def target_opt(df_daily_returns, target_ret=None):
#********* Markowitz Portfolio with target return ************************
cov_mat = df_daily_returns.cov()
avg_rets = df_daily_returns.mean()
if target_ret == None:
target_ret = avg_rets.quantile(0.7)
weights = pfopt.markowitz_portfolio(
cov_mat, avg_rets, target_ret) # returns a df series of weights
weights = pfopt.truncate_weights(weights) # Truncate some tiny weights
weights = weights[weights != 0] # remove any weights of 0
weights = weights.round(
decimals=4) # clean up weight values by rounding
ret = (weights *
avg_rets).sum() # float of the portfolio average daily return
# p = np.asmatrix(avg_rets) # this is where the error is occurring
# w = np.asmatrix(weights)
# C = np.asmatrix(np.cov(df_daily_returns))
# sigma = np.sqrt(w * C * w.T) #standard deviation
#***********************************************************************************************
# C is covariance matrix of the returns NOTE: if it used the simple std dev std(array(ret_vec).T*w)
# the result would be slightly different as it would not take covariances into account.!
std = (weights * df_daily_returns
).sum(1).std() # float of the portfolio standard deviation
return weights, ret, std
def test_truncate(self):
raw_weights = pd.Series([0.5, 0.4, 0.01, 0.09], index=['a', 'b', 'c', 'd'])
adj_weights = pfopt.truncate_weights(raw_weights, min_weight=0.05, rescale=False)
exp_weights = raw_weights[:]
exp_weights['c'] = 0.0
self.assertTrue((adj_weights == exp_weights).all())
def test_truncate(self):
raw_weights = pd.Series([0.5, 0.4, 0.01, 0.09], index=['a', 'b', 'c', 'd'])
adj_weights = pfopt.truncate_weights(raw_weights, min_weight=0.05, rescale=False)
exp_weights = raw_weights[:]
exp_weights['c'] = 0.0
self.assertTrue((adj_weights == exp_weights).all())
def test_rescale(self):
raw_weights = pd.Series([0.5, 0.4, 0.01, 0.8],
index=['a', 'b', 'c', 'd'])
adj_weights = pfopt.truncate_weights(raw_weights,
min_weight=0.0,
rescale=True)
self.assertTrue(np.isclose(adj_weights.sum(), 1.0))
def get_b(self, data, last_b):
x = data
x_mean = np.mean(x)
lam = max(0., (self.eps - np.dot(last_b, x)) / norm(x - x_mean) **2)
lam = min(1e+5, lam)
b = last_b + lam * (x - x_mean)
b = np.log(1. + np.exp(b))
b = b / b.sum()
b = pfopt.truncate_weights(b)
return b
def main():
returns, cov_mat, avg_rets = pfopt.create_test_data()
section("Example returns")
print(returns.head(10))
print("...")
section("Average returns")
print(avg_rets)
section("Covariance matrix")
print(cov_mat)
section("Minimum variance portfolio (long only)")
weights = pfopt.min_var_portfolio(cov_mat)
print_portfolio_info(returns, avg_rets, weights)
section("Minimum variance portfolio (long/short)")
weights = pfopt.min_var_portfolio(cov_mat, allow_short=True)
print_portfolio_info(returns, avg_rets, weights)
# Define some target return, here the 70% quantile of the average returns
target_ret = avg_rets.quantile(0.7)
section("Markowitz portfolio (long only, target return: {:.5f})".format(
target_ret))
weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret)
print_portfolio_info(returns, avg_rets, weights)
section("Markowitz portfolio (long/short, target return: {:.5f})".format(
target_ret))
weights = pfopt.markowitz_portfolio(cov_mat,
avg_rets,
target_ret,
allow_short=True)
print_portfolio_info(returns, avg_rets, weights)
section(
"Markowitz portfolio (market neutral, target return: {:.5f})".format(
target_ret))
weights = pfopt.markowitz_portfolio(cov_mat,
avg_rets,
target_ret,
allow_short=True,
market_neutral=True)
print_portfolio_info(returns, avg_rets, weights)
section("Tangency portfolio (long only)")
weights = pfopt.tangency_portfolio(cov_mat, avg_rets)
weights = pfopt.truncate_weights(weights) # Truncate some tiny weights
print_portfolio_info(returns, avg_rets, weights)
section("Tangency portfolio (long/short)")
weights = pfopt.tangency_portfolio(cov_mat, avg_rets, allow_short=True)
print_portfolio_info(returns, avg_rets, weights)
def main():
# returns, cov_mat, avg_rets = pfopt.create_test_data(num_days=1000)
returns, cov_mat, avg_rets = load_data()
# yy = returns['asset_a']
# xx = [ii for ii in range(len(yy))]
# plt.plot(xx, yy)
# plt.hlines(avg_rets[0], xmin=0, xmax=100, colors='black')
# plt.show()
# return 0
section("Example returns")
print(returns.head(5))
print("...")
