__R for Financial Engineering / Quantitative Finance__**Website**- http://qcfinance.in/r-for-financial-engineering-course/

**YouTube Channel**-http://www.youtube.com/user/shivbhaktajoshi

R is having mix of maths and data handling powers of SAS and given that it is open source and the best choice for research.

R is MATLAB when it comes to syntax.

When it comes to data handling it uses RMySQL in the very same way as SAS does.

Hence, it does things that SAS+MATLAB do for free.

SQL has inherent limitations of doing quant computations, and MATLAB is heave and requires too much resources. MATLAB is not very good to deal with SQL at system level.

But to start with, one also needs to know VBA for entry level and for easy task, VBA is preferred.

**Divisions:**

- 10 hours on handling data, functions, indexing, structures, data linkages, importing, SQL, etc
- 10 hours on Financial Commands, tools, pre-built libraries.

I will discuss in detail about R and its application in this post.

Keep checking it for updates.

Ref: R in a Nutshell

For getting some info you can join my MATLAB Course at:

http://stockcreditfinancecfa.blogspot.com/2012/07/50-hours-course-in-matlab-for-finance.html

Join our MATLAB for financial engineering course (http://www.wiziq.com/course/7225-matlab-for-financial-engineering) & get 15% discount. Ask for discount code, e-mail:- info@qcfinance.in.

**Course based on:**

http://www.unicom.co.uk/quant-finance/R-for-Finance.php

http://www.optirisk-systems.com/papers/FinancewithR.pdf

http://www.optirisk-systems.com/events/finance-with-r.asp

http://www.r-bloggers.com/search/finance

http://www.rinfinance.com/RinFinance2009/presentations/ChicagoWuertz.pdf

http://braverock.com/brian/R/PerformanceAnalytics/html/PerformanceAnalytics-package.html

http://www.quantmod.com/documentation/00Index.html

http://tradeblotter.files.wordpress.com/2012/06/uw_r_seminar_may_2012.pdf

__Introduction to R__- Some background on R, how to obtain and how to install it.
- R documentation and help and web resources available.
- R Console and R GUI.
- Data import, data types and variables.
- Elementary import functions.
- Data types.
- Variable generation, inspection and modification.
- Graphics in R.
- Command "plot" and where to plot.
- Setting the par.
- High-level and low-level plotting functions legends.
- Math in plots.
- Hands-on Session—data handling & graphics.
- Simulations in R.
- Simulating from given distributions.
- Risk management basics.
- Data Structures in R.
- Review of types of objects.
- Additional object types.
- Basics of the R Language.
- Conditionals & Loops.
- Writing R Functions: Basic functions.
- Object-orientation.
- Debugging.
- Application in Finance.
- Calculating Value at Risk.
- Packages for Portfolio Optimization.

**Functions of R/R Commands to be covered in the course:**

Caution: Always save workspace.

Use “c” when you want to use it a signal in black scholes formula.

read.table function will be used to get the data into R (/\ used opposite

than the one used in windows)

http://www.ats.ucla.edu/stat/r/faq/inputdata_R.htm.

**Example 1**
> test.txt <- read.table ("C:/Documents and Settings/shivbhakta.joshi/My

Documents/test.txt", header=T)

> print(test.txt)

> column <- test.txt[,c('make','price')].

**Example 2**
grb <- read.table ("C:/Documents and Settings/shivbhakta.joshi/My

Documents/GRB_afterglow.dat", header=T, skip=1)

x <- (grb[,1]).

**Grabbing data from tables into a single dimensional array**

x <- (grb[,1])

y <- log(grb[,1]).

__Another way to create returns for stock daily return is__

f=dft$age/dft$price (using dollar sign extraction)

Deleting column http://www.sr.bham.ac.uk/~ajrs/R/r-manipulate_data.html

> add <- add[-2] to delete the element 2nd

Adding data to array busing column command

> add <- c(1,d).

