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Developing Financial Analysis Tools

Video Description

Evaluate financial products in R by using financial packages via simple and easy-to-follow instructions

About This Video

  • Understand the basics of R and how they can be applied in various Quantitative Finance scenarios.

  • Learn various algorithmic trading techniques, ways to optimize them, and different methods to manage risk

  • Follow this comprehensive tutorial to learn and implement various quantitative finance concepts in R

  • In Detail

    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.

    Table of Contents

    1. Chapter 1 : Laying the Foundation of Financial Markets
      1. The Course Overview 00:01:58
      2. Performing Time Value of Money in R 00:08:13
      3. Evaluating the Equity Markets Using the quantmod Package in R 00:10:46
      4. Fundamental and Technical Analysis in R 00:17:55
      5. Bond Valuation in R 00:09:36
      6. Yield Curve 00:09:28
      7. Introduction to Derivatives 00:10:05
      8. Introduction to Shiny 00:10:18
    2. Chapter 2 : Financial Data Extraction and Cleaning
      1. Extracting Financial Data from Various Sources 00:13:35
      2. Extracting Data from the Web 00:11:34
      3. Understanding Tidy Data Using the tidyquant Package 00:08:02
      4. Visualizing Financial Data in R 00:07:19
      5. Generating a User Interface in Shiny 00:11:03
      6. Creating a Shiny Application 00:07:55
    3. Chapter 3 : Basic Statistics Using R
      1. Summarizing Financial Data in R 00:08:24
      2. Outlier Analysis in R 00:05:27
      3. Calculating Asset Return 00:08:07
      4. Understanding Population and Sample Data 00:06:30
      5. Evaluating Skewness and Kurtosis in Financial Data in R 00:06:36
      6. Probability Distribution of Asset Returns 00:08:51
      7. Market Volatility, Correlation, and Covariance in R 00:07:18
    4. Chapter 4 : Introduction to Time Series Modeling
      1. Basic Features of a Time Series 00:09:13
      2. Decomposition of Time Series Data 00:09:07
      3. Stationarity and Autocorrelation in R 00:08:30
      4. Stationarity and Autocorrelation in R (Continued) 00:07:59
      5. Time Series Modeling in R 00:10:02
      6. Time Series Modeling in R (Continued) 00:09:07
      7. ARIMA in R 00:12:11
    5. Chapter 5 : Advance Topics in Time Series Modeling
      1. Forecasting in R 00:11:05
      2. Accuracy of Forecast 00:10:29
      3. Cointegration 00:07:03
      4. Cointegration (Continued) 00:07:13
      5. Vector Autoregression (VAR) 00:13:50