def bdh(tickers,
flds=None,
start_date=None,
end_date='today',
adjust=None,
**kwargs) -> pd.DataFrame:
"""
Bloomberg historical data
Args:
tickers: ticker(s)
flds: field(s)
start_date: start date
end_date: end date - default today
adjust: `all`, `dvd`, `normal`, `abn` (=abnormal), `split`, `-` or None
exact match of above words will adjust for corresponding events
Case 0: `-` no adjustment for dividend or split
Case 1: `dvd` or `normal|abn` will adjust for all dividends except splits
Case 2: `adjust` will adjust for splits and ignore all dividends
Case 3: `all` == `dvd|split` == adjust for all
Case 4: None == Bloomberg default OR use kwargs
**kwargs: overrides
Returns:
pd.DataFrame
"""
logger = logs.get_logger(bdh, **kwargs)
if flds is None: flds = ['Last_Price']
e_dt = utils.fmt_dt(end_date, fmt='%Y%m%d')
if start_date is None:
start_date = pd.Timestamp(e_dt) - pd.Timedelta(weeks=8)
s_dt = utils.fmt_dt(start_date, fmt='%Y%m%d')
request = process.create_request(
service='//blp/refdata',
request='HistoricalDataRequest',
**kwargs,
)
process.init_request(request=request,
tickers=tickers,
flds=flds,
start_date=s_dt,
end_date=e_dt,
adjust=adjust,
**kwargs)
logger.debug(f'Sending request to Bloomberg ...\n{request}')
conn.send_request(request=request, **kwargs)
res = pd.DataFrame(process.rec_events(process.process_hist, **kwargs))
if kwargs.get('raw', False): return res
if res.empty or any(fld not in res for fld in ['ticker', 'date']):
return pd.DataFrame()
return (res.set_index(['ticker', 'date']).unstack(level=0).rename_axis(
index=None, columns=[None, None]).swaplevel(
0, 1, axis=1).reindex(columns=utils.flatten(tickers),
level=0).reindex(columns=utils.flatten(flds),
level=1))
def dividend(tickers,
typ='all',
start_date=None,
end_date=None,
**kwargs) -> pd.DataFrame:
"""
Bloomberg dividend / split history
Args:
tickers: list of tickers
typ: dividend adjustment type
`all`: `DVD_Hist_All`
`dvd`: `DVD_Hist`
`split`: `Eqy_DVD_Hist_Splits`
`gross`: `Eqy_DVD_Hist_Gross`
`adjust`: `Eqy_DVD_Adjust_Fact`
`adj_fund`: `Eqy_DVD_Adj_Fund`
`with_amt`: `DVD_Hist_All_with_Amt_Status`
`dvd_amt`: `DVD_Hist_with_Amt_Status`
`gross_amt`: `DVD_Hist_Gross_with_Amt_Stat`
`projected`: `BDVD_Pr_Ex_Dts_DVD_Amts_w_Ann`
start_date: start date
end_date: end date
**kwargs: overrides
Returns:
pd.DataFrame
"""
kwargs.pop('raw', None)
if isinstance(tickers, str): tickers = [tickers]
tickers = [t for t in tickers if ('Equity' in t) and ('=' not in t)]
fld = const.DVD_TPYES.get(typ, typ)
if (fld == 'Eqy_DVD_Adjust_Fact') and ('Corporate_Actions_Filter'
not in kwargs):
kwargs[
'Corporate_Actions_Filter'] = 'NORMAL_CASH|ABNORMAL_CASH|CAPITAL_CHANGE'
if start_date:
kwargs['DVD_Start_Dt'] = utils.fmt_dt(start_date, fmt='%Y%m%d')
if end_date:
kwargs['DVD_End_Dt'] = utils.fmt_dt(end_date, fmt='%Y%m%d')
return bds(tickers=tickers, flds=fld, col_maps=const.DVD_COLS, **kwargs)
