r in finance: introduction to r and its applications in finance

2016/5/13

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i n F i n a n c eIntroduction to R and Its Applications in Finance

Slides of this presentation: https://goo.gl/4oPIja

R script for this presentation: https://goo.gl/2YKQpq

The Finance and Financial Planning HDR Research Methodology Workshops

LiangC.Zhang

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@LiangCZhang i n F i n a n c e

Disclaimer

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� This workshop contains R tools for Technical trading &

Performance analysis that might assist investment decisions.

� All analysis are used for demonstrating R only.

� In other words,

� You are responsible for your own investment decisions if you

use these tools to manage your assets.

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Welcome to the Era of Open-source language/software 2015 Top 10 Popular Programming

Languages What is open-source

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� Its source code is available for modification or enhancement by anyone

� Access to examining the code

� Developed in a collaborative public manner

� Free of charge

http

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Source: IE

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Top n

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-source

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What is R

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� Open-source programming language and software

� A platform to build their own functions/packages/products

� The source code is open for inspection

� Free of charge� History

� 1993: A research project initiated by Prof. Ross Ihaka and Robert Gentleman

� 2000: After the hard work of computer scientists and statisticians R version 1.0.0

� Number of packages (Add-ins) grows exponentially

Source: https://gist.github.com/daroczig/3cf06d6db4be2bbe3368

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Why R in Finance?

Trend over the last decade.

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� Are you following or falling behind the trend?

� Data: Google trends over the past 10 years

� It measures how often people search for the given term.

Source: Google Trend, http://goo.gl/vO6LhA

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Why R in Finance?

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� Popular in industry

� ….more could be found here http://www.revolutionanalytics.com/companies-using-r

� Popular in Ivy league universities: Stanford, UCLA, Princeton…

� Princeton even provides docs for helping students and staff switch to R

� Directly load financial data into R from the web: Data mining/big data

� Producing beautiful plots: Today’s FOCUS!

� A all-in-one statistical software and programming language

� You don’t have to switch to other software. R can satisfy most your needs if you know how to search for the packages and have basic programming skills.

Credit risk analysis Reporting

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Use R!

Setting up R working environmentInstall R – Source code Install RStudio – Interface

Click and download!

https://www.r-project.org/ https://www.rstudio.com/products/RStudio/

Other alternative interfaces, see http://goo.gl/yh3Z7G

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Use R!

Setting up R working environment (cont.)� Put all files (data, script…)

in the same folder

� Always start with opening the script: EX: Open R in Finance.R

� For setting up working directory

� See whether all files in the tab of the bottom right pane

� Install packages for this event

� Need internet connection

� Select the lines of code and press Ctrl+Enter

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Use R!

What is an R function?

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� Recall � � ��

� You plug any value in x and you get the results y

� An R function follows exactly the same mathematical structure. But

� Results could be statistics, data sets, plots, table…

� x is called arguments in R

S��

1�∑ ��

!"

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Use R!

Functions & R Structure

Build-in Functions Functions from packages

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� These functions

are ready to use

whenever you

open R

� Know how to use

a function� ?functionName, EX

� ?install.packages

� These functions have to be installed and loaded first � install.packages("quantmod")

� library(quantmod)

� Know how to use these functions

� Download manual of the package

� EX, quantmod

� https://cran.r-project.org/web/packages/quantmod/index.html

R Structure

�

�

��

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Use R!

R in Finance at a Glance

12Full R Finance package list https://cran.r-project.org/web/views/Finance.html

Beginner Expert

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Examples of using R in Finance

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� Use your finance knowledge to help youselflearn R

� What you will learn

� Techniques that let you use R and its packages

� Using three packages to deal with different tasks

� Manipulating data using R build-in functions

� What you will not learn

� The interpretation of the results

� You should be familiar with these materials

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Financial Data Mining

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� Data mining: A fancy word for data downloading but automate the process

� Using R to connect financial data serversData Servers R Packages Free Access Available on CRAN Package/Data URL

