def get_free_market_value_date(self, date):
""" 得到股票自由流通市值 """
date = Date.change_to_str(date)
out_path = os.path.join(self.data_path_static, "Free_Market_Value")
out_file = os.path.join(out_path, 'Free_Market_Value_' + date + '.csv')
free_market_value = pd.read_csv(out_file, index_col=[0], encoding='gbk')
return free_market_value
def get_trade_status_date(self, date):
""" 得到股票交易状态 """
date = Date.change_to_str(date)
out_path = os.path.join(self.data_path_static, "trade_status")
out_file = os.path.join(out_path, 'trade_status_' + date + '.csv')
trade_status = pd.read_csv(out_file, index_col=[0], encoding='gbk')
return trade_status
def get_wind_fund_asset(self, date=None):
if date is None:
date = Date().get_normal_date_series(period='Q')[-2]
print(date)
date = Date.change_to_str(date)
out_path = Parameter().get_read_file("Fund_Asset")
out_file = "基金规模_" + date + '.csv'
out_file = os.path.join(out_path, out_file)
data = pd.read_csv(out_file, encoding='gbk', index_col=[0])
return data
def load_free_market_value_date(self, date):
""" 下载股票自由流通市值 """
print("######### Loading FreeMarketValue At %s #########" % date)
date = Date.change_to_str(date)
out_path = os.path.join(self.data_path_static, "Free_Market_Value")
code_list = self.get_all_stock_code_now()
code_list_str = ','.join(code_list)
data = w.wss(code_list_str, "mkt_freeshares", "unit=1;tradeDate=" + date)
data = pd.DataFrame(data.Data, index=['Free_Market_Value'], columns=data.Codes).T
out_file = os.path.join(out_path, "Free_Market_Value_" + date + '.csv')
data.to_csv(out_file)
def get_fund_pool_all(self, date=None, name=None):
if name is None:
name = '基金持仓基准基金池'
if date is None:
date = Date().get_normal_date_series(period='Q')[-1]
print(date)
date = Date.change_to_str(date)
out_sub_path = os.path.join(self.pool_load_out_path, name)
file = name + '_' + date + '.csv'
out_file = os.path.join(out_sub_path, file)
data = pd.read_csv(out_file, encoding='gbk', index_col=[0])
data = data.sort_values(by=['wind_code'], ascending=True)
return data
def get_fund_pool_name(self, date=None, name=None):
""" 下载基金池名称 """
if name is None:
name = '基金持仓基准基金池'
if date is None:
date = Date().get_normal_date_series(period='Q')[-2]
date = Date.change_to_str(date)
print(" Get Fund Pool Name %s %s " % (name, date))
out_file = os.path.join(self.data_path_pool, name,
name + '_' + str(date) + '.csv')
data = pd.read_csv(out_file, encoding='gbk', index_col=[0])
data = data.sort_values(by=['wind_code'], ascending=True)
data = list(data['sec_name'].values)
return data
def load_fund_pool(self, date, name, pool_number, source="wind_terminal"):
if source == "wind_terminal":
date = Date.change_to_str(date)
data = w.wset("sectorconstituent",
"date=%s;sectorid=%s" % (date, pool_number))
data = pd.DataFrame(data.Data, index=data.Fields).T
data.date = data.date.map(Date.change_to_str)
else:
data = None
out_sub_path = os.path.join(self.pool_load_out_path, name)
if not os.path.exists(out_sub_path):
os.makedirs(out_sub_path)
file = name + '_' + date + '.csv'
out_file = os.path.join(out_sub_path, file)
data.to_csv(out_file)
def get_fund_pool_all(self, date=None, name=None):
""" 得到基金池信息 """
if name is None:
name = '基金持仓基准基金池'
if date is None:
date = Date().get_normal_date_series(period='Q')[-1]
date = Date.change_to_str(date)
print(" Get Fund Pool %s %s " % (name, date))
out_file = os.path.join(self.data_path_pool, name,
name + '_' + str(date) + '.csv')
data = pd.read_csv(out_file, encoding='gbk', index_col=[0])
