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simulate.py
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simulate.py
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#function for the first quizz:
import sys
# QSTK Imports
import QSTK.qstkutil.qsdateutil as du
import QSTK.qstkutil.tsutil as tsu
import QSTK.qstkutil.DataAccess as da
# Third Party Imports
import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
def Compute(close_norm,allocations):
#normalized value of portfolio
np_alloc=np.array(allocations)
valuePortfolio=np.dot(close_norm,np_alloc)
print 'valuePortolio = ',valuePortfolio
#print 'normalized valuePortfolio ',valuePortfolio
#daily returns of the PF
daily_Portfolio=valuePortfolio.copy()
tsu.returnize0(daily_Portfolio)
print 'valuePortolio afer returnize= ',valuePortfolio
#calculate the quantities
avg_P=np.average(daily_Portfolio)
sigma_P=np.std(daily_Portfolio)
sharpe_P=np.sqrt(252)*avg_P/sigma_P
cum_P=valuePortfolio[valuePortfolio.size -1]
return sigma_P,avg_P,sharpe_P,cum_P
def GetNormalizedReturn(dt_start,dt_end, symbols,c_dataobj):
#
#
#
# Keys to be read from the data, it is good to read everything in one go.
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
# We need closing prices so the timestamp should be hours=16.
dt_timeofday = dt.timedelta(hours=16)
# Get a list of trading days between the start and the end.
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
# Timestamps and symbols are the ones that were specified before.
ldf_data = c_dataobj.get_data(ldt_timestamps, symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
#normalized returns data frame
df_rets=d_data['close'].copy()
close=df_rets.values
#normalized returns
close_norm=close/close[0,:]
#print 'close norm = ',close_norm.shape
return close_norm
def simulate(dt_start,dt_end, symbols, allocations):
#==========check allocations:
sum_alloc=0
for all in allocations:
sum_alloc+=all
#
if sum_alloc != 1.0:
print 'the allocations are wrong!'
sys.exit(1)
if len(allocations)!=len(symbols):
print 'the sizes are different!'
sys.exit(1)
#=========construct the portfolio
portfolio=zip(symbols,allocations)
print 'portfolio = ',portfolio
#check for bad symbols
#
c_dataobj=da.DataAccess('Yahoo')
ls_all_syms = c_dataobj.get_all_symbols()
# Bad symbols are symbols present in portfolio but not in all syms
ls_bad_syms = list(set(symbols) - set(ls_all_syms))
if len(ls_bad_syms) != 0:
print "Portfolio contains bad symbols : ", ls_bad_syms
sys.exit(1)
#
#
close_norm=GetNormalizedReturn(dt_start,dt_end, symbols,c_dataobj)
#optimize!
sharpe_max=-1
step=0.1
for a in range(0,11):
for b in range (0,11-a):
for c in range(0,11-a-b):
d=10-a-b-c
alloc_a=a*step
alloc_b=b*step
alloc_c=c*step
alloc_d=d*step
allocations=[alloc_a,alloc_b,alloc_c,alloc_d]
vol,daily,sharpe,cumul=Compute(close_norm,allocations)
#print 'sharpe =',sharpe
if sharpe>sharpe_max:
sharpe_max=sharpe
best_alloc=allocations
#print alloc_a,alloc_b,alloc_c,alloc_d
#raw_input('hey')
#
#
#
#
print 'the best sharpe is = ',sharpe_max
print 'with the allocation =',best_alloc
# vol,daily,sharpe,cumul=Compute(close_norm,allocations)
#return vol,daily,sharpe,cumul
if __name__=='__main__':
# List of symbols
ls_symbols=['BRCM', 'TXN', 'IBM', 'HNZ']
#ls_symbols=['C', 'GS', 'IBM', 'HNZ']
#ls_symbols=['BRCM', 'TXN', 'IBM', 'HNZ']
#ls_symbols=['GOOG', 'AAPL', 'GLD', 'XOM']
#ls_symbols=['AXP', 'HPQ', 'IBM', 'HNZ']
#ls_symbols=['AAPL', 'GLD', 'GOOG', 'XOM']
#ls_symbols = ["AAPL", "GLD", "GOOG", "$SPX", "XOM"]
# Start and End date of the charts
dt_start = dt.datetime(2010, 1, 1)
dt_end = dt.datetime(2010, 12, 31)
ls_allocations=(0.4,0.4,0.0,0.2)
#vol,daily,sharpe,cumul=simulate(dt_start,dt_end,ls_symbols,ls_allocations)
simulate(dt_start,dt_end,ls_symbols,ls_allocations)
#print 'volatility= ',vol
#print 'avg daily ret =',daily
#print 'sharpe ratio = ',sharpe
#print 'cumulative return =',cumul