Yahoo, FRED, Oanda, Google quantmod Yes Yes http://www.quantmod.com/

ECB, EIA, Eurostat … pdfetch Yes Yeshttps://cran.r-project.org/web/packages/pdfetch/index.html

Quandl Quandl Yes Yes http://www.quandl.com/help/packages/r

TrueFX TFX Yes Yes http://rpubs.com/gsee/TFX

Bloomberg Rbbg No No http://findata.org/rbloomberg/

Interactive Broker IBrokers No Yes https://www.interactivebrokers.com/en/main.php

Datastream rdatastream No No https://github.com/fcocquemas/rdatastream

Penn World Table pwt Yes Yes https://pwt.sas.upenn.edu/

Yahoo, FRED, Oanda fImport Yes Yes http://www.rmetrics.org/

ThinkNum Thinknum Yes Yes http://thinknum.com/

Source: Adapted from (The R Trader, 2013)

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Data for today’s demonstration

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� Stock prices of two banks, Commonwealth and ANZ

� Stock prices are most assessable and varied data (more variance)

� Most of you might use bank data

� Period: 1 Jan 2000 to 31 Mar 2016

� Short term data used in technical trading

� Last recent quarter (subsetting)

� 1 Jan 2016 to 31 Mar 2016

� Long term data used in performance analysis

� Whole data set but convert it to monthly returns (data transformation)

� 1 Jan 2000 to 31 Mar 2016

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Loading data from Yahoo FinanceNow, consider you need to use these two assets for your studies

Commonwealth Bank of Australia ANZ

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Company name (ticker/stock symbol)

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Data Subsetting for xts object

Code Results

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Loading data from Yahoo FinanceTake a look at what you have downloaded with R

Commonwealth Bank of Australia ANZ

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Data cleansing

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� Missing values

� Plotting

� dimensions

� Weird statistics

� No zero prices: no zero for the minimum

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Data cleansing

Look for missing values by plotting

A plot without missing values Man-made missing values

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Jan 01 2016 Jan 25 2016 Feb 15 2016 Mar 07 2016 Mar 28 2016

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CBA.AX$CBA.AX.Adjusted


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CBA.AX$CBA.AX.Adjusted[-20:-30]

Unequal space between the dash lines

R code for this figure

> plot(S.CBA.AX$CBA.AX.Adjusted)

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Technical trading

Candelstick chart

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� Recall

� R code� chartSeries(S.CBA.AX, theme =

chartTheme("white",up.col = "green",dn.col = "red"))

� Now, change the value for the arguments:

70

75

80

85

S.CBA.AX [2016-01-01/2016-03-31]

Last 74.92

Volume (100,000s):

3,039,100

0

20

40

60

80


Sourc

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Now, it is your turn

Changing the values for the arguments

� A quick way to do it is to

copy the line of code and

paste it in the script pane

� Create a new scrip for the

following practice

� Change the value for the

argument

� EX

� Change the data set

� Replace S.CBA.AX with S.ANZ.AX

� Change the theme background

� Replace “white” with “black”

� Change the dn.col

� Replace “red” with “pink”

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Technical trading

Candelstick chart

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� Add Bollinger bands to the chart

� Arguments� n = number of moving average

periods

� sd = number of standard deviations

� maType = type of moving average to be used

� R code� addBBands(n = 20, sd = 2, maType = "SMA")

� 20-day moving average with 2 sd error bands

70

75

80

85

S.CBA.AX [2016-01-01/2016-03-31]

Last 74.92

Bollinger Bands (20,2) [Upper/Lower]: 78.302/73.511

Volume (100,000s):

3,039,100

0

20

40

60

80


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Technical trading

Candelstick chart

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� Add simple moving average to the chart

� Arguments

� n = periods to average over

� col = colour of moving average line

� R code� addSMA(n=10,col = "blue")

70

75

80

85

S.CBA.AX [2016-01-01/2016-03-31]

Last 74.92

Bollinger Bands (20,2) [Upper/Lower]: 78.302/73.511

Volume (100,000s):

3,039,100

0

20

40

60

80


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Technical trading

Candelstick chart

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� Add Moving Average Convergence Divergence line to the chart

� Arguments

� fast=fast period

� slow=slow period

� signal=signal period

� type =type of MA to use.

