fynance.features.metrics.roll_calmar¶
-
fynance.features.metrics.
roll_calmar
(X, period=252.0, w=None, axis=0, dtype=None, ddof=0)¶ Compute the rolling Calmar ratio of each X’ series.
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
orw=0
, thenw=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
, wheret
represents the number of elements in time axis. Default is 0.
Returns: - np.ndarray[dtype, ndim=1 or 2]
Series of rolling Calmar ratio.
See also
Notes
Calmar ratio [3] is the rolling compouned annual return (
roll_annual_return
) over the rolling maximum drawdown (roll_mdd
). Let \(T\) the number of time observations, DD the vector of drawdown, \(\forall t \in [1:T]\):\[\begin{split}calmarRatio_t = \frac{annualReturn_t}{MDD_t} \\ \\\end{split}\]With, \(annualReturn_t = \frac{X_t}{X_1}^{\frac{period}{t}} - 1\) and \(MDD_t = max(DD_t)\), where \(DD_t = 1 - \frac{X_t}{max(X_{1:t})}\).
References
[3] https://en.wikipedia.org/wiki/Calmar_ratio Examples
Assume a monthly series of prices:
>>> X = np.array([70, 100, 80, 120, 160, 80]).astype(np.float64) >>> roll_calmar(X, period=12) array([ 0. , 0. , 3.52977926, 20.18950437, 31.35989887, 0.6122449 ])