Tail hedge#53
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| # Volatility, Mean (vol. adjusted) | ||
| freq_dict = {'daily': 21, 'weekly': 4, 'monthly': 1} | ||
| vols = np.log(df).diff(freq_dict[freq]).std() * np.sqrt(12.) |
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The ann. factor 12. needs to depend on the frequency chosen. Otherwise, it's wrong.
| # Volatility, Mean (vol. adjusted) | ||
| freq_dict = {'daily': 21, 'weekly': 4, 'monthly': 1} | ||
| vols = np.log(df).diff(freq_dict[freq]).std() * np.sqrt(12.) | ||
| mus = (np.log(df).diff(freq_dict[freq]) * 12.).mean().div(vols) |
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The ann. factor 12. needs to depend on the frequency chosen. Otherwise, it's wrong.
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| if window: | ||
| dd = _window_dd_single(df.iloc[:, 0], window=window) # Fixed window drawdown | ||
| ann_factor = 252. / float(window) |
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I think this only works for daily data. Please revise.
| dc = DayCounts('ACT/360', calendar='us_trading') | ||
| if ann_factor is None: | ||
| range_d1_d0 = dc.days(start_dates, end_dates) | ||
| ann_factor = [365.25 / float(d) for d in range_d1_d0] |
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I think this only works for daily data. Please revise.
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Added DayCount functions