def raw_futures_roll(self, instrument_code):
"""
Returns the raw difference between price and carry
:param instrument_code: instrument to get data for
:type instrument_code: str
:returns: Tx4 pd.DataFrame
>>> from systems.tests.testfuturesrawdata import get_test_object_futures
>>> from systems.basesystem import System
>>> (data, config)=get_test_object_futures()
>>> system=System([FuturesRawData()], data)
>>> system.rawdata.raw_futures_roll("EDOLLAR").ffill().tail(2)
2015-12-11 17:08:14 -0.07
2015-12-11 19:33:39 -0.07
dtype: float64
"""
carrydata = self.get_instrument_raw_carry_data(instrument_code)
raw_roll = carrydata.PRICE - carrydata.CARRY
raw_roll[raw_roll == 0] = np.nan
raw_roll = uniquets(raw_roll)
return raw_roll
def roll_differentials(self, instrument_code):
"""
Work out the annualisation factor
:param instrument_code: instrument to get data for
:type instrument_code: str
:returns: Tx4 pd.DataFrame
>>> from systems.tests.testfuturesrawdata import get_test_object_futures
>>> from systems.basesystem import System
>>> (data, config)=get_test_object_futures()
>>> system=System([FuturesRawData()], data)
>>> system.rawdata.roll_differentials("EDOLLAR").ffill().tail(2)
2015-12-11 17:08:14 -0.246407
2015-12-11 19:33:39 -0.246407
dtype: float64
"""
carrydata = self.get_instrument_raw_carry_data(instrument_code)
roll_diff = carrydata.apply(
fraction_of_year_between_price_and_carry_expiries,
floor_date_diff=1,
axis=1)
roll_diff = uniquets(roll_diff)
return roll_diff
def roll_differentials(self, floor_date_diff: float = 1/CALENDAR_DAYS_IN_YEAR)\
-> pd.Series:
raw_differential = self.raw_differential()
floored_differential = apply_abs_min(raw_differential, floor_date_diff)
unique_differential = uniquets(floored_differential)
return unique_differential
def _calc_roll_differentials(system, instrument_code, this_stage):
carrydata = this_stage.get_instrument_raw_carry_data(instrument_code)
roll_diff = carrydata.apply(expiry_diff, 1)
roll_diff = uniquets(roll_diff)
return roll_diff
def raw_futures_roll(self, instrument_code):
"""
Returns the raw difference between price and carry
:param instrument_code: instrument to get data for
:type instrument_code: str
:returns: Tx4 pd.DataFrame
>>> from systems.tests.testfuturesrawdata import get_test_object_futures
>>> from systems.basesystem import System
>>> (data, config)=get_test_object_futures()
>>> system=System([FuturesRawData()], data)
>>> system.rawdata.raw_futures_roll("EDOLLAR").ffill().tail(2)
2015-12-11 17:08:14 -0.07
2015-12-11 19:33:39 -0.07
dtype: float64
"""
carrydata = self.get_instrument_raw_carry_data(instrument_code)
raw_roll = carrydata.PRICE - carrydata.CARRY
raw_roll[raw_roll == 0] = np.nan
raw_roll = uniquets(raw_roll)
return raw_roll
def _calc_roll_differentials(system, instrument_code, this_stage):
carrydata = this_stage.get_instrument_raw_carry_data(
instrument_code)
roll_diff = carrydata.apply(expiry_diff, 1)
roll_diff = uniquets(roll_diff)
return roll_diff
def raw_futures_roll(self) -> pd.Series:
raw_roll = self.price - self.carry
raw_roll[raw_roll == 0] = np.nan
raw_roll = uniquets(raw_roll)
return raw_roll
def _calc_raw_futures_roll(system, instrument_code, this_stage):
carrydata = this_stage.get_instrument_raw_carry_data(instrument_code)
raw_roll = carrydata.PRICE - carrydata.CARRY
raw_roll[raw_roll == 0] = np.nan
raw_roll = uniquets(raw_roll)
return raw_roll
def _calc_raw_futures_roll(system, instrument_code, this_stage):
carrydata = this_stage.get_instrument_raw_carry_data(
instrument_code)
raw_roll = carrydata.PRICE - carrydata.CARRY
raw_roll[raw_roll == 0] = np.nan
raw_roll=uniquets(raw_roll)
return raw_roll
def costs_pandl_in_instrument_currency(self) -> pd.Series:
instrument_currency_costs_as_list = (
self.costs_from_trading_in_instrument_currency_as_list())
date_index = self.date_index_for_all_fills()
costs_as_pd_series = pd.Series(instrument_currency_costs_as_list,
date_index)
costs_as_pd_series = costs_as_pd_series.sort_index()
costs_as_pd_series = uniquets(costs_as_pd_series)
normalised_costs = self.normalise_costs_in_instrument_currency(
costs_as_pd_series)
return normalised_costs
def _get_filtered_forecast(
system, instrument_code, rule_variation_name, this_stage):
raw_forecast = this_stage.get_actual_raw_forecast(
instrument_code, rule_variation_name)
(fit_dates, filter_data)=this_stage.get_fitted_values(instrument_code, rule_variation_name)
filtered_list=[]
for (fit_period, data_this_period) in zip(fit_dates, filter_data):
(x_bins, fit_y)=data_this_period
filtered_list.append(filtering_function(raw_forecast, startdate=fit_period.period_start, enddate=fit_period.period_end, x_bins=x_bins, fit_y=fit_y))
filtered_forecast=pd.concat(filtered_list, axis=0)
return uniquets(filtered_forecast)
def _get_filtered_forecast(
system, instrument_code, rule_variation_name, this_stage):
raw_forecast = this_stage.get_actual_raw_forecast(
instrument_code, rule_variation_name)
(fit_dates, filter_data)=this_stage.get_fitted_values(instrument_code, rule_variation_name)
filtered_list=[]
for (fit_period, data_this_period) in zip(fit_dates, filter_data):
(x_bins, fit_y)=data_this_period
filtered_list.append(filtering_function(raw_forecast, startdate=fit_period.period_start, enddate=fit_period.period_end, x_bins=x_bins, fit_y=fit_y))
filtered_forecast=pd.concat(filtered_list, axis=0)
return uniquets(filtered_forecast)
def roll_differentials(self, instrument_code):
"""
Work out the annualisation factor
:param instrument_code: instrument to get data for
:type instrument_code: str
:returns: Tx4 pd.DataFrame
>>> from systems.tests.testfuturesrawdata import get_test_object_futures
>>> from systems.basesystem import System
>>> (data, config)=get_test_object_futures()
>>> system=System([FuturesRawData()], data)
>>> system.rawdata.roll_differentials("EDOLLAR").ffill().tail(2)
2015-12-11 17:08:14 -0.246407
2015-12-11 19:33:39 -0.246407
dtype: float64
"""
carrydata = self.get_instrument_raw_carry_data(instrument_code)
roll_diff = carrydata.apply(expiry_diff, 1)
roll_diff = uniquets(roll_diff)
return roll_diff
def get_capital_pd_series_for_strategy(self, strategy_name: str) -> pd.Series:
capital_series = self.get_capital_series_for_strategy(strategy_name)
pd_series = capital_series.as_pd_df()
pd_series = uniquets(pd_series).squeeze()
return pd_series
