In finance, quantitative analysts ensure portfolios are risk balanced, help find new trading opportunities, and evaluate asset prices using mathematical models.

Ensure portfolios are risk balanced, help find new trading opportunities, and evaluate asset prices using mathematical models.

 with R

Quantitative Analyst

Get comfortable with the very basics of R and learn how to use it as a calculator. Also, create your first variables in R and explore some of the base data types such as numerics and characters.

The Basics

In this chapter, you will learn all about vectors and matrices using historical stock prices for companies like Apple and IBM. You will then be able to feel confident creating, naming, manipulating, and selecting from vectors and matrices.

Vectors and Matrices

Arguably the most important data structure in R, the data frame is what most of your data will take the form of. It combines the structure of a matrix with the flexibility of having different types of data in each column.

Data Frames

Questions with answers that fall into a limited number of categories can be classified as factors. In this chapter, you will use bond credit ratings to learn all about creating, ordering, and subsetting factors.

Factors

Wouldn't it be nice if there was a way to hold related vectors, matrices, or data frames together in R? In this final chapter, you will explore lists and many of their interesting features by building a small portfolio of stocks.

Lists

In this finance-oriented introduction to R, you will learn essential data structures such as lists and data frames and have the chance to apply that knowledge to real-world financial examples. By the end of the course, you will be comfortable with the basics of manipulating your data to perform financial analysis in R.

Learn essential data structures such as lists and data frames and have the chance to apply that knowledge directly to financial examples.

Intro to R for Finance

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Introduction to R for Finance

Applied Finance

Welcome! Before we go deeper into the world of R, it will be nice to have an understanding of how dates and times are created. This chapter will teach you enough to begin working with dates, but only scratches the surface of what you can do with them.

Dates

Imagine you own stock in a company. If the stock goes above a certain price, you might want to sell. If the stock drops below a certain price, you might want to buy it while it's cheap! This kind of thinking can be implemented using operators and if statements. In this chapter, you will learn all about them, and create a program that tells you to buy or sell a stock.

If Statements and Operators

Loops can be useful for doing the same operation to each element of your data structure. In this chapter you will learn all about repeat, while, and for loops!

Loops

If data structures like data frames and vectors are how you hold your data, functions are how you tell R what to do with your data. In this chapter, you will learn about using built-in functions, creating your own unique functions, and you will finish off with a brief introduction to packages.

Functions

A popular alternative to loops in R are the apply functions. These are often more readable than loops, and are incredibly useful for scaling the data science workflow to perform a complicated calculation on any number of observations. Learn about them here!

Apply

If you enjoyed the Introduction to R for Finance course, then you will love Intermediate R for Finance. Here, you will first learn the basics about how dates work in R, an important skill for the rest of the course. Your next step will be to explore the world of if statements, loops, and functions. These are powerful ideas that are essential to any financial data scientist's toolkit. Finally, we will spend some time working with the family of apply functions as a vectorized alternative to loops. And of course, all examples will be finance related! Enjoy!

Learn about how dates work in R, and explore the world of if statements, loops, and functions. You'll practice this knowledge using financial examples.

Intermediate R for Finance

Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.

xts and zoo are just two of the many different types of objects that exist in R. This chapter will introduce the basic objects in xts and zoo and their components, and offers examples of how to construct and examine the data.

Introduction to eXtensible Time Series, using xts and zoo for time series

Now that you can create basic xts objects, it's time to see how powerful they can be. This chapter will cover the basics of one of the most useful features of xts: time based subsetting. From there you'll explore additional ways to extract data using time phrases, and conclude with how to do basic operations like adding and subtracting your xts objects.

First Order of Business - Basic Manipulations

One of the most important parts of working with time series data involves creating derived time series. To do this effectively, it is critical to keep track of dates and times. In this chapter you will look at how xts handles merging new columns and rows into existing data, how to deal with the inevitable missing observations in time series, and how to shift your series in time.

Merging and modifying time series

Now the fun begins! A very common usage pattern for time series is to calculate values for disjoint periods of time or aggregate values from a higher frequency to a lower frequency. For most series, you'll often want to see the weekly mean of a price or measurement. You may even find yourself looking at data that has different frequencies and you need to normalize to the lowest frequency. This chapter is where it all happens. Hang tight, and lets get going!

