im trying to replicate a certain code from yuxing Yan's python for finance. I am at a road block because I am getting very high minimized figures(in this case stock weights, which ca be both +(long) and (-short) after optimization with fmin().
can anyone help me with a fresh pair of eyes. I have seen some suggestion about avoiding passing negative or complex figures to fmin() but I can't afford to as its vital to my code
#Lets import our modules
from scipy.optimize import fmin #to minimise our negative sharpe-ratio
import numpy as np#deals with numbers python
from datetime import datetime#handles date objects
import pandas_datareader.data as pdr #to read download equity data
import pandas as pd #for reading and accessing tables etc
import scipy as sp
from scipy.stats import norm
import scipy.stats as stats
from scipy.optimize import fminbound
assets=('AAPL',
'IBM',
'GOOG',
'BP',
'XOM',
'COST',
'GS')
#start and enddate to be downloaded
startdate='2016-01-01'
enddate='2016-01-31'
rf_rate=0.0003
n=len(assets)
#_______________________________________________
#This functions takes the assets,start and end dates and
#returns portfolio return
#__________________________________________________
def port_returns (assets,startdate,enddate):
#We use adjusted clsoing prices of sepcified dates of assets
#as we will only be interested in returns
data = pdr.get_data_yahoo(assets, start=startdate, end=enddate)['Adj Close']
#We calculate the percentage change of our returns
#using pct_change function in python
returns=data.pct_change()
return returns
def portfolio_variance(returns,weight):
#finding the correlation of our returns by
#dropping the nan values and transposing
correlation_coefficient = np.corrcoef(returns.dropna().T)
#standard deviation of our returns
std=np.std(returns,axis=0)
#initialising our variance
port_var = 0.0
#creating a nested loop to calculate our portfolio variance
#where the variance is w12σ12 + w22σ22 + 2w1w2(Cov1,2)
#and correlation coefficient is given by covaraince btn two assets divided by standard
#multiplication of standard deviation of both assets
for i in range(n):
for j in range(n):
#we calculate the variance by continuously summing up the varaince between two
#assets using i as base loop, multiplying by std and corrcoef
port_var += weight[i]*weight[j]*std[i]*std[j]*correlation_coefficient[i, j]
return port_var
def sharpe_ratio(returns,weights):
#call our variance function
variance=portfolio_variance(returns,weights)
avg_return=np.mean(returns,axis=0)
#turn our returns to an array
returns_array = np.array(avg_return)
#Our sharpe ratio uses expected return gotten from multiplying weights and return
# and standard deviation gotten by square rooting our variance
#https://en.wikipedia.org/wiki/Sharpe_ratio
return (np.dot(weights,returns_array) - rf_rate)/np.sqrt(variance)
def negate_sharpe_ratio(weights):
#returns=port_returns (assets,startdate,enddate)
#creating an array with our weights by
#summing our n-1 inserted and subtracting by 1 to make our last weight
weights_new=np.append(weights,1-sum(weights))
#returning a negative sharpe ratio
return -(sharpe_ratio(returns_data,weights_new))
returns_data=port_returns(assets,startdate,enddate)
# for n stocks, we could only choose n-1 weights
ones_weights_array= (np.ones(n-1, dtype=float) * 1.0 )/n
weight_1 = fmin(negate_sharpe_ratio,ones_weights_array)
final_weight = np.append(weight_1, 1 - sum(weight_1))
final_sharpe_ratio = sharpe_ratio(returns_data,final_weight)
print ('Optimal weights are ')
print (final_weight)
print ('final Sharpe ratio is ')
print(final_sharpe_ratio)