def QA_fetch_financial_report_adv(code, start, end=None, ltype='EN'):
"""高级财务查询接口
Arguments:
code {[type]} -- [description]
start {[type]} -- [description]
Keyword Arguments:
end {[type]} -- [description] (default: {None})
"""
if end is None:
return QA_DataStruct_Financial(
QA_fetch_financial_report(code,
start,
ltype=ltype)
)
else:
series = pd.Series(
data=month_data,
index=pd.to_datetime(month_data, utc=False),
name='date'
)
timerange = series.loc[start:end].tolist()
return QA_DataStruct_Financial(
QA_fetch_financial_report(code,
timerange,
ltype=ltype)
)
def QA_fetch_financial_report_adv(code, start='all', type='report', end=None):
"""高级财务查询接口
Arguments:
code {[type]} -- [description]
start {[type]} -- [description]
Keyword Arguments:
end {[type]} -- [description] (default: {None})
"""
end = start if end is None else end
start = str(start)[0:10]
end = str(end)[0:10]
if start == 'all':
start = '1990-01-01'
end = str(datetime.date.today())
if end is None:
end = str(datetime.date.today())
date_list = list(
pd.DataFrame.from_dict(
QA_util_getBetweenQuarter(
start,
QA_util_datetime_to_strdate(QA_util_add_months(
end, -3)))).T.iloc[:, 1])
if type == 'report':
return QA_DataStruct_Financial(
QA_fetch_financial_report(code, date_list))
elif type == 'date':
return QA_DataStruct_Financial(
QA_fetch_financial_report(code, date_list, type='date'))
else:
daterange = pd.date_range(start, end)
timerange = [item.strftime('%Y-%m-%d') for item in list(daterange)]
if type == 'report':
return QA_DataStruct_Financial(
QA_fetch_financial_report(code, timerange))
elif type == 'date':
return QA_DataStruct_Financial(
QA_fetch_financial_report(code, timerange, type='date'))
def get(self):
code = self.get_argument('code', default='000001')
start_year = self.get_argument('start_year', default='2002')
res = QA_fetch_financial_report([code], report_dates(int(start_year)))
temp = pd.DataFrame({
'report_date':
res.report_date,
'code':
res.code,
'cashPaymentsForDistrbutionOfDividendsOrProfits':
res.cashPaymentsForDistrbutionOfDividendsOrProfits
})
temp['report_date'].apply(format_ts_date)
data = QA_util_to_json_from_pandas(temp)
return self.write({'result': data})
def get(self):
code = self.get_argument('code', default='000001')
start_year = self.get_argument('start_year', default='2002')
res = QA_fetch_financial_report([code], report_dates(int(start_year)))
temp = pd.DataFrame({
'report_date': res.report_date,
'code': res.code,
'ROE': res.ROE
})
temp['report_date_str'] = temp['report_date'].apply(format_ts_date)
temp['ROE'] = temp['ROE'].apply(format_number)
temp['ROE_str'] = temp['ROE'].apply(format_percent)
data = QA_util_to_json_from_pandas(temp)
return self.write({'result': data})
def get(self):
code = self.get_argument('code', default='000001')
start_date = self.get_argument('start_date', default='2018-01-01')
end_date = self.get_argument('end_date', default='2018-12-31')
start_year = self.get_argument('start_year', default='2017')
stock_prices = QA_fetch_stock_day(code,
start_date,
end_date,
format='pandas')
financial_reports = QA_fetch_financial_report([code],
report_dates(
int(start_year)))
# PE= 股价/每股收益
# PB= 股价/每股净资产
# PEG=PE/（企业年盈利增长率*100）
price_pd = pd.DataFrame({
'code': stock_prices.code,
'date': stock_prices.date,
'close': stock_prices.close
})
reports_pd = pd.DataFrame({
'code':
financial_reports.code,
'report_date':
financial_reports.report_date,
'EPS':
financial_reports.EPS,
'netAssetsPerShare':
financial_reports.netAssetsPerShare,
'netProfitGrowthRate':
financial_reports.netProfitGrowthRate
})
reports_pd['report_date_str'] = reports_pd['report_date'].apply(
format_ts_date)
eps_sum = reports_pd.loc[reports_pd["report_date_str"] ==
"2017-12-31"].EPS.sum()
aps_sum = reports_pd.loc[reports_pd["report_date_str"] ==
"2017-12-31"].netAssetsPerShare.sum()
pgr_sum = reports_pd.loc[reports_pd["report_date_str"] ==
"2017-12-31"].netProfitGrowthRate.sum()
price_pd['date_str'] = price_pd['date'].apply(
lambda x: x.strftime("%Y-%m-%d"))
price_pd['eps_sum'] = price_pd['close'].apply(
lambda x: round(eps_sum, 2))
price_pd['aps_sum'] = price_pd['close'].apply(
lambda x: round(aps_sum, 2))
price_pd['pgr_sum'] = price_pd['close'].apply(
lambda x: round(pgr_sum, 2))
price_pd['pe'] = price_pd['close'].apply(
lambda x: round(x / eps_sum, 2))
price_pd['pb'] = price_pd['close'].apply(
lambda x: round(x / aps_sum, 2))
price_pd['peg'] = price_pd['pe'].apply(lambda x: round(x / pgr_sum, 2))
return self.write({
'stock_prices':
QA_util_to_json_from_pandas(price_pd),
'financial_reports':
QA_util_to_json_from_pandas(reports_pd)
})