Apply and aggregate by time

Now that you are comfortable with most of the core features, its time to explore some of the lesser known (but powerful!) aspects of working with xts. In this final chapter you will use the internals of the index to find repeating itervals, see how xts provides intuitive time zone support, and experiment with ways to explore your data by time - including identifying frequency and coverage in time. Let's finish this course!

Extra features of xts

Chicago summer temperature data

Daily USD/EUR exchange rate

Time series are all around us, from server logs to high frequency financial data. Managing and manipulating ordered observations is central to all time series analysis. The xts and zoo packages provide a set of powerful tools to make this task fast and mistake free. In this course, you will learn everything from the basics of xts to advanced tips and tricks for working with time series data in R.

You will learn everything from the basics of working with the xts and zoo packages to advanced tips and tricks for working with time series data in R. 

Manipulating Time Series Data with xts and zoo in R

The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.

Data Manipulation

A wealth of financial and economic data are available online. Learn how getSymbols() and Quandl() make it easy to access data from a variety of sources.

Introduction and downloading data

You've learned how to import data from online sources, now it's time to see how to extract columns from the imported data. After you've learned how to extract columns from a single object, you will explore how to import, transform, and extract data from multiple instruments.

Extracting and transforming data

Learn how to simplify and streamline your workflow by taking advantage of the ability to customize default arguments to `getSymbols()`. You will see how to customize defaults by data source, and then how to customize defaults by symbol. You will also learn how to handle problematic instrument symbols.

Managing data from multiple sources

You've learned how to import, extract, and transform data from multiple data sources. You often have to manipulate data from different sources in order to combine them into a single data set. First, you will learn how to convert sparse, irregular data into a regular series.  Then you will review how to aggregate dense data to a lower frequency. Finally, you will learn how to handle issues with intra-day data.

Aligning data with different periodicities

You've learned the core workflow of importing and manipulating financial data. Now you will see how to import data from text files of various formats. Then you will learn how to check data for weirdness and handle missing values. Finally, you will learn how to adjust stock prices for splits and dividends.

Importing text data, and adjusting for corporate actions

Amazon CSV file

DC data

UNE CSV file

two_symbols CSV file

If you've ever done anything with financial or economic time series, you know the data come in various shapes, sizes, and periodicities. Getting the data into R can be stressful and time-consuming, especially when you need to merge data from several different sources into one data set. This course will cover importing data from local files as well as from internet sources.

This course will cover importing financial data from local files as well as from internet sources.

Importing and Managing Financial Data in R

Learn how to access financial data from local files as well as from internet sources.

This chapter will give you insights on how to organize and visualize time series data in R. You will learn several simplifying assumptions that are widely used in time series analysis, and common characteristics of financial time series.

Exploratory time series data analysis

In this chapter, you will conduct some trend spotting, and learn the white noise (WN) model, the random walk (RW) model, and the definition of stationary processes.

Predicting the future

In this chapter, you will review the correlation coefficient, use it to compare two time series, and also apply it to compare a time series with its past, as an autocorrelation. You will discover the autocorrelation function (ACF) and practice estimating and visualizing autocorrelations for time series data.

Correlation analysis and the autocorrelation function

In this chapter, you will learn the autoregressive (AR) model and several of its basic properties. You will also practice simulating and estimating the AR model in R, and compare the AR model with the random walk (RW) model.

Autoregression

In this chapter, you will learn the simple moving average (MA) model and several of its basic properties. You will also practice simulating and estimating the MA model in R, and compare the MA model with the autoregressive (AR) model.

A simple moving average

Many phenomena in our day-to-day lives, such as the movement of stock prices, are measured in intervals over a period of time. Time series analysis methods are extremely useful for analyzing these special data types. In this course, you will be introduced to some core time series analysis concepts and techniques.

Intermediate R

Learn the core techniques necessary to extract meaningful insights from time series data.

Time Series Analysis in R

Probability & Statistics

You will  investigate the nature of time series data and learn the basics of ARMA models that can explain the behavior of such data.  You will learn the basic R commands needed to help set up raw time series data to a form that can be analyzed using ARMA models.