� R code� addMACD(fast = 12, slow = 26, signal = 9,

type = "EMA")

70

75

80

85

S.CBA.AX [2016-01-01/2016-03-31]

Last 74.92

Bollinger Bands (20,2) [Upper/Low er]: 78.302/73.511

Volume (100,000s):

3,039,100

0

20

40

60

80

Moving Average Convergence Divergence (12,26,9):

MACD: -0.315

Signal: 0.037

-3

-2

-1

0

1

2

3

Jan 01 2016 Jan 18 2016 Feb 01 2016 Feb 15 2016 Feb 29 2016 Mar 14 2016 Mar 28 2016

MACD Line: 12-day EMA - 26-day EMA Signal Line: 9-day EMA of MACD Line MACD Histogram: MACD Line - Signal Line

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Technical trading

Candelstick chart

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� Still want to add more

lines to the chart?

� Look for any function

starting with add in the

quantmod manual

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Data Transformation

Change frequency R code

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� Daily to monthly

� From now on, we use full

data set

� M.CBA.AX <- to.period(CBA.AX,period = "months")

� M.ANZ.AX <- to.period(ANZ.AX,period = "months")

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Data Transformation

Convert prices to returns R code

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� Adjusted closing price

� incorporates the effects of

dividends

� Log returns (Yt)

� price.CBA.AX <- M.CBA.AX[,6]

� Return.CBA.AX <-

diff(log(price.CBA.AX))[-1]

� price.ANZ.AX <- M.ANZ.AX[,6]

� Return.ANZ.AX <-

diff(log(price.ANZ.AX))[-1]

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Performance Analysis

Downside risk measures

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� Be care of the default values

� EX: Value at Risk (VaR)� This could be easily got by running

this

� VaR(Return.CBA.AX)

� But

� Significance level or confidence level?

� Which method to calculate this measure?� It turns out that you are using modified

method which might not be what you want

Manual

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Density plot

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� Arguments� method = add what line to

the plot

� break = a single number giving the number of cells for the histogram

� R code� chart.Histogram(Return.CBA.AX, breaks =

50,method=(c("add.rug","add.normal","add.risk","add.density")))

� rug(Return.CBA.AX,col = "red") #change rugs' color

CBA.AX.Adjusted

ReturnsD

en

sity

-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15

05

10

15

95 %

ModV

aR

95%

VaR

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Time Series Plot

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Feb-2

000

Jan-2

001

Jan-2

002

Jan-2

003

Jan-2

004

Jan-2

005

Jan-2

006

Jan-2

007

Jan-2

008

Jan-2

009

Jan-2

010

Jan-2

011

Jan-2

012

Jan-2

013

Jan-2

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Jan-2

015

Jan-2

016

Mar-

2016

-0.15

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0.00

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0.15CBA.AX.AdjustedANZ.AX.Adjusted

Re

turn

s

Time Series Plot

� R code

� chart.TimeSeries(

� cbind(Return.CBA.AX,Return.ANZ.AX),

� legend.loc="topleft",

� date.format ="%b-%Y",

� las = 2,

� ylab = "Returns",

� main = "Time Series Plot",

� period.areas = c("2007-01-31::2008-12-31"),

� period.color = c("gray"))

Global financial crisis

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Drawdown Plot

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Feb/2

000

Jan/2

001

Jan/2

002

Jan/2

003

Jan/2

004

Jan/2

005

Jan/2

006

Jan/2

007

Jan/2

008

Jan/2

009

Jan/2

010

Jan/2

011

Jan/2

012

Jan/2

013

Jan/2

014

Jan/2

015

Jan/2

016

Mar/

2016

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Drawdown Plot

� chart.Drawdown(

� cbind(Return.CBA.AX,Return.ANZ.AX),

� legend.loc="bottomleft",

� ylab="Drawdowns",

� date.format= "%b/%Y",

� main ="Drawdown Plot",

� las = 2,

� period.areas = c("2007-01-31::2008-12-31","2015-01-31::2016-03-31"),

� period.color = c("gray"))

