Pairs Trading Analysis with Python

Course details

Learn pairs trading analysis through a practical course with Python programming language using MSCI® countries indexes ETFs historical data for back-testing. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.

Become a Pairs Trading Analysis Expert in this Practical Course with Python

• Read or download MSCI® Countries Indexes ETF prices data and perform pairs trading analysis operations by installing related packages and running code on Python IDE.
• Identify pairs of international countries stock indexes prices with similar behavior based on fundamental factors of countries with relevant commodities sector and from specific region.
• Test pairs short term statistical relationship through their price returns correlation coefficient.
• Assess single pairs spread co-integration or long term statistical relationship through Engle-Granger test.
• Evaluate if individual price time series are non-stationary and their spread is stationary through Augmented Dickey-Fuller and Phillips-Perron tests.
• Calculate trading strategies for co-integrated pairs spreads.
• Generate entry or exit trading signals based on rolling spread normalized time series or z-score crossing certain bands thresholds.
• Produce long or short trading positions associated to trading signals.
• Assess trading strategies performance against buy and hold benchmarks using annualized return, annualized standard deviation, annualized Sharpe ratio metrics and cumulative returns chart.

Become a Pairs Trading Analysis Expert and Put Your Knowledge in Practice

Learning pairs trading analysis is indispensable for finance careers in areas such as quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors quantitative trading research and development.

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using MSCI® Countries Indexes ETF prices historical data for back-testing to achieve greater effectiveness. 

Content and Overview

This practical course contains 48 lectures and 6 hours of content. It's designed for all pairs trading analysis knowledge levels and a basic understanding of Python programming language is useful but not required.

At first, you'll learn how to read or download MSCI® Countries Indexes ETF prices historical data to perform pairs trading analysis operations by installing related packages and running code on Python IDE.

Then, you'll identify pairs for international countries stock indexes prices with similar behavior based on fundamental factors of countries with relevant commodities sector and from specific region. After that, you'll test pairs short term statistical relationship through their price returns correlation coefficient.

Next, you'll asses single pairs spread co-integration or long term statistical relationship through Engle-Granger test. Later, you'll evaluate whether individual price time series are non-stationary and their spread is stationary using Augmented Dickey-Fuller and Phillips-Perron tests. 

After that, you'll calculate co-integrated pair spreads trading strategies.  Next, you'll generate entry or exit trading signals based on rolling spread normalized time series or z-score crossing certain bands thresholds. Later, you'll produce long or short trading positions based on previously generated trading signals.

Finally, you'll measure trading strategies performance against individual paired stock indexes buy and hold benchmarks through annualized return, annualized standard deviation, annualized Sharpe ratio and cumulative returns chart