portfolio sharp ratio

portfolio_sharp_ratio.py

import scipy
from yahoo_finance import YahooFinance


VALUE_TYPE = {'Date': 0, 'Open': 1, 'High': 2, 'Low': 3, 'Close': 4, 'Volume': 5, 'Adj_Close': 6}
start_date = "20121101"
end_date = "20121201"

yf = YahooFinance("AAPL,GOOG,GLD,USO")
percent = scipy.array([0.25, 0.15, 0.4, 0.2]) #investment distribution portfolio 

all_adj_prices = []
all_total_return = []
all_daily_return = []
all_avg_daily_return = []
all_std_daily_return = []
all_sharp_ratio = []

ticker = 0
for (symb, data) in yf.get_historical_prices(start_date, end_date):    
    data.reverse() #reverse the time series
    data.pop() #get rid of the header
    adj_prices = scipy.array(map(lambda x : float(x[VALUE_TYPE['Adj_Close']]), data))    
    all_adj_prices.append(adj_prices)
    total_return = (adj_prices[-1]/adj_prices[0]) - 1 #calculate the total return for apple this month
    all_total_return.append(total_return)
    daily_return = (adj_prices[1:]/adj_prices[0:-1]) - 1 #calculate the daily_return
    all_daily_return.append(daily_return)    
    avg_daily_return = scipy.mean(daily_return, 0) #average daily retuen
    all_avg_daily_return.append(avg_daily_return)
    std_daily_return = scipy.std(daily_return, 0) #deviation fo daily return
    all_std_daily_return.append(std_daily_return)
    sharp_ratio = scipy.sqrt(250) * (avg_daily_return / std_daily_return) # sharp ratio
    all_sharp_ratio.append(sharp_ratio)
    
  """
  our prices
  GOOG : 687.5,  687.9,  682.9,  681.7,  667.1,  652.2,  663. ,  665.9,
        659. ,  652.5,  647.2,  647.1,  668.2,  669.9,  665.8,  667.9,
        661.1,  670.7,  683.6,  691.8,  698.3
  
  USO : 32. ,  31.3,  31.6,  32.5,  31.2,  31.3,  31.7,  31.5,  31.4,
        31.7,  31.4,  31.9,  32.6,  31.9,  32.1,  32.3,  32.1,  31.9,
        31.7,  32.1,  32.5
  
  AAPL : 593.8,  574.1,  581.9,  580.2,  558. ,  537.7,  547. ,  542.8,
        542.9,  536.8,  525.6,  527.6,  565.7,  560.9,  561.7,  571.5,
        589.5,  584.7,  582.9,  589.3,  585.2

  GLD : 166. ,  162.6,  163.2,  166.3,  166.4,  167.9,  167.8,  167.4,
        167.1,  167.1,  166. ,  165.8,  167.8,  167.3,  167.5,  169.6,
        169.4,  168.7,  166.5,  167.1,  166.
  
  total return for each stock : [0.015709090909090895, 0.015625, -0.014482990906028781, 0.0]
  daily return :
  GOOG : 0.00058182, -0.0072685 , -0.00175721, -0.02141705, -0.02233548,
        0.01655934,  0.00437406, -0.01036192, -0.00986343, -0.00812261,
       -0.00015451,  0.03260702,  0.00254415, -0.00612032,  0.0031541 ,
       -0.01018116,  0.01452125,  0.01923364,  0.01199532,  0.00939578
       
  USO : -0.021875  ,  0.00958466,  0.02848101, -0.04      ,  0.00320513,
        0.01277955, -0.00630915, -0.0031746 ,  0.00955414, -0.00946372,
        0.01592357,  0.02194357, -0.02147239,  0.00626959,  0.00623053,
       -0.00619195, -0.00623053, -0.00626959,  0.0126183 ,  0.01246106 
  
  AAPL : -0.03317615,  0.01358648, -0.00292146, -0.03826267, -0.03637993,
        0.01729589, -0.00767824,  0.00018423, -0.01123596, -0.02086438,
        0.00380518,  0.0722138 , -0.00848506,  0.00142628,  0.01744704,
        0.03149606, -0.00814249, -0.0030785 ,  0.01097958, -0.00695741 
  
  GLD : -0.02048193,  0.00369004,  0.0189951 ,  0.00060132,  0.00901442,
       -0.00059559, -0.00238379, -0.00179211,  0.        , -0.00658288,
       -0.00120482,  0.01206273, -0.00297974,  0.00119546,  0.01253731,
       -0.00117925, -0.00413223, -0.0130409 ,  0.0036036 , -0.00658288
  
  average daily return for each stock : [0.00086921414889110646, 0.00090320887851679248, -0.00043738608805856115, 3.7192619905374034e-05]
  std deviation for each stock : [0.01340975818314491, 0.015929201121363604, 0.024332769635802963, 0.008623865082371179]
  sharp ratio for each stock : [1.0248866711092259, 0.8965287201907356, -0.28421266379411581, 0.068190648813791305]
  """
  
  corr = scipy.corrcoef(all_adj_prices)
  """
  [[ GOOG        USO         AAPL        GLD]
   [ 1.          0.35796266  0.83675929 -0.38083073]
   [ 0.35796266  1.          0.49457339  0.33637871]
   [ 0.83675929  0.49457339  1.         -0.04349294]
   [-0.38083073  0.33637871 -0.04349294  1.        ]]
  """

portfolio_total_return = None
portfolio_daily_return = None

for ticker in range(4):
    if ticker == 0:
        portfolio_daily_return = all_daily_return[ticker] * percent[ticker]
        portfolio_total_return = all_total_return[ticker] * percent[ticker]
    else:
        portfolio_daily_return += all_daily_return[ticker] * percent[ticker]
        portfolio_total_return += all_total_return[ticker] * percent[ticker]    

portfolio_avg_daily_return = scipy.mean(portfolio_daily_return, 0)
portfolio_std_daily_return = scipy.std(portfolio_daily_return, 0)
portfolio_sharp_ratio = scipy.sqrt(250) * (portfolio_avg_daily_return / portfolio_std_daily_return) # sharp ratio

"""
portfolio daily return : 
[ -2.05026424e-02   5.79317575e-03   6.46328287e-03  -2.65390640e-02
  -1.78521880e-02   1.28560048e-02  -3.40091395e-03  -3.35140050e-03
  -5.52711836e-03  -1.31125392e-02   3.63101326e-03   4.27413546e-02
  -6.57479444e-03   2.19962885e-04   1.12093815e-02   8.88849236e-03
  -1.38771006e-03   2.83893335e-05   1.00041289e-02   1.18563923e-04]

portfolio total return : 0.000477826364861
portfolio average daily return : 0.000185268957758
portfolio std deviation daily return : 0.0143152971765
portfolio sharp ratio: 0.20463140898

"""