Python for Finance

Chapter 2
Similarly, we could save all the functions to a vector (array); see the following code:
>>>import scipy as sp
>>> x=dir(sp)
>>> len(x)
588
>>>
Introduction to matplotlib
Graphs and other visual representations have become more important in explaining
many complex financial concepts, trading strategies, and formulas.
In this section, we discuss the matplotlib module, which is used to create various
types of graphs. In addition, the module will be used intensively in Chapter 10,
Options and Futures, when we discuss the famous Black-Scholes-Merton option model
and various trading strategies. The matplotlib module is designed to produce
publication-quality figures and graphs. The matplotlib module depends on NumPy
and SciPy, which were discussed in the previous sections. To save generated graphs,
there are several output formats available, such as PDF, Postscript, SVG, and PNG.
How to install matplotlib
If Python was installed by using the Anaconda super package, then matplotlib is
preinstalled already. After launching Spyder, type the following line to test. If there
is no error, it means that we have imported/uploaded the module successfully. This
is the beauty of using a super package such as Anaconda:
>>> import matplotlib
To install the matplotlib module or other modules independently, see the Module
dependency – how to install a module section.
Several graphical presentations using
matplotlib
The best way to understand the usage of the matplotlib module is through
examples. The following example could be the simplest one since it has just
three lines of Python code. The objective is to link several points. By default, the
matplotlib module assumes that the x axis starts at zero and moves by one on every
element of the array.
[ 45 ]