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fynance.features.metrics.roll_calmar

# fynance.features.metrics.roll_drawdown¶

fynance.features.metrics.roll_drawdown(X, w=None, raw=False, axis=0, dtype=None)

Measures the rolling drawdown of each X’ series.

Function to compute measure of the decline from a historical peak in some variable [5] (typically the cumulative profit or total open equity of a financial trading strategy).

Parameters: X : np.ndarray[dtype, ndim=1 or 2] Time-series of prices, performances or index. Must be positive values. w : int, optional Size of the lagged window of the rolling function, must be positive. If w is None or w=0, then w=X.shape[axis]. Default is None. raw : bool, optional If True then compute the raw drawdown. Else (default) compute the drawdown in percentage. axis : {0, 1}, optional Axis along wich the computation is done. Default is 0. dtype : np.dtype, optional The type of the output array. If dtype is not given, infer the data type from X input. np.ndarray[dtype, ndim=1 or 2] Series of drawdown for each series.

Notes

Let DD^w the drawdown vector with a lagged window of size w:

$\begin{split}DD^w_t =\begin{cases} max(X_{t - w + 1:t}) - X_t \text{, if raw=True} \\ 1 - \frac{X_t}{max(X_{t - w + 1:t})} \text{, otherwise} \\ \end{cases}\end{split}$

References

Examples

>>> X = np.array([70, 100, 80, 120, 160, 80]).astype(np.float64)
>>> roll_drawdown(X)
array([0. , 0. , 0.2, 0. , 0. , 0.5])
>>> roll_drawdown(X.reshape([6, 1])).T
array([[0. , 0. , 0.2, 0. , 0. , 0.5]])
>>> roll_drawdown(X, raw=True)
array([ 0.,  0., 20.,  0.,  0., 80.])
>>> X = np.array([100, 80, 70, 75, 110, 80]).astype(np.float64)
>>> roll_drawdown(X, raw=True, w=3)
array([ 0., 20., 30.,  5.,  0., 30.])