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R for Financial Data Analysis
The R for finance course provides a practical introduction to the R environment within financial services to enable quantitative analysts to quickly become more productive with using the R language.
Training materials can be customised for a particular environment, including the use of sample client data for the class examples and exercises.
Who Should Attend?
This course is suitable for R novices and improvers who wish to use R for statistical analysis of financial markets. Some basic knowledge of finance, linear models and time series analysis will be assumed. Some prior exposure to computer programming is an advantage, but is not assumed.
Course Goals
- To provide an understanding of the technology behind the R package
- To improve programming style and confidence
- To understand R's models related to the finance sector
- To enable users to access a wide range of available functionality
- To enable attendees to program in R within their own environment
Course Outline:
As with all our courses, attendees are provided with comprehensive training manuals complete with detailed examples and laminated tip sheets for future reference.
Should you wish to book a place on this course or have any questions please contact us at
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Should your organization have more than 3 possible attendees, why not talk to us about hosting a customized and focused course delivered at your own premises?
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Course content:
Day 1
Introduction
- What is R?
- The R community and web resource
- R-related journals and conferences
- Overview of how R can be used in the finance industry
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The R Environment
- The R command line
- The R GUI
- R Packages
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Data Objects
- Core data types, missing data
- Vectors
- Matrices
- Arrays
- Lists
- Data frames
- Factors
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R functions
- What is a function?
- Calling functions, arguments, return values
- Generic functions
- Flow control
- Debugging
- The help system
Day 2
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Important R functions
- Importing and exporting data (including databases and Excel spreadsheets)
- Probability distributions, summary statistics
- Functions for manipulating character data
- Functions for handling missing data
- The apply family
- Data manipulation
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Traditional Graphics
- Graphics devices and colours
- High level graphics functions
- Graph types
- Univariate graph functions
- Matrix plotting functions
- Low level graphics functions
- Setting graphics parameters
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Basic R Statistical and Mathematical Functions used in the Finance industry
- Matrix operations, linear equations
- Robust mean and covariance estimation
- Quadratic programming
- Optimization functions
- Splines, linear interpolation
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Day 3
Basic R Statistical and Mathematical Functions used in the Finance industry (contd)
- Normality tests
- Linear models and regression
- Fitting probability distributions
- Functions for additional distributions (hyperbolic, multivariate skew-t, etc.)
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Time Series Analysis, Manipulation and Visualization
- Time and date classes
- Creating and manipulating time series objects (including packages such as zoo)
- Fitting and analyzing ARMA, VAR and GARCH models
- Outliers, ACF, PACF
- Time series visualization tools
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Performance and Risk Measurement
- Calculating VaR and CVaR
- Lower partial moments, draw-downs
- Financial ratios and other performance measures
Additional packages as time permits
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