def queryForecastReport(code, starDate):
list = []
data = query_forecast_report(code, start_date=starDate)
while (data.error_code == '0') & data.next():
list.append(data.get_row_data())
data = pd.DataFrame(list, columns=data.fields)
return data
def get_forecast_report_data(stock_number, stock_name, start_date, end_date):
print('==========================================================')
print("开始进行: " + stock_name + "(" + stock_number + ")" + "的数据处理")
print("尝试登陆baostock")
#####login#####
lg = bs.login(user_id="anonymous", password="******")
if (lg.error_code == '0'):
print("登陆成功")
else:
print("登录失败")
#####get stock data#####
rs = bs.query_forecast_report(stock_number, start_date, end_date)
print('请求历史数据返回代码:' + rs.error_code)
print('请求历史数据返回信息:' + 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()
print(stock_name + "(" + stock_number + ")" + "的数据处理完成")
print('==========================================================')
return result
def getPerformanceForecast(id: str, begin: str = None, end: str = None):
data = BaoStock.query_forecast_report(id, begin, end).get_data()
result = []
for row in data.iterrows():
result.append({getPerformanceForecastMap[key][0]: getPerformanceForecastMap[key][1](
row[1][key]) for key in data.keys() if key in getPerformanceForecastMap})
return result
def get_forecast_report_df(code: str, start_date: str,
end_date: str) -> pd.DataFrame:
column_mapping = {
"profitForcastExpPubDate": "date",
"profitForcastExpStatDate": "period",
"value": "value",
}
resp = bs.query_forecast_report(code=code,
start_date=start_date,
end_date=end_date)
forecast_list = []
while (resp.error_code == "0") and resp.next():
forecast_list.append(resp.get_row_data())
forecast_df = pd.DataFrame(forecast_list, columns=resp.fields)
numeric_fields = ["profitForcastChgPctUp", "profitForcastChgPctDwn"]
try:
forecast_df[numeric_fields] = forecast_df[numeric_fields].apply(
pd.to_numeric, errors="ignore")
except KeyError:
return pd.DataFrame()
forecast_df["value"] = (forecast_df["profitForcastChgPctUp"] +
forecast_df["profitForcastChgPctDwn"]) / 200
forecast_df = forecast_df[list(column_mapping.keys())]
forecast_df.rename(columns=column_mapping, inplace=True)
forecast_df["field"] = "YOYNI"
return forecast_df
def query_forcast_report(code, start_date, end_date):
"""
季频公司业绩预告
方法说明：通过API接口获取季频公司业绩预告信息，可以通过参数设置获取起止年份数据，提供2003年至今数据。
返回类型：pandas的DataFrame类型。
参数含义：
code：股票代码，sh或sz.+6位数字代码，或者指数代码，如：sh.601398。sh：上海；sz：深圳。此参数不可为空；
start_date：开始日期，发布日期或更新日期在这个范围内；
end_date：结束日期，发布日期或更新日期在这个范围内。
"""
lg = bs.login()
if lg.error_code != '0':
logger.error('login respond error_msg:' + lg.error_msg)
rs_forecast = bs.query_forecast_report(code,
start_date=start_date,
end_date=end_date)
if rs_forecast.error_code != '0':
logger.error('query_forecast_reprot respond error_msg:' +
rs_forecast.error_msg)
rs_forecast_list = []
while (rs_forecast.error_code == '0') & rs_forecast.next():
rs_forecast_list.append(rs_forecast.get_row_data())
result_forecast = pd.DataFrame(rs_forecast_list,
columns=rs_forecast.fields)
bs.logout()
return result_forecast
def query_forcast_report(self,code, start_date, end_date):
'''
code：股票代码，sh或sz.+6位数字代码，或者指数代码，如：sh.601398。sh：上海；sz：深圳。此参数不可为空；
start_date：开始日期，发布日期或更新日期在这个范围内；
end_date：结束日期，发布日期或更新日期在这个范围内。
'''
rs_forecast = bs.query_forecast_report(code, start_date=start_date, end_date=end_date)
self.log('query_forecast_reprot respond error_msg:', rs_forecast)
rs_forecast_list = []
while (rs_forecast.error_code == '0') & rs_forecast.next():
# 分页查询，将每页信息合并在一起
rs_forecast_list.append(rs_forecast.get_row_data())
