okama.PortfolioDCF.plot_forecast_monte_carlo

PortfolioDCF.plot_forecast_monte_carlo(distr='norm', years=1, backtest=True, n=20, figsize=None)

Plot Monte Carlo simulation for portfolio future wealth indexes optionally together with historical wealth index.

Wealth index (Cumulative Wealth Index) is a time series that presents the value of portfolio over time period considering cash flows (portfolio withdrawals/contributions).

Random wealth indexes are generated according to a given distribution.

Parameters:

distr{‘norm’, ‘lognorm’}, default ‘norm’

Distribution type for the rate of return of portfolio. ‘norm’ - for normal distribution. ‘lognorm’ - for lognormal distribution.

yearsint, default 1

Investment period length for new wealth indexes

backtestbool, default ‘True’

Include historical wealth index if ‘True’.

nint, default 20

Number of random wealth indexes to generate with Monte Carlo simulation.

figsize(float, float), optional

Width, height in inches. If None default matplotlib figsize value is used.

Returns:

None

Examples

>>> import matplotlib.pyplot as plt
>>> pf = ok.Portfolio(assets=['SPY.US', 'AGG.US', 'GLD.US'],
...                   weights=[.60, .35, .05],
...                   rebalancing_period='year',
...                   initial_amount=300_000,
...                   cashflow=-1_000)
>>> pf.dcf.plot_forecast_monte_carlo(years=20, backtest=True, distr='lognorm', n=100)
>>> plt.show()