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maxdiv

Maximizes the portfolio's diversification ratio

Usageโ€‹

maxdiv [-cv {hist,ewma1,ewma2,ledoit,oas,shrunk,gl,jlogo,fixed,spectral,shrink}] [-de SMOOTHING_FACTOR_EWMA] [-vs SHORT_ALLOCATION] [-mt METHOD] [-ct CATEGORIES] [-p PERIOD] [-s START_PERIOD] [-e END_PERIOD] [-lr] [--freq {d,w,m}] [-mn MAX_NAN] [-th THRESHOLD_VALUE] [-v LONG_ALLOCATION] [--name NAME]

Parametersโ€‹

NameParameterDescriptionDefaultOptionalChoices
covariance-cv --covarianceMethod used to estimate covariance matrix. 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 methodhistTruehist, ewma1, ewma2, ledoit, oas, shrunk, gl, jlogo, fixed, spectral, shrink
smoothing_factor_ewma-de --d-ewmaSmoothing factor for ewma estimators0.94TrueNone
short_allocation-vs --value-shortAmount to allocate to portfolio in short positions0.0TrueNone
nan_fill_method-mt --methodMethod used to fill nan values in time series, by default time. Possible values are: 'linear': linear interpolation 'time': linear interpolation based on time index 'nearest': use nearest value to replace nan values 'zero': spline of zeroth order 'slinear': spline of first order 'quadratic': spline of second order 'cubic': spline of third order 'barycentric': builds a polynomial that pass for all pointstimeTruelinear, time, nearest, zero, slinear, quadratic, cubic, barycentric
categories-ct --categoriesShow selected categoriesTrueNone
historic_period-p --periodPeriod to get yfinance data from. Possible frequency strings are: 'd': means days, for example '252d' means 252 days 'w': means weeks, for example '52w' means 52 weeks 'mo': means months, for example '12mo' means 12 months 'y': means years, for example '1y' means 1 year 'ytd': downloads data from beginning of year to today 'max': downloads all data available for each asset3yTrue1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 3y, 5y, 10y, ytd, max
start_period-s --startStart date to get yfinance data from. Must be in 'YYYY-MM-DD' formatTrueNone
end_period-e --endEnd date to get yfinance data from. Must be in 'YYYY-MM-DD' formatTrueNone
log_returns-lr --log-returnsIf use logarithmic or arithmetic returns to calculate returnsFalseTrueNone
return_frequency--freqFrequency used to calculate returns. Possible values are: 'd': for daily returns 'w': for weekly returns 'm': for monthly returnsdTrued, w, m
max_nan-mn --maxnanMax percentage of nan values accepted per asset to be considered in the optimization process0.05TrueNone
threshold_value-th --thresholdValue used to replace outliers that are higher to threshold in absolute value0.3TrueNone
long_allocation-v --valueAmount to allocate to portfolio1TrueNone
name--nameSave portfolio with personalized or default nameMAXDIV_0TrueNone

Examplesโ€‹

2022 Apr 05, 14:18 (๐Ÿฆ‹) /portfolio/po/ $ maxdiv

[3 Years] Display a maximal diversification portfolio

Weights
โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“
โ”ƒ โ”ƒ Value โ”ƒ
โ”กโ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ
โ”‚ AAPL โ”‚ 0.0 % โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚ AMZN โ”‚ 22.62 % โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚ BA โ”‚ 11.53 % โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚ FB โ”‚ 12.06 % โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚ MSFT โ”‚ 0.0 % โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚ T โ”‚ 40.01 % โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚ TSLA โ”‚ 13.75 % โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

Annual (by 252) expected return: 24.68%
Annual (by โˆš252) volatility: 26.16%
Sharpe ratio: 0.9435