Python for Finance

Chapter 8
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• Hasbrouck, Joel, 1992, Using the TORQ database, New York University,
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• Moskowitz, T., and Grinblatt, M., 1999, Do industries explain momentum?
Journal of Finance 54(4), 2017–2069, http://faculty.som.yale.edu/
Tobiasmoskowitz/documents/DoIndustriesExplainMomentum.pdf
• Pastor and Stambaugh, 2003, Liqudity measure and expected stock returns, Journal
of Political Economy, 642-685, http://people.stern.nyu.edu/lpederse/
courses/LAP/papers/TransactionCosts/PastorStam.pdf
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Market, Journal of Finance, 39, 1127-1139, http://onlinelibrary.wiley.
com/doi/10.1111/j.1540-6261.1984.tb03897.x/pdf
Appendix A – Python program to generate
GDP dataset usGDPquarterly2.pkl
The first program generates a Python dataset with a .pkl extension:
import pandas_datareader.data as web
import datetime
begdate = datetime.datetime(1900, 1, 1)
enddate = datetime.datetime(2017, 1, 27)
x= web.DataReader("GDP", "fred", begdate,enddate)
x.to_pickle("c:/temp/ugGDPquarterly2.pkl")
To retrieve the dataset, we use the pandas.read_pickle() function. See the
following code:
import pandas as pd
a=pd.read_pickle("c:/temp/usGDPquarterly2.pkl")
print(a.head())
print(a.tail())
GDP
DATE
1947-01-01 243.1
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