Monte Carlo

The Monte Carlo method is implemented in finquant.monte_carlo.

The module provides a class MonteCarlo which is an implementation of the Monte Carlo method and a class MonteCarloOpt which allows the user to perform a Monte Carlo run to find optimised financial portfolios, given an initial portfolio.

class finquant.monte_carlo.MonteCarlo(num_trials=1000)

An object to perform a Monte Carlo run/simulation.

__init__(num_trials=1000)
Parameters:

num_trials (int) – Number of iterations of the Monte Carlo run/simulation, default: 1000

run(fun, **kwargs)
Parameters:

  • fun (Callable[..., Any]) – Function to call at each iteration of the Monte Carlo run.

  • kwargs (Dict[str, Any]) – (optional) Additional arguments that are passed to fun.

Result:

Array of quantities returned from fun at each iteration.

Return type:

ndarray[float64, Any]

class finquant.monte_carlo.MonteCarloOpt(returns, num_trials=1000, risk_free_rate=0.005, freq=252, initial_weights=None)

An object to perform a Monte Carlo run/simulation for finding optimised financial portfolios.

Inherits from MonteCarlo.

__init__(returns, num_trials=1000, risk_free_rate=0.005, freq=252, initial_weights=None)
Parameters:

  • returns (DataFrame) – DataFrame of returns of stocks Note: If applicable, the given returns should be computed with the same risk free rate and time window/frequency (arguments risk_free_rate and freq as passed in here.

  • num_trials (int) – Number of portfolios to be computed, each with a random distribution of weights/allocation in each stock, default: 1000

  • risk_free_rate (Union[floating, float]) – Risk free rate as required for the Sharpe Ratio, default: 0.005

  • freq (Union[integer, int]) – Number of trading days in a year, default: 252

  • initial_weights (Optional[ndarray[float64, Any]]) – Weights of initial/given portfolio, only used to plot a marker for the initial portfolio in the optimisation plot, default: None

optimisation()

Optimisation of the portfolio by performing a Monte Carlo simulation.

Return type:

Tuple[DataFrame, DataFrame]

Returns:

opt_w:

DataFrame with optimised investment strategies for maximum Sharpe Ratio and minimum volatility.

opt_res:

DataFrame with Expected Return, Volatility and Sharpe Ratio for portfolios with minimum Volatility and maximum Sharpe Ratio.

plot_results()

Plots the results of the Monte Carlo run, with all of the randomly generated weights/portfolios, as well as markers for the portfolios with the minimum Volatility and maximum Sharpe Ratio.

Return type:

None

properties()

Prints out the properties of the Monte Carlo optimisation.

Return type:

None