charliedream1/ai_quant_trade

🤖 AI Quantitative Trading Bot

One-Stop AI Quantitative Trading Platform · From Learning & Simulation to Live Trading

中文版 | 日本語版

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• 1. ✨ Core Highlights
• 2. 🔥 New Features
• 3. 📖 Introduction
• 4. 🌟 awesome_projects (Highly Recommended)
• 5. 🧩 Integrated Quant Workspace (src)
• 6. 🚀 Quick Start
• 7. 📊 Local Quantitative Strategies
• 8. 💰 Live Trading
• 9. 🛠️ Trading Assistant Tools
• 10. ⛏️ Alpha Mining
• 11. 💾 Data Processing
• 12. 📝 Text Analysis
• 13. 🤖 LLM Applications
• 14. 📚 Programming & AI Basics
• 15. 🌐 Online Research Platforms
• 16. 📖 Quant Resource Collection
• 17. 🎁 Companion Resources
• 18. 💖 Support Me
• 19. 💬 Discussion
• 20. 🐛 Technical Support
• 21. ❓ FAQ
• 22. 📄 Citation

• 🏢 Institutional Investors
• 👨‍💻 Retail Traders (with programming background)
• 🌱 Retail Traders (no programming background)

ai_quant_trade
├── ai_notes ........... Financial quantitative trading knowledge (Markdown / Jupyter Notebook knowledge system)
│   ├── 资源 ........... Continuously collected excellent resources from across the web
│   ├── 实战 ........... Hands-on usage, frameworks, libraries, and pitfalls
│   └── 热点 ........... Financial market hot topics, tech hot topics, paper interpretations
├── src ................ Integrated workspace services and modular Gradio UI
├── egs ................ Workspace YAML configuration and entry points
├── tests .............. Workspace unit, integration and browser checks
├── doc ................ Design documents
├── docs ............... User guides, principles and the integrated documentation site
├── start.py ........... Workspace service launcher
├── egs_aide ........... Trading assistant tools (monitoring tools, etc.)
├── egs_alpha .......... Alpha library & alpha mining
├── egs_data ........... Data acquisition & processing (Wind / open-source tools)
├── egs_fin_nlp ........ Text analysis (sentiment analysis, etc.)
├── egs_llm ............ LLM applications (stock prediction / financial analysis)
├── egs_online_platform  Online research platform strategies (JoinQuant / Uqer)
├── egs_trade .......... Local quantitative stock trading strategies
│   ├── paper_trade .... Live trading simulation (Wind)
│   ├── rl ............. Reinforcement learning for trading
│   ├── ms_qlib ........ Microsoft Qlib framework
│   └── vanilla ........ Traditional rule-based strategies
├── runtime ............ Model deployment and real-world usage
├── tools .............. Auxiliary tools
└── README.md

The integrated workspace uses the root requirements.txt / pyproject.toml, plus optional model dependencies and project environments. Legacy egs_*/ examples keep their own dependency files; install only the environment for the project you choose.

📁 Directory: awesome_projects

⭐ The Highlight Section of This Repo: Curated, organized, and reviewed top-quality quantitative resources from across the entire web, all in one place!

This is the most essential "resource treasure trove" of this repo — we spent tremendous effort curating, organizing and reviewing tens of thousands of materials from across the web, classified by the full quantitative trading workflow so that you can quickly find the tools and materials you need, avoiding detours.

• 🔍 Best of the Best: Selected from the vast sea of resources across the web to avoid repeated pitfalls
• 📂 Clear Categorization: Classified by the full quant workflow (data → strategy → backtest → trading), easy to find what you need
• 📝 With Reviews: Not just links, but also pros/cons analysis and getting-started guides
• 🔄 Continuously Updated: Keeping up with technology development, continuously adding new resources

• 🤖 LLM Applications in Finance: Covers the latest research and practice including FinGPT, FinMem, Self-Reflective, Stock-chain, TradingAgents, FinRobot, etc.
• 🛠️ Skill Pack Collection (60+): Includes Chan Theory, technical analysis, quant statistics, fundamental analysis, crypto, macro analysis, etc.
• 📊 Backtest Framework Multi-dimensional Comparison: Hands-on comparison of Backtrader, Zipline, RQAlpha, PyAlgoTrade, QuantDigger, etc.
• 🔬 Research Report Reproduction: Selected high-quality broker research reports with reproduction code
• 💹 Complete Trading Strategies: From traditional double MA to RL and GNN, covering all types of strategy resources

💡 Usage Tip: Browse the awesome_projects directory as needed; each project entry now includes Stars, License, maintenance status, documentation/homepage and a value review.

