Python for Finance

Chapter 2
2010-01-08 110.929466
>> >df.tail()
>>>
Open High Low Close Volume
Date
2016-11-16 158.460007 159.550003 158.029999 159.289993 2244100
2016-11-17 159.220001 159.929993 158.850006 159.800003 2256400
2016-11-18 159.800003 160.720001 159.210007 160.389999 2958700
2016-11-21 160.690002 163.000000 160.369995 162.770004 4601900
2016-11-22 163.000000 163.000000 161.949997 162.669998 2707900
Adj Close
Date
2016-11-16 159.289993
2016-11-17 159.800003
2016-11-18 160.389999
2016-11-21 162.770004
2016-11-22 162.669998
>>>
This module will be explained again in more detail in Chapter 4, Sources of Data.
Two financial calculators
In the next chapter, many basic financial concepts and formulas will be introduced
and discussed. Usually, when taking corporate finance or financial management,
students rely on either Excel or a financial calculator to conduct their estimations.
Since Python is the computational tool, a financial calculator written in Python
would definitely enhance our understanding of both finance and Python.
Here is the first financial calculator, written in Python, from Numpy.lib.financial;
see the following code:
>>> import numpy.lib.financial as fin
>>> dir(fin)
['__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__
loader__', '__name__', '__package__', '__spec__', '_convert_when',
'_g_div_gp', '_rbl', '_when_to_num', 'absolute_import', 'division',
'fv', 'ipmt', 'irr', 'mirr', 'np', 'nper', 'npv', 'pmt', 'ppmt',
'print_function', 'pv', 'rate']
>>>
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