Python financial application programming self-study strategy

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This issue features Python financial application programming course materials

Including : Python and finance, data analysis, pricing and quantitative investment, software materials used, courseware, code

What are the most competitive skills of our time? The CEO of JPMorgan Chase tells you --- programming. All asset management analysts new to JPMorgan this year had to learn a programming language, Python.

"The only language of modern asset management is a programming language," said Mary Callahan Erodes, an asset manager at JPMorgan. Because of the forced popularization of programming languages, nowJPMorganof employees in the asset management department can use the same programming language as the technology department, which will help develop better wealth management products to serve customers. Next year, JPMorgan will expand mandatory learning to include numerical algorithms and machine learning.

With the rise of algorithmic trading, more and more traditional investment banks have begun to transform into technology companies. In addition to JPMorgan, other investment banks have also joined the coding learning team. The financial industry actually fully satisfies two important conditions for large-scale commercial algorithm application.

First, the quality and quantity of data must meet certain requirements, with particular emphasis on the entire data process and daily data update, which determines whether the foundation of the algorithm is solid;

Second, is there a relatively clear definition of the problem in the field.

Consistently known for its high-quality intern programGoldman Sachs GroupPublished a survey of 2,500 people inGoldman SachsIn the summer intern survey, when asked "Which language will be more important in the future" in your opinion, 72% of the 2,500 outstanding young people born in the 80s and 90s surveyed around the world chose Python.

Programming is becoming the most popular or must-have skill for young people and even the whole world.Goldman SachsIntern Adam Korn bluntly stated that "it is difficult for fund managers who want to engage in trading or analysis to survive without programming."

Currently, Goldman Sachs has about 9,000 computer engineers, accounting for 1/3 of Goldman's total employees. Next up will be investment banking. Traditionally, this field requires strong interpersonal skills, butGoldman SachsThere are 146 different steps required to be taken in any one IPO, many of which can be accomplished algorithmically.

According to the latest survey by efiancialcareers, Java and Python are the two hottest programming languages on Wall Street right now:

Java has been the hottest programming language on Wall Street for many years. Java engineers are found in everything from low-latency processing applications to order management systems or risk assessment platforms, and Java is also well-suited for data simulation and modeling. Furthermore, both Java and JavaScript (front-end design) are key languages when it comes to building user-friendly, fast-loading secure websites.

In addition to the strong demand for Java engineers, the other reason is that Wall Street has high requirements for the skills and qualifications of Java developers. According to the statistics of Jay Gaines & Company, the average number of applicants for Java engineer positions is only 7, which is far lower than For engineers in other languages, the salary and benefits of Java engineers are also about 10% higher on average than other developers.

Python is a rising star on digital Wall Street, and Python is ideal for developing analytical tools and quantitative analysis models that are critical to the trading strategies of investment banks and hedge funds.

One of the advantages of Python is that it is easy to get started, and the development speed is faster than traditional languages . Jared Butler, director of North American fintech recruiting at Selby Jennings, believes that Python will surpass Java in the investment banking world for three reasons:

First of all, Python's code efficiency is high, 10 lines of Python code can complete the work of 20 lines of C++ code, and the probability of errors is lower. With increased regulation and the spread of best practices, Python's performance and usability are increasingly recognized.

Second, Python will become more popular as more and more technologists appear on the banking side. Because Python allows developers to better collaborate with analysts and researchers on projects.

Third, Python is an excellent scripting language, and its applications are getting wider and wider. Especially with the increasing importance of big data, Python and Scala will play a more critical role.

However, Python developers are not very competitive in terms of compensation, which is also because Python is too easy to get started. Many HR managers don't even mention Python in their skill requirements anymore because learning Python is fairly easy for experienced developers.

This material course introduces the basics of using Python for data analysis and financial application development. The course starts with an introduction to simple financial applications and walks you through the basics of Python.

Skills used include Python, probability and statistics, stochastic analysis, Hadoop and NoSQL, financial derivatives pricing, event-driven quantitative investment systems

Course Catalog

Chapter 1 Overview of Python and Financial Applications

Chapter 2 Basic Data Types and Data Structures of Python

Chapter 3 Python Data Visualization

Chapter 4 Financial Time Series Analysis

Chapter 5 Input and Output Operations

Chapter 6 Improving Python Efficiency

Chapter 7 Mathematical Tools

Chapter 8 Stochastic Analysis Stochastic Analysis

Chapter 9 Statistical Analysis

Chapter 10 Numerical Analysis Techniques

Chapter 11 Manipulating Excel with Python