okama.PortfolioDCF.plot_forecast_monte_carlo
- PortfolioDCF.plot_forecast_monte_carlo(backtest=True, 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:
- backtestbool, default ‘True’
Include historical wealth index if ‘True’.
- 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') >>> # Set Monte Carlo parameters >>> pf.dcf.set_mc_parameters(distribution="norm", period=50, number=200) >>> # 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.plot_forecast_monte_carlo(backtest=True) >>> plt.yscale("log") # Y-axis has logarithmic scale >>> plt.show()