result_forecast = pd.DataFrame(rs_forecast_list, columns=rs_forecast.fields)
return result_forecast
def get_forcast(code,start='2019-01-01',end='2020-02-02'):
result_list = []
rs_forecast_list = []
# print(code,start, end)
rs = bs.query_performance_express_report(code, start_date=start, end_date=end)
while (rs.error_code == '0') & rs.next():
result_list.append(rs.get_row_data())
result = pd.DataFrame(result_list, columns=rs.fields)
rs_forecast = bs.query_forecast_report(code, start_date=start, end_date=end)
while (rs_forecast.error_code == '0') & rs_forecast.next():
rs_forecast_list.append(rs_forecast.get_row_data())
result_forecast = pd.DataFrame(rs_forecast_list, columns=rs_forecast.fields)
return result,result_forecast
def query_forcast_report(self):
"""
季频公司业绩预告：query_forcast_report()
方法说明：查询季频公司业绩预告信息，可以通过参数设置获取起止年份数据，提供2003年至今数据。
返回类型：pandas的DataFrame类型。
"""
all_stock = pd.read_csv(self.path + "all_stock.csv")
all_stock.drop_duplicates(subset=['code'], keep='first', inplace=True)
#### 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)
#### 获取公司业绩预告 ####
result_all = pd.DataFrame([])
for code in all_stock.code:
rs_forecast = bs.query_forecast_report(code,
start_date=self.start_date,
end_date=self.end_date)
#print('query_forecast_reprot respond error_code:'+rs_forecast.error_code)
#print('query_forecast_reprot respond error_msg:'+rs_forecast.error_msg)
rs_forecast_list = []
while (rs_forecast.error_code == '0') & rs_forecast.next():
# 分页查询，将每页信息合并在一起
rs_forecast_list.append(rs_forecast.get_row_data())
result_forecast = pd.DataFrame(rs_forecast_list,
columns=rs_forecast.fields)
result_all = result_all.append(result_forecast)
#### 结果集输出到csv文件 ####
forecast_report = pd.read_csv(self.path + "forecast_report.csv")
result_all = result_all.append(forecast_report)
result_all.drop_duplicates(subset=[
'code', 'profitForcastExpPubDate', 'profitForcastExpStatDate'
],
keep='first',
inplace=True)
result_all.to_csv(self.path + "forecast_report.csv",
encoding="gbk",
index=False)
#print(result_forecast)
#### 登出系统 ####
bs.logout()
return
def GetCurrentStockFundamentals(self, *args, **kwargs):
post_data_code = CheckKeyOrAbort(kStockCodeKey, kwargs)
profit_data_handler = bs.query_profit_data(post_data_code)
profit_data = profit_data_handler.get_data()
operation_data_handler = bs.query_operation_data(post_data_code)
operation_data = operation_data_handler.get_data()
growth_data_handler = bs.query_growth_data(post_data_code)
growth_data = growth_data_handler.get_data()
balance_data_handler = bs.query_balance_data(post_data_code)
balance_data = balance_data_handler.get_data()
cash_flow_data_handler = bs.query_balance_data(post_data_code)
cash_flow_data = cash_flow_data_handler.get_data()
dupont_data_handler = bs.query_dupont_data(post_data_code)
dupont_data = dupont_data_handler.get_data()
performance_data_handler = bs.query_performance_express_report(
post_data_code)
performance_data = performance_data_handler.get_data()
forecast_data_handler = bs.query_forecast_report(post_data_code)
forecast_data = forecast_data_handler.get_data()
return profit_data, operation_data, growth_data, balance_data, cash_flow_data, dupont_data, performance_data, forecast_data
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)
#### 获取公司业绩预告 ####
rs_forecast = bs.query_forecast_report("sh.600000",
start_date="2010-01-01",
end_date="2017-12-31")
print('query_forecast_reprot respond error_code:' + rs_forecast.error_code)
print('query_forecast_reprot respond error_msg:' + rs_forecast.error_msg)
rs_forecast_list = []
while (rs_forecast.error_code == '0') & rs_forecast.next():
