def bds(tickers, flds, **kwargs) -> pd.DataFrame:
"""
Bloomberg block data
Args:
tickers: ticker(s)
flds: field
**kwargs: other overrides for query
Returns:
pd.DataFrame: block data
"""
logger = logs.get_logger(bds, **kwargs)
service = conn.bbg_service(service='//blp/refdata', **kwargs)
request = service.createRequest('ReferenceDataRequest')
if isinstance(tickers, str):
data_file = storage.ref_file(ticker=tickers,
fld=flds,
has_date=True,
ext='pkl',
**kwargs)
if files.exists(data_file):
logger.debug(f'Loading Bloomberg data from: {data_file}')
return pd.DataFrame(pd.read_pickle(data_file))
process.init_request(request=request,
tickers=tickers,
flds=flds,
**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_ref, **kwargs))
if kwargs.get('raw', False): return res
if res.empty or any(fld not in res for fld in ['ticker', 'field']):
return pd.DataFrame()
data = (res.set_index(['ticker', 'field']).droplevel(
axis=0, level=1).rename_axis(index=None).pipe(
pipeline.standard_cols, col_maps=kwargs.get('col_maps', None)))
if data_file:
logger.debug(f'Saving Bloomberg data to: {data_file}')
files.create_folder(data_file, is_file=True)
data.to_pickle(data_file)
return data
return pd.DataFrame(
pd.concat(
[bds(tickers=ticker, flds=flds, **kwargs) for ticker in tickers],
sort=False))
def bdp(tickers, flds, cache=False, **kwargs):
"""
Get reference data and save to
Args:
tickers: tickers
flds: fields to query
cache: bool - use cache to store data
**kwargs: overrides
Returns:
pd.DataFrame
Examples:
>>> bdp('IQ US Equity', 'Crncy', raw=True)
ticker field value
0 IQ US Equity Crncy USD
>>> bdp('IQ US Equity', 'Crncy').reset_index()
ticker crncy
0 IQ US Equity USD
"""
logger = logs.get_logger(bdp, level=kwargs.pop('log', logs.LOG_LEVEL))
con, _ = create_connection()
ovrds = assist.proc_ovrds(**kwargs)
logger.info(
f'loading reference data from Bloomberg:\n'
f'{assist.info_qry(tickers=tickers, flds=flds)}'
)
data = con.ref(tickers=tickers, flds=flds, ovrds=ovrds)
if not cache: return [data]
qry_data = []
for r, snap in data.iterrows():
subset = [r]
data_file = storage.ref_file(
ticker=snap.ticker, fld=snap.field, ext='pkl', cache=cache, **kwargs
)
if data_file:
if not files.exists(data_file): qry_data.append(data.iloc[subset])
files.create_folder(data_file, is_file=True)
data.iloc[subset].to_pickle(data_file)
return qry_data
def bdp_bds_cache(func, tickers, flds, **kwargs) -> ToQuery:
"""
Find cached `BDP` / `BDS` queries
Args:
func: function name - bdp or bds
tickers: tickers
flds: fields
**kwargs: other kwargs
Returns:
ToQuery(ticker, flds, kwargs)
"""
cache_data = []
log_level = kwargs.get('log', logs.LOG_LEVEL)
logger = logs.get_logger(bdp_bds_cache, level=log_level)
has_date = kwargs.pop('has_date', func == 'bds')
cache = kwargs.get('cache', True)
tickers = utils.flatten(tickers)
flds = utils.flatten(flds)
loaded = pd.DataFrame(data=0, index=tickers, columns=flds)
for ticker, fld in product(tickers, flds):
data_file = storage.ref_file(
ticker=ticker,
fld=fld,
has_date=has_date,
cache=cache,
ext='pkl',
**{k: v
for k, v in kwargs.items() if k not in EXC_COLS})
if not files.exists(data_file): continue
logger.debug(f'reading from {data_file} ...')
