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# fynance.features.metrics.perf_returns¶

fynance.features.metrics.perf_returns(R, kind='raw', base=100.0, axis=0, dtype=None)

Compute performance of returns along time axis.

Parameters: R : np.ndarray[dtype, ndim=1 or 2] Time-series of returns. kind : {‘raw’, ‘log’, ‘pct’} If ‘raw’ (default), then considers returns as following $$R_t = X_t - X_{t-1}$$. If ‘log’, then considers returns as following $$R_t = log(\frac{X_t}{X_{t-1}})$$. If ‘pct’, then considers returns as following $$R_t = \frac{X_t - X_{t-1}}{X_{t-1}}$$. base : float, optional Initial value for measure the performance, default is 100. 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] Performances along time axis.

Examples

>>> R = np.array([0., 20., 30., -10., 20., 20., -20.])
>>> perf_returns(R, base=100.)
array([100., 120., 150., 140., 160., 180., 160.])