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# fynance.features.indicators.cci¶

fynance.features.indicators.cci(X, high=None, low=None, w=20, axis=0, dtype=None)

Compute Commodity Channel Index of size w for each X’ series’.

CCI is an oscillator introduced by Donald Lamber in 1980 [2]. It is calculated as the difference between the typical price of a commodity and its simple moving average, divided by the moving mean absolute deviation of the typical price.

Parameters: X : np.ndarray[dtype, ndim=1 or 2] Elements to compute the indicator. If X is a two-dimensional array, then an indicator is computed for each series along axis. high, low : np.ndarray[dtype, ndim=1 or 2], optional Series of high and low prices, if None then p_t is computed with only closed prices. Must have the same shape as X. w : int, optional Size of the lagged window of the moving average, must be positive. If w is None or w=0, then w=X.shape[axis]. Default is 20. 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] Commodity Channal Index for each series.

Notes

The index is usually scaled by an inverse factor of 0.015 to provide more readable numbers:

$\begin{split}cci = \frac{1}{0.015} \frac{p_t - sma^w_t(p)}{mad^w_t(p)} \\ \text{where, }p = \frac{p_{close} + p_{high} + p_{low}}{3}\end{split}$

References

Examples

>>> X = np.array([60, 100, 80, 120, 160, 80]).astype(np.float64)
>>> cci(X, w=3, dtype=np.float64)
array([   0.        ,   66.66666667,    0.        ,  100.        ,
100.        , -100.        ])