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portfolio.py
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portfolio.py
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# 仓位管理：基于马科维茨投资组合理论，计算一段时间序列内投资组合的风险、仓位配比和夏普率，有市场方向和最佳收益方向两种结果
import numpy as np
import datetime
import pymysql
import copy
# Import Yahoo API as ts 【之前code基于tushare, 暂时考虑便利直接import api as ts.】
# 注意数据格式的匹配度。
# 返回的resu中 特征值按由小到大排列，对应的是其特征向量
def get_portfolio(stock_list, state_dt, para_window):
# 建数据库连接，设置ETC最终选用API.
db = pymysql.connect(host='localhost', user='root', passwd='your password', db='your dbname', charset='utf8mb4')
cursor = db.cursor()
ts.set_token('your token')
pro = ts.pro_api()
portfilio = stock_list
# 建评估时间序列, para_window参数代表回测窗口长度
model_test_date_start = (datetime.datetime.strptime(state_dt, '%Y-%m-%d') - datetime.timedelta(days=para_window)).strftime(
'%Y%m%d')
model_test_date_end = (datetime.datetime.strptime(state_dt, "%Y-%m-%d")).strftime('%Y%m%d')
df = pro.trade_cal(exchange_id='', is_open=1, start_date=model_test_date_start, end_date=model_test_date_end)
date_temp = list(df.iloc[:, 1])
model_test_date_seq = [(datetime.datetime.strptime(x, "%Y%m%d")).strftime('%Y-%m-%d') for x in date_temp]
list_return = []
for i in range(len(model_test_date_seq) - 4):
ri = []
for j in range(len(portfilio)):
# 每次计算后四天的平均收益率
sql_select = "select * from stock_all a where a.stock_code = '%s' and a.state_dt >= '%s' and a.state_dt <= '%s' order by state_dt asc" %\
(portfilio[j], model_test_date_seq[i], model_test_date_seq[i + 4])
cursor.execute(sql_select)
done_set = cursor.fetchall()
db.commit()
temp = [x[3] for x in done_set]
base_price = 0.00
after_mean_price = 0.00
if len(temp) <= 1:
r = 0.00
else:
base_price = temp[0]
after_mean_price = np.array(temp[1:]).mean()
r = float(after_mean_price / base_price) - 1.00
ri.append(r)
del done_set
del temp
del base_price
del after_mean_price
list_return.append(ri)
# 基于马科维茨理论, 最小特征值
# 求协方差矩阵
cov = np.cov(np.array(list_return).T)
# 求特征值和其对应的特征向量
ans = np.linalg.eig(cov)
# 排序，特征向量中负数置0，非负数线性归一
ans_index = copy.copy(ans[0])
ans_index.sort()
resu = []
for k in range(len(ans_index)):
con_temp = []
con_temp.append(ans_index[k])
content_temp1 = ans[1][np.argwhere(ans[0] == ans_index[k])[0][0]]
content_temp2 = []
content_sum = np.array([x for x in content_temp1 if x >= 0.00]).sum()
for m in range(len(content_temp1)):
if content_temp1[m] >= 0 and content_sum > 0:
content_temp2.append(content_temp1[m] / content_sum)
else:
content_temp2.append(0.00)
con_temp.append(content_temp2)
# 计算夏普率
sharp_temp = np.array(copy.copy(list_return)) * content_temp2
sharp_exp = sharp_temp.mean()
sharp_base = 0.015 / 250
sharp_std = np.std(sharp_temp)
if sharp_std == 0.00:
sharp = 0.00
else:
sharp = (sharp_exp - sharp_base) / sharp_std
con_temp.append(sharp)
resu.append(con_temp)
return resu
if __name__ == '__main__':
# 使用部分class <DataGenerator.GetTechinicalIndicators.> 及logger函数以统一整体算法workflow。
pf = ['600536.SH', '000063.SZ', '000977.SZ', '600588.SH', '600845.SH'] #此处换为对应生成50支对应Equity pick market ticker.
ans = get_portfolio(pf, '2019-08-23', 2019)
print('************** Market Trend ****************')
print('Risk : ' + str(round(ans[0][0], 4)))
print('Sharp ratio : ' + str(round(ans[0][2], 4)))
for i in range(5):
print('----------------------------------------------')
print('Stock_code : ' + str(pf[i]) + ' Position : ' + str(round(ans[0][1][i] * 100, 2)) + '%')
print('----------------------------------------------')
print('************** Best Return *****************')
print('Risk : ' + str(round(ans[1][0], 4)))
print('Sharp ratio : ' + str(round(ans[1][2], 4)))
for j in range(5):
print('----------------------------------------------')
print('Stock_code : ' + str(pf[j]) + ' Position : ' + str(
round(ans[1][1][j] * 100, 2)) + '%')
print('----------------------------------------------')