session 1

first session is here

R

R is a popular programming language used by data analysts inside corporations and academia. It is becoming their day-to-day part of financial modeling, data-mining and statistical analysis. Companies like Google (for ad pricing trends), Bank of America (for research) and many more are using R. A lot of the hedge funds have trading algorithms that are written using R-environment which are taking the place of the human trading decisions. One of the reasons I guess people are using R because it doesn’t require in-depth programming skills and it’s very easy to use.

2nd Session

The material is here.

Sorry to keep you guys waiting today. Got held up in a rather unfortunate incident - seems my involement is jinxed this year! The injury is minor, but it being on the left wrist prevents me from driving for a while. But am not sure it would be a good idea to keep you guys in limbo. Would talk to your course coordinator in case they have a 'back-up'. Else, if it's ok with your insti, we could do it in your next term.

Sorry again, in case I left you high and dry. Well, the least I can do is leave you with the video :)

For those who were interested in 'learning' (as against just meeting the grade), I have some partial material ready for the course outline I shared with you and will keep posting it on this blog once a week or so - roughly with the same frequency as our classes would have been.

Good luck to you all!

Today's Class [10/10/09]

I have taken ill overnight with what seems like a viral. So, sorry guys, there won't be a class today. I don't have the class rep's number and I am not able to reach the course co-ordinator on phone. So in the unlikely case any of you has this site open, could you please pass this on to the class.

Excel Tips: I

1. Locating Microsoft Office Excel 2003 commands in v. 2007: Here or here

2. Excel keyboard shortcuts (applies to 2003) - but afaik all 2003 keyboard shortcuts work in 2007 too...something you may wanna try.

1st Session: Course Introduction & some Useful Excel Functions

Here is what we are going to discuss tomorrow:

1. About the course

2. Course evaluation

3. Introduction to - or revision of, as the case may be - some advanced Excel functions and procedures useful for financial applications. In no particular order, we'll cover:

a) Accessing mathematical / statistical functions
b) Using keyboard short-cuts etc
c) Look-up functions
d) Absolute vs Relative Referencing
e) Array functions
f) Data Tables etc.

4. Introduction to VBA - time permitting.

I hope your course coordinator has been able to arrange the book by now. If not, I would suggest you push for it.

Update: Material for the 1st Session is here.

Computer Applications in Finance - Course Outline

Welcome everybody! Here is the course outline for your 'Computer Applications in Finance' course. I am told our first meeting is slated 3rd October - but I am sharing the course outline now in case any of you is interested to give it a look-er. Other than posting lecture notes and comments on the class discussion, I'll also be using this blog to post course-related links and solution to assignments.

Feedbacks and suggestions are always welcome. Discretion on what I choose to do with them, of course, remains mine :-) Just kidding - pls feel free to post comments on the course outline or any other suggestions / questions you may have. I'll respond sooner than later.

[ Yes, I am ditching good ol' MATLAB for Excel this year. I hate to concede, but sometimes Excel just makes more practical sense!]

R Basics - I

Reading Data in R

The vector is the simplest way to store more than one value in R. The 'c' function (mnemonic for catenate or combine) allows you to quickly enter data into R:

> x = c(1, 2, 5, 10) 

# or x <- c(1, 2, 5, 10)

[TBC]

R

Good as MATLAB is for prototyping, its scalability in working with larger datasets - I have in mind high frequency financial data - is a problem (or may be I just don't know how to yet). 

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I needn't talk about the virtues of R. It has a strong user-base and also has many contributed financial / econometrics packages (e.g. Rmetrics and AER) to make our life easier. The hope is to be able to read high-frequency datasets in a reasonable amount of time (may be with a little bit of help from Perl?) and then exploit R's statistical abilities. 

The first thing we need to figure out is how to read data in R and then learn about vectorization. The rest should be easier.