It is a good application to estimate annualized return distribution and represent it as a graph. To make our exercise more meaningful, we download Microsoft 's daily price data. Then, we estimate its daily returns and convert them into annual ones. Based on those annual returns, we generate its distribution by applying bootstrapping with replacements 5,000 times as shown in the following code:
from matplotlib.finance import quotes_historical_yahoo import matplotlib.pyplot as plt import numpy as np import scipy as sp # Step 1: input area ticker='MSFT' # input value 1 begdate=(1926,1,1) # input value 2 enddate=(2013,12,31) # input value 3 n_simulation=5000 # input value 4 # Step 2: retrieve price data and estimate log returns x=quotes_historical_yahoo(ticker,begdate,enddate,asobject=True,adjusted=True) logret = log(x.aclose[1:]/x.aclose[:-1]) # Step 3: estimate annual returns date=[] d0=x.date for i in range(0,size(logret)): date.append...