Pairs Trading Analysis with Python
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Pairs Trading Analysis with Python

Learn pairs trading analysis from basic to expert level through a practical course with Python programming language.
5.0 (1 rating)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
10 students enrolled
Created by Diego Fernandez
Last updated 8/2017
English
Current price: $10 Original price: $50 Discount: 80% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 6.5 hours on-demand video
  • 7 Articles
  • 12 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • 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 return chart.
View Curriculum
Requirements
  • Python programming language is required. Downloading instructions included.
  • Python Distribution (PD) and Integrated Development Environment (IDE) are recommended. Downloading instructions included.
  • Practical example data and Python code files provided with the course.
  • Prior basic Python programming language knowledge is useful but not required.
Description

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

Who is the target audience?
  • Undergraduates or postgraduates who want to learn about pairs trading analysis using Python programming language.
  • Finance professionals or academic researchers who wish to deepen their knowledge in quantitative finance.
  • Experienced investors who desire to research pairs trading strategies.
  • This course is NOT about “get rich quick” trading strategies or magic formulas.
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Curriculum For This Course
48 Lectures
06:15:43
+
Course Overview
7 Lectures 28:17

In this lecture you will view course disclaimer and learn which are its objectives, how you will benefit from it, its previous requirements and my profile as instructor.

Preview 04:38

In this lecture you will learn that it is recommended to view course in an ascendant manner as each section builds on last one and also does its complexity. You will also study course structure and main sections (pairs identification, pairs spread co-integration, pairs trading signals, pairs strategies performance comparison).

Preview 01:48

In this lecture you will learn pairs trading analysis definition, Miniconda Distribution for Python 3.6 64-bit (PD) and Python PyCharm Integrated Development Environment (IDE) downloading websites.

Pairs Trading Analysis
04:26

In this lecture you will learn pairs trading analysis data reading or downloading into Python PyCharm Integrated Development Environment (IDE), data sources, code files originally in .TXT format that need to be converted in .PY format with multiple regression analysis computation instructions, Python packages Miniconda Distribution for Python 3.6 64-bit (PD) installation (numpy, pandas, pandas-datareader, matplotlib, statsmodels and arch) and related code (import <package> as <name>, read_csv(), DataReader()  functions).

Pairs Trading Analysis Data
17:16

Before starting course please download .TXT data file in .CSV format as additional resources.

Course Data File
00:03

Before starting course please download .TXT Python code files as additional resources.

Course Code Files
00:03

You can download .PDF section slides file as additional resources.

Course Overview Slides
00:02
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Pairs Identification
8 Lectures 42:42

You can download .PDF section slides file as additional resources.

Pairs Identification Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to single pairs identification.

Pairs Identification Overview
03:33

In this lecture you will learn single pair identification definition and main calculations (shift(), print(), corr(), subplots(), plot(), legend(), twinx(), suptitle(), show() functions).

Preview 07:20

In this lecture you will learn single pair identification definition and main calculations (shift(), print(), corr(), subplots(), plot(), legend(), twinx(), suptitle(), show() functions).

Single Pair Identification 2
06:02

In this lecture you will learn single pair identification definition and main calculations (shift(), print(), corr(), subplots(), plot(), legend(), twinx(), suptitle(), show() functions).

Single Pair Identification 3
06:36

In this lecture you will learn single pair identification definition and main calculations (shift(), print(), corr(), subplots(), plot(), legend(), twinx(), suptitle(), show() functions).

Single Pair Identification 4
06:26

In this lecture you will learn single pair identification definition and main calculations (shift(), print(), corr(), subplots(), plot(), legend(), twinx(), suptitle(), show() functions).

Single Pair Identification 5
06:20

In this lecture you will learn single pair identification definition and main calculations (shift(), print(), corr(), subplots(), plot(), legend(), twinx(), suptitle(), show() functions).

Single Pair Identification 6
06:23
+
Pairs Spread Co-Integration
14 Lectures 01:19:21

You can download .PDF section slides file as additional resources.

Pairs Spread Co-Integration Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to pairs spread co-integration (single pair spreads and single pair spreads co-integration). 