**Using Data frames**

Data.frame is used to combine the arrays back into table format

people = data.frame (x,y)

Write.CSV command executed

write.csv(people,"filename.csv")

grb <- read.table ("C:/Documents and Settings/satyadhar.joshi/My

Documents/GRB_afterglow.dat", header=T, skip=1)

x <- (grb[,1])

y <- log(grb[,1])

people = data.frame (x,y)

write.csv(people,"filename.csv")

These methods can be used to compute beta from columns of a csv file.

**Working formula of Black Scholes**

BS <-

function(S, K, T, r, sig, type="C"){

d1 <- (log(S/K) + (r + sig^2/2)*T) / (sig*sqrt(T))

d2 <- d1 - sig*sqrt(T)

if(type=="C"){

value <- S*pnorm(d1) - K*exp(-r*T)*pnorm(d2)

}

if(type=="P"){

value <- K*exp(-r*T)*pnorm(-d2) - S*pnorm(-d1)

}

return(value)

}

Black Scholes <- function(s, k, r=.1, t=5, sigma=.9,call=TRUE) {

#calculate call/put option

d1 <- (log(s/k)+(r+sigma^2/2)*t)/(sigma*sqrt(t))

d2 <- d1 - sigma * sqrt(t)

ifelse(call==TRUE,s*pnorm(d1) - k*exp(-r*t)*pnorm(d2),k*exp(-r*t)

* pnorm(-d2) - s*pnorm(-d1))

}

**Predefined Black schools model:**

http://shafik.net/~shafik/FinancialEngineering/Code/

black_scholes ("c",5600,5600,.1,.08,.22 )

this depicts use of functions in R.

**Merton Distance to Default (different as per needs)**

(modified from Black Scholes formula, still

unsure about pnorm() function)

d1 <- (log(s/k)+(sigma^2/2))/(sigma)

pnorm(d1)

3 files are needed to run the demos which are available on internet by various authors:

- black_scholes.R
- GRB_afterglow.dat
- test.txt.

**ARIMA and Forecasting Using R**

__Commands:__

library(Quandl)

> Quandl.auth("1LkmpypqJskJzKcpd2TV")

>stockData=Quandl("YAHOO/INDEX_GSPC", start_date="2004-01-01", end_date="2014-04-17")

stockData[,7]

// ran Arima here:

results2=arima(stockData[,7], order = c(3,0,0))

fit <- arima(myts, order=c(p, d, q)

fit <- arima(stockData[,7],order = c(3,0,0))

results=arima(stockData[,7])

fit <- arima(stockData[,7],order = c(3,0,0))

predict(arima(stockData[,7], order = c(3,0,0))

results=arima(stockData[,7])

x2=(forecast(fit, 100))

plot(x2).

**Excel and R:**http://rcom.univie.ac.at/download.html#statconnDCOM

http://www.r-bloggers.com/a-million-ways-to-connect-r-and-excel/

http://cran.r-project.org/web/packages/XLConnect/vignettes/XLConnect.pdf

http://cran.r-project.org/web/packages/XLConnect/XLConnect.pdf

https://stat.ethz.ch/R-manual/R-patched/library/base/html/getwd.html

__Playing with arrays and matrix__http://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-do-my-matrices-lose-dimensions_003f

https://stat.ethz.ch/pipermail/r-help/2008-February/154088.html

http://stat.ethz.ch/R-manual/R-devel/library/base/html/rev.html.

__ARIMA and Predict statement__http://stat.ethz.ch/R-manual/R-patched/library/stats/html/predict.arima.html

http://people.duke.edu/~rnau/411arim.htm

http://www.statmethods.net/advstats/timeseries.html

http://stackoverflow.com/questions/14272937/time-series-prediction-using-r

http://www.inside-r.org/packages/cran/forecast/docs/forecast.Arima.

__Market check__http://www.bloomberg.com/quote/SPX:IND/chart.

__Other good References__
http://heather.cs.ucdavis.edu/~matloff/132/NSPpart.pdf.

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**One on One Customized Training**to our clients.
These Trainings are provided on request by our clients & are customized according to their individual needs.

The course structure & timings for these training are highly flexible, classes are scheduled as per the convenience of our clients.

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