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Possible values are 'hist': historical method 'ewma1': exponential weighted moving average with adjust=True 'ewma2': exponential weighted moving average with adjust=False 'ledoit': Ledoit and Wolf shrinkage method 'oas': oracle shrinkage method 'shrunk': scikit-learn shrunk method 'gl': graphical lasso method 'jlogo': j-logo covariance 'fixed': takes average of eigenvalues above max Marchenko Pastour limit 'spectral': makes zero eigenvalues above max Marchenko Pastour limit 'shrink': Lopez de Prado's book shrinkage method"),(0,r.kt)("td",{parentName:"tr",align:null},"hist"),(0,r.kt)("td",{parentName:"tr",align:null},"True"),(0,r.kt)("td",{parentName:"tr",align:null},"hist, ewma1, ewma2, ledoit, oas, shrunk, gl, jlogo, fixed, spectral, shrink")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"smoothing_factor_ewma"),(0,r.kt)("td",{parentName:"tr",align:null},"Smoothing factor for ewma estimators"),(0,r.kt)("td",{parentName:"tr",align:null},"0.94"),(0,r.kt)("td",{parentName:"tr",align:null},"True"),(0,r.kt)("td",{parentName:"tr",align:null},"None")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"short_allocation"),(0,r.kt)("td",{parentName:"tr",align:null},"Amount to allocate to portfolio in short positions"),(0,r.kt)("td",{parentName:"tr",align:null},"0.0"),(0,r.kt)("td",{parentName:"tr",align:null},"True"),(0,r.kt)("td",{parentName:"tr",align:null},"None")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"risk_measure"),(0,r.kt)("td",{parentName:"tr",align:null},"Risk measure used to optimize the portfolio. Possible values are: 'MV' : Variance 'MAD' : Mean Absolute Deviation 'MSV' : Semi Variance (Variance of negative returns) 'FLPM' : First Lower Partial Moment 'SLPM' : Second Lower Partial Moment 'CVaR' : Conditional Value at Risk 'EVaR' : Entropic Value at Risk 'WR' : Worst Realization 'ADD' : Average Drawdown of uncompounded returns 'UCI' : Ulcer Index of uncompounded returns 'CDaR' : Conditional Drawdown at Risk of uncompounded returns 'EDaR' : Entropic Drawdown at Risk of uncompounded returns 'MDD' : Maximum Drawdown of uncompounded returns"),(0,r.kt)("td",{parentName:"tr",align:null},"MV"),(0,r.kt)("td",{parentName:"tr",align:null},"True"),(0,r.kt)("td",{parentName:"tr",align:null},"MV, MAD, MSV, FLPM, SLPM, CVaR, EVaR, WR, ADD, UCI, CDaR, EDaR, MDD")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"nan_fill_method"),(0,r.kt)("td",{parentName:"tr",align:null},"Method used to fill nan values in time series, by default time. 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The period of the risk-free rate must be annual"),(0,r.kt)("td",{parentName:"tr",align:null},"0.03456"),(0,r.kt)("td",{parentName:"tr",align:null},"True"),(0,r.kt)("td",{parentName:"tr",align:null},"None")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"significance_level"),(0,r.kt)("td",{parentName:"tr",align:null},"Significance level of CVaR, EVaR, CDaR and EDaR"),(0,r.kt)("td",{parentName:"tr",align:null},"0.05"),(0,r.kt)("td",{parentName:"tr",align:null},"True"),(0,r.kt)("td",{parentName:"tr",align:null},"None")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"long_allocation"),(0,r.kt)("td",{parentName:"tr",align:null},"Amount to allocate to portfolio"),(0,r.kt)("td",{parentName:"tr",align:null},"1"),(0,r.kt)("td",{parentName:"tr",align:null},"True"),(0,r.kt)("td",{parentName:"tr",align:null},"None")),(0,r.kt)("tr",{parentName:"tbody"},(0,r.kt)("td",{parentName:"tr",align:null},"name"),(0,r.kt)("td",{parentName:"tr",align:null},"Save portfolio with personalized or default name"),(0,r.kt)("td",{parentName:"tr",align:null},"MAXUTIL_0"),(0,r.kt)("td",{parentName:"tr",align:null},"True"),(0,r.kt)("td",{parentName:"tr",align:null},"None")))),(0,r.kt)("hr",null),(0,r.kt)("h2",{id:"examples"},"Examples"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-python"},"2022 Apr 05, 13:21 (\ud83e\udd8b) /portfolio/po/ $ maxutil\n\n [3 Years] Display a maximal risk averse utility function\n portfolio using volatility as risk measure\n\n Weights\n\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n\u2503 \u2503 Value \u2503\n\u2521\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2529\n\u2502 AAPL \u2502 0.0 % \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 AMZN \u2502 0.0 % \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 BA \u2502 0.0 % \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 FB \u2502 0.0 % \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 MSFT \u2502 0.0 % \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 T \u2502 0.0 % \u2502\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524\n\u2502 TSLA \u2502 100.0 % \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nAnnual (by 252) expected return: 124.85%\nAnnual (by \u221a252) volatility: 68.44%\nSharpe ratio: 1.8215\n")),(0,r.kt)("hr",null))}d.isMDXComponent=!0}}]);
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