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Finance & AccountingFinancial Modeling & AnalysisAlgorithmic Trading

Machine Trading Analysis with R

Learn machine trading analysis from basic to expert level through a practical course with R statistical software.
Rating: 4.0 out of 54.0 (44 ratings)
330 students
Created by Diego Fernandez
Last updated 10/2017
English
English [Auto]

What you'll learn

  • Read or download S&P 500® Index ETF prices data and perform machine trading analysis operations by installing related packages and running script code on RStudio IDE.
  • Define target and predictor algorithm features for supervised regression machine learning task.
  • Select relevant predictor features subset through univariate filter methods, deterministic wrapper methods and embedded methods.
  • Implement Student t-test, ANOVA F-test for univariate methods, recursive feature elimination for deterministic wrapper methods and least absolute shrinkage and selection operator for embedded methods.
  • Extract predictor features transformations through principal component analysis.
  • Train algorithm for mapping optimal relationship between target and predictor features through ensemble methods, maximum margin methods and multi-layer perceptron methods.
  • Apply extreme gradient boosting machine regression for ensemble methods, radial basis function support vector machine regression for maximum margin methods and artificial neural network regression for multi-layer perceptron methods.
  • Test algorithm for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics.
  • Assess mean absolute error, root mean squared error for scale-dependent metrics and mean absolute percentage error, mean absolute scaled error for scale-independent metrics.
  • Calculate machine trading strategies for algorithms with highest forecasting accuracy.
  • Generate buy or sell trading signals based on target feature prediction crossing centerline cross-over threshold.
  • Produce long-only trading positions associated to trading signals.
  • Evaluate machine trading strategies performance against buy and hold benchmark using annualized return, annualized standard deviation, annualized Sharpe ratio metrics and cumulative returns, maximum drawdown charts.

Requirements

  • R statistical software is required. Downloading instructions included.
  • RStudio Integrated Development Environment (IDE) is recommended. Downloading instructions included.
  • Practical example data and R script code files provided with the course.
  • Prior basic R statistical software knowledge is useful but not required.

Description

Learn machine trading analysis through a practical course with R statistical software using S&P 500® Index ETF 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 Machine Trading Analysis Expert in this Practical Course with R

  • Read or download S&P 500® Index ETF prices data and perform machine trading analysis operations by installing related packages and running script code on RStudio IDE.
  • Define target and predictor algorithm features for supervised regression machine learning task.
  • Select relevant predictor features subset through univariate filter methods, deterministic wrapper methods and embedded methods.
  • Implement Student t-test, ANOVA F-test for univariate methods, recursive feature elimination for deterministic wrapper methods and least absolute shrinkage and selection operator for embedded methods.
  • Extract predictor features transformations through principal component analysis.
  • Train algorithm for mapping optimal relationship between target and predictor features through ensemble methods, maximum margin methods and multi-layer perceptron methods.
  • Apply extreme gradient boosting machine regression for ensemble methods, radial basis function support vector machine regression for maximum margin methods and artificial neural network regression for multi-layer perceptron methods.
  • Test algorithm for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics.
  • Assess mean absolute error, root mean squared error for scale-dependent metrics and mean absolute percentage error, mean absolute scaled error for scale-independent metrics.
  • Calculate machine trading strategies for algorithms with highest forecasting accuracy.
  • Generate buy or sell trading signals based on target feature prediction crossing centerline cross-over threshold. 
  • Produce long-only trading positions associated to trading signals.
  • Evaluate machine trading strategies performance against buy and hold benchmark using annualized return, annualized standard deviation, annualized Sharpe ratio metrics and cumulative returns, maximum drawdown charts.

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

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

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500® Index ETF prices historical data for back-testing to achieve greater effectiveness. 

Content and Overview

This practical course contains 43 lectures and 5 hours of content. It’s designed for all machine trading analysis knowledge levels and a basic understanding of R statistical software is useful but not required.

At first, you’ll learn how to read or download S&P 500® Index ETF prices historical data to perform machine trading analysis operations by installing related packages and running script code on RStudio IDE.

Then, you’ll define target and predictor features for supervised regression machine learning task. After that, you’ll select relevant predictor features subset through univariate filter methods, deterministic wrapper methods and embedded methods. Next, you’ll implement Student t-test, ANOVA F-test for univariate methods, recursive feature elimination for deterministic wrapper methods and least absolute shrinkage and selection operator for embedded methods. Later, you’ll extract predictor features transformations through principal component analysis.

Next, you’ll train algorithm for mapping optimal relationship between target and predictor features through ensemble methods, maximum margin methods and multi-layer perceptron methods. Then, you’ll apply gradient boosting machine regression for ensemble methods, radial basis function support vector machine regression for maximum margin methods and artificial neural network regression for multi-layer perceptron methods. After that, you’ll test algorithm for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics. Later, you’ll assess mean absolute error, root mean squared error for scale-dependent metrics and mean absolute percentage error, mean absolute scaled error for scale-independent metrics.

After that, you’ll calculate machine trading strategies for algorithms with highest forecasting accuracy. Then, you’ll generate buy or sell trading signals based on target feature prediction crossing centerline cross-over threshold. Next, you’ll produce long-only trading positions associated to trading signals.

Finally, you’ll measure machine trading strategies performance against buy and hold benchmark through annualized return, annualized standard deviation, annualized Sharpe ration and cumulative returns, maximum drawdown charts

Who this course is for:

  • Undergraduates or postgraduates who want to learn about machine trading analysis using R statistical software.
  • Finance professionals or academic researchers who wish to deepen their knowledge in computational finance.
  • Experienced investors who desire to research machine trading strategies.
  • This course is NOT about “get rich quick” trading strategies or magic formulas.

Instructor

Diego Fernandez
Exfinsis
Diego Fernandez
  • 3.7 Instructor Rating
  • 2,497 Reviews
  • 12,869 Students
  • 36 Courses

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.

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