import csv
import pandas as pd
import random
import time

start_time=time.time()
sp=pd.read_csv("../../FundData/SP500.csv")
sp['ret_s']=sp['Adj Close']/sp['Adj Close'].shift(12)

bd=pd.read_csv("../../FundData/ICE US Investment Grade.csv")
bd.columns=['Date','BondPrice']
bd['ret_b']=bd['BondPrice']/bd['BondPrice'].shift(12)

ret_rf=1+.02/12


all=pd.merge(sp,bd,on='Date')
all=all[['Date','ret_s','ret_b']]

def util(w,A,gamma,alpha,ret_s,ret_b):
    u=gamma/(1-gamma)*(A*(w[0]*ret_rf+w[1]*ret_s + w[0]*ret_b) + gamma/alpha)**(1-gamma)
    return u

def util2(w,A,gamma,eta,ret_s, ret_b, ret_rf):
    u=(1-gamma)/gamma*(A*(w[0]*ret_rf+w[1]*ret_s+w[0]*ret_b)/(1-gamma)+eta)**gamma
    return u

gamma=-1
alpha=.008
A=100
eU=[]
best=-999999999
best_port=(0,0,0,-9999999)
for i in range(21):
    for k in range((21-i)):
        w=(5*i/100,5*k/100,5*(20-i-k)/100)
        u=[]
        for j in range(12,len(all['Date'])):
            u.append(util(w,A,gamma,alpha,all['ret_s'].iloc[j],all['ret_b'].iloc[j]))
        eU.append((w[0],w[1],w[2],sum(u)/len(u)))
        if eU[-1][3]>best_port[3]:
            best_port=eU[-1]
        # print(i,k,100-i-k,eU[-1])

for x in eU:
    print(x)
print(best_port)

print("--------%s seconds ----------" % (time.time()-start_time))