Pairs Spread Co-Integration Overview
04:00

In this lecture you will learn single pair spread definition and main calculations (OLS(), params(), subplots(), plot(), axhline(), legend(), suptitle(), show()  functions). 

Single Pair Spread 1
05:24

In this lecture you will learn single pair spread definition and main calculations (OLS(), params(), subplots(), plot(), axhline(), legend(), suptitle(), show()  functions). 

Single Pair Spread 2
04:33

In this lecture you will learn single pair spread definition and main calculations (OLS(), params(), subplots(), plot(), axhline(), legend(), suptitle(), show()  functions). 

Single Pair Spread 3
04:17

In this lecture you will learn single pair spread definition and main calculations (OLS(), params(), subplots(), plot(), axhline(), legend(), suptitle(), show()  functions). 

Single Pair Spread 4
04:10

In this lecture you will learn single pair spread definition and main calculations (OLS(), params(), subplots(), plot(), axhline(), legend(), suptitle(), show()  functions). 

Single Pair Spread 5
04:09

In this lecture you will learn single pair spread definition and main calculations (OLS(), params(), subplots(), plot(), axhline(), legend(), suptitle(), show()  functions). 

Single Pair Spread 6
04:20

In this lecture you will learn single pair spread co-integration definition and main calculations (ADF(), isnan(), shift(), PhillipsPerron() functions).

Single Pair Spread Co-Integration 1
09:51

In this lecture you will learn single pair spread co-integration definition and main calculations (ADF(), isnan(), shift(), PhillipsPerron() functions).

Single Pair Spread Co-Integration 2
08:26

In this lecture you will learn single pair spread co-integration definition and main calculations (ADF(), isnan(), shift(), PhillipsPerron() functions).

Single Pair Spread Co-Integration 3
07:39

In this lecture you will learn single pair spread co-integration definition and main calculations (ADF(), isnan(), shift(), PhillipsPerron() functions).

Single Pair Spread Co-Integration 4
07:32

In this lecture you will learn single pair spread co-integration definition and main calculations (ADF(), isnan(), shift(), PhillipsPerron() functions).

Single Pair Spread Co-Integration 5
07:18

In this lecture you will learn single pair spread co-integration definition and main calculations (ADF(), isnan(), shift(), PhillipsPerron() functions).

Single Pair Spread Co-Integration 6
07:40
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Pairs Trading Strategies
12 Lectures 02:27:07

You can download .PDF section slides file as additional resources.

Pairs Trading Strategies Slides
00:02

In this lecture you will learn section lectures’ detail and main themes to be covered related to pairs trading strategies (single pair spreads z-score and trading signals).

Pairs Trading Strategies Overview
08:56

In this lecture you will learn single pair spread z-score definition and main calculations (rolling(), cov(), IndexSlice(), reset_index(), var(), subplots(), plot(), axhline(), legend(), suptitle(), show(), mean(), std() functions).

Single Pair Spread Z-Score 1
13:32

In this lecture you will learn single pair spread z-score definition and main calculations (rolling(), cov(), IndexSlice(), reset_index(), var(), subplots(), plot(), axhline(), legend(), suptitle(), show(), mean(), std() functions).

Single Pair Spread Z-Score 2
11:34

In this lecture you will learn single pair spread z-score definition and main calculations (rolling(), cov(), IndexSlice(), reset_index(), var(), subplots(), plot(), axhline(), legend(), suptitle(), show(), mean(), std() functions).

Single Pair Spread Z-Score 3
12:13

In this lecture you will learn single pair spread z-score definition and main calculations (rolling(), cov(), IndexSlice(), reset_index(), var(), subplots(), plot(), axhline(), legend(), suptitle(), show(), mean(), std() functions).

Single Pair Spread Z-Score 4
12:03

In this lecture you will learn single pair spread z-score definition and main calculations (rolling(), cov(), IndexSlice(), reset_index(), var(), subplots(), plot(), axhline(), legend(), suptitle(), show(), mean(), std() functions).

Single Pair Spread Z-Score 5
13:50

In this lecture you will learn single pair spread trading signals definition and main calculations (shift(), enumerate(), iterrows(), subplots(), plot(), axhline(), legend(), suptitle(), show() functions, ‘for’ loops, ‘if’ conditionals, ‘and’ operators).

