fynance.features.metrics.roll_annual_return

fynance.features.metrics.roll_annual_return(X, period=252, w=None, axis=0, dtype=None, ddof=0)

Compute rolling compouned annual returns of each X’ series.

The annualised return [1] is the process of converting returns on a whole period to returns per year.

Parameters:

X : np.ndarray[dtype, ndim=1 or 2]

Time-series of price, performance or index.

period : int, optional

Number of period per year, default is 252 (trading days per year).

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.

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.

ddof : int, optional

Means Delta Degrees of Freedom, the divisor used in calculations is t - ddof, where t represents the number of elements in time axis. Default is 0.

Returns:

np.ndarray[dtype, ndim=1 or 2]

Values of rolling compouned annual returns of each series.

See also

mdd, drawdown, sharpe, annual_volatility

Notes

The rolling annual compouned returns is computed such that \(\forall t \in [1: T]\):

\[annualReturn_t = \frac{X_t}{X_1}^{\frac{period}{t}} - 1\]

References

[1]	https://en.wikipedia.org/wiki/Rate_of_return#Annualisation

Examples

Assume series of monthly prices:

>>> X = np.array([100, 110, 80, 120, 160, 108]).astype(np.float64)
>>> roll_annual_return(X, period=12)
array([ 0.        ,  0.771561  , -0.5904    ,  0.728     ,  2.08949828,
        0.1664    ])
>>> X = np.array([[100, 101], [80, 81], [110, 108]]).astype(np.float64)
>>> roll_annual_return(X, period=12, axis=1)
array([[ 0.        ,  0.06152015],
       [ 0.        ,  0.07738318],
       [ 0.        , -0.10425081]])