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 index(request):
returns, cov_mat, avg_rets = optimizer.create_test_data()
section("Example returns")
print(returns.head(10))
print("...")
section("Average returns")
print(avg_rets)
section("Covariance matrix")
print(cov_mat)
# have to keep target within domain of expected returns of assets
# else cvxopt/numpy will return domain error or convergence problem
target_ret = avg_rets.quantile(0.7)
weights = optimizer.markowitz_portfolio(cov_mat,
avg_rets,
0.0049,
allow_short=False,
market_neutral=False)
print_portfolio_info(returns, avg_rets, weights)
context = {'avg_rets': avg_rets, 'cov_mat': cov_mat, 'weights': weights}
return render(request, 'create_portfolio/base.html', context)
def main():
raw_tickers = input('Enter stocks separated by a comma: ')
investment = int(input("Enter total investment to allocate: "))
stock_tickers = raw_tickers.split(", ")
print(stock_tickers)
listed_prices, returns, cov_mat, avg_rets = gd.get_stock_data(
stock_tickers)
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)
allocation = investment * weights
print(allocation)
# 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)
allocation = investment * weights
print(allocation)
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 get_markowitz_analysis(self,forecasts=0):
start_index = len(self.historical_returns) - 253
end_index = len(self.historical_returns) - 1
return_grid = 100 * self.returns_grid[:, start_index:end_index].T
returns = pd.DataFrame(return_grid)
avgs = [self.yearly_expected_ret(returns[col]) for col in returns.columns]
cov_mat = pd.DataFrame(np.cov(return_grid.T))
if forecasts == 0:
avg_rets = pd.Series(avgs, index=returns.columns)
else:
avg_rets = pd.Series(forecasts.values(), index=returns.columns)
w_opt = pfopt.tangency_portfolio(cov_mat, avg_rets, allow_short=False)
ret_opt = (w_opt * avg_rets).sum()
std_opt = (w_opt * returns).sum(1).std()
smallest_target = max(min(avg_rets), 0)
biggest_target = max(avg_rets)
target_returns = np.arange(smallest_target, biggest_target, .0005)
X = []
Y = []
for yi in target_returns:
w = pfopt.markowitz_portfolio(cov_mat, avg_rets, yi)
ret = (w * avg_rets).sum()
std = (w * returns).sum(1).std()
Y.append(ret)
X.append(std)
#coefs = np.polyfit(Y,X,2) #highest power first
curve = {"risks":X,"returns":Y,"min_return":smallest_target,"max_return":biggest_target}
tangency_port = {'weights': dict(w_opt),'X':std_opt,'Y':ret_opt}
return {"tangency_port":tangency_port,"curve":curve}
def markowitz_portfolios(self):
pf = self.panelframe
returns = (pf['Close'] - pf['Close'].shift(1)) / pf['Close'].shift(1)
returns.fillna(0, inplace=True)
market = returns['market']
returns = returns.iloc[:, :-1]
cov_mat = np.cov(returns, rowvar=False, ddof=1)
cov_mat = pd.DataFrame(cov_mat,
columns=returns.keys(),
index=returns.keys())
avg_rets = returns.mean(0).astype(np.float64)
mrk = []
weights = pfopt.min_var_portfolio(cov_mat)
case = self._one_pfopt_case(cov_mat, returns, market, weights,
'Minimum variance portfolio')
mrk.append(case)
for t in [0.50, 0.75, 0.90]:
target = avg_rets.quantile(t)
weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target)
case = self._one_pfopt_case(
cov_mat, returns, market, weights,
'Target: more than {:.0f}% of stock returns'.format(t * 100))
mrk.append(case)
weights = pfopt.tangency_portfolio(cov_mat, avg_rets)
case = self._one_pfopt_case(cov_mat, returns, market, weights,
'Tangency portfolio')
mrk.append(case)
return mrk
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 test_long_only(self):
returns, cov_mat, avg_rets = create_test_data()
target_ret = avg_rets.quantile(0.7)
calc_weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret).values
