okama.EfficientFrontierReb.get_monte_carlo

EfficientFrontierReb.get_monte_carlo(n=100)

Generate N random rebalanced portfolios with Monte Carlo simulation.

Risk (annualized standard deviation) and Return (CAGR) are calculated for a set of random weights.

Parameters:

nint, default 100

Number of random portfolios to generate with Monte Carlo simulation.

Returns:

DataFrame

Table with Return (CAGR) and Risk values for random portfolios (portfolios with random asset weights).

Examples

>>> ls_m = ['SPY.US', 'GLD.US', 'PGJ.US', 'RGBITR.INDX', 'MCFTR.INDX']
>>> curr_rub = 'RUB'
>>> x = ok.EfficientFrontierReb(assets=ls_m,
...                             first_date='2005-01',
...                             last_date='2020-11',
...                             ccy=curr_rub,
...                             rebalancing_period='year',  # set rebalancing period to one year
...                             n_points=20,
...                             verbose=False)
>>> monte_carlo = x.get_monte_carlo(n=1000)  # it can take some time ...
>>> monte_carlo.head(5)
       CAGR      Risk
0  0.182937  0.178518
1  0.184915  0.172965
2  0.154892  0.141681
3  0.185500  0.168739
4  0.176748  0.192657

Monte Carlo simulation results can be plotted togeather with the optimized portfolios on the Efficient Frontier.

>>> import matplotlib.pyplot as plt
>>> df_reb_year = x.ef_points  # optimize portfolios for EF. Calculations will take some time ...
>>> fig = plt.figure()
>>> # Plot the assets points (optional).
>>> x.plot_assets(kind='cagr')
>>> ax = plt.gca()
>>> # Plot random portfolios (Monte Carlo simulation)
>>> ax.scatter(monte_carlo.Risk, monte_carlo.CAGR)
>>> # Plot the Efficient Frontier
>>> ax.plot(df_reb_year.Risk, df_reb_year.CAGR, label='Annually rebalanced')
>>> # Set the axis labels and Title
>>> ax.set_title('Multi-period Efficient Frontier & Monte Carlo simulation')
>>> ax.set_xlabel('Risk (Standard Deviation)')
>>> ax.set_ylabel('CAGR')
>>> ax.legend()
>>> plt.show()