Using the portfolio functions

  1. Choose a “port” by browsing portfoliostatic.
  2. Import it
  3. Pass it either a covariance or a precision matrix

Example usage

We need a cov matrix

   import numpy as np
   cov = np.array([[ 1.09948514, -1.02926114,  0.22402055,  0.10727343],
           [-1.02926114,  2.54302628,  1.05338531, -0.12481515],
           [ 0.22402055,  1.05338531,  1.79162765, -0.78962956],
           [ 0.10727343, -0.12481515, -0.78962956,  0.86316527]])

Then:

 from precise.skaters.portfoliostatic.unitport import unit_port
 w = unit_port(cov=cov)

Model ensembles, stacking, mixtures of experts et cetera

Same thing, sort of. See this notebook example or these examples.

Interpreting portfolio names

PyPortfolioOpt Intent
ppo Uses the PyPortfolioOpt package
ppo_vol … and minimum volatility therein
ppo_quad … and maximum quadratic utility therein
ppo_sharpe … and maximum Sharpe ratio therein
Riskfolio-Lib Intent
rpl Uses the RiskFolio-Lib package
rpl_hrp … and Hierarchical Risk Parity therein
rlp_hrp_cdar … and Conditional Drawdown at Risk of uncompounded cumulative returns
rlp_hrp_flpm … and First Lower Partial Moment therein
  … et cetera (see the rest)
Homespun Intent
diag Use only diagonal entries of cov
weak Method that “weakens” some cov entries to make portfolio long only
hrp Varieties of hierarchical allocation
hrp_diag_diag … and uses “diag” allocation/portfolio, like Lopez de Prado’s 2016 paper
hrp_weak_weak … and uses “weak” allocation and also “weak” portfolio construction.
schur Homespun method that exploits Schur complements
schur_weak_diag … and uses weak allocation and diag portfolio at the leaves

At present “weak” and “schur” are the only methods you may have trouble finding implemented elsewhere. The latter is my attempt to unify seemingly disparate approaches: namely those using a global optimization versus those using divide and conquer.

Other places to rummage

… for functionality.

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