In [1]:

    

import numpy as np


    

In [2]:

    

import pandas as pd


    

In [3]:

    

import matplotlib.pyplot as plt


    

In [4]:

    

df=pd.read_excel('IGE.xls')


    

In [5]:

    

df.sort_values(by='Date', inplace=True)


    

In [6]:

    

dailyret=df.loc[:, 'Adj Close'].pct_change() # daily returns


    

In [7]:

    

excessRet=dailyret-0.04/252 #  excess daily returns = strategy returns - financing cost, assuming risk-free rate of


    

In [8]:

    

sharpeRatio=np.sqrt(252)*np.mean(excessRet)/np.std(excessRet)


    

In [9]:

    

sharpeRatio


    

    
Out[9]:





0.789580250130583

In [10]:

    

df2=pd.read_excel('SPY.xls')


    

In [11]:

    

df=pd.merge(df, df2, on='Date', suffixes=('_IGE', '_SPY'))


    

In [12]:

    

df['Date']=pd.to_datetime(df['Date'])


    

In [13]:

    

df.set_index('Date', inplace=True)


    

In [14]:

    

df.sort_index(inplace=True)


    

In [15]:

    

dailyret=df[['Adj Close_IGE', 'Adj Close_SPY']].pct_change() # daily returns


    

In [16]:

    

dailyret.rename(columns={"Adj Close_IGE": "IGE", "Adj Close_SPY": "SPY"}, inplace=True)


    

In [17]:

    

netRet=(dailyret['IGE']-dailyret['SPY'])/2


    

In [18]:

    

sharpeRatio=np.sqrt(252)*np.mean(netRet)/np.std(netRet)


    

In [19]:

    

sharpeRatio


    

    
Out[19]:





0.7839419359681374

In [20]:

    

cumret=np.cumprod(1+netRet)-1


    

In [21]:

    

plt.plot(cumret)


    

    
Out[21]:





[<matplotlib.lines.Line2D at 0x237672b7e48>]






    

In [22]:

    

from calculateMaxDD import calculateMaxDD


    

In [23]:

    

maxDrawdown, maxDrawdownDuration, startDrawdownDay=calculateMaxDD(cumret.values)


    

In [24]:

    

maxDrawdown


    

    
Out[24]:





-0.09529268047208683

In [25]:

    

maxDrawdownDuration


    

    
Out[25]:





497.0

In [26]:

    

startDrawdownDay


    

    
Out[26]:





1223