In the previous recipe, we tried out a trading idea. However, we have no benchmark that can tell us if the result we got was any good. It is common in such cases to trade at random, under the assumption that we should be able to beat a random process. We will simulate trading by taking some random days from a trading year. This should illustrate working with random numbers using NumPy.
If necessary, install Matplotlib. Refer to the See Also section for the corresponding recipe.
First, we need an array filled with random integers.
- Generate random indices.
Generate random integers with the NumPy
randintfunction. This will be linked to random days of a trading year:return numpy.random.randint(0, high, size)
- Simulate trades.
Simulate trades with the random indices from the previous step. Use the NumPy
takefunction to extract random close prices from the array of step 1:buys = numpy.take(close, get_indices(len(close), nbuys)) sells = numpy.take(close, get_indices(len(close), nbuys)) profits[i] = sells.sum() - buys.sum()
- Plot a histogram of the profits.
Let's plot a histogram of the profits for a large number of simulations:
matplotlib.pyplot.hist(profits) matplotlib.pyplot.show()
The resulting histogram of 2000 simulations for AAPL with 5 buys and sells in a year:
The following is the complete code:
from matplotlib.finance import quotes_historical_yahoo
from datetime import date
import numpy
import sys
import matplotlib.pyplot
def get_indices(high, size):
#2. Generate random indices
return numpy.random.randint(0, high, size)
#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])
nbuys = int(sys.argv[2])
N = int(sys.argv[3])
profits = numpy.zeros(N)
for i in xrange(N):
#3. Simulate trades
buys = numpy.take(close, get_indices(len(close), nbuys))
sells = numpy.take(close, get_indices(len(close), nbuys))
profits[i] = sells.sum() - buys.sum()
print "Mean", profits.mean()
print "Std", profits.std()
#4. Plot a histogram of the profits
matplotlib.pyplot.hist(profits)
matplotlib.pyplot.show()
#python random_periodic.py AAPL 5 2000
#Mean -2.566465
#Std 133.746039463We used the randint function, which can be
found in the
numpy.random module. This module contains more convenient random generators, as described in the following table:
- The Installing Matplotlib recipe in Chapter 1, Winding Along with IPython