� Drawdown

summary Measuring loss in percentage between the peak and the trough

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Extensions of using R in Finance

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� Big Data

� Suppose we have the assess to the intra-day stock prices

� The size of whole data set becomes bigger and bigger as D and I increases

� Speed up the data process

� See Zou & Zhang (2014)

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Extensions of using R in Finance

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� Demonstrate your financial models/products on the web using

R Shiny

� EX: Invent a algorithm to predict when to sell or buy the stocks

� http://www.showmeshiny.com

Source:h

ttp://5

2.2

.13.9

7:3

838/fam

a_fren

ch/

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Conclusion:

Where to find the future of finance: Data

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Conclusion: Embrace innovation, embrace challenge, and embrace R

Software Your field of knowledge

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� Excel:

� R Through Excel (Heiberger & Neuwirth, 2009)

� SAS and SPSS: � R for SAS and SPSS Users

(Muenchen, 2011)

� STATA:

� R for Stata Users (Muenchen & Hilbe, 2010)

� MATLAB:

� R and MATLAB (Hiebeler, 2015)

� Finance� Analyzing financial data and

implementing financial models using R (Ang, 2015)

� An introduction to analysis of financial data with R (Tsay, 2014)

� Financial risk forecasting (Daníelsson, 2011)

� Computational finance (Arratia, 2014)

How to learn R by yourself: Always start with something your are familiar with

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References

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� Ang, C. S. (2015). Analyzing financial data and implementing financial models using R. New York, NY: Springer.

� Arratia, A. (2014). Computational finance: An introductory course with R. New York, NY: Springer

� Daníelsson, J. (2011). Financial risk forecasting: The theory and practice of forecasting market risk with implementation in R and Matlab. Hoboken, NJ: John Wiley & Sons.

� Muenchen, R. A., & Hilbe, J. (2010). R for Stata users. New York, NY: Springer.

� Muenchen, R. A. (2011). R for SAS and SPSS users. New York, NY: Springer.

� Hiebeler, D. E. (2015). R and MATLAB: CRC Press.

� Heiberger, R. M., & Neuwirth, E. (2009). R through Excel: A spreadsheet interface for statistics, data analysis, and graphics. New York, NY: Springer.

� Ryan, J. A., & Ulrich, J. M. (2014). xts: eXtensible Time Series. Retrieved from https://cran.r-project.org/web/packages/xts/index.html

� Peterson, B. G., & Carl, P. (2014). PerformanceAnalytics: Econometric tools for performance and risk analysis. Retrieved from http://CRAN.R-project.org/package=PerformanceAnalytics

� The R Trader. (2013). Financial data accessible from R –Part III. Retrieved from http://www.thertrader.com/2013/11/08/financial-data-accessible-from-r-part-iii/

� Tsay, R. S. (2014). An introduction to analysis of financial data with R. New York, NY: John Wiley & Sons.

� Ryan, J. A., Ulrich, J. M., & Thielen, W. (2015). quantmod: Quantitative financial modelling framework. R package version 0.3-5. URL http://www. quantmod. com URL http://r-forge. r-project. org/projects/quantmod. Retrieved from https://cran.r-project.org/web/packages/quantmod/index.html

� Zou, J., & Zhang, H. (2014, 27-30 Oct. 2014). High-frequency financial statistics with parallel R and Intel Xeon Phi coprocessor. Paper presented at the Big Data (Big Data), 2014 IEEE International Conference.

Note: Some references are not here since they are shown with the web links.

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Thanks for your attention!

Liang C. Zhang ((((良丞良丞良丞良丞張張張張))))W: liangchengzhang.weebly.com

E: [email protected]

@LiangCZhang

r in finance: introduction to r and its applications in finance

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