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R for Financial Services

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.

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.

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.

The cost of this course is £1200 for commercial attendees and £800 for academic attendees.

Should you want to book a place on this course or have any questions please contact us at .

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
  • How is R used in the finance industry

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The R Environment

  • The R command line
  • Overview of R objects and object creation
  • Search paths, the workspace, working directory and package directories
  • R packages
  • The R GUI

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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
  • Flow control
  • Debugging
  • The help system

Day 2

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Important R functions

  • Importing and exporting data
  • Probability distributions, summary statistics
  • Functions for manipulating character data
  • Functions for handling missing data
  • The apply family

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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

Day 3

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Lattice Graphics

  • The Lattice Philosophy
  • Panels and strips
  • High level functions
  • Controlling lattice

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Basic R Statistical and Mathematical Functions used in the Finance industry

  • Matrix operations, linear equations
  • Quadratic programming
  • Optimization functions
  • Splines
  • Linear models and regression
  • Nonlinear regression

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Time Series

  • Creating and manipulating time series objects
  • Fitting time series models, diagnostics
  • Time series graphics

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Overview of additional R finance packages

  • zoo
  • Rmetrics
  • PerformanceAnalytics
Additional packages as time permits

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