Source code for qf_lib.containers.series.returns_series

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from datetime import datetime
from typing import Union

from pandas import DatetimeIndex, Timedelta, Index

from qf_lib.containers.series.qf_series import QFSeries


[docs]class ReturnsSeries(QFSeries): """ Series of returns. It is a base class for series which specify the type of the returns (e.g. LogReturnsSeries). This is an abstract class and should not be instantiated. """ @property def _constructor(self): raise NotImplementedError() @property def _constructor_expanddim(self): raise NotImplementedError()
[docs] def to_prices(self, initial_price: float = None, suggested_initial_date: Union[datetime, int, float] = None, frequency=None) -> "PricesSeries": if suggested_initial_date is None: suggested_initial_date = self._get_initial_date(frequency) if initial_price is None: initial_price = 1.0 if suggested_initial_date is not datetime: prices_dates = Index([suggested_initial_date]).append(self.index.copy()) # if it is numeric or string based else: prices_dates = DatetimeIndex([suggested_initial_date]).append(self.index.copy()) prices_values = self._to_prices_values(initial_price) from qf_lib.containers.series.prices_series import PricesSeries return PricesSeries(data=prices_values, index=prices_dates).__finalize__(self)
def _get_initial_date(self, frequency=None): if frequency is None: interval, _ = self.infer_interval() else: interval = Timedelta(frequency.nr_of_calendar_days(), unit='D') first_date = self.index[0] return first_date - interval def _to_prices_values(self, initial_price): # method must be implemented by classes inheriting from ReturnsSeries raise NotImplementedError()