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from pandas import concat
from qf_lib.common.enums.frequency import Frequency
from qf_lib.common.utils.volatility.get_volatility import get_volatility
from qf_lib.containers.series.qf_series import QFSeries
from qf_lib.containers.series.simple_returns_series import SimpleReturnsSeries
[docs]class VolatilityManager:
"""
VolatilityManager uses rolling window to asses the historical volatility of a series.
It is then using the results to find appropriate weights to be held in time
in order to keep the volatility constant over time.
Parameters
----------
series: QFSeries
series to be volatility managed
frequency: Frequency
frequency of the series that is passed
"""
def __init__(self, series: QFSeries, frequency: Frequency = Frequency.DAILY):
self.returns_tms = series.to_simple_returns()
self.frequency = frequency
[docs] def get_managed_series(self, vol_level: float, window_size: int = 20, lag: int = 1,
min_leverage: float = 0.25, max_leverage: float = 1) -> SimpleReturnsSeries:
"""
Parameters
----------
vol_level: float
volatility level to be maintained expressed in number. for example 0.2 means 20% volatility
window_size: int
length of the window to asses the volatility
lag: int
how many periods do we need in order to implement the reallocation.
1 means that already on close of the current day we adjust for the realised volatility of that day
min_leverage: float
min leverage the the function is allowed to apply
max_leverage: float
max leverage the the function is allowed to apply
Returns
-------
SimpleReturnsSeries
SimpleReturnsSeries containing returns of the series based on the input series passed in the constructor
that is volatility managed according to the above parameters
"""
def volatility_fun(window):
return get_volatility(SimpleReturnsSeries(window), self.frequency)
rolling_vol_tms = self.returns_tms.rolling_window(window_size=window_size, func=volatility_fun)
# weights that we would need to make the series have constant volatility
target_weights_tms = vol_level / rolling_vol_tms
# shift the results to reflect the fact that adjustment can be made the next day the earliest
target_weights_tms = target_weights_tms.shift(periods=lag).dropna()
# apply constraints on leverage
target_weights_tms[target_weights_tms > max_leverage] = max_leverage
target_weights_tms[target_weights_tms < min_leverage] = min_leverage
# apply the weights to the series of returns
managed_returns_tms = self.returns_tms.loc[target_weights_tms.index]
managed_returns_tms = managed_returns_tms * target_weights_tms
skipped_part = self.returns_tms.iloc[:window_size]
managed_returns_tms = concat([skipped_part, managed_returns_tms], verify_integrity=True)
return managed_returns_tms, target_weights_tms