exp_weights = [0.23506651774627838, 0.28683592298360255, 0.0015464104918494993,
0.3685342846149573, 0.10801686416331224]
self.assertTrue(np.allclose(calc_weights, exp_weights))
def test_long_only(self):
returns, cov_mat, avg_rets = create_test_data()
target_ret = avg_rets.quantile(0.7)
calc_weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret).values
exp_weights = [0.23506651774627838, 0.28683592298360255, 0.0015464104918494993,
0.3685342846149573, 0.10801686416331224]
self.assertTrue(np.allclose(calc_weights, exp_weights))
def test_allow_short(self):
returns, cov_mat, avg_rets = create_test_data()
target_ret = avg_rets.quantile(0.7)
calc_weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret, allow_short=True).values
exp_weights = [0.24132125485063094, 0.28750580615601806, -0.0065950973956575331,
0.36542391192949747, 0.11234412445951109]
self.assertTrue(np.allclose(calc_weights, exp_weights))
def test_allow_short(self):
returns, cov_mat, avg_rets = create_test_data()
target_ret = avg_rets.quantile(0.7)
calc_weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret,
allow_short=True).values
exp_weights = [0.24132125485063094, 0.28750580615601806, -0.0065950973956575331,
0.36542391192949747, 0.11234412445951109]
self.assertTrue(np.allclose(calc_weights, exp_weights))
def test_market_neutral(self):
returns, cov_mat, avg_rets = create_test_data()
target_ret = avg_rets.quantile(0.7)
calc_weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret,
allow_short=True, market_neutral=True).values
exp_weights = [-0.088226487071008788, 0.15873382946203626, -0.24120662493449421,
0.26091591077682091, -0.090216628233354162]
self.assertTrue(np.isclose(calc_weights.sum(), 0.0))
self.assertTrue(np.allclose(calc_weights, exp_weights))
def test_market_neutral(self):
returns, cov_mat, avg_rets = create_test_data()
target_ret = avg_rets.quantile(0.7)
calc_weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target_ret,
allow_short=True, market_neutral=True).values
exp_weights = [-0.088226487071008788, 0.15873382946203626, -0.24120662493449421,
0.26091591077682091, -0.090216628233354162]
self.assertTrue(np.isclose(calc_weights.sum(), 0.0))
self.assertTrue(np.allclose(calc_weights, exp_weights))
def markowitz_portfolios(self):
"""
Estimate Markowitz portfolios
Inputs: daily returns by stock, avg returns by stock, cov_matrix
"""
# pf = self._daily
# returns = (pf['close'] - pf['close'].shift(1))/pf['close'].shift(1)
# returns.fillna(0, inplace=True)
# market = returns['market']
# returns = returns.iloc[:, :-1]
# cov_mat = np.cov(returns, rowvar=False, ddof=1)
# cov_mat = pd.DataFrame(
# cov_mat,
# columns=returns.keys(),
# index=returns.keys())
# avg_rets = returns.mean(0).astype(np.float64)
# prepare inputs
returns = self.stocks_daily
market = returns['SPY']
returns.drop('SPY', axis=1, inplace=True)
avg_rets = returns.mean(0).astype(np.float64)
cov_mat = self.stocks_covar
cov_mat.drop('SPY', axis=0, inplace=True)
cov_mat.drop('SPY', axis=1, inplace=True)
mrk = []
weights = pfopt.min_var_portfolio(cov_mat)
case = self._one_pfopt_case(returns, market, weights,
'Minimum variance portfolio')
mrk.append(case)
for t in [0.50, 0.75, 0.90]:
target = avg_rets.quantile(t)
weights = pfopt.markowitz_portfolio(cov_mat, avg_rets, target)
case = self._one_pfopt_case(
returns, market, weights,
'Target: more than {:.0f}% of stock returns'.format(t * 100))
mrk.append(case)
weights = pfopt.tangency_portfolio(cov_mat, avg_rets)
case = self._one_pfopt_case(returns, market, weights,
'Tangency portfolio')
mrk.append(case)
return mrk
def markowitz_efficient_frontier(returns_data):
avg_rets = returns_data.mean()
cov_mat = returns_data.cov()
