okama.PortfolioDCF.set_mc_parameters
- PortfolioDCF.set_mc_parameters(distribution, period, number)
Add Monte Carlo simulation parameters to PortfolioDCF.
- Parameters:
- distribution: str
The type of a distribution to generate random rate of return. Allowed values for distribution: -‘norm’ for normal distribution -‘lognorm’ for lognormal distribution -‘t’ for Student’s (t-distribution)
- period: int
Forecast period for portfolio wealth index time series (in years).
- number: int
Number of random wealth indexes to generate with Monte Carlo simulation.
Examples
>>> import matplotlib.pyplot as plt >>> pf = ok.Portfolio(first_date="2015-01", last_date="2024-10") # create Portfolio with default parameters >>> # Set Monte Carlo parameters >>> pf.dcf.set_mc_parameters(distribution="lognorm", period=10, number=100) >>> # Set the cash flow strategy. It's required to generate random wealth indexes. >>> ind = ok.IndexationStrategy(pf) # create IndexationStrategy linked to the portfolio >>> ind.initial_investment = 10_000 # add initial investments size >>> ind.frequency = "year" # set cash flow frequency >>> ind.amount = -1_500 # set withdrawal size >>> ind.indexation = "inflation" >>> # Assign the strategy to Portfolio >>> pf.dcf.cashflow_parameters = ind >>> pf.dcf.use_discounted_values = False # do not discount initial investment value >>> # Plot wealth index with cash flow >>> pf.dcf.wealth_index.plot() >>> plt.show()