本文整理汇总了Python中pythalesians.timeseries.calcs.timeseriescalcs.TimeSeriesCalcs.rolling_volatility方法的典型用法代码示例。如果您正苦于以下问题：Python TimeSeriesCalcs.rolling_volatility方法的具体用法？Python TimeSeriesCalcs.rolling_volatility怎么用？Python TimeSeriesCalcs.rolling_volatility使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pythalesians.timeseries.calcs.timeseriescalcs.TimeSeriesCalcs的用法示例。
在下文中一共展示了TimeSeriesCalcs.rolling_volatility方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: calculate_leverage_factor
# 需要导入模块: from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs [as 别名]
# 或者: from pythalesians.timeseries.calcs.timeseriescalcs.TimeSeriesCalcs import rolling_volatility [as 别名]
def calculate_leverage_factor(self, returns_df, vol_target, vol_max_leverage, vol_periods = 60, vol_obs_in_year = 252,
vol_rebalance_freq = 'BM', returns = True, period_shift = 0):
"""
calculate_leverage_factor - Calculates the time series of leverage for a specified vol target
Parameters
----------
returns_df : DataFrame
Asset returns
vol_target : float
vol target for assets
vol_max_leverage : float
maximum leverage allowed
vol_periods : int
number of periods to calculate volatility
vol_obs_in_year : int
number of observations in the year
vol_rebalance_freq : str
how often to rebalance
returns : boolean
is this returns time series or prices?
period_shift : int
should we delay the signal by a number of periods?
Returns
-------
pandas.Dataframe
"""
tsc = TimeSeriesCalcs()
if not returns: returns_df = tsc.calculate_returns(returns_df)
roll_vol_df = tsc.rolling_volatility(returns_df,
periods = vol_periods, obs_in_year = vol_obs_in_year).shift(period_shift)
# calculate the leverage as function of vol target (with max lev constraint)
lev_df = vol_target / roll_vol_df
lev_df[lev_df > vol_max_leverage] = vol_max_leverage
# only allow the leverage change at resampling frequency (eg. monthly 'BM')
lev_df = lev_df.resample(vol_rebalance_freq)
returns_df, lev_df = returns_df.align(lev_df, join='left', axis = 0)
lev_df = lev_df.fillna(method='ffill')
return lev_df示例2: calculate_leverage_factor
# 需要导入模块: from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs [as 别名]
# 或者: from pythalesians.timeseries.calcs.timeseriescalcs.TimeSeriesCalcs import rolling_volatility [as 别名]
def calculate_leverage_factor(self, returns_df, vol_target, vol_max_leverage, vol_periods = 60, vol_obs_in_year = 252,
vol_rebalance_freq = 'BM', data_resample_freq = None, data_resample_type = 'mean',
returns = True, period_shift = 0):
"""
calculate_leverage_factor - Calculates the time series of leverage for a specified vol target
Parameters
----------
returns_df : DataFrame
Asset returns
vol_target : float
vol target for assets
vol_max_leverage : float
maximum leverage allowed
vol_periods : int
number of periods to calculate volatility
vol_obs_in_year : int
number of observations in the year
vol_rebalance_freq : str
how often to rebalance
vol_resample_freq : str
do we need to resample the underlying data first? (eg. have we got intraday data?)
returns : boolean
is this returns time series or prices?
period_shift : int
should we delay the signal by a number of periods?
Returns
-------
pandas.Dataframe
"""
tsc = TimeSeriesCalcs()
if data_resample_freq is not None:
return
# TODO not implemented yet
if not returns: returns_df = tsc.calculate_returns(returns_df)
roll_vol_df = tsc.rolling_volatility(returns_df,
periods = vol_periods, obs_in_year = vol_obs_in_year).shift(period_shift)
# calculate the leverage as function of vol target (with max lev constraint)
lev_df = vol_target / roll_vol_df
lev_df[lev_df > vol_max_leverage] = vol_max_leverage
# should we take the mean, first, last in our resample
if data_resample_type == 'mean':
lev_df = lev_df.resample(vol_rebalance_freq).mean()
elif data_resample_type == 'first':
lev_df = lev_df.resample(vol_rebalance_freq).first()
elif data_resample_type == 'last':
lev_df = lev_df.resample(vol_rebalance_freq).last()
else:
# TODO implement other types
return
returns_df, lev_df = returns_df.align(lev_df, join='left', axis = 0)
lev_df = lev_df.fillna(method='ffill')
lev_df.ix[0:vol_periods] = numpy.nan # ignore the first elements before the vol window kicks in
return lev_df