Book Image

Python for Finance

By : Yuxing Yan
Book Image

Python for Finance

By: Yuxing Yan

Overview of this book

A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.
Table of Contents (14 chapters)
13
Index

Python for high-frequency data

High-frequency data is referred to second-by-second or millisecond-by-millisecond transaction and quotation data. The New York Stock Exchange's TAQ (Trade and Quotation) database is a typical example (http://www.nyxdata.com/data-products/daily-taq). The following program can be used to retrieve high-frequency data from Google Finance:

>>>import re, string
>>>import pandas as pd
>>>ticker='AAPL'         # input a ticker
>>>f1="c:/temp/ttt.txt"  # ttt will be replace with aboove sticker
>>>f2=f1.replace("ttt",ticker)
>>>outfile=open(f2,"w")
>>>path="http://www.google.com/finance/getprices?q=ttt&i=300&p=10d&f=d,o,h,l,c,v"
>>>path2=path.replace("ttt",ticker)
>>>df=pd.read_csv(path2,skiprows=8,header=None)
>>>df.to_csv(outfile,header=False,index=False)
>>>outfile.close()

In the preceding program...