CutWithdrawalsIfDrawdown

class CutWithdrawalsIfDrawdown(parent, frequency='year', initial_investment=1000.0, time_series_dic={}, time_series_discounted_values=False, amount=0.0, indexation=None, crash_threshold_reduction=[(0.2, 0.4), (0.5, 1)])

Bases: IndexationStrategy

Withdrawal strategy that reduces the withdrawal amount if the portfolio drawdown exceeds a certain threshold.

The reduction coefficients are defined in crash_threshold_reduction list of tuples.

Parameters:

parentPortfolio

The parent Portfolio instance.

frequencystr, optional

Frequency of cash flows. Default is “year”.

initial_investmentfloat, optional

Initial investment amount. Default is 1000.0.

time_series_dicdict, optional

Dictionary with dates and cash flow values. Default is empty dict.

time_series_discounted_valuesbool, optional

If True, values in time_series_dic are considered as discounted (PV). Default is False.

amountfloat, optional

Regular withdrawal amount (negative value) before reduction. Default is 0.0. The frequency of withdrawals is determined by the frequency parameter.

indexationstr or float, optional

Indexation rate for the withdrawal amount. Default is None.

crash_threshold_reductionlist[tuple[float, float]], optional

List of tuples (threshold, reduction_coefficient). Example: [(0.20, 0.40)] means if drawdown > 20%, reduce withdrawal by 40%. Default is [(.20, .40), (.50, 1)].

Examples

>>> import matplotlib.pyplot as plt

>>> pf = ok.Portfolio(
...     ["SPY.US", "BND.US"],
...     weights=[0.6, 0.4],
...     first_date="2010-01",
...     last_date="2024-10",
...     ccy="USD",
...     inflation=True,
... )
>>> cwid = ok.CutWithdrawalsIfDrawdown(
...     parent=pf,
...     initial_investment=1_000_000,
...     frequency="year",
...     amount=-60_000,
...     indexation="inflation",
...     crash_threshold_reduction=[(0.10, 0.25), (0.20, 0.50), (0.35, 1)],
... )
>>> pf.dcf.cashflow_parameters = cwid
>>> pf.dcf.wealth_index(discounting="fv", include_negative_values=False).plot()
>>> plt.show()

Methods & Attributes

NAME
amount	Portfolio regular withdrawals size.
crash_threshold_reduction	List of tuples (threshold, reduction_coefficient) that define how much withdrawals are reduced based on the portfolio drawdown depth.
frequency	The frequency of regular withdrawals or contributions in the strategy.
indexation	Portfolio cash flow indexation rate.
initial_investment	Initial investment (FV) amount at the start of the calculation period.
make_series_from_list(l)	Convert list of tuples to pandas Series.
periods_per_year	Get the number of periods per year based on the frequency.
time_series_dic	Cash flow time series in form of dictionary.

property frequency

The frequency of regular withdrawals or contributions in the strategy.

Allowed values for frequency:

• ‘none’ no frequency (default value)

• ‘year’ annual cash flows

• ‘half-year’ 6 months cash flows

• ‘quarter’ 3 months cash flows

• ‘month’ 1 month cash flows

Returns:

str

The frequency of withdrawals or contributions.

property indexation

Portfolio cash flow indexation rate.

Returns:

float

Cash flow indexation rate.

property initial_investment

Initial investment (FV) amount at the start of the calculation period.

For historical backtesting, this is the investment at first_date. For Monte Carlo simulations, this is the investment at last_date. Initial investment must be positive.

Returns:

float

Portfolio initial investment.

property periods_per_year

Get the number of periods per year based on the frequency.

Returns:

int

Number of periods per year.

property time_series_dic

Cash flow time series in form of dictionary.

Negative number corresponds to withdrawals, positive number corresponds to contributions.

Examples

>>> import matplotlib.pyplot as plt

>>> pf = ok.Portfolio(first_date="2015-01", last_date="2024-10")  # create Portfolio with default parameters
>>> # create simple dictionary with cash flow amounts and dates
>>> d = {"2018-02": 2_000, "2024-03": -4_000}
>>> ts = ok.TimeSeriesStrategy(pf)  # create TimeSeresStrategy linked to the portfolio
>>> ts.time_series_dic = d  # use the dictionary to set cash flow
>>> ts.initial_investment = 1_000  # add initial investments size (optional)
>>> # Assign the strategy to Portfolio
>>> pf.dcf.cashflow_parameters = ts
>>> # Plot wealth index with cash flow
>>> pf.dcf.wealth_index(discounting="fv", include_negative_values=False).plot()
>>> plt.show()

NAME = 'CWD'

property amount

Portfolio regular withdrawals size. Must be negative. Cash flow value is indexed each period by ‘indexation’.

The frequency of withdrawals or contributions is determined by the frequency parameter.

Returns:

float

Portfolio regular cash flow size.

property crash_threshold_reduction

List of tuples (threshold, reduction_coefficient) that define how much withdrawals are reduced based on the portfolio drawdown depth.

make_series_from_list(l)

Convert list of tuples to pandas Series.

Parameters:

llist[tuple[float, float]]

List of tuples.

Returns:

pd.Series

Series with threshold as index and reduction as value.