# 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,
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import numpy as np
from qf_lib.common.enums.frequency import Frequency
from qf_lib.common.utils.returns.get_aggregate_returns import get_aggregate_returns
from qf_lib.containers.series.qf_series import QFSeries
[docs]def gain_to_pain_ratio(qf_series: QFSeries) -> float:
"""
Calculates the gain to pain ratio for a given timeseries of returns.
gain_to_pain_ratio is calculated for monthly returns
gain_to_pain_ratio = sum(all returns) / abs(sum(negative returns)
Parameters
----------
qf_series: QFSeries
financial series
Returns
-------
float
< 0 is bad
> 1 is good
> 1.5 is exceptionally good
"""
aggregated_series = get_aggregate_returns(qf_series, Frequency.MONTHLY, multi_index=True)
negative_returns = aggregated_series.loc[aggregated_series < 0]
negative_sum = np.abs(negative_returns.sum())
if negative_sum != 0:
gain_to_pain = aggregated_series.sum() / negative_sum
else:
gain_to_pain = float("inf")
return gain_to_pain