The new src/ is a runnable research workspace: a modular Gradio UI, shared REST API and CLI, and one strategy/backtest pipeline. It connects market research, strategy construction, simulation, AI sessions and local knowledge in one project. The older egs_*/ examples remain available as independent projects.

Platform handbook · Workspace guide · Getting started · Architecture and principles · CLI reference

Nine modules in one page — overview → market research → ETF research → learning → collapse navigation.

Strategy construction and backtest review — load a graph preset, inspect and drag a node, configure a backtest, then inspect its output and frozen parameters.

AI sessions and knowledge references — attach a note, send with Enter, inspect the persisted status, ask a RAG question and find the Skill-report actions.

This README and the Japanese README share recordings of the English interface with English captions.

These GIFs are recorded from real clicks in an isolated mock-data service. Chat replies are explicitly mock; opening the Skill panel does not run a report. They demonstrate workflow and UI behavior, not investment performance or successful paid CLI/model execution. Recording details.

The workspace requires Python 3.10–3.14 (current patch releases; minimum 3.10.1) and the pinned root dependencies. Follow environment setup, then run from the repository root:

.venv-secure/Scripts/python.exe -B egs/entrypoints/check_runtime.py --role web
.venv-secure/Scripts/python.exe -B start.py --host 127.0.0.1 --port 8000

Open http://127.0.0.1:8000/app/. On Linux, create the declared environment and use .venv-secure/bin/python. Business settings live in YAML under egs/; private keys stay in local .env / .llm_env. See access and source-review rules before remote use or custom Python execution. Qlib signal analysis and the native Qlib Workflow are separate capabilities; the latter needs its own data and configuration. Broker/live execution also requires a configured channel and checks.

For the integrated src workspace, follow installation and quick start; run start.py from the repository root with Python 3.10–3.14. See workspace capabilities and workflows for the main operations.

For a legacy example, clone the repository, enter that egs_*/ project and use its own requirements and README. These examples may have different Python and optional-library requirements; they do not all share the workspace runtime.

📁 Code Directory: egs_trade

🎯 Each example comes with a comprehensive tutorial — from principles and usage to code walkthrough.

You can build an independent quantitative trading system locally, covering the following strategy types:

📁 Code Directory: egs_trade/rl

Since the AlphaGo vs. Ke Jie match in 2017, deep reinforcement learning has taken off.

Compared with ML and DL, RL is goal-oriented (uses interaction as the objective), while many other methods consider isolated sub-problems (such as "stock price prediction", "market prediction", "trading decision", etc.) and cannot directly obtain interactive actions. RL is directly oriented to "completing the commander's task" and can produce a sequence of actions.

Strategy List:

Backtest Results:

Although traditional strategies may seem outdated, they are more operable and still have some practical value. DL and ML often need to be combined with rules to work effectively.

1. Double Moving Average Strategy + Simple Handwritten Backtest Framework

   • Detailed Tutorial
   • Includes strategy code + self-built pure handwritten backtest framework
   • Includes nice plotting indicating buy/sell points
   • 🎯 Goal: Through this example, understand how to build a complete quantitative trading framework

2. Portfolio Management 7-Lesson Course

📁 Code Directory: egs_trade

1. Wind Local Live Trading Simulation: Double MA Strategy
   • Live trading simulation implemented with Wind software
   • Wind is often the data source of choice for major financial institutions; due to its high price, it's more suitable for institutions
   • 🏢 Target Users: Institutions

📁 Code Directory: egs_aide

1. Stock Monitoring with Excel V1

   • 👀 Hard to be spotted while monitoring stocks
   • 📋 Customizable stock watchlist
   • ⚡ Use Excel for fast calculation and data processing

2. "Stealth Stock Trading" Tool V2: Modular Monitoring System 🆕

   

   • 🏗️ Architecture Refactor: Upgraded from a single file to a modular excel_monitor package with Sheet Handler pattern; each Sheet refresh is isolated
   • 🔔 Alert Monitoring: Customizable upper/lower price/change limits; triggered rows turn red + popup reminders
   • 📈 K-Line Charts: Draw K-line charts with a button click in Excel (mplfinance candlestick + MA lines), no need to switch software
   • 💰 Capital Sentiment: New Sheet aggregating northbound capital + Weibo sentiment + news sentiment + Guba hot posts
   • 🔍 Stock Pool Selection: Built-in all A-shares; fuzzy search by code/name/pinyin initial + dropdown selection, no need to look up codes
   • 🔄 Multi-Source Fallback: qstock main source + akshare/Eastmoney/Tencent/NetEase/efinance backups; auto-switch when one fails
   • ⚙️ Hot Config Reload: YAML + Excel "Config" Sheet; watchlist/refresh interval can be hot-applied in Excel without restart
   • 🧪 Out-of-the-Box: One command auto-generates the Excel template (7 Sheets), no need to prepare stock lists in advance
   • ✅ Unit Tests: pytest covers core logic (156 items), stable for long-running sessions

3. Streamlit Real-Time Market Monitor

   • 🌐 Web-based real-time market dashboard

📁 Code Directory: egs_alpha

📁 Code Directory: egs_data

• Detailed usage of various common data sources
• Unified data source interface

📁 Code Directory: egs_fin_nlp

📁 Code Directory: egs_llm

For easier maintenance, the original ai_wiki directory content (system operations, programming basics, AI basics, AI practice, etc.) has been independently synchronized to the repo AI LLM Pitfall Guide.