Single Pair Trading Signals 1
18:51

In this lecture you will learn single pair spread trading signals definition and main calculations (shift(), enumerate(), iterrows(), subplots(), plot(), axhline(), legend(), suptitle(), show() functions, ‘for’ loops, ‘if’ conditionals, ‘and’ operators).

Single Pair Trading Signals 2
13:28

In this lecture you will learn single pair spread trading signals definition and main calculations (shift(), enumerate(), iterrows(), subplots(), plot(), axhline(), legend(), suptitle(), show() functions, ‘for’ loops, ‘if’ conditionals, ‘and’ operators).

Single Pair Trading Signals 3
14:28

In this lecture you will learn single pair spread trading signals definition and main calculations (shift(), enumerate(), iterrows(), subplots(), plot(), axhline(), legend(), suptitle(), show() functions, ‘for’ loops, ‘if’ conditionals, ‘and’ operators).

Single Pair Trading Signals 4
13:48

In this lecture you will learn single pair spread trading signals definition and main calculations (shift(), enumerate(), iterrows(), subplots(), plot(), axhline(), legend(), suptitle(), show() functions, 'for' loops, 'if' conditionals, 'and' operators).

Single Pair Trading Signals 5
14:22
+
Pairs Strategies Performance Comparison
7 Lectures 01:18:12

You can download .PDF section slides file as additional resources.

Pairs Strategies Performance Comparison Slides
00:02

In this lecture you will learn section lectures’ detail and main themes to be covered related to pairs strategies performance comparison.

Pairs Strategies Performance Comparison Overview
08:11

In this lecture you will learn single pair strategy performance definition and main calculations (shift(), enumerate(), iterrows(), isnan(), cumprod(), len(), std(), sqrt(), DataFrame() functions, ‘for’ loops, ‘if’ conditionals, ‘and’, ‘or’ operators).

Single Pair Strategy Performance 1
14:31

In this lecture you will learn single pair strategy performance definition and main calculations (shift(), enumerate(), iterrows(), isnan(), cumprod(), len(), std(), sqrt(), DataFrame() functions, ‘for’ loops, ‘if’ conditionals, ‘and’, ‘or’ operators).

Single Pair Strategy Performance 2
15:12

In this lecture you will learn single pair strategy performance definition and main calculations (shift(), enumerate(), iterrows(), isnan(), cumprod(), len(), std(), sqrt(), DataFrame() functions, ‘for’ loops, ‘if’ conditionals, ‘and’, ‘or’ operators).

Single Pair Strategy Performance 3
12:56

In this lecture you will learn single pair strategy performance definition and main calculations (shift(), enumerate(), iterrows(), isnan(), cumprod(), len(), std(), sqrt(), DataFrame() functions, ‘for’ loops, ‘if’ conditionals, ‘and’, ‘or’ operators).

Single Pair Strategy Performance 4
13:24

In this lecture you will learn single pair strategy performance definition and main calculations (shift(), enumerate(), iterrows(), isnan(), cumprod(), len(), std(), sqrt(), DataFrame() functions, ‘for’ loops, ‘if’ conditionals, ‘and’, ‘or’ operators).

Single Pair Strategy Performance 5
13:56
About the Instructor
Diego Fernandez
3.8 Average rating
429 Reviews
2,966 Students
21 Courses
Exfinsis

Diego Fernandez is author of high-quality online courses and ebooks at Exfinsis for anyone who wants to become an expert in financial data analysis.

His main areas of expertise are financial analysis and data science. Within financial analysis he has focused on computational finance, quantitative finance and trading strategies analysis. Within data science he has concentrated on machine learning, applied statistics and econometrics. For all of this he has become proficient in Microsoft Excel®, R statistical software® and Python programming language® analysis tools. 

He has important online business development experience at fast-growing startups and blue-chip companies in several European countries. He has always exceeded expected professional objectives by starting with a comprehensive analysis of business environment and then efficiently executing formulated strategy.

He also achieved outstanding performance in his undergraduate and postgraduate degrees at world-class academic institutions. This outperformance allowed him to become teacher assistant for specialized subjects and constant student leader within study groups. 

His motivation is a lifelong passion for financial data analysis which he intends to transmit in all of the courses.