Developing Financial Analysis Tools
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Developing Financial Analysis Tools

Evaluate financial products in R by using financial packages via simple and easy-to-follow instructions
0.0 (0 ratings)
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.
1 student enrolled
Created by Packt Publishing
Last updated 8/2017
Current price: $10 Original price: $125 Discount: 92% off
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  • 5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Install R and R Studio and get help in R.
  • Work with R and the R Studio console.
  • Perform basic arithmetic and linear algebra calculations in R
  • Explore the various data structures in R
  • Import and export datasets from various sources to R
  • Write loops in R and an introduction to internal looping functions
View Curriculum
  • An understanding of the basic financial concepts will be useful.

As most of the data on the web or residing in a database is not structured in the right way, the course will assist viewers in developing skills to manipulate, transform, and evaluate raw input data. Through the concept of tidy data and visualization tools, viewers will be able to analyze trends and study the financial markets.

Once users have developed a good understanding of financial markets and financial data, the next three sections (3, 4, and 5) will introduces users to the concepts of basic statistics, time series analysis, and forecasting. Viewers will use a variety of basic R functions and forecast package to understand statistics and perform time series analysis.

By the end of this volume users will be able to use R, learn the use of Shiny apps, understand the concept of tidy data, and generate R markdown files for sharing information.

About the author

Atmajitsinh Gohil works as a senior consultant at a consultancy firm in New York City. After graduating, he worked in the financial industry as a Fixed Income Analyst. He writes about data manipulation, data exploration, visualization, and basic R plotting functions.

He has a master's degree in financial economics from the State University of New York (SUNY), Buffalo. He also graduated with a master of arts degree in economics from University of Pune, India. He loves to read blogs on data visualization and loves to go out on hikes in his free time.

Who is the target audience?
  • If you want to learn how to use R to build quantitative finance models with ease, this video is for you. Analysts who want to learn R to solve their quantitative finance problems will also find this video useful.
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Curriculum For This Course
33 Lectures
Laying the Foundation of Financial Markets
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This video provides an overview of the entire course.

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Fundamental and Technical Analysis in R

This video provides an introduction to the basics of bond valuation using R. The main purpose of this video is to teach you the concepts of bond valuation in R.

Bond Valuation in R

In this video, you will learn to import a yield curve in R and understand the concept of yield to maturity, basics of a yield curve, and different shapes of a yield curve.

Yield Curve

Learn the basics of derivative markets and stock options, extract option prices from the web, and evaluate the payoff structure of a call and put options.

Introduction to Derivatives

The main purpose of this video is to build a simple Shiny application and learn about the basic structure of an application.

Introduction to Shiny
Financial Data Extraction and Cleaning
6 Lectures 59:26

This video covers how to use googlesheets package in R to import and export financial data.

Preview 13:35

Not all the datasets are available for download in CSV, text, or Excel formats. In this video, you will learn to scrape online data, which can be used to detect trends, perform analysis, or generate a database.

Extracting Data from the Web

In this video, you will learn about the process involved in data exploration and the philosophy of tidy data in R.

Understanding Tidy Data Using the tidyquant Package

In this video, you will learn techniques of visualizing financial data for exploratory data analysis in R using the ggplot2 package.

Visualizing Financial Data in R

In this video, you will learn how to build a simple user interface for your Shiny application.

Generating a User Interface in Shiny

In this video, you will learn about the structure of the server.r file and its usefulness in building a Shiny app.

Creating a Shiny Application
Basic Statistics Using R
7 Lectures 51:09

In this video, we will understand the importance of Exploratory Data Analysis and learn to use some of the important functions from the dplyr package.

Preview 08:24

In this video, we aim to define outliers using the Tukeys range test and visualize the same using Boxplots. We will also study techniques to reduce the impact of outliers on our analysis.

Outlier Analysis in R

In this video, we introduce the concept of asset returns and why it is preferred over using asset prices.

Calculating Asset Return

In this video,we will look at the concepts of population and sample, and understand Central Limit Theorem. We will also use R sampling functions to calculate sample statistics.

Understanding Population and Sample Data

In this video, we aim to study and visualize the different types of distributions of asset returns using the concept of skewness, kurtosis, excess kurtosis, and QQ plots in R.

Evaluating Skewness and Kurtosis in Financial Data in R

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Probability Distribution of Asset Returns

What is market volatility, correlation, and covariance. How are all these computed?

Market Volatility, Correlation, and Covariance in R
Introduction to Time Series Modeling
7 Lectures 01:06:06

An introduction of time series data and creating time series objects in R.

Preview 09:13

Learn to perform additive and multiplicative decomposition of time series data in R.

Decomposition of Time Series Data

Understand the concept of Autocorrelation in Time Series data and how to calculate the same in R.

Stationarity and Autocorrelation in R

The objective is to introduce the Box- Pierce test, Ljung-Box test and understand the concept of Partial Autocorrelation.

Stationarity and Autocorrelation in R (Continued)

Understand the concept of Moving Average process, its properties, and how to simulate Moving Average process in R.

Time Series Modeling in R

What is Autoregressive process and how to simulate the same in R.

Time Series Modeling in R (Continued)

Learn about ARMA process, ARIMA process and how to systematically model a time series data using Box-Jenkins approach.

Advance Topics in Time Series Modeling
5 Lectures 49:37

An introduction to What is Forecasting.

Preview 11:05

Understand various measures such as MAPE, MSE, RMSE, MAE and Theil’s U to calculate the accuracy of forecast.

Accuracy of Forecast

Understand the concept of cointegration and how to test for cointegrated time series.


The objective is to introduce the concepts of Spurious Regression and Error Correction Models.

Cointegration (Continued)

The objective is to understand VAR models, interpretation of VAR models and estimate VAR model using R.

Vector Autoregression (VAR)
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Packt Publishing
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