Drawdown Summary

perfana.core.risk.drawdown_summary(data, weights=None, geometric=True, rebalance=True, *, top=5)[source]

A summary of each drawdown instance. Output is ranked by depth of the drawdown.

If data is DataFrame-like, weights must be specified. If data is Series-like, weights can be left empty.

Parameters

  • data (Union[DataFrame, Iterable[Union[int, float]], ndarray, Series]) – The assets returns vector or matrix

  • weights (Union[Iterable[Union[int, float]], ndarray, Series, None]) – Weights of the portfolio. This must be 1 dimensional and must match the dimension of the data’s last axis.

  • geometric – If True, calculates the geometric mean, otherwise, calculates the arithmetic mean.

  • rebalance – If True, portfolio is assumed to be rebalanced at every step.

  • top (Optional[int]) – If None, returns all episodes. If specified, returns the top n episodes ranked by the depth of drawdown.

Returns

A data frame summarizing each drawdown episode

Return type

DataFrame

Examples

>>> from perfana.datasets import load_hist
>>> from perfana.core import drawdown_summary
>>> hist = load_hist().iloc[:, :7]
>>> weights = [0.25, 0.18, 0.24, 0.05, 0.04, 0.13, 0.11]
>>> drawdown_summary(hist, weights)
       Start     Trough        End  Drawdown  Length  ToTrough  Recovery
0 2007-11-30 2009-02-28 2014-02-28 -0.400798      76        16        60
1 2000-04-30 2003-03-31 2004-02-29 -0.203652      47        36        11
2 1990-01-31 1990-11-30 1991-05-31 -0.150328      17        11         6
3 1998-04-30 1998-10-31 1999-06-30 -0.149830      15         7         8
4 1994-02-28 1995-03-31 1996-01-31 -0.132766      24        14        10
>>> drawdown_summary(hist.iloc[:, 0])
       Start     Trough        End  Drawdown  Length  ToTrough  Recovery
0 2007-11-30 2009-02-28 2014-05-31 -0.549134      79        16        63
1 2000-04-30 2003-03-31 2006-12-31 -0.474198      81        36        45
2 1990-01-31 1990-09-30 1994-01-31 -0.286489      49         9        40
3 1998-08-31 1998-09-30 1999-01-31 -0.148913       6         2         4
4 2018-10-31 2018-12-31 2019-03-31 -0.130014       6         3         3