scikit-learn, yellowbricks, shap, optuna, and more. It provides Interactive Broker connectivity via ib_async and includes major Python packages for statistical and time series analysis. The image is optimized for size, includes jedi language server, jupyterlab-lsp, and common command line utilities. Users can install new packages with sudo, leverage apt cache, and bring their own dot files and SSH keys. The tool is designed for ephemeral containers, ensuring data persistence and flexibility for quantitative analysis tasks.
Qbot
Qbot is an AI-oriented automated quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It provides a full closed-loop process from data acquisition, strategy development, backtesting, simulation trading to live trading. The platform emphasizes AI strategies such as machine learning, reinforcement learning, and deep learning, combined with multi-factor models to enhance returns. Users with some Python knowledge and trading experience can easily utilize the platform to address trading pain points and gaps in the market.
FinMem-LLM-StockTrading
This repository contains the Python source code for FINMEM, a Performance-Enhanced Large Language Model Trading Agent with Layered Memory and Character Design. It introduces FinMem, a novel LLM-based agent framework devised for financial decision-making, encompassing three core modules: Profiling, Memory with layered processing, and Decision-making. FinMem's memory module aligns closely with the cognitive structure of human traders, offering robust interpretability and real-time tuning. The framework enables the agent to self-evolve its professional knowledge, react agilely to new investment cues, and continuously refine trading decisions in the volatile financial environment. It presents a cutting-edge LLM agent framework for automated trading, boosting cumulative investment returns.