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from __future__ import annotations |
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import abc |
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from datetime import timedelta |
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class SmoothingAlgorithm(abc.ABC): |
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@abc.abstractmethod |
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def __init__(self, **kwargs): |
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raise NotImplementedError |
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@abc.abstractmethod |
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def update(self, new_value: float, elapsed: timedelta) -> float: |
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"""Updates the algorithm with a new value and returns the smoothed |
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value. |
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""" |
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raise NotImplementedError |
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class ExponentialMovingAverage(SmoothingAlgorithm): |
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""" |
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The Exponential Moving Average (EMA) is an exponentially weighted moving |
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average that reduces the lag that's typically associated with a simple |
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moving average. It's more responsive to recent changes in data. |
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""" |
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def __init__(self, alpha: float = 0.5) -> None: |
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self.alpha = alpha |
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self.value = 0 |
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def update(self, new_value: float, elapsed: timedelta) -> float: |
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self.value = self.alpha * new_value + (1 - self.alpha) * self.value |
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return self.value |
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class DoubleExponentialMovingAverage(SmoothingAlgorithm): |
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""" |
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The Double Exponential Moving Average (DEMA) is essentially an EMA of an |
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EMA, which reduces the lag that's typically associated with a simple EMA. |
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It's more responsive to recent changes in data. |
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""" |
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def __init__(self, alpha: float = 0.5) -> None: |
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self.alpha = alpha |
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self.ema1 = 0 |
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self.ema2 = 0 |
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def update(self, new_value: float, elapsed: timedelta) -> float: |
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self.ema1 = self.alpha * new_value + (1 - self.alpha) * self.ema1 |
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self.ema2 = self.alpha * self.ema1 + (1 - self.alpha) * self.ema2 |
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return 2 * self.ema1 - self.ema2 |
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