Time Series Data and Models

You will discover the wonderful world of ARMA models and how to fit these models to time series data. You will learn how to identify a model, how to choose the correct model, and how to verify a model once you fit it to data.  You will learn how to use R time series commands from the stats and astsa packages.

Fitting ARMA models

Now that you know how to fit ARMA models to stationary time series, you will learn about integrated ARMA (ARIMA) models for nonstationary time series.  You will fit the models to real data using R time series commands from the stats and astsa packages.

ARIMA Models

You will learn how to fit and forecast seasonal time series data using seasonal ARIMA models. This is accomplished using what you learned in the previous chapters and by learning how to extend the R time series commands available in the stats and astsa packages.

Seasonal ARIMA

In this course, you will become an expert in fitting ARIMA models to time series data using R.  First, you will explore the nature of time series data using the tools in the R stats package.  Next, you learn how to fit various ARMA models to simulated data (where you will know the correct model) using the R package astsa.  Once you have mastered the basics, you will learn how to fit integrated ARMA models, or ARIMA models to various real data sets. You will learn how to check the validity of an ARIMA model and you will learn how to forecast time series data.  Finally, you will learn how to fit ARIMA models to seasonal data, including forecasting using the astsa package.

In this course, you will become an expert in fitting ARIMA models to time series data using R, from fitting to simulated data and seasonal data to using astsa.

ARIMA Models in R

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

You've been hired to understand the travel needs of tourists visiting the Boston area. As your first assignment on the job, you'll practice the  skills you've learned for time series data manipulation in R by exploring  data on flights arriving at Boston's Logan International Airport (BOS)  using xts & zoo.

Flight Data

In this chapter, you'll expand your time series data library to include weather data in the Boston area. Before you can conduct any analysis, you'll need to do some data manipulation, including merging multiple xts objects  and isolating certain periods of the data. It's a great opportunity for  more practice!

Weather Data

Now it's time to go further afield. In addition to flight delays, your client is interested in how Boston's tourism industry is affected by economic trends. You'll need to manipulate some time series data on economic indicators,  including GDP per capita and unemployment in the United States in general and  Massachusetts (MA) in particular.

Economic Data

Having exhausted other options, your client now believes Boston's tourism industry must be related to the success of local sports teams. In your final task on this project, your supervisor has asked you to assemble some time series data on Boston's sports teams over the past few years.

Sports Data

Flights arriving at Boston Logan airport

Boston monthly average visibility

Wind speeds in Boston

Boston monthly temperature data

US and Massachusetts unemployment

US GDP data

Boston-area sports teams data

In this course, you will strengthen your knowledge of time series topics through interactive exercises and interesting datasets. You’ll explore a variety of datasets about Boston, including data on flights, weather, economic trends, and local sports teams.

Strengthen your knowledge of the topics covered in Manipulating Time Series in R with xts and zoo using real case study data.

Case Studies: Manipulating Time Series Data in R

Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

Case Studies

The first thing to do in any data analysis task is to plot the data. Graphs enable many features of the data to be visualized, including patterns, unusual observations, and changes over time. The features that are seen in plots of the data must then be incorporated, as far as possible, into the forecasting methods to be used.

Exploring and visualizing time series in R

In this chapter, you will learn general tools that are useful for many different forecasting situations. It will describe some methods for benchmark forecasting, methods for checking whether a forecasting method has adequately utilized the available information, and methods for measuring forecast accuracy. Each of the tools discussed in this chapter will be used repeatedly in subsequent chapters as you develop and explore a range of forecasting methods.

Benchmark methods and forecast accuracy

Forecasts produced using exponential smoothing methods are weighted averages of past observations, with the weights decaying exponentially as the observations get older. In other words, the more recent the observation, the higher the associated weight. This framework generates reliable forecasts quickly and for a wide range of time series, which is a great advantage and of major importance to applications in business.

Exponential smoothing

ARIMA models provide another approach to time series forecasting. Exponential smoothing and ARIMA models are the two most widely-used approaches to time series forecasting, and provide complementary approaches to the problem. While exponential smoothing models are based on a description of the trend and seasonality in the data, ARIMA models aim to describe the autocorrelations in the data.

