Aug
12
My friend & I have collected dozens of videos relevant to quantitative finance study, including basic finance, Credit, Derivatives, Mathematics, Risk, Trading, etc. The main purpose is to build a convenient online video place to share and learn math finance.Please help it grow by visiting QuanTube, rating the video, and most importantly, submit a better video you come across, we appreciate that.
Aug
10
Couple of months ago I created a PDF document including most of articles on this blog, (read Top Quant codes collection you shouldn't miss). Over one year passed I realize I need to make an updated one as many more articles have been added since then. It helps me to backup and some of my readers to view easily.Quantitative Finance Collector is simply a record of my financial engineering learning journey as a master in quantitative finance, a PhD candidate in finance and a Quantitative researcher, with most of the entries written at school. Download the whole articles. (updated 08/2010)
It has 460 pages long, covering all artilces up to 09/08/2010. (wow, much more than I expected), some of them are irrelavant to quant finance, and please forgive me as there are absolutely lots of English grammer errors and poor expression. So feel free to distribute or delete it.
Aug
9
A follow-up of my previous post Excellent Free CSV Splitter. I asked a question at LinkedIn about how to handle large CSV files in R / Matlab. Specifically,
A useful summary of suggested solution:
1, 1) import the large file via "scan" in R;
2) convert to a data.frame --> to keep data formats
3) use cast --> to group data in the most "square" format as possible, this step involves the Reshape package, a very good one.
Quotation
suppose I have a large CSV file with over 30 million number of rows, both Matlab / R lacks memory when importing the data. Could you share your way to handle this issue? what I am thinking is:
a) split the file into several pieces (free, straightforward but hard to maintain);
b) use MS SQL/MySQL (have to learn it, MS SQL isn't free, not straightforward).
a) split the file into several pieces (free, straightforward but hard to maintain);
b) use MS SQL/MySQL (have to learn it, MS SQL isn't free, not straightforward).
A useful summary of suggested solution:
1, 1) import the large file via "scan" in R;
2) convert to a data.frame --> to keep data formats
3) use cast --> to group data in the most "square" format as possible, this step involves the Reshape package, a very good one.
Aug
6
Share an excellent free CSV splitter I found recently, as my csv file is too large to be openned in Matlab & R, I have to split the csv into several smaller files. As far as I have tried, Matlab & R warn "short of memory" for reading csv file larger than 10,000,000 number of rows (it may be varied across computers), while my tick-by-tick corporate bond data has nearly 30,000,000 number of rows.
This CSV splitter allows you to split your large file into several smaller files either by number of lines or by max pieces,
The amazing point of it is the smaller files keep the original header of the big csv file, very cool. Download the free csv splitter here.
This CSV splitter allows you to split your large file into several smaller files either by number of lines or by max pieces,
The amazing point of it is the smaller files keep the original header of the big csv file, very cool. Download the free csv splitter here.
Aug
5
Long time no blog. Just to let you know I am still alive, busy with my own PhD research, collecting & cleaning data, programming, making my hands dirty...
Data massaging is not fun, what makes us more upset is different data providers have their own data format, name, code, etc., matching the data from several sources is not so easy, for example, WRDS includes CUSIP code while Datastream provides ISIN. I didn't understand why they do business like that but now I get it, similar as those cell phone manufacturers have distinct chargers and plug-in, not because it's hard to standardize, but a way to impose customers to use always their own products.
Anyway, you can convert ISIN code to CUSIP easily once you understand the rule, ISIN is a 12-digit number while CUSIP is a 9-digit one (at least the case for US corporate bond), so what you need to do is to first strip off the first 2 characters representing country code and then remove the last digit which is a check digit for catching error.
Suppose your ISIN code is in cell A1, ISIN CUSIP conversion can be done easily in Excel as "=left(right(A1, 10), 9)", for instance, ISIN US885797AB65 equals CUSIP 885797AB6.
Data massaging is not fun, what makes us more upset is different data providers have their own data format, name, code, etc., matching the data from several sources is not so easy, for example, WRDS includes CUSIP code while Datastream provides ISIN. I didn't understand why they do business like that but now I get it, similar as those cell phone manufacturers have distinct chargers and plug-in, not because it's hard to standardize, but a way to impose customers to use always their own products.
Anyway, you can convert ISIN code to CUSIP easily once you understand the rule, ISIN is a 12-digit number while CUSIP is a 9-digit one (at least the case for US corporate bond), so what you need to do is to first strip off the first 2 characters representing country code and then remove the last digit which is a check digit for catching error.
Suppose your ISIN code is in cell A1, ISIN CUSIP conversion can be done easily in Excel as "=left(right(A1, 10), 9)", for instance, ISIN US885797AB65 equals CUSIP 885797AB6.
Quantitative Finance Collector is simply a record of my financial engineering learning journey as a master in quantitative finance, a PhD candidate in finance and a Quantitative researcher, with most of the entries written at school. 
