Method cones

volatility/volest.py:143–248

Plots volatility cones Parameters ---------- windows : [int, int, ...] List of rolling windows for which to calculate the estimator cones quantiles : [lower, upper] List of lower and upper quantiles for which to plot the cones

(self, windows=[30, 60, 90, 120], quantiles=[0.25, 0.75])

        )
   
    def cones(self, windows=[30, 60, 90, 120], quantiles=[0.25, 0.75]):
        """Plots volatility cones
        
        Parameters
        ----------
        windows : [int, int, ...]
            List of rolling windows for which to calculate the estimator cones
        quantiles : [lower, upper]
            List of lower and upper quantiles for which to plot the cones
        """

        price_data = self._price_data

        if len(windows) < 2:
            raise ValueError(
                'Two or more window periods required')
        if len(quantiles) != 2:
            raise ValueError(
                'A two element list of quantiles is required, lower and upper')
        if quantiles[0] + quantiles[1] != 1.0:
            raise ValueError(
                'The sum of the quantiles must equal 1.0')
        if quantiles[0] > quantiles[1]:
            raise ValueError(
                'The lower quantiles (first element) must be less than the upper quantile (second element)')
        
        max_ = []
        min_ = []
        top_q = []
        median = []
        bottom_q = []
        realized = []
        data = []

        for window in windows:
            
            estimator = self._get_estimator(
                window=window,
                price_data=price_data
            )

            max_.append(estimator.max())
            top_q.append(estimator.quantile(quantiles[1]))
            median.append(estimator.median())
            bottom_q.append(estimator.quantile(quantiles[0]))
            min_.append(estimator.min())
            realized.append(estimator[-1])

            data.append(estimator)
        
        if self._estimator is "Skew" or self._estimator is "Kurtosis":
            f = lambda x: "%i" % round(x, 0)
        else:
            f = lambda x: "%i%%" % round(x*100, 0)

        # figure
        fig = plt.figure(figsize=(8, 6))
        fig.autofmt_xdate()