def missing_info(**kwargs) -> str:
"""
Full infomation for missing query
"""
func = kwargs.pop('func', 'unknown')
if 'ticker' in kwargs: kwargs['ticker'] = kwargs['ticker'].replace('/', '_')
for dt in ['dt', 'start_dt', 'end_dt', 'start_date', 'end_date']:
if dt not in kwargs: continue
kwargs[dt] = utils.fmt_dt(kwargs[dt])
info = utils.to_str(kwargs, fmt='{value}', sep='/')[1:-1]
return f'{func}/{info}'
def beqs(screen,
asof=None,
typ='PRIVATE',
group='General',
**kwargs) -> pd.DataFrame:
"""
Bloomberg equity screening
Args:
screen: screen name
asof: as of date
typ: GLOBAL/B (Bloomberg) or PRIVATE/C (Custom, default)
group: group name if screen is organized into groups
Returns:
pd.DataFrame
"""
logger = logs.get_logger(beqs, **kwargs)
service = conn.bbg_service(service='//blp/refdata', **kwargs)
request = service.createRequest('BeqsRequest')
request.set('screenName', screen)
request.set('screenType',
'GLOBAL' if typ[0].upper() in ['G', 'B'] else 'PRIVATE')
request.set('Group', group)
if asof:
overrides = request.getElement('overrides')
ovrd = overrides.appendElement()
ovrd.setElement('fieldId', 'PiTDate')
ovrd.setElement('value', utils.fmt_dt(asof, '%Y%m%d'))
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 res.empty:
if kwargs.get('trial', 0): return pd.DataFrame()
else:
return beqs(screen=screen,
asof=asof,
typ=typ,
group=group,
trial=1,
**kwargs)
if kwargs.get('raw', False): return res
cols = res.field.unique()
return (res.set_index(['ticker', 'field']).unstack(level=1).rename_axis(
index=None,
columns=[None, None
]).droplevel(axis=1,
level=0).loc[:,
cols].pipe(pipeline.standard_cols))
def trail_info(**kwargs) -> dict:
"""
Convert info to proper format for databse
Returns:
dict
"""
kwargs['func'] = kwargs.pop('func', 'unknown')
if 'ticker' in kwargs:
kwargs['ticker'] = kwargs['ticker'].replace('/', '_')
for dt in ['dt', 'start_dt', 'end_dt', 'start_date', 'end_date']:
if dt not in kwargs: continue
kwargs[dt] = utils.fmt_dt(kwargs[dt])
return kwargs
def dividend(tickers, typ='all', start_date=None, end_date=None, **kwargs):
"""
Dividend history
Args:
tickers: list of tickers
typ: `all`, `dvd`, `split`, `split`, `gross`, `adjust`, `adj_fund`,
`with_amt`, `dvd_amt`, `gross_amt`, `projected`
start_date: start date
end_date: end date
**kwargs: overrides
Returns:
pd.DataFrame
Examples:
>>> dividend(
... tickers=['C US Equity', 'NVDA US Equity', 'MS US Equity'],
... start_date='2018-01-01', end_date='2018-05-01'
... ).rename_axis(None).loc[:, [
... 'ex_date', 'rec_date', 'dvd_amt'
... ]].round(2)
ex_date rec_date dvd_amt
C US Equity 2018-02-02 2018-02-05 0.32
MS US Equity 2018-04-27 2018-04-30 0.25
MS US Equity 2018-01-30 2018-01-31 0.25
NVDA US Equity 2018-02-22 2018-02-23 0.15
"""
if isinstance(tickers, str): tickers = [tickers]
tickers = [t for t in tickers if ('Equity' in t) and ('=' not in t)]
fld = {
