def Zijinmian():
today = datetime.datetime.today()
year = today.year
month = today.month
dic = {'m2': []}
# 登陆系统
lg = bs.login()
# 获取货币供应量
if month < 10:
enddate = str(year) + '-' + '0' + str(month)
else:
enddate = str(year) + '-' + str(month)
rs5 = bs.query_money_supply_data_month(start_date="2020-01",
end_date=enddate)
# 打印结果集
data_list = []
while (rs5.error_code == '0') & rs5.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs5.get_row_data())
result = pd.DataFrame(data_list, columns=rs5.fields)
print(np.array(result['m2Month']))
# 登出系统
bs.logout()
dic['m2'] = np.array(result['m2Month']).tolist()
dic['m2'] = list(map(eval, dic['m2']))
return json.dumps(dic, ensure_ascii=False)
def query_money_supply_data_month(start_date=None, end_date=None):
"""
货币供应量
方法说明：通过API接口获取货币供应量，可以通过参数设置获取对应起止日期的数据。
返回类型：pandas的DataFrame类型。
参数含义：
start_date：开始日期，格式XXXX-XX，发布日期在这个范围内，可以为空；
end_date：结束日期，格式XXXX-XX，发布日期在这个范围内，可以为空。
"""
lg = bs.login()
if lg.error_code != '0':
logger.error('login respond error_msg:' + lg.error_msg)
rs = bs.query_money_supply_data_month(start_date=start_date,
end_date=end_date)
if rs.error_code != '0':
logger.error('query_money_supply_data_month respond error_msg:' +
rs.error_msg)
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
bs.logout()
return result
def query_money_supply_data_month(self, start_date = None, end_date = None):
'''
start_date：开始日期，格式XXXX-XX，发布日期在这个范围内，可以为空；
end_date：结束日期，格式XXXX-XX，发布日期在这个范围内，可以为空。
'''
rs = bs.query_money_supply_data_month(start_date=start_date, end_date=end_date)
self.log('query_money_supply_data_month respond error_msg:', rs)
# 打印结果集
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
return result
def save_money_supply_data_month(start_date, end_date):
'''
获取货币供应量
:param start_date:
:param end_date:
:return:
'''
bs.login()
rs = bs.query_money_supply_data_month(start_date=start_date,
end_date=end_date)
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
engine = create_engine(
'mysql+pymysql://root:root@localhost:3306/stock_quant?charset=utf8')
result.to_sql('bs_money_supply_data_month', engine, index=True)
bs.logout()
def download(beginDate: str, endDate: str, fileName):
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
# 查询季频估值指标盈利能力
list = []
# yearList = range(2003, 2020)
# for year in yearList:
# rs = bs.query_money_supply_data_month(str(year)+"-01", str(year)+"-12")
rs = bs.query_money_supply_data_month(beginDate, endDate)
# print('query_loan_rate_data respond error_code:'+rs.error_code)
# print('query_loan_rate_data respond error_msg:'+rs.error_msg)
while (rs.error_code == '0') & rs.next():
list.append(rs.get_row_data())
saveToCsv(list, fileName)
def get_money_supply_data(self,
start_date: str = None,
end_date: str = None,
save=False):
"""
货币供应量
"""
if end_date == None:
end_date = self.end_date
elif start_day == None:
start_day = start_date
data_saver = []
rs = bs.query_money_supply_data_month(start_date, end_date)
while (rs.error_code == "0") and rs.next():
data_saver.append(rs.get_row_data())
if save == True:
result = pd.DataFrame(data_saver, columns=rs.fields)
result.to_csv("../data_home/%s.csv" % targetStock)
return data_saver, rs.fields
def query_money_supply_data_month(self):
# 登陆系统
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
today_ISO_yymm = self.end_date[0:7]
# 获取货币供应量
rs = bs.query_money_supply_data_month(start_date="2010-01",
end_date=today_ISO_yymm)
print('query_money_supply_data_month respond error_code:' +
rs.error_code)
print('query_money_supply_data_month respond error_msg:' +
rs.error_msg)
# 打印结果集
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
# 结果集输出到csv文件
result = result[result.m0Month >= '0']
result.sort_values(by=['statYear', 'statMonth'],
ascending=False,
inplace=True)
result.to_csv(self.path + "money_supply_data_month.csv",
encoding="gbk",
index=False)
#print(result)
# 登出系统
bs.logout()
return
import baostock as bs
import pandas as pd
# 登陆系统
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:'+lg.error_code)
print('login respond error_msg:'+lg.error_msg)
# 获取货币供应量
rs = bs.query_money_supply_data_month(start_date="2010-01", end_date="2021-09")
print('query_money_supply_data_month respond error_code:'+rs.error_code)
print('query_money_supply_data_month respond error_msg:'+rs.error_msg)
# 打印结果集
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
# 结果集输出到csv文件
result.to_csv("./数据文件/货币流通量/2021-09.csv", encoding="gbk", index=False)
print(result)
# 登出系统
bs.logout()
log = Logger(level='debug')
log.logger.info('baostock_auto_downloader_' + version + ' start!')
# logon to Baostock and download
# 登陆系统
lg = bs.login()
# 显示登陆返回信息
log.logger.info('login respond error_code:' + lg.error_code)
log.logger.info('login respond error_msg:' + lg.error_msg)
today_ISO = datetime.today().date().isoformat()
today_ISO_yymm = today_ISO[0:7]
# 获取货币供应量
rs = bs.query_money_supply_data_month(start_date="2019-01",
end_date=today_ISO_yymm)
log.logger.info('query_money_supply_data_month respond error_code:' +
rs.error_code)
log.logger.info('query_money_supply_data_month respond error_msg:' +
rs.error_msg)
# 打印结果集
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
# 结果集输出到csv文件
result = result[result.m0Month >= '0']
result.sort_values(by=['statYear', 'statMonth'],
ascending=False,
def normal_money_supply_data_month(self, start_date, end_date):
self.getStockInstance()
rs = bs.query_money_supply_data_month(start_date[0:7], end_date[0:7])
self._test_rs(rs)
return rs
