TopGit tracks charliedream1/ai_quant_trade on GitHub as part of the AI Tools family. The project has 6.7k stars. Stock AI Trader: 1-stop platform for learning, sim & live trading. Covers: stock basics, strategies, LLMs, factor mining, ML/DL/RL, graph nets, HFT, C++ deploy & JoinQuant code. 股票AI操盘手：一站式学习、模拟、实盘平台。涵盖：股票基础、策略、大模型、因子挖掘、机器学习/深度学习/强化学习、图网络、高频交易、C++部署及聚宽代码。
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🆕 Unsloth Reasoning Stock Forecasting LLM (code in this repo, detailed guide & model on the Planet)
2025.01.03
LLM-based Financial Market Analysis (video tutorial on the Planet or WeChat account)
📂 2023 Updates
Date
Feature
2023.04.09
StructBERT Market Sentiment Analysis
2023.03.28
RL Multi-Stock Trading: 53% Annualized Return
2023.02.28
ML Auto-Mining 5,000 Alphas & Stock Trend Prediction
2023.02.05
Stock Monitoring with Excel
2023.01.01
Local Deep RL Strategy
📂 2022 Updates
Date
Feature
2022.11.07
Wind Local Live Trading Simulation
2022.08.03
Basic Backtest Framework + Double Moving Average Strategy
3. 📖 Introduction
Target Audience
🏢 Institutional Investors
👨💻 Retail Traders (with programming background)
🌱 Retail Traders (no programming background)
Project Structure
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
├── docs ............... Usage documentation for this repo
├── 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
├── quant_brain ........ Core algorithm library
├── runtime ............ Model deployment and real-world usage
├── tools .............. Auxiliary tools
└── README.md
Each egs_*/ sub-project now carries its own requirements.txt —
pick the project you want to run, then pip install -r requirements.txt
inside its directory. There is no global dependency file at the repo root.
4. 🌟 awesome_projects (Highly Recommended)
📁 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!
🎯 What Is This?
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.
✨ Four Major Features
🔍 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
Traditional / DL / RL / report reproduction / portfolio
🛠️ 06-assistant-tools
Assistant Tools
K-line patterns, monitoring, office tools
📊 07-visualization
Visualization
Quant charts and visualization
🧠 08-knowledge-graphs
Knowledge Graph
Traditional & LLM-based solutions
⚡ 09-high-frequency-trading
High-Frequency Trading
Crypto HFT
🤖 10-llm
LLM in Finance
FinGPT, FinRobot, TradingAgents, Agent, RAG, etc.
🌐 11-research-platforms
Research Platforms
Free quant platform collection
💻 12-trading-platforms
Trading Platforms
EasyTrader, VN.Py, etc.
📄 13-research-reports
Research Reports
Report reproduction & sources
🔥 Highlighted Recommendations
🤖 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.
5. 🚀 Quick Start
This repository is not packaged as a Python package yet. Please clone the entire project, then navigate to each egs directory for detailed usage instructions and principle explanations.
# 1. Clone the repo
git clone https://github.com/charliedream1/ai_quant_trade.git
# 2. Pick a project and install its own deps (no root requirements.txt)
cd egs_trade/vanilla/double_ma
pip install -r requirements.txt
# 3. Follow the project's own README to get started
Python baseline: 3.8+ on x86_64. Apple Silicon (M1/M2) users, please follow the conda setup notes in each project's README.
6. 📊 Local Quantitative Strategies
📁 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:
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:
No.
Strategy
Paper
1
Prototype
—
2
FinRL Tutorial 0 - NeurIPS2018
Practical Deep Reinforcement Learning Approach for Stock Trading
Backtest Results:
No.
Strategy
Market
Annualized Return
Max Drawdown
Sharpe Ratio
1
Prototype
China A-Shares
—
—
—
2
FinRL Tutorial 0 - NeurIPS2018
US Dow Jones 30
53.1%
-10.4%
2.17
📐 Traditional Strategies
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.
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
Portfolio Management 7-Lesson Course
7. 💰 Live Trading
📁 Code Directory: egs_trade
Live Trading Simulation
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
8. 🛠️ Trading Assistant Tools
📁 Code Directory: egs_aide
Stock Monitoring with Excel V1
👀 Hard to be spotted while monitoring stocks
📋 Customizable stock watchlist
⚡ Use Excel for fast calculation and data processing
"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
📈 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
Streamlit Real-Time Market Monitor
🌐 Web-based real-time market dashboard
9. ⛏️ Alpha Mining
📁 Code Directory: egs_alpha
Alpha Mining Strategies
No.
