monte_carlo_wealth

PortfolioDCF.monte_carlo_wealth(discounting='fv', include_negative_values=True)

Calculate portfolio random wealth indexes with cash flows (withdrawals/contributions) using Monte Carlo simulation.

Monte Carlo simulation generates n random monthly time series. Each wealth index is calculated with rate of return time series of a given distribution.

First date of forecasted returns is portfolio last_date. First value for the forecasted wealth indexes is the last historical portfolio index value. It is useful for a chart with historical wealth index and forecasted values.

Parameters:

discounting{‘fv’, ‘pv’}, default ‘fv’

‘fv’ - calculate Future Value (not discounted) wealth indexes. ‘pv’ - calculate Present Value (discounted) wealth indexes.

include_negative_valuesbool, default True

If True, negative values in the wealth index are preserved. If False, negative values are replaced with 0.

Returns:

DataFrame

Table with n random wealth indexes monthly time series.

Examples

>>> import matplotlib.pyplot as plt

>>> pf = ok.Portfolio(
...     ["SPY.US", "AGG.US", "GLD.US"],
...     weights=[0.60, 0.35, 0.05],
...     rebalancing_strategy=ok.Rebalance(period="month"),
... )
>>> pf.dcf.set_mc_parameters(distribution="t", period=10, mc_number=100)  # Set Monte Carlo parameters
>>> # set cash flow parameters
>>> ind = ok.IndexationStrategy(pf)  # create cash flow strategy linked to the portfolio
>>> ind.initial_investment = 10_000  # add initial investment to cash flow strategy
>>> ind.amount = -500  # set withdrawal size
>>> ind.frequency = "year"  # set withdrawal frequency
>>> pf.dcf.cashflow_parameters = ind  # assign cash flow strategy to portfolio
>>> pf.dcf.monte_carlo_wealth(discounting="fv").plot()
>>> plt.legend("")  # don't show legend for each line
>>> plt.show()