Hands-On Algorithmic Trading with Python

The pace of automation in the investment management industry has become frenetic in the last decade because of algorithmic trading and machine learning technologies. Industry experts estimate that 70%-80% of the daily trading volume in US equity markets is executed algorithmically i.e. by computer programs following a set of predefined rules. While all algorithmic trading is executed by computers, the rules for generating trades may be designed by humans or discovered by machine learning algorithms from data or both. Discipline in the face of grueling markets is a key success factor in trading and investing. Emotional irrationality, behavioral biases, inability to multitask effectively and slow execution speeds put manual trading by retail investors at a massive disadvantage.

Retail investors are aware of these disadvantages and there is considerable interest in algorithmic trading, especially using the Python ecosystem of libraries. This course is about taking the first step in leveling the playing field for retail equity investors and traders. It provides a critical overview of the concepts, process and technologies for developing your own proprietary algorithmic trading strategies. There is no secret formula or algorithm for trading and investing success - there are no substitutes for continual learning, experimentation, experience and hard work. This course will give you an edge in your explorations of this exciting and complex domain. Note that live trading is out of scope for the course.

What you’ll learn—and how you can apply it

By the end of this video course you’ll understand:

• The advantages and disadvantages of algorithmic trading
• The different types of models used to generate trading and investment strategies
• The fundamental concepts, processes, and technologies used for researching, designing, and developing them
• Pitfalls of backtesting algorithmic strategies
• The pros and cons of several risk-adjusted metrics for evaluating their performance
• The paramount importance of risk management and position sizing

And you’ll be able to:

• Use the Pandas library to import, analyze, and visualize data from market, fundamental, and alternative sources available for free on the web
• Design and automate your own specific investment and trading strategies in Python
• Use the tools you learn in this course to start creating, designing and prototyping your own algorithmic trading strategy in Python
• Backtest and evaluate the performance of your strategies using the Zipline library

This video course is for you because…

• You’re a retail equity investor, financial analyst, or trader who wants to develop algorithmic trading strategies and mitigate the disadvantages of emotional, manual trading
• You're looking for a insightful overview of the key financial, statistical and algorithmic trading concepts and technologies used by practitioners
• You have Python development experience and want to learn how to apply that to open up opportunities in the financial services and investment industry

Prerequisites:

• You should have basic experience trading and investing in equities
• You should have basic knowledge of Python and Pandas DataFrames
• You should be able to create a Google Colab document: https://colab.research.google.com/

Recommended preparation:

• “Algorithmic trading in less than 100 lines of Python code” (article)
• “Data Analysis with Pandas" (Chapter 5 in Python for Finance, 2nd Edition)
• “The Trinity of Errors in Financial Models” (article)

Recommended follow-up:

• Read Python for Algorithmic Trading (book)
• Read Machine Learning and Data Science Blueprints for Finance (book)
• Read Probabilistic Machine Learning for Finance and Investing (book)