A Comprehensive Handbook on Trading & Quantitative Finance | 投资交易知识与量化实践手册
TradeNote is a concise handbook on trading and investment knowledge, with a focus on in-depth strategy analysis of derivatives (US stock options, Hong Kong warrants/CBBCs, etc.), the mathematical principles and practical applications of the Kelly Criterion, while also covering Python quantitative trading and AI Agent trading assistance. These notes are divided into five volumes with 16 chapters, approximately 135 pages in total.
This project is intended for the following users:
- 🎯 Beginners who want to systematically learn trading and investment knowledge
- 📈 Traders who want to deeply understand derivatives
- 🐍 Developers who want to implement quantitative trading strategies using Python
- 🤖 Technologists interested in AI Agent applications in financial trading
| Volume | Name | Chapters | Pages | Overview |
|---|---|---|---|---|
| I | Trading & Investment Fundamentals | Chapter 1 | ~10 pp | Global market overview, macro/fundamental/technical analysis, A-share/HK/US market comparison |
| II | Derivatives Markets ⭐ | Chapters 2–9 | ~60 pp | Options theory & pricing, buyer/seller strategies, advanced combination strategies, volatility trading, HK warrants/CBBCs |
| III | Python Quantitative Trading | Chapters 10–11 | ~18 pp | Python basics, data acquisition, backtesting systems, stock selection & timing, derivatives quant, live trading |
| IV | AI Agent Applications | Chapters 12–13 | ~18 pp | AI trading overview, AI stock selection & timing, AI execution & risk management, strategy evolution, building a personal trading assistant |
| V | Trading Psychology & Kelly Criterion ⭐ | Chapters 14–16 | ~26 pp | In-depth analysis of the Kelly Criterion, trading psychology, capital management, trading system construction |
- 🔥 Derivatives markets are the core: 8 chapters, ~60 pages, accounting for 44% of the content
- 🎯 Kelly Criterion as a standalone chapter: ~12 pages of systematic analysis, from mathematical derivation to derivatives practice and Python implementation
- 💻 Theory meets practice: Each section includes Python code examples and real-world case studies
- 🌏 Cross-market perspective: Covers A-share, Hong Kong, and US markets simultaneously
TradeNote/
├── .gitignore # Git ignore rules
├── README.md # Project documentation
├── TradeNote.md # Full notes text (all 16 chapters + appendices)
└── code/ # Standalone Python scripts
├── requirements.txt Dependency list
├── ch10_quant_basics/ Chapter 10: Quantitative Trading Basics & Backtesting
│ ├── 01_numpy_basics.py NumPy Sharpe ratio calculation
│ ├── 02_pandas_basics.py Pandas data processing & moving averages
│ ├── 03_matplotlib_basics.py Matplotlib visualization
│ ├── 04_data_fetch.py AKShare/yfinance data acquisition
│ ├── 05_data_clean_store.py Data cleaning & storage
│ ├── 06_simple_backtest.py Simple backtesting engine
│ └── 07_performance_metrics.py Performance evaluation metrics
├── ch11_quant_strategies/ Chapter 11: Quantitative Strategy Practice
│ ├── 01_pairs_trading.py Pairs trading (cointegration test)
│ └── 02_hmm_market_regime.py HMM market regime detection
├── ch12_ai_agent/ Chapter 12: AI Agent Trading Applications
│ └── 01_llm_financial_analysis.py LLM financial analysis prompt
├── ch13_ai_assistant/ Chapter 13: Building a Personal AI Trading Assistant
│ ├── 01_rag_knowledge_base.py RAG knowledge base construction
│ └── 02_strategy_generation.py Automated strategy generation
└── ch14_kelly_criterion/ Chapter 14: In-depth Analysis of the Kelly Criterion ⭐
├── 01_kelly_classic.py Classic/continuous/fractional Kelly
├── 02_kelly_multi_asset.py Multi-asset Kelly (with constrained optimization)
├── 03_kelly_monte_carlo.py Monte Carlo simulation comparison
├── 04_kelly_bayesian.py Bayesian dynamic Kelly
├── 05_kelly_derivatives.py Derivatives Kelly (tail risk/fat-tailed/robust correction)
└── 06_kelly_case_studies.py 5 real-world case studies
All content in this repository is for educational and reference purposes only and does not constitute any form of investment advice or recommendation.
- Financial markets carry risks; past performance does not guarantee future returns
- Any trading strategy may result in partial or total loss of principal
- Users should fully understand the relevant risks and make independent judgments based on their own financial situation and risk tolerance before engaging in actual trading
- The authors and contributors shall not be held liable for any direct or indirect losses arising from the use of the content in this repository
Investing involves risks; enter the market with caution.
Copyright (c) 2026 TradeNote Authors
This project uses a dual-license model:
| Scope | License | License File |
|---|---|---|
Code (all files under code/) |
GNU Affero General Public License v3.0 (AGPL-3.0) | LICENSE-AGPL |
Documentation (all other content, including TradeNote.md) |
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) | LICENSE-CC-BY-NC-SA |
- You may use, modify, and distribute the code under the terms of AGPL-3.0
- Copyleft: any modified version must also be licensed under AGPL-3.0
- Network use is distribution: if you run a modified version on a server and others interact with it, you must make the source code available
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made
- NonCommercial — You may not use the material for commercial purposes
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original