Python Quantitative Trading

Name: Python Quantitative Trading
ISBN: 9787121361401

张彦桥 / 梁雷超Publishing House of Electronics Industry · ChinaOct 2020

3.2

Word count: 150K words
Chapters: 67
ISBN: 9787121361401

Report an error Find this book

Summary

Designed to get beginners quickly started with quantitative trading, this book explains in a step-by-step manner the knowledge needed to enter the field of quantitative trading, along with a wealth of development and trading techniques, making it highly practical. vn.py is an institutional-grade quantitative trading software; mastering the principles of the vn.py framework and becoming proficient in its use will help beginners rapidly build their own quantitative trading systems. The Python language has very powerful data analysis libraries, giving it a natural advantage for developing trading strategies, and its ease of learning has also made it popular among beginners. This book is written around the popular combination of Python + vn.py, starting from the origins and development of quantitative trading, then briefly introducing the basics of Python quantitative programming and analyzing the vn.py architecture in detail, before thoroughly and comprehensively introducing CTA strategies, turtle strategies, and the development process for new strategies.

Editor's review

Python Quantitative Trading is a hands-on guide focused on building quantitative trading systems with the Python technology stack. Written by Zhang Yanqiao and published by Publishing House of Electronics Industry, the book has received quite positive reviews in the field of technical practice since its release. By integrating data analysis, strategy modeling, and automated execution, it lays out a clear path from theoretical understanding to hands-on development.

In terms of structure, the book does not settle for superficial conceptual introductions but goes deep into the core workflow of quantitative trading. It systematically explains how to use libraries such as Pandas and NumPy for financial data processing and analysis, how to backtest and optimize strategies based on popular frameworks such as Backtrader, and finally how to connect to live trading interfaces and control risk. This progressive, closely linked arrangement reflects the author's precise grasp of the reader's learning curve. The book contains a large number of runnable code examples; these are not simple syntax demonstrations but are designed around real trading scenarios, offering high reference and reuse value.

The author, Zhang Yanqiao, has extensive practical experience in the fintech field, which ensures the book's practicality. The viewpoints and cases in the book focus more on engineering implementation and strategy logic than on subjective predictions of market movements; this restrained, rational technical perspective is precisely the essence of quantitative trading. For developers hoping to bridge the gap between programming and finance, the book provides a solid scaffold.

Overall, this is a technical book that is "practice-oriented and strongly logical." Its value lies in breaking down seemingly esoteric quantitative trading into a series of executable, verifiable technical steps, lowering the barrier to entry in this field. It is a solid starting point for building a personal knowledge system for quantitative trading.

Who it's for

A good fit for

Developers who know basic Python syntax and hope to move into the field of financial data analysis
Individual investors with a basic understanding of financial markets such as stocks and futures who are eager to use programmatic methods to test trading ideas
Students in finance- or computer-related majors seeking a reference textbook for applying theoretical knowledge to quantitative trading practice
IT professionals interested in fintech who want to understand the basic composition and implementation of quantitative trading systems

Not a good fit for

Complete beginners with no programming background who expect to quickly obtain "get-rich code"
Readers seeking in-depth financial theory derivations or academic research on asset pricing models

Reading tips

Read the chapters in order and be sure to run and modify every piece of code in the book; this is the key to understanding the quantitative workflow
While reading, work with a specific financial market instrument (such as a stock or an index) for data acquisition and strategy writing to make the experience more immersive
Focus on understanding the logic and limitations of the strategy backtesting section, and recognize the essential difference between strong historical backtest results and live profitability

About the publisher

Publishing House of Electronics Industry China · Founded in 1982

The Publishing House of Electronics Industry is a comprehensive publisher distinguished by its focus on electronic information and related fields. Judging from the more than 3,000 titles in its catalogue, its publishing covers programming and design, artificial intelligence, computer science, finance and economics, photography, and social sciences, attending both to cutting-edge technology and to general readers. Representative titles include Factor Investing: Methods and Practice, x86 Assembly Language: From Real Mode to Protected Mode, The RISC-V Reader, Artificial Intelligence: Speech Recognition, Understanding and Practice, Phased Array Radar Technology, Radar Target Characteristics, and 100 Go Mistakes and How to Avoid Them, most of which centre on technical methods and engineering practice. Its authors include Shi Chuan, Li Zhong, Wang Xiaobo, David Patterson, Andrew Waterman, and Yu Zihao, among others from China and abroad. Owing to limited public information, no further publishing features are elaborated here.

