def optimize_for_sharpe_ratio(allocation, start_value, df, symbols):
symbols = symbols[1:]
dr = utils.daily_returns(df)
df = df[symbols]
# print df
corr = np.asarray(utils.get_correlation(dr))
corr = np.asarray([1,-.12,.00209,-.004,-.007,.005])
# print symbols
cons = ({'type': 'eq', 'fun': lambda x: 1 - sum(x)})
bnds = tuple((0, 1) for item in allocation)
result = op.minimize(__get_sharpe_ratio, allocation, args=(df, symbols, start_value,corr), method='SLSQP', bounds=bnds,
constraints=cons)
print "Correlation to SPY: ", corr[1:]
return np.round(result.x, 2)
def __get_sharpe_ratio(allocation, df, symbol, start_value,corr):
dr = utils.daily_returns(df)
# corr = np.asarray(utils.get_correlation(dr))
sd = dr.std()
dr = dr.mean()
var = __variance(len(symbol),allocation,sd,corr)
df_normed = df / df.ix[0, :]
df_alloced = df_normed * allocation
pos_vals = df_alloced * start_value
portfolio_value = pos_vals.sum(axis=1)
return (np.sum(dr * allocation)/np.sqrt(var)) * -1
def __get_sharpe_ratio(allocation, df, symbol, start_value):
df_normed = df / df.ix[0, :]
df_alloced = df_normed * allocation
pos_vals = df_alloced * start_value
# portfolio_value = pos_vals.sum(axis=1)
#
port_daily_ret = utils.daily_returns(pos_vals)
portfolio_daily_return_mean = port_daily_ret.mean()
portfolio_standard_deviation = port_daily_ret.std()
# plt.scatter(portfolio_daily_return_mean,portfolio_standard_deviation)
# plt.show()
# print portfolio_daily_return_mean,portfolio_standard_deviation
sharpe = (math.sqrt(252) * (portfolio_daily_return_mean / portfolio_standard_deviation)) * -1
print "Allocations: ", np.round(allocation, 2)
# print "Portfolio Value: ", portfolio_value.tail(5)
print "Sharpe Ratio: ", sharpe * -1
print "symbols: ", symbol
return sharpe
import pandas as pd
import scipy
import utils
symbols = ['IBM','AAPL','GLD','XOM']
L = len(symbols)
addressable_dates = pd.date_range('2010-02-01', '2010-12-31')
df = utils.get_data(symbols, addressable_dates)
df = df.dropna()
daily_ret = utils.daily_returns(df)
daily_ret_mean = scipy.array(daily_ret.mean())
std = utils.std_daily_return(daily_ret)
stds = scipy.array(std)
print stds
print symbols
print daily_ret_mean
corr = scipy.array(daily_ret.corr())
# cor = scipy.corrcoef(x)
# print cor
def main_run():
# h = [.01, .01, .01,.01, .01,.01, .01,.01]
# # h = pd.Series([0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025,0.0025],dtype=float)
#
# # x = [3., 2., 1.5,2., 1.5,3., 4.5,4.9]
# # y = [4.2,1.9,1.9,2.1,2.0 ,2.5,3.9,4.5]
#
# x = [1.5, 2., 1.5,2., 1.5,3., 4.5,4.9]
# y = [4.2,1.9,1.9,2.1,2.0 ,2.5,3.9,4.5]
# z = [0.012,0.032,0.035,0.902,0.052,0.302,-0.909,0.902]
# # df.corr()
# b = [1., -1., -1.,1., 1.,-1., 1,-1]
# p = [0.12,0.55,0.45,0.3,0.53,0.57,0.19,0.58]
start_value = 1000000
# symbols = ['AC','ALI','BDO','BPI','DMC','GLO',
# 'GTCAP','HCP','JFC','MBT','MPI','MEG',
# 'RLC','SECB','SM','SMPH','TEL','URC']
# symbols = ['AAPL','IBM','XOM','GLD']
# symbols = ['goog','aapl','msft','amzn']
symbols = ['AAPL', 'MSFT','ORCL','QCOM','BBY','MU','GILD','YUM','NFLX','VZ','APA','RRC','MDLZ','CSCO','V','MET','SBUX','GGP','UA','GM']
h = np.ones(len(symbols))
addressable_dates = pd.date_range('2012-01-01','2015-12-31')
df = utils.get_data_online(symbols, addressable_dates)
df = pd.DataFrame(df,index=df.index,dtype=np.float64)
df = df.dropna()
x = np.asarray(utils.daily_returns(df[symbols[1:]]).std())
# print x
y = np.asarray(utils.daily_returns(df[symbols[1:]]).mean())
# print y
# b = [1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.]
z = np.asarray(utils.get_correlation(df)[1:])
# print z
# p = [.5,.5,.5,.5,.5,.5,.5,.5,.5,.5,.5,.5,.5,.5,.5,.5,.5,.5]
b = np.ones(len(symbols) - 1)
# print b
p = Accuracy.get_accuracy(symbols,addressable_dates)
# print p
sharpe_ratios = utils.get_sharpe_ratio(utils.daily_returns(df[symbols[1:]]))
# print sharpe_ratios
data = (x,y,z,b,p)
x_sorted = np.sort(x)
y_sorted = np.sort(y)
# print "h",np.transpose( h) * y
# print "cost", error(h)
# cons = ({'type':'eq', 'fun': lambda x: 1 - sum(x)})
cons = ({'type': 'eq', 'fun': lambda x: 1 - sum(x)})
# bounds = tuple((0,1) for it in h)
bnds = tuple((0,1) for it in h)
bounds = bnds
# bounds = (0,1)
min_result = spo.minimize(error,h,args=(data,), method='SLSQP',bounds=bounds, constraints=cons, options={'disp':True})
print "Parameters = {}, Y = {}".format(np.round(min_result.x,2), np.abs( min_result.fun)) # for tracing
# print "xdata",xdata
model_bounds = max(x.max(),y.max())
xdata = np.linspace(0,5,8)
y_main = func(xdata,model_bounds,1,-model_bounds)
print "bias", b * np.round(min_result.x,3)
print "risk: ", x
print "rewards: ", y
print "correlation: ", z
print "accuracy: ", p
print "y main",y_main
plt.plot(xdata,y_main)
y = func(xdata,2.5,1.3,0.5)
ydata = y + 0.2 * np.random.normal(size=len(xdata))
coeffs, pcov = curve_fit(func,xdata,ydata)
yaj = func(xdata, coeffs[0], coeffs[1], coeffs[2])
print pcov
# plt.scatter(xdata,ydata)
# plt.plot(xdata,yaj)
# c,p = curve_fit(sigmoid,x_sorted,y_sorted)
plt.scatter(x_sorted,y_sorted)
plt.show()
return None
