Using Cython with NumPy

We can integrate Cython and NumPy code in the same way that we can integrate Cython and Python code. Let's go through an example that analyzes the ratio of up days (days on which a stock closes higher than the previous day) for a stock. We will apply the formula for binomial proportion confidence. You can refer to http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval for more information. This indicates how significant the ratio is.

How to do it...

This section describes how we can use Cython with NumPy. To demonstrate this, perform the following steps:

  1. Write the .pyx file.

    Let's write a .pyx file that contains a function to calculate the ratio of up days and associated confidence. First, this function computes the differences of the prices. Then, we count the number of positive differences, giving us a ratio for the proportion of up days. Finally, we apply the formula for the confidence from the Wikipedia page in the introduction.

    import numpy

def pos_confidence(numbers):
diffs = numpy.diff(numbers)
n = float(len(diffs))
p = len(diffs[diffs > 0])/n
confidence = numpy.sqrt(p * (1 - p)/ n)

return (p, confidence)
  2. Write the setup.py file.

    We will use the setup.py file from the previous example, as the template. There are some obvious things that need to be changed, such as the name of the .pyx file.

    from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext

ext_modules = [Extension("binomial_proportion", ["binomial_proportion.pyx"])]

setup(
        name = 'Binomial proportion app',
        cmdclass = {'build_ext': build_ext},
        ext_modules = ext_modules
     )

    We can now build—see the previous recipe for more details.

  3. Use the Cython module.

    After building, we can use the Cython module from the previous step by importing. We will write a Python program that downloads stock price data with matplotlib. Then, we will apply the confidence function to the close prices.

    from matplotlib.finance import quotes_historical_yahoo
from datetime import date
import numpy
import sys
from binomial_proportion import pos_confidence

#1. Get close prices.
today = date.today()
start = (today.year - 1, today.month, today.day)

quotes = quotes_historical_yahoo(sys.argv[1], start, today)
close = numpy.array([q[4] for q in quotes])
print pos_confidence(close)

    The output of the program for AAPL is shown as follows:

    (0.56746031746031744, 0.031209043355655924)

How it works...

We computed the probability of an up day for AAPL shares and the corresponding confidence. We put NumPy code in a .pyx file and built it just like in the previous tutorial, creating a Cython module. At the end, we imported and used the Cython module.

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