版权信息
内容简介
前言
第1章量化交易速览
1.1 为何选择量化交易
1.2 量化交易的先驱们
1.3 美国量化投资的发展历史
1.4 中国量化投资的发展历史
1.5 国内常用的量化交易策略
1.6 宽客
1.7 宽客的两大阵形：P宗与Q宗
1.8 宽客的3种职能分类
1.9 宽客的四大派系
第2章 Python量化编程基础
2.1 Python运行环境搭建
2.2 数据
2.3 函数
2.4 条件判断
2.5 循环
2.6 类和实例
2.7 NumPy与Pandas
2.8 scikit-learn机器学习库
2.9 Matplotlib绘图库
第3章 vn.py入门
3.1 vn.py介绍
3.2 搭建vn.py运行环境
3.3 VnTrader界面功能介绍
3.4 vn.py架构
3.5 底层接口
3.6 事件引擎
3.7 上层应用
第4章在vn.py中实现CTA策略
4.1 数据解决方案
4.2 K线生成模块
4.3 K线管理模块
4.4 CTA策略模块
4.5 策略回测模块
第5章经典CTA策略
5.1 双均线策略
5.2 Dual Thrust策略
5.3 AtrRsi策略
5.4 金肯特纳通道策略
5.5 布林带通道策略
5.6 跨时间周期策略
5.7 多信号组合策略
第6章海龟策略本地化实证
6.1 海龟策略速览
6.2 本地化实现困境与解决方案
6.3 vn.py实现的海龟策略
6.4 品种选择验证
6.5 长短周期信号检验
6.6 上一笔赢利过滤检验
6.7 手续费、滑点测试
6.8 单位头寸限制检验
6.9 关于海龟策略的其他研究方向
第7章新策略实战
7.1 开发新的策略
7.2 多策略的组合回测
7.3 模拟测试
7.4 真实交易环境
7.5 实际操作注意事项
附录A 主流交易品种
A.1 股票
A.2 期货
A.3 期权
A.4 外汇
参考文献

FAQ

How much math and finance knowledge does this book require?

The book focuses on engineering implementation and does not demand advanced mathematics, but readers need basic statistical concepts (such as mean and standard deviation) and a basic understanding of financial markets (such as candlesticks and order types). Complex financial models are not the focus of this book.

Which Python version are the code examples based on? Are they outdated?

The book is mainly based on the Python 3.x environment and uses mature, stable libraries such as Pandas and NumPy. The core data processing and strategy-building logic remains valid over the long term, but for the API parts involving specific third-party trading interfaces, readers should note that they may change as platforms are updated.

Can I achieve stable profits after reading this book?

Absolutely not. The core value of this book is teaching how to use Python tools to build and test trading strategies—that is, the methodology of "how to do it." The effectiveness of the strategies themselves, risk management, and psychological factors in live trading are more complex and critical topics that no single book can guarantee. Readers should regard it as a tool for technical introduction and thinking training.

Comments

Find this book

This site is an index: you start “Search all sources” in your browser, and the entries it lists all point to third-party sites; please judge their availability yourself.

About the author

张彦桥 Author

Zhang Yanqiao is an author about whom limited public information is available. One work is credited to this name in the library: Python Quantitative Trading, commonly classified under programming and design. Judging from the title and category, the book is intended for readers interested in quantitative trading and seeks to combine Python programming with financial trading strategies, making it an applied work that brings programming techniques into a specific domain. Books of this kind usually need to cover language fundamentals, data processing, and strategy implementation, and the author tends to emphasize putting programming methods into operable trading scenarios. Because only this one work is included in the library, the author's nationality, dates of birth and death, educational background, and professional experience are not clearly recorded, making it difficult to describe the author's broader writing trajectory or personal style. Readers interested in the practical use of Python in quantitative trading may find this book a reference for relevant ideas, but judgments about its depth and scope should still be based on the original text and further materials.

梁雷超 Author

Liang Leichao is an author about whom limited public information is available. His one known representative work is Python Quantitative Trading, classified under programming and design and aimed at readers who wish to use Python to enter the field of quantitative trading. Judging from the subject matter, the author focuses on the practical application of a programming language to financial data processing and the implementation of trading strategies, rather than on programming syntax in general. The writing tends to combine tools and methods with real trading scenarios, helping readers understand how quantitative trading is actually carried out. Because only this single work is included in the database, the author's full background, research interests and broader body of writing cannot be confirmed; readers may treat this book as a starting point for understanding his writing style, to be supplemented if more material becomes available.