calmar¶

Defined in fynance.features.metrics

calmar(X, period=252, axis=0, dtype=None, ddof=0)[source]

Compute the Calmar Ratio for each X’ series.

Parameters:

Xnp.ndarray[dtype, ndim=1 or 2]: Time-series price, performance or index.
periodint, 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.
dtypenp.dtype, optional: The type of the output array. If dtype is not given, infer the data type from X input.
ddofint, 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:

dtype or np.ndarray([dtype, ndim=1]): Values of Calmar ratio for each series.

See also

mdd, drawdown, sharpe, roll_calmar

Notes

Calmar ratio [3] is the compouned annual return (annual_return) over the maximum drawdown (mdd). Let \(T\) the number of time observations, DD the vector of drawdown:

\[calmarRatio = \frac{annualReturn}{MDD}\]

With, \(annualReturn = \frac{X_T}{X_1}^{\frac{period}{T}} - 1\) and \(MDD = max(DD_{1:T})\).

Where, \(DD_t = 1 - \frac{X_t}{max(X_{1:t})}\), \(\forall t \in [1:T]\).

References

[3]

https://en.wikipedia.org/wiki/Calmar_ratio

Examples

Assume a series of monthly prices:

>>> X = np.array([70, 100, 80, 120, 160, 105, 80]).astype(np.float64)
>>> calmar(X, period=12, ddof=1)
array(0.6122449)
>>> calmar(X.reshape([7, 1]), period=12)
array([0.51446018])