# 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 typing import Union
from qf_lib.containers.dataframe.qf_dataframe import QFDataFrame
from qf_lib.containers.series.qf_series import QFSeries
[docs]class RollingWindowsEstimator:
"""
Class for estimating parameters of rolling windows.
"""
[docs] @classmethod
def estimate_rolling_window_size(cls, container: Union[QFDataFrame, QFSeries]) -> int:
"""
Estimates the size of the rolling window based on the number of samples.
Parameters
----------
container
container with data analysed using rolling window
Returns
-------
window_size
the calculated size of the rolling window
"""
num_of_samples = container.shape[0]
if 12 <= num_of_samples < 20:
window_size = 3
elif 20 <= num_of_samples < 50:
window_size = 6
elif 50 <= num_of_samples < 120:
window_size = 12
elif 120 <= num_of_samples < 300:
window_size = 30
elif 300 <= num_of_samples < 500:
window_size = 75
elif 500 <= num_of_samples:
window_size = 125
else:
raise ValueError("Too few samples to estimate a rolling window's size.")
return window_size
[docs] @classmethod
def estimate_rolling_window_step(cls, container: Union[QFDataFrame, QFSeries]) -> int:
"""
Estimates the step of the rolling window based on the number of samples.
Parameters
----------
container
container with data analysed using rolling window
Returns
-------
window_step
the calculated step (shift) of the rolling window
"""
num_of_samples = container.shape[0]
if 12 <= num_of_samples < 120:
step = 2
elif 120 <= num_of_samples < 500:
step = 10
elif 500 <= num_of_samples < 1500:
step = 20
elif 1500 <= num_of_samples:
step = 50
else:
raise ValueError("Too few samples to estimate a rolling window's size.")
return step