def get_monte_carlo(self, n: int = 100) -> pd.DataFrame:
"""
Generate N random risk / cagr point for rebalanced portfolios.
Risk and cagr are calculated for a set of random weights.
"""
weights_df = Float.get_random_weights(n, self.ror.shape[1])
# Portfolio risk and cagr for each set of weights
portfolios_ror = weights_df.aggregate(Rebalance.rebalanced_portfolio_return_ts, ror=self.ror, period=self.reb_period)
random_portfolios = pd.DataFrame()
for _, data in portfolios_ror.iterrows():
risk_monthly = data.std()
mean_return = data.mean()
risk = Float.annualize_risk(risk_monthly, mean_return)
cagr = Frame.get_cagr(data)
row = {
'Risk': risk,
'CAGR': cagr
}
random_portfolios = random_portfolios.append(row, ignore_index=True)
return random_portfolios
def get_monte_carlo(self, n: int = 100, kind: str = "mean") -> pd.DataFrame:
"""
Generate N random risk / cagr point for portfolios.
Risk and cagr are calculated for a set of random weights.
"""
weights_series = Float.get_random_weights(n, self.ror.shape[1])
# Portfolio risk and return for each set of weights
random_portfolios = pd.DataFrame(dtype=float)
for weights in weights_series:
risk_monthly = Frame.get_portfolio_risk(weights, self.ror)
mean_return_monthly = Frame.get_portfolio_mean_return(weights, self.ror)
risk = Float.annualize_risk(risk_monthly, mean_return_monthly)
mean_return = Float.annualize_return(mean_return_monthly)
if kind.lower() == "cagr":
cagr = Float.approx_return_risk_adjusted(mean_return, risk)
row = dict(Risk=risk, CAGR=cagr)
elif kind.lower() == "mean":
row = dict(Risk=risk, Return=mean_return)
else:
raise ValueError('kind should be "mean" or "cagr"')
random_portfolios = random_portfolios.append(row, ignore_index=True)
return random_portfolios