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, we have two input variables: ticker and path. After we choose...