def get_weights(self):
code_list = list_jq2wind(self.code_list)
code_weights = {}
return_value = self._get_return(code_list)
def optimization_target(weight_temp):
weight_temp = weight_temp[:, np.newaxis]
portfolio_return_value = np.matmul(weight_temp.transpose(), return_value)[0, :] # 组合的投资回报序列
mean = np.mean(portfolio_return_value)
var = np.var(portfolio_return_value)
skew = scipy.stats.skew(portfolio_return_value)
kurtosis = scipy.stats.kurtosis(portfolio_return_value)
loss = self.w2 * var + self.w3 * skew + self.w4 * kurtosis # 对方差、偏度、峰度进行优化，期望值最小越好
return loss
def constraint(weight_temp):
return np.sum(weight_temp) - 1.0
n = len(self.code_list) # 资产个数
x_0 = np.ones((n,)) * 1.0 / n
bounds = n * ((0.0, None),) # 权重非负约束条件
res = scipy.optimize.minimize(optimization_target, x_0, method='SLSQP', bounds=bounds, constraints={'type': 'eq', 'fun': constraint})
# print(res)
if res.success: # 优化成功
x = res.x
else: # 优化失败，用等权
x = x_0
for i in range(len(code_list)):
code = code_list[i]
code_weights[list_wind2jq([code])[0]] = x[i]
return code_weights
def get_weights(self):
code_list = list_jq2wind(self.code_list)
weight_value = self._calc_weights(code_list)
code_weights = {}
for i in range(len(code_list)):
code = code_list[i]
code_weights[list_wind2jq([code])[0]] = weight_value[i]
return code_weights
def get_weights(self):
code_list = list_jq2wind(self.code_list)
code_weights = {}
w.start()
weight_data = np.array(w.wss(code_list, "mkt_cap_ard", "unit=1;tradeDate=" + self.date + ";currencyType=").Data[0])
weight_data = weight_data / np.sum(weight_data)
for i in range(len(code_list)):
code = code_list[i]
code_weights[list_wind2jq([code])[0]] = weight_data[i]
return code_weights
def get_weights(self):
code_list = list_jq2wind(self.code_list)
w.start()
return_value = np.array(w.wsd(code_list, "pct_chg", "ED-" + str(self.N - 1) + "TD", self.date, "").Data)
return_value = return_value.transpose()
return_value = pd.DataFrame(return_value, columns=code_list)
optimizer = HRPOpt(return_value)
hrp_portfolio = optimizer.hrp_portfolio()
hrp_portfolio = dict(zip(list_wind2jq(list(hrp_portfolio.keys())), hrp_portfolio.values()))
return hrp_portfolio
def get_weights(self):
w.start()
index_data = w.wset("indexconstituent", "date="+self.date+";windcode="+self.index_code).Data
index_data_code = index_data[1]
index_data_weight = index_data[3]
code_weights_all = dict(zip(index_data_code, index_data_weight))
code_weights = {}
for code in self.code_list:
try:
code_weights[code] = code_weights_all[list_jq2wind([code])[0]]
except KeyError:
code_weights[code] = 0.0
all_weights = np.sum(np.array([t for t in code_weights.values()]))
for code in self.code_list:
try:
code_weights[code] = code_weights_all[list_jq2wind([code])[0]] / all_weights
except KeyError:
code_weights[code] = 0.0
return code_weights
def get_weights(self):
code_list = list_jq2wind(self.code_list)
SW1_code_list = [t[0] for t in SW1_INDEX]
weight_value = self._calc_weights(SW1_code_list) # 提取行业权重
industry_list = w.wss(code_list, "indexcode_sw", "tradeDate="+self.date+";industryType=1").Data[0]
weight_value_temp = []
for i in range(len(code_list)):
industry_temp = industry_list[i]
if industry_temp is None: # 个股无行业分类数据的处理
weight_value_temp.append(0.0)
else:
industry_index = SW1_code_list.index(industry_temp)
weight_value_temp.append(weight_value[industry_index])
weight_value_temp = np.array(weight_value_temp)
weight_value_temp = weight_value_temp / np.sum(weight_value_temp) # 权重归一化
code_weights = dict([[list_wind2jq([code_list[i]])[0], weight_value_temp[i]] for i in range(len(code_list))])
return code_weights
def get_weights(self):
code_list = list_jq2wind(self.code_list)
stock_ZX1_industry_code_list = w.wss(
code_list, "indexcode_citic",
"industryType=1;tradeDate=" + self.date).Data[0]
ZX1_industry_code_list = list(set(stock_ZX1_industry_code_list))
weight_value = self._calc_weights(list(
set(ZX1_industry_code_list))) # 计算股票涉及到的行业权重
# industry_list = w.wss(code_list, "indexcode_sw", "tradeDate="+self.date+";industryType=1").Data[0]
weight_value_temp = []
for i in range(len(code_list)):
industry_temp = stock_ZX1_industry_code_list[i]
if industry_temp is None: # 个股无行业分类数据的处理
weight_value_temp.append(0.0)
else:
industry_index = ZX1_industry_code_list.index(industry_temp)
weight_value_temp.append(weight_value[industry_index])
weight_value_temp = np.array(weight_value_temp)
weight_value_temp = weight_value_temp / np.sum(
weight_value_temp) # 权重归一化
code_weights = dict(
[[list_wind2jq([code_list[i]])[0], weight_value_temp[i]]
for i in range(len(code_list))])
return code_weights