It records a large number of problems and solutions encountered in actual development, and tracks cutting-edge technology developments in real-time. Welcome to follow and Star ⭐

✨ AI LLM Pitfall Guide

• Github: https://github.com/charliedream1/ai_wiki
• Gitee (Domestic Mirror): https://gitee.com/charlie1/ai_wiki.git
• Introduction: Share various practical cases, track cutting-edge technology developments, covering the full stack of AI knowledge — including LLMs, programming techniques, machine learning, deep learning, reinforcement learning, GNN, speech recognition, NLP, image recognition, etc.

📁 Code Directory: egs_online_platform

Domestic quant platforms such as JoinQuant, Uqer, MiRuo, GuoRen, and BigQuant can be tried by interested readers.

Research platforms are cloud platforms tailored for quant enthusiasts (quants), providing free stock data acquisition, accurate backtesting, high-speed live trading interfaces, easy-to-use API documentation, and a strategy library from easy to difficult, making it convenient to quickly implement and validate strategies.

⚠️ Note: The following strategies are only valid for the backtest periods described and have not been carefully tuned or verified for full-period performance. No strategy can guarantee effectiveness across all periods, so please be cautious when using them in live trading.

🔗 JoinQuant Platform · Welcome to follow me: 量客攻城狮

• For detailed strategy introductions and source code, please click the corresponding strategy link
• JoinQuant Usage Introduction: egs_online_platform/聚宽_JoinQuant
• This part of the code can only run on JoinQuant Platform

Stock Quant Strategies:

Stock Analysis Research:

• Hands-on Tutorial on "ML - Dynamic Multi-Factor Stock Selection" (with nanny-level tutorial)
• Dragon-Tiger List Data Filtering
• Concept Sector Data Acquisition and Stock Selection
• Detailed Explanation: Stock Data Acquisition & Graphical Analysis (with detailed code)

(Our article with 26,000+ reads on Zhihu) The Ultimate Collection of AI Stock Quantitative Trading Tools and Open-Source Projects

We have re-categorized and reviewed all tools, collected in the ai_notes folder for easy lookup.

🎯 In Development:

• Continuously reviewing all tools to facilitate selection
• Continuously documenting the pros and cons of each tool to form comparison tables for easy selection
• Continuously documenting usage: We don't make comprehensive tutorials, only list the most commonly used and practical features, so you can get started quickly

This code repository adheres to the principle of parallel paid and free offerings.

Register on the official Knowledge Planet website for guaranteed user rights. Planet Content Introduction

👇 Scan the QR code below or click the link to enter the Planet and view more detailed introductions 🎏

Planet Video Introduction:

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Planet Join Links:

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🎯 This repo will continue to be updated, but some code will be privately maintained and only visible on the Planet. The corresponding features will be noted in the repo.

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🔥 Real-time updates on the latest news 🎁 Follow and like any article, DM the admin to receive a free exquisite quant material package!

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Code Repository (Forever Free)

✨ AI Quantitative Trading Bot

• Github: https://github.com/charliedream1/ai_quant_trade
• Gitee (Domestic Mirror): https://gitee.com/charlie1/ai_quant_trade.git

Companion Repo

✨ AI驯龙笔记 (AI Taming Notes)

• Github: https://github.com/charliedream1/ai_wiki
• Gitee (Domestic Mirror): https://gitee.com/charlie1/ai_wiki.git
• Introduction: Share various practical cases, track cutting-edge technology developments, covering the full stack of AI knowledge — including LLMs, programming techniques, machine learning, deep learning, reinforcement learning, GNN, speech recognition, NLP, image recognition, etc.

Your support is the driving force for me to move forward. Even "0.1 RMB" makes me happy. Thank you for your support (^o^)/

    

Feel free to start a discussion in Github Discussions.

• Feel free to submit issues at Github Issues
• Join the Knowledge Planet for more technical support
  • AI智投星球: Focused on AI quantitative trading knowledge sharing
  • LLM Pitfall Guide: Focused on programming, LLMs, and AI application empowerment

Please see the documentation → FAQ

@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}},
}