Strategy
Paper
1
ML Auto-Mining 5,000 Alphas & Stock Trend Prediction
—
Alpha Library
No.
Alpha Library
1
alpha101
2
stockstats
3
ta_lib
10. 💾 Data Processing
📁 Code Directory: egs_data
Detailed usage of various common data sources
Unified data source interface
11. 📝 Text Analysis
📁 Code Directory: egs_fin_nlp
No.
Tool
1
StructBERT Market Sentiment Analysis
12. 🤖 LLM Applications
📁 Code Directory: egs_llm
No.
Tool
1
LLM-based Financial Market Analysis (video tutorial on the Planet or WeChat account)
2
Unsloth Reasoning Stock Forecasting Model Training (open-source code; detailed guide & model on the Planet)
13. 📚 Programming & AI Basics
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 ⭐
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.
14. 🌐 Online Research Platforms
📁 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
🔗 JoinQuant Platform · Welcome to follow me: 量客攻城狮
For detailed strategy introductions and source code, please click the corresponding strategy link
(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
16. 🎁 Companion Resources
This code repository adheres to the principle of parallel paid and free offerings.
💎 Paid Resources — Knowledge Planet
Register on the official Knowledge Planet website for guaranteed user rights. Planet Content Introduction
🔥 As low as 0.1 RMB/day | Exclusive Crash Course | Painless Learning | 📺 Video Tutorials | Q&A |
Open-Source Pitfall Guide | Self-Developed Tool Code | 3-Minute Video Paper Speed | Library |
One of the lowest-priced quant planets on the web | 3-day no-questions-asked refund
👇 Scan the QR code below or click the link to enter the Planet and view more detailed introductions 🎏
AI速成营: In-depth supplements on programming, LLMs, AI basics, principles and finance practice & job-seeking cases, complementary to AI智投星球
Planet Introduction:
Planet Content Introduction
New User Guide
👇 Scan the QR code for a more detailed introduction to the "Planet" (there are funny comics inside)!
🎯 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.
🆓 Free Resources
WeChat Official Account
🔥 Real-time updates on the latest news
🎁 Follow and like any article, DM the admin to receive a free exquisite quant material package!
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.
17. 💖 Support Me
Your support is the driving force for me to move forward. Even "0.1 RMB" makes me happy. Thank you for your support (^o^)/
18. 💬 Discussion
Feel free to start a discussion in Github Discussions.
19. 🐛 Technical Support
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
Yes — charliedream1/ai_quant_trade ships under the Apache-2.0 license, which makes its source code freely readable (and, depending on license terms, forkable and reusable). Source: github.com/charliedream1/ai_quant_trade.
What else is in the AI Tools space?
charliedream1/ai_quant_trade is tracked by TopGit under the AI Tools category, alongside 11 GitHub-tagged topics. Trending and Topics pages list peer repositories of comparable stars and language.
What is charliedream1/ai_quant_trade?
charliedream1/ai_quant_trade (charliedream1/ai_quant_trade) is a Jupyter Notebook project on GitHub. From the project's own README: Stock AI Trader: 1-stop platform for learning, sim & live trading. Covers: stock basics, strategies, LLMs, factor mining, ML/DL/RL, graph nets, HFT, C++ deploy & JoinQuant code. 股票AI操盘手：一站式学习、模拟、实盘平台。涵盖：股票基础、策略、大模型、因子挖掘、机器学习/深度学习/强化学习、图网络、高频交易、C++部署及聚宽代码。
What license does charliedream1/ai_quant_trade use?
charliedream1/ai_quant_trade is released under the Apache-2.0 license. Always verify the LICENSE file directly on GitHub for the authoritative terms — license strings can be edited out of sync with a project's actual stance.
Where can I see charliedream1/ai_quant_trade in action?
The project maintains a homepage at https://charliedream1.github.io/ai_quant_trade/. The README tab on this page also usually contains screenshots and a quickstart.
Where do I read more about charliedream1/ai_quant_trade?
This TopGit page is a snapshot — the READ ME tab shows the project's own README content (links stripped, images preserved). The GitHub repository at github.com/charliedream1/ai_quant_trade is the definitive source.
Why is charliedream1/ai_quant_trade categorized under AI Tools?
TopGit places charliedream1/ai_quant_trade in the AI Tools category based on its GitHub topics and description (tagged: "cpp", "jupyter-notebook", "keras"). Categories are assigned from real repository metadata, not editorial guesswork.
Read full README in the tab above.
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