def __init__(self, *args, **kwargs):
self._logger = logging.getLogger("jpbank.quants")
self._logger.info("{0} initializing...".format(self.__class__.__name__))
self._start_date = kwargs.get('start_date', date(2001, 1, 1))
self._end_date = kwargs.get('end_date', date.today())
self._ar_diff = kwargs.get('ar_diff', 3)
self._frequency = kwargs.get('frequency', 'weekly')
if self._frequency == 'daily':
self._date_list = cf.create_daily_datelist(self._start_date, self._end_date)
self._term = int(kwargs.get('term_year', 3) * 261)
elif self._frequency == 'weekly':
self._date_list = cf.create_weekly_datelist(self._start_date, self._end_date)
self._term = int(kwargs.get('term_year', 3) * 52)
else:
self._date_list = cf.create_monthly_datelist(self._start_date, self._end_date)
self._term = int(kwargs.get('term_year', 3) * 12)
self._ccy_list = kwargs.get('ccy_list',
np.sort(['USDJPY Index', 'EURJPY Index', 'AUDJPY Index', 'GBPJPY Index',
'CADJPY Index', 'CHFJPY Index', 'NZDJPY Index']).tolist())
self._order = kwargs.get('order', 0)
if self._order == -1:
self._reg = 'nc'
elif self._order == 0:
self._reg = 'c'
elif self._oder == 1:
self._reg = 'ct'
self._fx_rate_df = np.log(self._get_fx_rate())
self._logger.info("{0} initialized.".format(self.__class__.__name__))
def create_macro_index(self, ticker, shifts=False):
macro_query = "SELECT ValueDate, Ticker, Last \
FROM bbg_marketprice \
WHERE ValueDate BETWEEN '{0}' AND '{1}' \
AND Ticker = '{2}'"\
.format(self._start_date,
self._end_date,
ticker)
with util.DBConnector(DBName='marketdb') as db_conn:
macro_df = db_conn.get_data(macro_query).pivot(index='ValueDate',
columns='Ticker',
values='Last')\
.fillna(method='ffill')
#import pdb;pdb.set_trace()
if shifts: macro_df = macro_df.shift(1).dropna(axis=0)
date_df = pd.DataFrame(cf.create_daily_datelist(start_date=self._start_date,
end_date=self._end_date),
columns=['ValueDate'])
macro_df = pd.merge(date_df,
macro_df.reset_index('ValueDate'),
on='ValueDate',
how='left')\
.set_index('ValueDate')\
.fillna(method='ffill')\
.fillna(method='bfill')
return macro_df
def create_factor(self, ticker_list):
target_df = self._input_data.query("@self._start_date <= ValueDate <= @self._end_date & Ticker in @ticker_list")\
.pivot(index='ValueDate',
columns='Ticker',
values='Last')\
.fillna(method='ffill')
date_df = pd.DataFrame(cf.create_daily_datelist(
start_date=self._start_date, end_date=self._end_date),
columns=['ValueDate'])
return pd.merge(date_df,
target_df.reset_index('ValueDate'),
on='ValueDate',
how='left')\
.set_index('ValueDate')\
.fillna(method='ffill')\
.fillna(method='bfill')
def create_macro_index(self, ticker, shifts=False):
macro_df = self._input_data.query("'{0}' <= ValueDate <= '{1}'Ticker '{2}'"
.format(self._start_date,
self._end_date,
ticker)).pivot(index='ValueDate',
columns='Ticker',
values='Last')\
.fillna(method='ffill')
if shifts: macro_df = macro_df.shift(1).dropna(axis=0)
date_df = pd.DataFrame(cf.create_daily_datelist(
start_date=self._start_date, end_date=self._end_date),
columns=['ValueDate'])
macro_df = pd.merge(date_df,
macro_df.reset_index('ValueDate'),
on='ValueDate',
how='left')\
.set_index('ValueDate')\
.fillna(method='ffill')\
.fillna(method='bfill')
return macro_df
def create_factor(self, ticker_list):
factor_query = "SELECT ValueDate, Ticker, Last \
FROM bbg_marketprice \
WHERE ValueDate BETWEEN '{0}' AND '{1}' \
AND Ticker IN ('{2}')"\
.format(self._start_date,
self._end_date,
"','".join(ticker_list))
with util.DBConnector(DBName='marketdb') as db_conn:
factor_df = db_conn.get_data(factor_query).pivot(index='ValueDate',
columns='Ticker',
values='Last')\
.fillna(method='ffill')
date_df = pd.DataFrame(cf.create_daily_datelist(start_date=self._start_date,
end_date=self._end_date),
columns=['ValueDate'])
factor_df = pd.merge(date_df,
factor_df.reset_index('ValueDate'),
on='ValueDate',
how='left')\
.set_index('ValueDate')\
.fillna(method='ffill')\
.fillna(method='bfill')
return factor_df
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._logger.info("{0} initializing...".format(
self.__class__.__name__))
self._start_date = kwargs.get('start_date', None)
self._end_date = kwargs.get('end_date', None)
interval = kwargs.get('interval', 1)
frequency = kwargs.get('frequency', 'weekly')
if frequency == 'daily':
date_list = cf.create_daily_datelist(self._start_date,
self._end_date)
elif frequency == 'weekly':
date_list = cf.create_weekly_datelist(self._start_date,
self._end_date)
else:
date_list = cf.create_monthly_datelist(self._start_date,
self._end_date)
coint_vec_file = kwargs.get('coint_Vec_file', './input/coint_vec.csv')
self._weight_df = cf.convert_date_format(pd.read_csv(coint_vec_file))\
.set_index('ValueDate').loc[date_list]
self._fx_rate_df = np.log(
cf.get_fx_rate(self._start_date, self._end_date,
self._weight_df.columns.tolist())).loc[date_list]
#import pdb;pdb.set_trace()
self._coint_index_df = pd.DataFrame(
(self._fx_rate_df *
self._weight_df[self._fx_rate_df.columns]).sum(axis=1),
columns=['Price']).loc[date_list]
#self._index_return_df = pd.DataFrame(self._coint_index_df[interval:] - self._coint_index_df[:-interval],
# index = self._fx_rate_df.index[:-interval],
# columns = ['Return'])#.loc[date_list]
self._logger.info("{0} initialized.".format(self.__class__.__name__))