section("Average returns")
print(avg_rets)
section("Covariance matrix")
print(cov_mat)
section("Minimum variance portfolio (long only)")
weights = pfopt.min_var_portfolio(cov_mat)
print_portfolio_info(returns, avg_rets, weights)
section("Minimum variance portfolio (long/short)")
weights = pfopt.min_var_portfolio(cov_mat, allow_short=True)
print_portfolio_info(returns, avg_rets, weights)
# Define some target return, here the 70% quantile of the average returns
target_ret = avg_rets.quantile(0.7)
section("Markowitz portfolio (long only, target return: {:.5f})".format(target_ret))
weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret)
print_portfolio_info(returns, avg_rets, weights)
section("Markowitz portfolio (long/short, target return: {:.5f})".format(target_ret))
weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret, allow_short=True)
print_portfolio_info(returns, avg_rets, weights)
section("Markowitz portfolio (market neutral, target return: {:.5f})".format(target_ret))
weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret, allow_short=True,
market_neutral=True)
print_portfolio_info(returns, avg_rets, weights)
section("Tangency portfolio (long only)")
weights = pfopt.tangency_portfolio(cov_mat, avg_rets)
weights = pfopt.truncate_weights(weights) # Truncate some tiny weights
print_portfolio_info(returns, avg_rets, weights)
section("Tangency portfolio (long/short)")
weights = pfopt.tangency_portfolio(cov_mat, avg_rets, allow_short=True)
print_portfolio_info(returns, avg_rets, weights)
def tangency_opt(df_daily_returns):
cov_mat = df_daily_returns.cov()
avg_rets = df_daily_returns.mean()
weights = pfopt.tangency_portfolio(cov_mat, avg_rets)
weights = pfopt.truncate_weights(weights) # Truncate some tiny weights
weights = weights[weights != 0]
weights = weights.round(decimals=4)
ret = (weights * avg_rets).sum()
ret = ret.round(decimals=4)
std = (weights * df_daily_returns).sum(1).std()
std = std.round(decimals=4)
return weights, ret, std
def min_variance(df_daily_returns):
#***************** Minimum variance portfolio ***********************
cov_mat = df_daily_returns.cov()
avg_rets = df_daily_returns.mean()
weights = pfopt.min_var_portfolio(cov_mat)
weights = pfopt.truncate_weights(weights) # Truncate some tiny weights
weights = weights[weights != 0]
weights = weights.round(decimals=4)
ret = (weights * avg_rets).sum()
ret = ret.round(decimals=4)
std = (weights * df_daily_returns).sum(1).std()
std = std.round(decimals=4)
return weights, ret, std
def main():
returns, cov_mat, avg_rets = pfopt.create_test_data()
section("Example returns")
print(returns.head(10))
print("...")