Forecasting with ARIMA models

The time series models in the previous chapters work well for many time series, but they are often not good for weekly or hourly data, and they do not allow for the inclusion of other information such as the effects of holidays, competitor activity, changes in the law, etc. In this chapter, you will look at some methods that handle more complicated seasonality, and you consider how to extend ARIMA models in order to allow other information to be included in the them.

Advanced methods

Excelfile in the first exercise

Forecasting involves making predictions about the future. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call centre next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an important aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.

Learn how to make predictions about the future using time series forecasting in R.

Forecasting in R

This chapter will introduce you to basic R time series visualization tools.

R Time Series Visualization Tools

Univariate plots are designed to learn as much as possible about the distribution, central tendency and spread of the data at hand. In this chapter you will be presented with some visual tools used to diagnose univariate times series.

Univariate Time Series

What to do if you have to deal with multivariate time series? In this chapter, you will learn how to identify patterns in the distribution, central tendency and spread over pairs or groups of data.

Multivariate Time Series

Let's put everything you learned so far in practice! Imagine you already own a portfolio of stocks and you have some spare cash to invest, how can you wisely select a new stock to invest your additional cash? Analyzing the statistical properties of individual stocks vs. an existing portfolio is a good way of approaching the problem.

Case study: Visually selecting a stock that improves your existing portfolio

Returns for XOM, C, MSFT, DOW, and YHOO

Existing portfolio

Stock data for GS, KO, DIS, and CAT

Daily stocks for YHOO, MSFT, C, and DOW

Daily returns for Apple

Old versus new portfolio

As the saying goes, “A chart is worth a thousand words”. This is why visualization is the most used and powerful way to get a better understanding of your data. After this course you will have a very good overview of R time series visualisation capabilities and you will be able to better decide which model to choose for subsequent analysis. You will be able to also convey the message you want to deliver in an efficient and beautiful way.

Learn how to visualize time series in R, then practice with a stock-picking case study.

Visualizing Time Series Data in R

Data Visualization

Asset returns and portfolio weights; those are the building blocks of a portfolio return. This chapter is about computing those portfolio weights and returns in R.

The building blocks

The history of portfolio returns reveals valuable information about how much the investor can expect to gain or lose. This chapter introduces the R functionality to analyze the investment performance based on a statistical analysis of the portfolio returns. It includes graphical analysis and the calculation of performance statistics expressing average return, risk, and risk-adjusted return over rolling estimation samples.

Analyzing performance

In addition to studying portfolio performance based on the observed portfolio return series, it is relevant to determine how individual (expected) returns, volatilities, and correlations interact to determine the total portfolio performance.

Performance drivers

We have up to now considered the portfolio weights as given. In this chapter, you learn how to determine in R the portfolio weights that are optimal in terms of achieving a target return with minimum variance, while satisfying constraints on the portfolio weights.

Optimizing the portfolio

Stock prices for Apple and Microsoft

Bonds prices

Commodities prices

Equities prices

Stock prices for DIJA

Real estate prices

Daily prices in S&P 500

A golden rule in investing is to always test the portfolio strategy on historical data, and, once you are trading the strategy, to constantly monitor its performance. In this course, you will learn this by critically analyzing portfolio returns using the package PerformanceAnalytics. The course also shows how to estimate the portfolio weights that optimally balance risk and return. This is a data-driven course that combines portfolio theory with the practice in R, illustrated on real-life examples of equity portfolios and asset allocation problems. If you'd like to continue exploring the data after you've finished this course, the data used in the first three chapters can be obtained using the tseries-package. The code to get them can be found <a href='http://s3.amazonaws.com/assets.datacamp.com/course/portfolio-analysis/data_portfolio_analysis.R' target='_blank'>here</a>. The data used in chapter 4 can be downloaded <a href='http://s3.amazonaws.com/assets.datacamp.com/course/portfolio-analysis/prices.rds' target='_blank'>here</a>.

Apply your R skills to backtest, analyze, and optimize financial portfolios.The course also shows how to estimate the weights that optimize risk and return.

Introduction to Portfolio Analysis in R

Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.

This chapter will give you a brief review of Modern Portfolio Theory and introduce you to the PortfolioAnalytics package by solving a couple portfolio optimization problems.