'all': 'DVD_Hist_All', 'dvd': 'DVD_Hist',
'split': 'Eqy_DVD_Hist_Splits', 'gross': 'Eqy_DVD_Hist_Gross',
'adjust': 'Eqy_DVD_Adjust_Fact', 'adj_fund': 'Eqy_DVD_Adj_Fund',
'with_amt': 'DVD_Hist_All_with_Amt_Status',
'dvd_amt': 'DVD_Hist_with_Amt_Status',
'gross_amt': 'DVD_Hist_Gross_with_Amt_Stat',
'projected': 'BDVD_Pr_Ex_Dts_DVD_Amts_w_Ann',
}.get(typ, typ)
if (fld == 'Eqy_DVD_Adjust_Fact') and ('Corporate_Actions_Filter' not in kwargs):
kwargs['Corporate_Actions_Filter'] = 'NORMAL_CASH|ABNORMAL_CASH|CAPITAL_CHANGE'
if fld in [
'DVD_Hist_All', 'DVD_Hist', 'Eqy_DVD_Hist_Gross',
'DVD_Hist_All_with_Amt_Status', 'DVD_Hist_with_Amt_Status',
]:
if start_date: kwargs['DVD_Start_Dt'] = utils.fmt_dt(start_date, fmt='%Y%m%d')
if end_date: kwargs['DVD_End_Dt'] = utils.fmt_dt(end_date, fmt='%Y%m%d')
kwargs['col_maps'] = {
'Declared Date': 'dec_date', 'Ex-Date': 'ex_date',
'Record Date': 'rec_date', 'Payable Date': 'pay_date',
'Dividend Amount': 'dvd_amt', 'Dividend Frequency': 'dvd_freq',
'Dividend Type': 'dvd_type', 'Amount Status': 'amt_status',
'Adjustment Date': 'adj_date', 'Adjustment Factor': 'adj_factor',
'Adjustment Factor Operator Type': 'adj_op',
'Adjustment Factor Flag': 'adj_flag',
'Amount Per Share': 'amt_ps', 'Projected/Confirmed': 'category',
}
return bds(tickers=tickers, flds=fld, raw=False, **kwargs)
def bdh(tickers, flds, start_date, end_date='today', adjust=None, **kwargs):
"""
Bloomberg historical data
Args:
tickers: ticker(s)
flds: field(s)
start_date: start date
end_date: end date - default today
adjust: `all`, `dvd`, `normal`, `abn` (=abnormal), `split`, `-` or None
exact match of above words will adjust for corresponding events
Case 0: `-` no adjustment for dividend or split
Case 1: `dvd` or `normal|abn` will adjust for all dividends except splits
Case 2: `adjust` will adjust for splits and ignore all dividends
Case 3: `all` == `dvd|split` == adjust for all
Case 4: None == Bloomberg default OR use kwargs
**kwargs: overrides
Returns:
pd.DataFrame
Examples:
>>> bdh(
... tickers='VIX Index', flds=['High', 'Low', 'Last_Price'],
... start_date='2018-02-05', end_date='2018-02-07',
... ).round(2).transpose().rename_axis((None, None))
2018-02-05 2018-02-06 2018-02-07
VIX Index High 38.80 50.30 31.64
Low 16.80 22.42 21.17
Last_Price 37.32 29.98 27.73
>>> bdh(
... tickers='AAPL US Equity', flds='Px_Last',
... start_date='20140605', end_date='20140610', adjust='-'
... ).round(2)
ticker AAPL US Equity
field Px_Last
2014-06-05 647.35
2014-06-06 645.57
2014-06-09 93.70
2014-06-10 94.25
>>> bdh(
... tickers='AAPL US Equity', flds='Px_Last',
... start_date='20140606', end_date='20140609',
... CshAdjNormal=False, CshAdjAbnormal=False, CapChg=False,
... ).round(2)
ticker AAPL US Equity
field Px_Last
2014-06-06 645.57
2014-06-09 93.70
"""
logger = logs.get_logger(bdh, level=kwargs.pop('log', logs.LOG_LEVEL))
# Dividend adjustments
if isinstance(adjust, str) and adjust:
if adjust == 'all':
kwargs['CshAdjNormal'] = True
kwargs['CshAdjAbnormal'] = True
kwargs['CapChg'] = True
else:
kwargs['CshAdjNormal'] = 'normal' in adjust or 'dvd' in adjust
kwargs['CshAdjAbnormal'] = 'abn' in adjust or 'dvd' in adjust
kwargs['CapChg'] = 'split' in adjust
con, _ = create_connection()
elms = assist.proc_elms(**kwargs)
ovrds = assist.proc_ovrds(**kwargs)
if isinstance(tickers, str): tickers = [tickers]
if isinstance(flds, str): flds = [flds]
s_dt = utils.fmt_dt(start_date, fmt='%Y%m%d')
e_dt = utils.fmt_dt(end_date, fmt='%Y%m%d')
logger.info(
f'loading historical data from Bloomberg:\n'
f'{assist.info_qry(tickers=tickers, flds=flds)}'
)
return con.bdh(
tickers=tickers, flds=flds, elms=elms, ovrds=ovrds,
start_date=s_dt, end_date=e_dt,
).rename_axis(None)
def dividend(tickers,
typ='all',
start_date=None,
end_date=None,
**kwargs) -> pd.DataFrame:
"""
Bloomberg dividend / split history
Args:
tickers: list of tickers
typ: dividend adjustment type
`all`: `DVD_Hist_All`
`dvd`: `DVD_Hist`
`split`: `Eqy_DVD_Hist_Splits`
`gross`: `Eqy_DVD_Hist_Gross`
`adjust`: `Eqy_DVD_Adjust_Fact`
`adj_fund`: `Eqy_DVD_Adj_Fund`
`with_amt`: `DVD_Hist_All_with_Amt_Status`
`dvd_amt`: `DVD_Hist_with_Amt_Status`
`gross_amt`: `DVD_Hist_Gross_with_Amt_Stat`
`projected`: `BDVD_Pr_Ex_Dts_DVD_Amts_w_Ann`
start_date: start date
end_date: end date
**kwargs: overrides
Returns:
pd.DataFrame
"""
kwargs.pop('raw', None)
if isinstance(tickers, str): tickers = [tickers]
tickers = [t for t in tickers if ('Equity' in t) and ('=' not in t)]
fld = {
'all': 'DVD_Hist_All',
'dvd': 'DVD_Hist',
'split': 'Eqy_DVD_Hist_Splits',
'gross': 'Eqy_DVD_Hist_Gross',
'adjust': 'Eqy_DVD_Adjust_Fact',
'adj_fund': 'Eqy_DVD_Adj_Fund',
'with_amt': 'DVD_Hist_All_with_Amt_Status',
'dvd_amt': 'DVD_Hist_with_Amt_Status',
'gross_amt': 'DVD_Hist_Gross_with_Amt_Stat',
'projected': 'BDVD_Pr_Ex_Dts_DVD_Amts_w_Ann',
}.get(typ, typ)
if (fld == 'Eqy_DVD_Adjust_Fact') and ('Corporate_Actions_Filter'
not in kwargs):
kwargs[
'Corporate_Actions_Filter'] = 'NORMAL_CASH|ABNORMAL_CASH|CAPITAL_CHANGE'
if fld in [
'DVD_Hist_All',
'DVD_Hist',
'Eqy_DVD_Hist_Gross',
'DVD_Hist_All_with_Amt_Status',
'DVD_Hist_with_Amt_Status',
]:
if start_date:
kwargs['DVD_Start_Dt'] = utils.fmt_dt(start_date, fmt='%Y%m%d')
if end_date:
kwargs['DVD_End_Dt'] = utils.fmt_dt(end_date, fmt='%Y%m%d')
return bds(tickers=tickers,
flds=fld,
col_maps={
'Declared Date': 'dec_date',
'Ex-Date': 'ex_date',
'Record Date': 'rec_date',
'Payable Date': 'pay_date',
'Dividend Amount': 'dvd_amt',
'Dividend Frequency': 'dvd_freq',
'Dividend Type': 'dvd_type',
'Amount Status': 'amt_status',
'Adjustment Date': 'adj_date',
'Adjustment Factor': 'adj_factor',
'Adjustment Factor Operator Type': 'adj_op',
'Adjustment Factor Flag': 'adj_flag',
'Amount Per Share': 'amt_ps',
'Projected/Confirmed': 'category',
},
**kwargs)