def record(self, entity, start, end, size, timestamps):
the_quarters = get_year_quarters(start)
if not is_same_date(entity.timestamp, start) and len(the_quarters) > 1:
the_quarters = the_quarters[1:]
param = {
'security_item': entity,
'quarters': the_quarters,
'level': self.level.value
}
security_item = param['security_item']
quarters = param['quarters']
level = param['level']
result_df = pd.DataFrame()
for year, quarter in quarters:
query_url = self.url.format(security_item.code, year, quarter)
response = requests.get(query_url)
response.encoding = 'gbk'
try:
dfs = pd.read_html(response.text)
except ValueError as error:
self.logger.error(
f'skip ({year}-{quarter:02d}){security_item.code}{security_item.name}({error})'
)
time.sleep(10.0)
continue
if len(dfs) < 5:
time.sleep(10.0)
continue
df = dfs[4].copy()
df = df.iloc[1:]
df.columns = [
'timestamp', 'open', 'high', 'close', 'low', 'volume',
'turnover'
]
df['name'] = security_item.name
df['level'] = level
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['provider'] = 'sina'
result_df = pd.concat([result_df, df])
self.logger.info(
f'({security_item.code}{security_item.name})({year}-{quarter:02d})'
)
time.sleep(10.0)
result_df = result_df.sort_values(by='timestamp')
return result_df.to_dict(orient='records')
def record(self, entity, start, end, size, timestamps):
the_quarters = get_year_quarters(start)
# treat has recorded the season if contains some date
if not is_same_date(entity.timestamp, start) and len(the_quarters) > 1:
the_quarters = the_quarters[1:]
for year, quarter in the_quarters:
kdatas = []
for fuquan in ['bfq', 'hfq']:
the_url = self.get_kdata_url(entity.code, year, quarter,
fuquan)
resp = requests.get(the_url)
trs = Selector(text=resp.text).xpath(
'//*[@id="FundHoldSharesTable"]/tr[position()>1 and position()<=last()]'
).extract()
for idx, tr in enumerate(trs):
tds = Selector(text=tr).xpath('//td//text()').extract()
tds = [x.strip() for x in tds if x.strip()]
open = tds[1]
high = tds[2]
close = tds[3]
low = tds[4]
volume = tds[5]
turnover = tds[6]
if fuquan == 'hfq':
factor = tds[7]
the_timestamp = to_pd_timestamp(tds[0])
the_id = generate_kdata_id(entity_id=entity.id,
timestamp=the_timestamp,
level=self.level)
if fuquan == 'hfq':
# we got bfq at first and then update hfq data
existed = [
item for item in kdatas if item['id'] == the_id
]
if existed:
kdata = existed[0]
else:
self.logger.error(
"bfq not got for:{}".format(the_id))
kdata = {
'id': the_id,
'timestamp': the_timestamp,
'name': entity.name,
'level': self.level.value,
'open': to_float(open) / to_float(factor),
'close': to_float(close) / to_float(factor),
'high': to_float(high) / to_float(factor),
'low': to_float(low) / to_float(factor),
'volume': to_float(volume),
'turnover': to_float(turnover)
}
kdatas.append(kdata)
kdata['hfq_open'] = to_float(open)
kdata['hfq_high'] = to_float(high)
kdata['hfq_close'] = to_float(close)
kdata['hfq_low'] = to_float(low)
kdata['factor'] = to_float(factor)
self.latest_factors[entity.id] = to_float(factor)
else:
kdatas.append({
'id': the_id,
'timestamp': the_timestamp,
'name': entity.name,
'level': self.level.value,
'open': to_float(open),
'close': to_float(close),
'high': to_float(high),
'low': to_float(low),
'volume': to_float(volume),
'turnover': to_float(turnover)
})
return kdatas