Python金融编程第二版：数据驱动金融大师

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"Python for Finance 2nd" 是一本由 Yves Hilpisch 撰写的书籍，专注于在金融领域应用 Python 进行数据驱动的分析。这本书是第二版，被多个高等教育机构用作 Python 量化金融课程的教材，并且与 CQF（量化金融证书）课程相关。对于从事量化金融工作的人来说，这本书被认为是一个必备的参考资料。本书的内容涵盖了以下几个重要的知识点： 1. **Python基础知识**：书中会介绍Python编程的基础，包括语法、数据结构（如列表、元组、字典）、控制流（循环、条件语句）、函数定义以及模块导入等，这些都是进行量化金融分析的必备技能。 2. **金融数据分析**：Python 在金融中的一个重要应用是处理和分析大量金融数据。读者将学习如何使用Python库如Pandas进行数据清洗、处理缺失值、时间序列分析、统计建模等。 3. **数值计算与优化**：Python提供了诸如NumPy和SciPy这样的库，用于进行数值计算和优化问题，如线性代数操作、微积分、最优化算法等，这对于金融模型的建立和求解至关重要。 4. **金融建模**：书里会涉及金融工程的基本概念，如资产定价模型（如Black-Scholes模型）、风险管理和对冲策略、投资组合优化等，这些都需要用Python进行建模和模拟。 5. **机器学习与人工智能**：随着大数据和AI的发展，Python在金融领域的应用也扩展到了机器学习和深度学习。读者可以学习如何利用Python库如Scikit-Learn、TensorFlow进行预测建模、异常检测、分类和聚类等任务。 6. **数据可视化**：Python的Matplotlib和Seaborn库提供了强大的数据可视化功能，读者可以学习如何创建专业且具有洞察力的金融图表，如股票价格走势、风险敞口分布等。 7. **实时数据获取与API接口**：书中可能还会讲解如何通过Python从各种金融数据源（如Yahoo Finance、Quandl、Bloomberg等）获取实时和历史数据，以及如何使用API接口进行数据交互。 8. **Python生态系统**：介绍Python在金融领域的一些关键库，如Quantopian的zipline库用于回测交易策略，PyAlgoTrade、Backtrader等用于算法交易，以及yfinance、pandas_datareader等用于金融数据获取。 9. **分布式计算与云计算**：随着数据量的增长，理解如何利用分布式计算框架（如Apache Spark）和云计算服务（如AWS、Google Cloud）进行大规模金融数据分析也是现代金融专业人士需要掌握的技能。 10. **代码实践与案例研究**：书中的例子和实战项目将帮助读者将理论知识应用到实际问题中，提升解决金融问题的能力。 "Python for Finance 2nd" 是一本深入浅出地介绍如何使用Python进行量化金融分析的权威著作，无论是对于金融专业人士还是对金融感兴趣的技术人士，都是一个宝贵的学习资源。

operations and their trading and investment performances. At the beginning of 2018,

the first dedicated book on “financial machine learning” was published, which under‐

scores this trend. Without a doubt, there are more to come. This leads to what might

be called AI-first finance, where flexible, parameterizable ML and DL algorithms

replace traditional financial theory—theory that might be elegant but no longer very

useful in the new era of data-driven, AI-first finance.

Python is the right programming language and ecosystem to tackle the challenges of

this era of finance. Although this book covers basic ML algorithms for unsupervised

and supervised learning (as well as deep neural networks, for instance), the focus is

on Python’s data processing and analysis capabilities. To fully account for the impor‐

tance of AI in finance—now and in the future—another book-length treatment is

necessary. However, most of the AI, ML, and DL techniques require such large

amounts of data that mastering data-driven finance should come first anyway.

This second edition of Python for Finance is more of an upgrade than an update. For

example, it adds a complete part (Part IV) about algorithmic trading. This topic has

recently become quite important in the financial industry, and is also quite popular

with retail traders. It also adds a more introductory part (Part II) where fundamental

Python programming and data analysis topics are presented before they are applied

in later parts of the book. On the other hand, some chapters from the first edition

have been deleted completely. For instance, the chapter on web techniques and pack‐

ages (such as Flask) was dropped because there are more dedicated and focused

books about such topics available today.

For the second edition, I tried to cover even more finance-related topics and to focus

on Python techniques that are particularly useful for financial data science, algorith‐

mic trading, and computational finance. As in the first edition, the approach is a

practical one, in that implementation and illustration come before theoretical details

and I generally focus on the big picture rather than the most arcane parameterization

options of a certain class, method, or function.

Having described the basic approach for the second edition, it is worth emphasizing

that this book is neither an introduction to Python programming nor to finance in

general. A vast number of excellent resources are available for both. This book is

located at the intersection of these two exciting fields, and assumes that the reader

has some background in programming (not necessarily Python) as well as in finance.

Such readers learn how to apply Python and its ecosystem to the financial domain.

The Jupyter Notebooks and codes accompanying this book can be accessed and exe‐

cuted via our Quant Platform. You can sign up for free at http://py4fi.pqp.io.

My company (The Python Quants) and myself provide many more resources to mas‐

ter Python for financial data science, artificial intelligence, algorithmic trading, and

computational finance. You can start by visiting the following sites:

xiv | Preface

• Our company website

• My private website

• Our Python books website

• Our online training website

• The Certificate Program website

From all the offerings that we have created over the last few years, I am most proud of

our Certificate Program in Python for Algorithmic Trading. It provides over 150 hours

of live and recorded instruction, over 1,200 pages of documentation, over 5,000 lines

of Python code, and over 50 Jupyter Notebooks. The program is offered multiple

times per year and we update and improve it with every cohort. The online program

is the first of its kind, in that successful delegates obtain an official university certifi‐

cate in cooperation with htw saar University of Applied Sciences.

In addition, I recently started The AI Machine, a new project and company to stand‐

ardize the deployment of automated, algorithmic trading strategies. With this project,

we want to implement in a systematic and scalable fashion what we have been teach‐

ing over the years in the field, in order to capitalize on the many opportunities in the

algorithmic trading field. Thanks to Python—and data-driven and AI-first finance—

this project is possible these days even for a smaller team like ours.

I closed the preface for the first edition with the following words:

I am really excited that Python has established itself as an important technology in the

financial industry. I am also sure that it will play an even more important role there in

the future, in fields like derivatives and risk analytics or high performance computing.

My hope is that this book will help professionals, researchers, and students alike make

the most of Python when facing the challenges of this fascinating field.

When I wrote these lines in 2014, I couldn’t have predicted how important Python

would become in finance. In 2018, I am even happier that my expectations and hopes

have been so greatly surpassed. Maybe the first edition of the book played a small

part in this. In any case, a big thank you is in order to all the relentless open source

developers out there, without whom the success story of Python couldn’t have been

written.

Conventions Used in This Book

The following typographical conventions are used in this book:

Italic

Indicates new terms, URLs, and email addresses.

Preface | xv

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