def recov_date(self, return_date=False):
"""Drawdown recovery date.
Date at which `self` recovered to previous high-water mark.
Parameters
----------
return_date : bool, default False
If True, return a `datetime.date` object.
If False, return a Pandas Timestamp object.
Returns
-------
{datetime.date, pandas._libs.tslib.Timestamp, pd.NaT}
Returns NaT if recovery has not occured.
"""
dd = self.drawdown_idx()
# False beginning on trough date and all later dates.
mask = nancumprod(dd != nanmin(dd)).astype(bool)
res = dd.mask(mask) == 0
# If `res` is all False (recovery has not occured),
# .idxmax() will return `res.index[0]`.
if not res.any():
recov = pd.NaT
else:
recov = res.idxmax()
if return_date:
return recov.date()
return recov
def max_drawdown(self):
"""Most negative drawdown derived from drawdown index.
The largest difference between a high-water point and a
subsequent low. A portfolio may also be said to be in a
position of drawdown from a decline from a high-water mark
until a new high-water mark is reached.
[Source: CFA Institute]
Returns
-------
float
"""
return nanmin(self.drawdown_idx())
def drawdown_start(self, return_date=False):
"""The date of the peak at which most severe drawdown began.
Parameters
----------
return_date : bool, default False
If True, return a `datetime.date` object.
If False, return a Pandas Timestamp object.
Returns
-------
datetime.date or pandas._libs.tslib.Timestamp
"""
# Thank you @cᴏʟᴅsᴘᴇᴇᴅ
# https://stackoverflow.com/a/47892766/7954504
dd = self.drawdown_idx()
mask = nancumsum(dd == nanmin(dd.min)).astype(bool)
start = dd.mask(mask)[::-1].idxmax()
if return_date:
return start.date()
return start