Python Algorithm Trading Part 1: Python Data Analysis for Algorithm Trading

First, popularity in the job market!

As of now (2024), the undisputed #1 programming popularity is Python. Programming popularity is also linked to opportunities in the job market. Learning Python will provide you with more opportunities.

PYPL (Popularity of Programming Language)

Second, then why Pandas?

This is a question about the essence of data analysis. The essence of data analysis, called EDA (Exploratory Data Analysis), is the ability to process raw data into a desired form. The tool that can do this EDA most effectively is Pandas.

Third, why study Python with financial data?

Did you know that Wes McKinney , the creator of the Pandas library essential for data analysis in Python, was a quant working in the financial sector? Stock data is an ideal analysis target for applying complex and diverse analysis techniques and statistical models.

Pairs Trading, which will be implemented in this lecture, defines stock pairs that show similar patterns and uses statistical methodology and machine learning to determine algorithmic investments.

Fourth, in general data analysis classes, we write scripts in a functional manner.
Why study object-oriented data analysis?

• Data is dynamic: investment strategies that worked in the past may not be suitable today.

• Respond to continuous change: Your code needs to be updated periodically to accommodate changing data characteristics.

  
  

For this reason, learning object-oriented programming in data analysis is an important skill for effective code management and productivity improvement beyond simple function implementation. Once you become familiar with object-oriented grammar, you can quickly understand the code below in a few seconds. The ability to interpret object-oriented grammar is a magic like speed reading in reading .

Fifth, Python is slow? Is that really true? The answer is Yes or No.

Python can be sped up in two ways. In deep learning, GPUs can be used to speed up calculations, while in data analysis , parallel processing of the CPU can be used to speed up the process .

This lecture will guide you on how to effectively utilize CPU cores .

Sixth, configure the analytics environment on an Azure virtual machine.

• Using Azure Virtual Machines in your analytics environment :

  • In this lecture, we will build a stable Python analysis environment using Azure virtual machines.

  • Minimize variability in local environments and provide a standardized learning environment.

  • Learn how to set up a virtual environment and manage packages using Anaconda.

• Alternatives if you have trouble using the cloud :

  • We also share a separate notebook to enable implementation of Python analysis using Kaggle Notebooks.

  • The Kaggle platform offers the advantage of being able to start analyzing data right away without any installation or configuration.

  • This allows for flexible learning in a variety of environments.

The last seventh, Python Crash Course, is easy to understand even without basic knowledge.

• The basic Python syntax and concepts required for this course are intensively covered in “Section 4. Python Crash Course for Financial Analysis.”

• This section starts with the basics for those new to Python, and dives deep into the core syntax and functions needed for financial data analysis.

• This will give students a solid foundation to follow along smoothly with the more complex analysis and programming content that is presented later in the course.

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Things to note before taking the class