from pypfopt.efficient_frontier import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns
# Read in price data
df = pd.read_csv("Asset_Regime.csv", parse_dates=True, index_col="date")
# Calculate expected returns and sample covariance
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# Optimise for maximal Sharpe ratio
ef = EfficientFrontier(mu, S)
raw_weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
print(cleaned_weights)
ef.portfolio_performance(verbose=True)
Mis datos se ven como
US Equities World Equities US Treasuries High Yield Corp Bonds Real Estate Commodities
date
Jan-85 0.081301 0.028511 0.031500 0.037908 0.048963 0.056605 0.021351
Feb-85 0.030075 -0.009204 -0.044692 0.012689 -0.042029 0.016448 -0.015217
Mar-85 -0.007299 0.075134 0.028719 0.004323 0.032666 -0.006716 0.037171
Apr-85 -0.012255 -0.002459 0.023084 0.018215 0.037125 0.000906 -0.035116
May-85 0.064516 0.040245 0.086780 0.042363 0.104199 0.027241 0.004351
y vuelve
{'US Equities': nan, 'World Equities': nan, 'US Treasuries': nan, 'High Yield': nan, 'Corp Bonds': nan, 'Real Estate': nan, 'Commodities': nan}
Expected annual return: nan%
Annual volatility: nan%
Sharpe Ratio: nan
(nan, nan, nan)