data = data.sort_values(by=['wind_code'], ascending=True)
if 'setupdate' in data.columns:
data['setupdate'] = data['setupdate'].map(Date().change_to_str)
data.index = data['wind_code']
data = data[~data.index.duplicated()]
return data
def load_trade_status_today(self):
"""
下载股票交易状态
超过9：15 就用今天的数据
不超过9：15 就用昨天的数据
"""
today = datetime.today()
today_str = Date.change_to_str(today)
before_date = Date().get_trade_date_offset(today, -1)
out_path = os.path.join(self.data_path_static, "trade_status")
out_file = os.path.join(out_path, 'trade_status_' + today_str + '.csv')
code_list = self.get_all_stock_code_now()
code_list_str = ','.join(code_list)
if today.time() > time(9, 15):
print("######### Loading Trade Status At %s #########" % today_str)
trade_status = w.wsq(code_list_str, "rt_trade_status")
trade_status_pd = pd.DataFrame(trade_status.Data, index=['Trade_Status'], columns=trade_status.Codes).T
trade_status_pd = trade_status_pd[(trade_status_pd['Trade_Status'] != 1.0)
& (trade_status_pd['Trade_Status'] != 4.0)]
if len(trade_status_pd) > 1000:
trade_status = w.wss(code_list_str, "trade_status", "tradeDate=" + today_str)
trade_status_pd = pd.DataFrame(trade_status.Data, index=['Trade_Status'], columns=trade_status.Codes).T
trade_status_pd = trade_status_pd[trade_status_pd['Trade_Status'] != "交易"]
trade_status_pd.to_csv(out_file)
else:
trade_status_pd.to_csv(out_file)
else:
print("######### Loading Trade Status At %s #########" % before_date)
trade_status = w.wss(code_list_str, "trade_status", "tradeDate=" + before_date)
trade_status_pd = pd.DataFrame(trade_status.Data, index=['Trade_Status'], columns=trade_status.Codes).T
trade_status_pd = trade_status_pd[trade_status_pd['Trade_Status'] != "交易"]
trade_status_pd.to_csv(out_file)
def load_fund_pool(self, date, name, pool_number):
""" 下载基金池 为了便于比较每个类型加了 部门几个基金 """
date = Date.change_to_str(date)
data = w.wset("sectorconstituent",
"date=%s;sectorid=%s" % (date, pool_number))
data = pd.DataFrame(data.Data, index=data.Fields).T
data.date = data.date.map(Date.change_to_str)
data['Type'] = name
if name == "中证500基金":
d = pd.DataFrame([['001733.OF', "泰达宏利量化"]],
columns=['wind_code', 'sec_name'])
data = data.append(d)
data = data.reset_index(drop=True)
if name == "沪深300基金":
d = pd.DataFrame([['004484.OF', '泰达宏利业绩驱动']],
columns=['wind_code', 'sec_name'])
data = data.append(d)
data = data.reset_index(drop=True)
if name == "指数增强型基金":
d = pd.DataFrame(
[['001733.OF', "泰达宏利量化"], ['004484.OF', '泰达宏利业绩驱动']],
columns=['wind_code', 'sec_name'])
data = data.append(d)
data = data.reset_index(drop=True)
if name == "被动指数型基金":
d = pd.DataFrame([['162213.OF', "泰达宏利沪深300"]],
columns=['wind_code', 'sec_name'])
data = data.append(d)
data = data.reset_index(drop=True)
if name == "偏债混合型基金":
d = pd.DataFrame([['003912.OF', "泰达宏利启富"],
['004000.OF', '泰达宏利睿选稳健'],
['003501.OF', '泰达宏利睿智稳健'],
['162211.OF', '泰达宏利品质生活'],
['001419.OF', '泰达宏利新思路']])
d.columns = ['wind_code', 'sec_name']
data = data.append(d)
data = data.reset_index(drop=True)
if name == "偏股混合型基金":
d = pd.DataFrame([['001017.OF', "泰达宏利改革动力"],
['002263.OF', '泰达宏利同顺大数据'],
["001170.OF", "泰达宏利复兴伟业"],
["005903.OF", "泰达宏利绩优增长"]])
d.columns = ['wind_code', 'sec_name']
data = data.append(d)
data = data.reset_index(drop=True)
out_sub_path = os.path.join(self.data_path_pool, name)
if not os.path.exists(out_sub_path):
os.makedirs(out_sub_path)
print(" Loading Fund Pool %s At %s" % (name, date))
out_file = os.path.join(out_sub_path, name + '_' + date + '.csv')
data.to_csv(out_file)
print(data.head())
