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

Compute 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.

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).

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.

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

Values of compouned annual returns of each series.


Let T the number of timeframes in X’ series, the annual compouned returns is computed such that:

\[annualReturn = \frac{X_T}{X_1}^{\frac{period}{T}} - 1\]




Assume series of monthly prices:

>>> X = np.array([100, 110, 80, 120, 160, 108]).astype(np.float64)
>>> print(round(annual_return(X, period=12), 4))
>>> X = np.array([[100, 110], [80, 120], [160, 108]]).astype(np.float64)
>>> annual_return(X, period=12, ddof=1)
array([15.777216  , -0.10425081])