def bdib(ticker: str,
dt,
session='allday',
typ='TRADE',
**kwargs) -> pd.DataFrame:
"""
Bloomberg intraday bar data
Args:
ticker: ticker name
dt: date to download
session: [allday, day, am, pm, pre, post]
typ: [TRADE, BID, ASK, BID_BEST, ASK_BEST, BEST_BID, BEST_ASK]
**kwargs:
ref: reference ticker or exchange
used as supplement if exchange info is not defined for `ticker`
batch: whether is batch process to download data
log: level of logs
Returns:
pd.DataFrame
"""
from xbbg.core import trials
logger = logs.get_logger(bdib, **kwargs)
ex_info = const.exch_info(ticker=ticker, **kwargs)
if ex_info.empty: raise KeyError(f'Cannot find exchange info for {ticker}')
ss_rng = process.time_range(dt=dt,
ticker=ticker,
session=session,
tz=ex_info.tz,
**kwargs)
data_file = storage.bar_file(ticker=ticker, dt=dt, typ=typ)
if files.exists(data_file) and kwargs.get(
'cache', True) and (not kwargs.get('reload', False)):
res = (pd.read_parquet(data_file).pipe(
pipeline.add_ticker, ticker=ticker).loc[ss_rng[0]:ss_rng[1]])
if not res.empty:
logger.debug(f'Loading Bloomberg intraday data from: {data_file}')
return res
if not process.check_current(dt=dt, logger=logger, **kwargs):
return pd.DataFrame()
cur_dt = pd.Timestamp(dt).strftime('%Y-%m-%d')
q_tckr = ticker
if ex_info.get('is_fut', False):
is_sprd = ex_info.get(
'has_sprd', False) and (len(ticker[:-1]) != ex_info['tickers'][0])
if not is_sprd:
q_tckr = fut_ticker(gen_ticker=ticker, dt=dt, freq=ex_info['freq'])
if q_tckr == '':
logger.error(f'cannot find futures ticker for {ticker} ...')
return pd.DataFrame()
info_log = f'{q_tckr} / {cur_dt} / {typ}'
trial_kw = dict(ticker=ticker, dt=dt, typ=typ, func='bdib')
num_trials = trials.num_trials(**trial_kw)
if num_trials >= 2:
if kwargs.get('batch', False): return pd.DataFrame()
logger.info(f'{num_trials} trials with no data {info_log}')
return pd.DataFrame()
while conn.bbg_session(**kwargs).tryNextEvent():
pass
time_rng = process.time_range(dt=dt,
ticker=ticker,
session='allday',
**kwargs)
request = process.create_request(
service='//blp/refdata',
request='IntradayBarRequest',
settings=[
('security', ticker),
('eventType', typ),
('interval', kwargs.get('interval', 1)),
('startDateTime', time_rng[0]),
('endDateTime', time_rng[1]),
],
**kwargs,
)
logger.debug(f'Sending request to Bloomberg ...\n{request}')
conn.send_request(request=request, **kwargs)
res = pd.DataFrame(process.rec_events(func=process.process_bar, **kwargs))
if res.empty or ('time' not in res):
logger.warning(f'No data for {info_log} ...')
trials.update_trials(cnt=num_trials + 1, **trial_kw)
return pd.DataFrame()
data = (res.set_index('time').rename_axis(index=None).rename(
columns={
'numEvents': 'num_trds'
}).tz_localize('UTC').tz_convert(ex_info.tz).pipe(pipeline.add_ticker,
ticker=ticker))
if kwargs.get('cache', True):
storage.save_intraday(data=data[ticker],
ticker=ticker,
dt=dt,
typ=typ,
**kwargs)
return data.loc[ss_rng[0]:ss_rng[1]]
def bdib(ticker, dt, typ='TRADE', batch=False, log=logs.LOG_LEVEL) -> pd.DataFrame:
"""
Download intraday data and save to cache
Args:
ticker: ticker name
dt: date to download
typ: [TRADE, BID, ASK, BID_BEST, ASK_BEST, BEST_BID, BEST_ASK]
batch: whether is batch process to download data
log: level of logs
Returns:
pd.DataFrame
"""
from xbbg.core import missing
logger = logs.get_logger(bdib, level=log)
t_1 = pd.Timestamp('today').date() - pd.Timedelta('1D')
whole_day = pd.Timestamp(dt).date() < t_1
if (not whole_day) and batch:
logger.warning(f'querying date {t_1} is too close, ignoring download ...')
return pd.DataFrame()
cur_dt = pd.Timestamp(dt).strftime('%Y-%m-%d')
asset = ticker.split()[-1]
info_log = f'{ticker} / {cur_dt} / {typ}'
if asset in ['Equity', 'Curncy', 'Index', 'Comdty']:
exch = const.exch_info(ticker=ticker)
if exch.empty: return pd.DataFrame()
else:
logger.error(f'unknown asset type: {asset}')
return pd.DataFrame()
time_fmt = '%Y-%m-%dT%H:%M:%S'
time_idx = pd.DatetimeIndex([
f'{cur_dt} {exch.allday[0]}', f'{cur_dt} {exch.allday[-1]}']
).tz_localize(exch.tz).tz_convert(DEFAULT_TZ).tz_convert('UTC')
if time_idx[0] > time_idx[1]: time_idx -= pd.TimedeltaIndex(['1D', '0D'])
q_tckr = ticker
if exch.get('is_fut', False):
if 'freq' not in exch:
logger.error(f'[freq] missing in info for {info_log} ...')
is_sprd = exch.get('has_sprd', False) and (len(ticker[:-1]) != exch['tickers'][0])
if not is_sprd:
q_tckr = fut_ticker(gen_ticker=ticker, dt=dt, freq=exch['freq'])
if q_tckr == '':
logger.error(f'cannot find futures ticker for {ticker} ...')
return pd.DataFrame()
info_log = f'{q_tckr} / {cur_dt} / {typ}'
miss_kw = dict(ticker=ticker, dt=dt, typ=typ, func='bdib')
cur_miss = missing.current_missing(**miss_kw)
if cur_miss >= 2:
if batch: return pd.DataFrame()
logger.info(f'{cur_miss} trials with no data {info_log}')
return pd.DataFrame()
logger.info(f'loading data from Bloomberg: {info_log} ...')
con, _ = create_connection()
data = con.bdib(
ticker=q_tckr, event_type=typ, interval=1,
start_datetime=time_idx[0].strftime(time_fmt),
end_datetime=time_idx[1].strftime(time_fmt),
)
if not isinstance(data, pd.DataFrame):
raise ValueError(f'unknown output format: {type(data)}')
if data.empty:
logger.warning(f'no data for {info_log} ...')
missing.update_missing(**miss_kw)
return pd.DataFrame()
data = data.tz_localize('UTC').tz_convert(exch.tz)
storage.save_intraday(data=data, ticker=ticker, dt=dt, typ=typ)
return pd.DataFrame() if batch else assist.format_intraday(data=data, ticker=ticker)