R Programming for Finance Training Course

Course Code

rforfinance

Duration

28 hours (usually 4 days including breaks)

Requirements

  • An understanding of finance (securities, derivatives, etc.)
  • A solid grasp of mathematics
  • Programming experience in any language is helpful but not required

Overview

R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to use R to develop practical applications for solving a number of specific finance related problems.

By the end of this training, participants will be able to:

  • Understand the fundamentals of the R programming language
  • Select and utilize R packages and techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
  • Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
  • Troubleshoot, integrate deploy and optimize an R application

Audience

  • Developers
  • Analysts
  • Quants

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice

Note

  • This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.

Course Outline

Introduction

Setting up the IDE (Integrated Development Environment)

  • RStudio

R Programming Fundamentals

  • R objects: vectors, matrices, arrays, data frames and lists
  • Flow control: branching, looping and truth testing

Accessing Data with R

  • Reading and writing CSV data
  • Accessing data in an SQL database

Visualizing Data with R

  • Plotting with R

Analyzing Data with R

  • Manipulating data frames
  • Descriptive statistics

Inference and Time Series Analysis

  • Analyzing time series data in financial markets
  • Volatility modeling for high frequency financial data

Simulating Asset Price Trajectories

  • Monte Carlo simulation

Asset Allocation and Portfolio Optimization

  • Performing capital allocation, asset allocation, and risk assessment
  • Regression analysis

Risk Analysis and Investment Performance

  • Defining and solving portfolio optimization problems
  • VaR and ES

Fixed-Income Analysis and Option Pricing

  • Performing fixed-income analysis and option pricing

Taking Your R Application into Production

  • Integrating your application with Excel and other web applications

Application Performance

  • Optimizing your application
  • R multiprocessing

Troubleshooting

Closing Remarks

Testimonials

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