# calculate the Markowitz optimal allocations for each target return value
optimal_allocation_list = []
for k in np.arange(0.01, 0.99, 0.01):
tgt_return = avg_rets.quantile(k)
optimal_weights = pfopt.markowitz_portfolio(cov_mat=cov_mat,
exp_rets=avg_rets,
target_ret=tgt_return)
optimal_allocation_list.append(optimal_weights)
matrix_rets = avg_rets.as_matrix()
matrix_cov = cov_mat.as_matrix()
returns = [x.dot(matrix_rets) for x in optimal_allocation_list]
risks = [
np.sqrt(x.dot(matrix_cov.dot(x))) for x in optimal_allocation_list
]
return returns, risks
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)
1:] #minor_xs表示minor_axis轴，得到收盘价每日收益率
data_1 = PN.minor_xs('close') #这里得到每日收盘价格
print(data_r.head())
#求出年收益及其年收益率的协方差
exp_rets = data_r.mean() * 252 #data_r.mean()表示求均值，在这里是求年收益(对比下精度如何)
cov_mat = data_r.cov() * 252 #这里求年收益率的协方差
exp = (data_1.iloc[-1] - data_1.iloc[0]) / data_1.iloc[0] #这里进行一个对比,发现还是有点差距
print(exp_rets)
print(cov_mat)
#计算：
#计算目标收益的权重 (markowitz_portfolio方法)
portfolio_1 = opt.markowitz_portfolio(cov_mat,
exp_rets,
0.2,
allow_short=False,
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)
def optimize(self, data, portfolio_version_id):
# data = request.get_json()
strDateFrom = data['dateFrom']
strDateTo = data['dateTo']
# access to database in MySql
assets = db.session.query(PortfolioAsset).filter_by(
version_id=portfolio_version_id).join(Asset).all()
# check if user exists
if len(assets) == 0:
return {}
stocks = []
for asset in assets:
stocks.append(asset.asset.ticker)
# request data from datareader and store them in prices (pandas data form)
req = {'tickers': stocks, 'start': strDateFrom, 'end': strDateTo}
prices = DataReader().read_data_pd_format(req)
# convert daily stock prices into daily returns
returns = prices.pct_change(-1)
# drop infs and nans
returns = returns.replace([np.inf, -np.inf], np.nan)
returns = returns.dropna()
# estimate mean return and covariance matrix
ExpRet = returns.mean()
CovRet = returns.cov()
# compute portfolios with min var and max return
minvar_port = popt.min_var_portfolio(CovRet)
minret = max(np.dot(minvar_port, ExpRet), 0)
maxret = max(ExpRet)
# calculate range of attainable returns for optimal portfolios
mus = np.linspace(start=minret, stop=maxret, num=100)
# calculate efficient frontier
portfolios = [
popt.markowitz_portfolio(CovRet, ExpRet, mu) for mu in mus
]
dts, portfolio_indexes = self.get_value_indexes(prices, portfolios)
# expected returns
exRet = [np.dot(portfolio, ExpRet) * 252 for portfolio in portfolios]
# expected volatility
exVol = [
np.sqrt(np.dot(np.dot(portfolio, CovRet), portfolio) * 252)
for portfolio in portfolios
]
return {
'assets': stocks,
'portfolios': pd.Series(portfolios).to_json(orient='values'),
'annualizedExpectedReturns': exRet,
'annualizedExpectedVolatility': exVol,
'dates': dts.tolist(),
'portfolio_indexes': np.array(portfolio_indexes).tolist()
}
retData.corr()
import numpy as np
import pandas as pd
import portfolioopt as pfopt
train_set = retData['2014-01-01':'2015-12-31']
test_set = retData['2016']
cov_mat = train_set.cov()
avg_rets = train_set.mean()
#only long
target_ret = 0.0006
weights = pfopt.markowitz_portfolio(cov_mat,
avg_rets,
target_ret,
allow_short=False)
weights
test_return = np.dot(test_set, np.matrix(weights).T)
test_return = pd.DataFrame(test_return, index=test_set.index)
test_cum_return = (1 + test_return).cumprod() - 1
test_cum_return.columns = ['markowitz_portfolio']
test_cum_return.head()
sim_weight = np.random.uniform(0, 1, (100, 3))
sim_weight_normalized = np.apply_along_axis(lambda x: x / sum(x), 1,
sim_weight)
sim_return = np.dot(test_set, np.matrix(sim_weight_normalized).T)
sim_return = pd.DataFrame(sim_return, index=test_set.index)
