# Copyright 2016-present CERN – European Organization for Nuclear Research
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from datetime import datetime
import numpy as np
from qf_lib.containers.series.qf_series import QFSeries
[docs]class PricesSeries(QFSeries):
"""
Series of prices (for example prices of the SPY).
"""
def __init__(self, data=None, index=None, dtype=None, name=None, copy=False, fastpath=False):
super().__init__(data, index, dtype, name, copy, fastpath)
@property
def _constructor(self):
return PricesSeries
@property
def _constructor_expanddim(self):
from qf_lib.containers.dataframe.prices_dataframe import PricesDataFrame
return PricesDataFrame
[docs] def to_log_returns(self) -> "LogReturnsSeries":
from qf_lib.containers.series.log_returns_series import LogReturnsSeries
shifted = self.copy().shift(1)
rets = self / shifted
rets = np.log(rets)
dates = self.index[1:].copy()
returns = rets.iloc[1:]
return LogReturnsSeries(index=dates, data=returns).__finalize__(self)
[docs] def to_simple_returns(self) -> "SimpleReturnsSeries":
from qf_lib.containers.series.simple_returns_series import SimpleReturnsSeries
shifted = self.copy().shift(1)
rets = self / shifted - 1 # type: PricesSeries
dates = self.index[1:].copy()
returns = rets.iloc[1:]
return SimpleReturnsSeries(index=dates, data=returns).__finalize__(self)
[docs] def to_prices(self, initial_price: float = None, suggested_initial_date: datetime = None, frequency=None) \
-> ["PricesSeries"]:
if initial_price is None:
return self.copy()
return self / self[0] * initial_price
[docs] def total_cumulative_return(self) -> float:
return self.values[-1] / self.values[0] - 1.0