section("Average returns")
print(avg_rets)
section("Covariance matrix")
print(cov_mat)
section("Minimum variance portfolio (long only)")
weights = pfopt.min_var_portfolio(cov_mat)
print_portfolio_info(returns, avg_rets, weights)
section("Minimum variance portfolio (long/short)")
weights = pfopt.min_var_portfolio(cov_mat, allow_short=True)
print_portfolio_info(returns, avg_rets, weights)
# Define some target return, here the 70% quantile of the average returns
target_ret = avg_rets.quantile(0.7)
section("Markowitz portfolio (long only, target return: {:.5f})".format(target_ret))
weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret)
print_portfolio_info(returns, avg_rets, weights)
section("Markowitz portfolio (long/short, target return: {:.5f})".format(target_ret))
weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret, allow_short=True)
print_portfolio_info(returns, avg_rets, weights)
section("Markowitz portfolio (market neutral, target return: {:.5f})".format(target_ret))
weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret, allow_short=True,
market_neutral=True)
print_portfolio_info(returns, avg_rets, weights)
section("Tangency portfolio (long only)")
weights = pfopt.tangency_portfolio(cov_mat, avg_rets)
weights = pfopt.truncate_weights(weights) # Truncate some tiny weights
print_portfolio_info(returns, avg_rets, weights)
section("Tangency portfolio (long/short)")
weights = pfopt.tangency_portfolio(cov_mat, avg_rets, allow_short=True)
print_portfolio_info(returns, avg_rets, weights)
print('-' * len(caption))
def print_portfolio_info(returns, avg_rets, weights):
"""
Print information on expected portfolio performance.
"""
ret = (weights * avg_rets).sum()
std = (weights * returns).sum(1).std()
sharpe = ret / std
print("Optimal weights:\n{}\n".format(weights))
print("Expected return: {}".format(ret))
print("Expected variance: {}".format(std**2))
print("Expected Sharpe: {}".format(sharpe))
# Define some target return, here the 70% quantile of the average returns
target_ret = avg_rets.quantile(0.9)
weights = pfopt.min_var_portfolio(cov_mat)
print_portfolio_info(returns, avg_rets, weights)
section("Markowitz portfolio (long only, target return: {:.5f})".format(
target_ret))
weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret)
print_portfolio_info(returns, avg_rets, weights)
section("Tangency portfolio (long only)")
weights = pfopt.tangency_portfolio(cov_mat, avg_rets)
weights = pfopt.truncate_weights(weights) # Truncate some tiny weights
print_portfolio_info(returns, avg_rets, weights)
def test_rescale(self):
raw_weights = pd.Series([0.5, 0.4, 0.01, 0.8], index=['a', 'b', 'c', 'd'])
adj_weights = pfopt.truncate_weights(raw_weights, min_weight=0.0, rescale=True)
self.assertTrue(np.isclose(adj_weights.sum(), 1.0))
market_neutral=False)
#需输入协方差矩阵cov_mat,年预期收益exp_rets,0.2代表想要的年收益,allow_short表示是否允许做空,market_neutral表示是否具有市场中性
print(portfolio_1)
#得到的结果表示若要实现0.2的年收益,则分别需买入这些股票的比重分别为
#计算最小方差的权重 (opt.min_var_portfolio)
portfolio_mv = opt.min_var_portfolio(cov_mat, allow_short=False)
print(portfolio_mv)
#计算最优组合的权重 (opt.tangency_portfolio) (夏普比率最高的比重)
portfolio_tp = opt.tangency_portfolio(
cov_mat, exp_rets, allow_short=False) #需输入协方差矩阵cov_mat,年预期收益exp_rets
print(portfolio_tp)
#去除少于0.01权重的股票,低于0.01权重的不建议购买
weigth_t = opt.truncate_weights(portfolio_tp, min_weight=0.01, rescale=True)
print(weigth_t)
#计算组合风险
import numpy as np
Portfolio_v = np.dot(weigth_t.T,
np.dot(cov_mat,
weigth_t)) #weigth_t.T表示weigth_t的转置,cov_mat是协方差矩阵
P_sigma = np.sqrt(Portfolio_v) #开方求标准差
P_sigma
#Markowitz可视化 (求最高夏普比率) ？？？
import matplotlib.pyplot as plt
port_returns = []
port_variance = []
for p in range(4000): #随机4000个值
