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 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 test_allow_short(self): returns, cov_mat, avg_rets = create_test_data() calc_weights = pfopt.tangency_portfolio(cov_mat, avg_rets, allow_short=True).values exp_weights = [0.048052417309504825, 0.63522794399754601, -0.53498204281249118, 0.93698599544795846, -0.085284313942518161] self.assertTrue(np.allclose(calc_weights, exp_weights))
def test_long_only(self): returns, cov_mat, avg_rets = create_test_data() calc_weights = pfopt.tangency_portfolio(cov_mat, avg_rets).values exp_weights = [0.013637652162222968, 0.37065128018786714, 1.6549667634656901e-09, 0.61571105705720952, 8.9377335719926867e-09] self.assertTrue(np.allclose(calc_weights, exp_weights))
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 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 market_tangent_point(returns_data): avg_rets = returns_data.mean() cov_mat = returns_data.cov() # calculate the Markowitz optimal allocations for each target return value optimal_weights = pfopt.tangency_portfolio(cov_mat=cov_mat, exp_rets=avg_rets) matrix_rets = avg_rets.as_matrix() matrix_cov = cov_mat.as_matrix() reward = optimal_weights.dot(matrix_rets) risk = np.sqrt(optimal_weights.dot(matrix_cov.dot(optimal_weights))) return reward, risk
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
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)
## preprocess daily returns of previous month ## dailyreturn = pd.read_csv('returns_17_10.csv') data = dailyreturn d = {} for i in data.columns.values: dval = data[i].tolist() d[i] = dval # d is the dictionary we can use to put into the blm model stk_list = list(d.keys()) data = list(d.values()) n = len(data[0]) cov_m = pd.DataFrame(np.cov(data)) rtn_m = pd.Series([np.mean(each) for each in data]) #tangency_portfolio(pandas.DataFrame, pandas.Series) w_neutral = pfopt.tangency_portfolio(cov_m, rtn_m, allow_short=True) w_neutral = pd.DataFrame([round(each, 4) for each in w_neutral], index=stk_list, columns=['Weights']) ### The asolute views obtained from view prediction AI-model ############## view = {} view['view1'] = (['LQD'], [1], 0.003507782) view['view2'] = (['SPY'], [1], 0.000190048) view['view3'] = (['VOT'], [1], 0.007558567) view['view4'] = (['IWV'], [1], 0.001878394) view['view5'] = (['IWP'], [1], 0.004234864) view['view6'] = (['IJK'], [1], 0.019953683) view['view7'] = (['IVV'], [1], 0.01048204) ##############
#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) #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) ???