def request(self, url=None, method='get', param=None, path_fields=None):
security_item = param['security_item']
start_timestamp = param['start_timestamp']
end_timestamp = param['end_timestamp']
# 不复权
df = get_price(
to_jq_security_id(security_item),
start_date=to_time_str(start_timestamp),
end_date=end_timestamp,
frequency=param['jq_level'],
fields=['open', 'close', 'low', 'high', 'volume', 'money'],
skip_paused=True,
fq=None)
df.index.name = 'timestamp'
df.reset_index(inplace=True)
df['name'] = security_item.name
df.rename(columns={'money': 'turnover'}, inplace=True)
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['provider'] = Provider.JOINQUANT.value
df['level'] = param['level']
# remove the unfinished kdata
if is_in_trading(security_type='stock',
exchange='sh',
timestamp=df.iloc[-1, :]['timestamp']):
df = df.iloc[:-1, :]
return df.to_dict(orient='records')
def on_finish(self, security_item):
kdatas = get_kdata(
provider=self.provider,
security_id=security_item.id,
level=self.level.value,
order=self.data_schema.timestamp.asc(),
return_type='domain',
session=self.session,
filters=[
self.data_schema.hfq_close.is_(None),
self.data_schema.timestamp >= to_pd_timestamp('2005-01-01')
])
if kdatas:
start = kdatas[0].timestamp
end = kdatas[-1].timestamp
# get hfq from joinquant
df = get_price(to_jq_security_id(security_item),
start_date=to_time_str(start),
end_date=now_time_str(),
frequency='daily',
fields=['factor', 'open', 'close', 'low', 'high'],
skip_paused=True,
fq='post')
if df is not None and not df.empty:
# fill hfq data
for kdata in kdatas:
time_str = to_time_str(kdata.timestamp)
if time_str in df.index:
kdata.hfq_open = df.loc[time_str, 'open']
kdata.hfq_close = df.loc[time_str, 'close']
kdata.hfq_high = df.loc[time_str, 'high']
kdata.hfq_low = df.loc[time_str, 'low']
kdata.factor = df.loc[time_str, 'factor']
self.session.commit()
latest_factor = df.factor[-1]
# factor not change yet, no need to reset the qfq past
if latest_factor == self.current_factors.get(security_item.id):
sql = 'UPDATE {} SET qfq_close=hfq_close/{},qfq_high=hfq_high/{}, qfq_open= hfq_open/{}, qfq_low= hfq_low/{} where ' \
'security_id=\'{}\' and level=\'{}\' and (qfq_close isnull or qfq_high isnull or qfq_low isnull or qfq_open isnull)'.format(
self.data_schema.__table__, latest_factor, latest_factor, latest_factor, latest_factor,
security_item.id, self.level.value)
else:
sql = 'UPDATE {} SET qfq_close=hfq_close/{},qfq_high=hfq_high/{}, qfq_open= hfq_open/{}, qfq_low= hfq_low/{} where ' \
'security_id=\'{}\' and level=\'{}\''.format(self.data_schema.__table__, latest_factor,
latest_factor, latest_factor, latest_factor,
security_item.id,
self.level.value)
self.logger.info(sql)
self.session.execute(sql)
self.session.commit()
# TODO:use netease provider to get turnover_rate
self.logger.info('use netease provider to get turnover_rate')
def request(self, url=None, method='get', param=None, path_fields=None):
security_item = param['security_item']
start_timestamp = param['start_timestamp']
# 不复权
df = get_price(
to_jq_security_id(security_item),
start_date=to_time_str(start_timestamp),
end_date=now_time_str(),
frequency='daily',
fields=['open', 'close', 'low', 'high', 'volume', 'money'],
skip_paused=True,
fq=None)
df.index.name = 'timestamp'
df.reset_index(inplace=True)
df['name'] = security_item.name
df.rename(columns={'money': 'turnover'}, inplace=True)
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['provider'] = Provider.JOINQUANT.value
df['level'] = param['level']
return df.to_dict(orient='records')