# 分页查询，将每页信息合并在一起
rs_forecast_list.append(rs_forecast.get_row_data())
result_forecast = pd.DataFrame(rs_forecast_list, columns=rs_forecast.fields)
#### 结果集输出到csv文件 ####
result_forecast.to_csv("D:\\forecast_report.csv", encoding="gbk", index=False)
print(result_forecast)
#### 登出系统 ####
bs.logout()
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)
#### 获取公司业绩预告 ####
rs_forecast = bs.query_forecast_report("sh.600000", start_date="2020-01-01", end_date="2020-07-13")
print('query_forecast_reprot respond error_code:'+rs_forecast.error_code)
print('query_forecast_reprot respond error_msg:'+rs_forecast.error_msg)
rs_forecast_list = []
while (rs_forecast.error_code == '0') & rs_forecast.next():
# 分页查询，将每页信息合并在一起
rs_forecast_list.append(rs_forecast.get_row_data())
result_forecast = pd.DataFrame(rs_forecast_list, columns=rs_forecast.fields)
#### 结果集输出到csv文件 ####
result_forecast.to_csv("D:\\forecast_report.csv", encoding="gbk", index=False)
print(result_forecast)
#### 登出系统 ####
bs.logout()
def quarter_forecast_report(self, code, end_date, start_date='2003-01-01'):
self.getStockInstance()
rs = bs.query_forecast_report(code, start_date, end_date)
self._test_rs(rs)
return rs
query_date = "SELECT Date from tdx.recordtime WHERE ID = 'fcast'"
cur_date.execute(query_date)
s_date = cur_date.fetchone()
start_date = str(s_date[0])
end_date = time.strftime("%Y-%m-%d")
forcast_head = ['code','profitForcastExpPubDate','profitForcastExpStatDate','profitForcastType','profitForcastAbstract','profitForcastChgPctUp','profitForcastChgPctDwn']
count = 0 # 親數據
for code_item in code_item_list:
code = code_item[1]+"."+code_item[0]
rs_forcast = bs.query_forecast_report(code=code_item[1] + "." + code_item[0], start_date=start_date,end_date=end_date)
while (rs_forcast.error_code == '0') & rs_forcast.next():
growth_list = rs_forcast.get_row_data()
insert_sql = "INSERT INTO forcast("
value_sql = "VALUES("
value_list = []
for index in range(len(growth_list)):
if growth_list[index] != '':
insert_sql += forcast_head[index]+','
value_sql += "'%s',"
value_list.append(growth_list[index])
insert_sql = insert_sql[:-1]
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)
#### 获取公司业绩预告 ####
rs_forecast = bs.query_forecast_report("sz.000001",
start_date="2017-01-01",
end_date="2019-12-31")
print('query_forecast_reprot respond error_code:' + rs_forecast.error_code)
print('query_forecast_reprot respond error_msg:' + rs_forecast.error_msg)
rs_forecast_list = []
while (rs_forecast.error_code == '0') & rs_forecast.next():
# 分页查询，将每页信息合并在一起
rs_forecast_list.append(rs_forecast.get_row_data())
result_forecast = pd.DataFrame(rs_forecast_list, columns=rs_forecast.fields)
#### 结果集输出到csv文件 ####
result_forecast.to_csv("D:\\forecast_report.csv", encoding="gbk", index=False)
print(result_forecast)
#### 登出系统 ####
bs.logout()
def query_stock_forecast_report_summary(self, code, start_date, end_date):
forecast_report_data = bs.query_forecast_report(code, start_date=start_date, end_date=end_date)
return forecast_report_data
# 通过BaoStock获取业绩预报，
#### 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)
#### 获取公司业绩预告 ####
rs_forecast = bs.query_forecast_report("sz.300001",
start_date="2020-01-01",
end_date="2020-04-11")
print('query_forecast_reprot respond error_code:' + rs_forecast.error_code)
print('query_forecast_reprot respond error_msg:' + rs_forecast.error_msg)
rs_forecast_list = []
while (rs_forecast.error_code == '0') & rs_forecast.next():
# 分页查询，将每页信息合并在一起
rs_forecast_list.append(rs_forecast.get_row_data())
result_forecast = pd.DataFrame(rs_forecast_list, columns=rs_forecast.fields)