cache_data.append(pd.read_pickle(data_file))
loaded.loc[ticker, fld] = 1
to_qry = loaded.where(loaded == 0)\
.dropna(how='all', axis=1).dropna(how='all', axis=0)
return ToQuery(tickers=to_qry.index.tolist(),
flds=to_qry.columns.tolist(),
cached_data=cache_data)
def _bds_(
ticker: str,
fld: str,
logger: logs.logging.Logger,
use_port: bool = False,
**kwargs,
) -> pd.DataFrame:
"""
Get data of BDS of single ticker
"""
if 'has_date' not in kwargs: kwargs['has_date'] = True
data_file = storage.ref_file(ticker=ticker, fld=fld, ext='pkl', **kwargs)
if files.exists(data_file):
logger.debug(f'Loading Bloomberg data from: {data_file}')
return pd.DataFrame(pd.read_pickle(data_file))
request = process.create_request(
service='//blp/refdata',
request='PortfolioDataRequest' if use_port else 'ReferenceDataRequest',
**kwargs,
)
process.init_request(request=request, tickers=ticker, flds=fld, **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_ref, **kwargs))
if kwargs.get('raw', False): return res
if res.empty or any(fld not in res for fld in ['ticker', 'field']):
return pd.DataFrame()
data = (res.set_index(['ticker', 'field']).droplevel(
axis=0, level=1).rename_axis(index=None).pipe(pipeline.standard_cols,
col_maps=kwargs.get(
'col_maps', None)))
if data_file:
logger.debug(f'Saving Bloomberg data to: {data_file}')
files.create_folder(data_file, is_file=True)
data.to_pickle(data_file)
return data
def bds(tickers, flds, cache=False, **kwargs):
"""
Download block data from Bloomberg
Args:
tickers: ticker(s)
flds: field(s)
cache: whether read from cache
**kwargs: other overrides for query
-> raw: raw output from `pdbdp` library, default False
Returns:
pd.DataFrame: block data
Examples:
>>> import os
>>>
>>> pd.options.display.width = 120
>>> s_dt, e_dt = '20180301', '20181031'
>>> dvd = bds(
... 'NVDA US Equity', 'DVD_Hist_All',
... DVD_Start_Dt=s_dt, DVD_End_Dt=e_dt, raw=True,
... )
>>> dvd.loc[:, ['ticker', 'name', 'value']].head(8)
ticker name value
0 NVDA US Equity Declared Date 2018-08-16
1 NVDA US Equity Ex-Date 2018-08-29
2 NVDA US Equity Record Date 2018-08-30
3 NVDA US Equity Payable Date 2018-09-21
4 NVDA US Equity Dividend Amount 0.15
5 NVDA US Equity Dividend Frequency Quarter
6 NVDA US Equity Dividend Type Regular Cash
7 NVDA US Equity Declared Date 2018-05-10
>>> dvd = bds(
... 'NVDA US Equity', 'DVD_Hist_All',
... DVD_Start_Dt=s_dt, DVD_End_Dt=e_dt,
... )
>>> dvd.reset_index().loc[:, ['ticker', 'ex_date', 'dividend_amount']]
ticker ex_date dividend_amount
0 NVDA US Equity 2018-08-29 0.15
1 NVDA US Equity 2018-05-23 0.15
>>> if not os.environ.get('BBG_ROOT', ''):
... os.environ['BBG_ROOT'] = f'{files.abspath(__file__, 1)}/tests/data'
>>> idx_kw = dict(End_Dt='20181220', cache=True)
>>> idx_wt = bds('DJI Index', 'Indx_MWeight_Hist', **idx_kw)
>>> idx_wt.round(2).tail().reset_index(drop=True)
index_member percent_weight
0 V UN 3.82
1 VZ UN 1.63
2 WBA UW 2.06
3 WMT UN 2.59
4 XOM UN 2.04
>>> idx_wt = bds('DJI Index', 'Indx_MWeight_Hist', **idx_kw)
>>> idx_wt.round(2).head().reset_index(drop=True)
index_member percent_weight
0 AAPL UW 4.65
1 AXP UN 2.84
2 BA UN 9.29
3 CAT UN 3.61
4 CSCO UW 1.26
"""
logger = logs.get_logger(bds, level=kwargs.pop('log', logs.LOG_LEVEL))
has_date = kwargs.pop('has_date', True)
con, _ = create_connection()
ovrds = assist.proc_ovrds(**kwargs)
logger.info(
f'loading block data from Bloomberg:\n'
f'{assist.info_qry(tickers=tickers, flds=flds)}'
)
data = con.bulkref(tickers=tickers, flds=flds, ovrds=ovrds)
if not cache: return [data]
qry_data = []
for (ticker, fld), grp in data.groupby(['ticker', 'field']):
data_file = storage.ref_file(
ticker=ticker, fld=fld, has_date=has_date, ext='pkl', cache=cache, **kwargs
)
if data_file:
if not files.exists(data_file): qry_data.append(grp)
files.create_folder(data_file, is_file=True)
grp.reset_index(drop=True).to_pickle(data_file)
return qry_data