PORTFOLIO MANAGEMENT

PORTFOLIO OPTIMIZATION

  • We build well-diversified portfolios of trading systems.  Our portfolios’ dynamic composition results from a portfolio optimization algorithm.
  • We have read dozens of papers on the subject, and on top of them, we have built our private algorithms.
  • These start where the portfolio optimization techniques of Meucci (2006) and De Prado (2018) end.
  • We keep our algorithms private in the best interest of our clients.
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NESTED WALKFORWARD

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To account for training and selection bias, our chosen portfolio is based on a Bayesian model selection approach on top of a nested walk-forward algorithm.

Nested Walk Forward, as the word suggests, is based on a two stages analysis:

  1. In the first one, the different optimization algorithms are trained in-sample, and the out-of-sample results are collected.
  2. In the second one, the out-of-sample results of the first stage are combined by the Bayesian model selection algorithm to find the best weights for the second out-of-sample period.