Introduction and Portfolio Theory

The focus of this chapter is a detailed overview of the recommended workflow for solving portfolio optimization problems with PortfolioAnalytics. You will learn how to create a portfolio specification, add constraints, objectives, run the optimization, and analyze the results of the optimization output.

Portfolio Optimization Workflow

In this chapter, you will learn about estimating moments, characteristics of the distribution of asset returns, as well as custom objective functions.

Objective Functions and Moment Estimation

In the final chapter of the course, you will solve a portfolio optimization problem that mimics a real world real world example of constructing a portfolio of hedge fund strategy with different style definitions.

Application

Portfolio specifications object I

Portfolio specifications object II

Set of random portfolios I

Set of random portfolios II

This course builds on the fundamental concepts from Introduction to Portfolio Analysis in R and explores advanced concepts in the portfolio optimization process. It is critical for an analyst or portfolio manager to understand all aspects of the portfolio optimization problem to make informed decisions. In this course, you will learn a quantitative approach to apply the principles of modern portfolio theory to specify a portfolio, define constraints and objectives, solve the problem, and analyze the results. This course will use the R package PortfolioAnalytics to solve portfolio optimization problems with complex constraints and objectives that mirror real world problems.

An intermediate course to further develop your R finance skills to backtest, analyze, and optimize financial portfolios.

Portfolio Analysis in R 

Advance you R finance skills to backtest, analyze, and optimize financial portfolios.

Intermediate Portfolio Analysis in R

The fixed income market is large and filled with complex instruments. In this course, we focus on plain vanilla bonds to build solid fundamentals you will need to tackle more complex fixed income instruments.  In this chapter, we demonstrate the mechanics of valuing bonds by focusing on an annual coupon, fixed rate, fixed maturity, and option-free bond.

Introduction and Plain Vanilla Bond Valuation

Estimating Yield To Maturity - The YTM measures the expected return to bond investors if they hold the bond until maturity. This number summarizes the compensation investors demand for the risk they are bearing by investing in a particular bond. We will discuss how one can estimate YTM of a bond.

Yield to Maturity

Interest rate risk is the biggest risk that bond investors face. When interest rates rise, bond prices fall. Because of this, much attention is paid to how sensitive a particular bond's price is to changes in interest rates. In this chapter, we start the discussion with a simple measure of bond price volatility - the Price Value of a Basis Point. Then, we discuss duration and convexity, which  are two common measures that are used to manage interest rate risk.

Duration and Convexity

We will put all of the techniques that the student has learned from Chapters One through Three into one comprehensive example. The student will be asked to value a bond by using the yield on a comparable bond and estimate the bond's duration and convexity.

Comprehensive Example

After this course on quantitative finance with R, you will be able to use R to develop a model to value a fixed interest rate bond, estimate and analyze a bond's yield (i.e., a measure of the opportunity cost of bond investors), and model techniques used to protect bond portfolios from changes in interest rates. Why value bonds? Bonds are securities issued by governments or corporations that pay interest over a fixed schedule and are the most well-known type of fixed income securities. The US fixed income market is 1.5x larger than the US stock market, but, unlike stocks, most fixed income instruments, including bonds, trade very infrequently. Consequently, a bond's price may be a less reliable indicator of its value and analytical techniques are necessary when analyzing and valuing bonds.

Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.

Bond Valuation and Analysis in R

This chapter begins with a general introduction to credit risk models. We'll explore a real-life data set, then preprocess the data set such that it's in the appropriate format before applying the credit risk models.

Introduction and data preprocessing

Logistic regression is still a widely used method in credit risk modeling. In this chapter, you will learn how to apply logistic regression models on credit data in R.

Logistic regression

Classification trees are another popular method in the world of credit risk modeling. In this chapter, you will learn how to build classification trees using credit data in R.

Decision trees

This hands-on-course with real-life credit data will teach you how to model credit risk by using logistic regression and decision trees in R.Modeling credit risk for both personal and company loans is of major importance for banks. The probability that a debtor will default is a key component in getting to a measure for credit risk. While other models will be introduced in this course as well, you will learn about two model types that are often used in the credit scoring context; logistic regression and decision trees. You will learn how to use them in this particular context, and how these models are evaluated by banks.

Learn to create models to evaluate financial credit risk like a professional

Credit Risk Modeling in R

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

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