# 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.
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from datetime import datetime, timedelta
from math import floor
from typing import Sequence
from qf_lib.common.enums.price_field import PriceField
from qf_lib.common.tickers.tickers import Ticker
from qf_lib.containers.dataframe.qf_dataframe import QFDataFrame
from qf_lib.containers.series.qf_series import QFSeries
from qf_lib.data_providers.data_provider import DataProvider
[docs]class MarketStressIndicator:
"""Calculates market stress indicator.
Parameters
-----------
tickers: Sequence[Ticker]
tickers building the stress indicator
weights: Sequence[float]
weights of the tickers in the indicator, do not need to sum to 1, will be normalized anyway
data_provider: DataProvider
data provider that will be used to access the history of the individual tickers
"""
def __init__(self, tickers: Sequence[Ticker], weights: Sequence[float], data_provider: DataProvider):
self.tickers = tickers
self.weights = weights
self.data_provider = data_provider
[docs] def get_indicator(self, years_rolling: float, start_date: datetime, end_date: datetime, step: int = 1) -> QFSeries:
"""Returns the timeseries of the indicator.
Parameters
------------
years_rolling: float
How may years of the history is used for to evaluate the single point
start_date: datetime
start date of the indicator returned
end_date: datetime
end date of the indicator returned
step: int
how many day is the rolling window shifted. It aslo tells us the step of the returned indicator in days
Returns
-------
QFSeries
Timeseries of market stress indicator
"""
underlying_start_date = start_date - timedelta(days=floor(years_rolling * 365 * 1.1))
data = self.data_provider.get_price(self.tickers, PriceField.Close, underlying_start_date, end_date)
data = data.fillna(method='ffill')
# data = data.dropna() # this line can be enabled but it will shift starting point by the years_rolling
window_size = floor(252 * years_rolling)
stress_indicator_tms = data.rolling_time_window(
window_length=window_size, step=step, func=self._rolling_stress_indicator)
stress_indicator_tms = stress_indicator_tms.loc[start_date:]
return stress_indicator_tms
def _rolling_stress_indicator(self, data_frame_window: QFDataFrame):
zscore_df = QFDataFrame()
for name, series in data_frame_window.items():
zscore_df[name] = (series - series.mean()) / series.std()
last_row = zscore_df.tail(1)
result = last_row.dot(self.weights) # produces a weighted sum of the z-scored values
result = result[0] / sum(self.weights) # result was a single element series, return the value only
return result