Is your Sharpe Ratio is Lying to you? Use this instead

“Although skewness and kurtosis does not affect the point estimate of Sharpe ratio, it greatly impacts its confidence bands, and consequently its statistical significance” Bailey and López de Prado (2012).
In the last video we explained the downfalls of relying on the Central Limit Theorem (CLT) and using the mean and standard deviation to calculate a point estimate of the Sharpe Ratio.
In this video, we’ll delve into the limitations of the Sharpe Ratio, introduce you to the concept of Probabilistic Sharpe Ratio (PSR) developed by Bailey and López de Prado, and guide you through its Python implementation. So, if you’re eager to enhance your understanding of risk and performance metrics, you’re in the right place.
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[@SpencerSmith-v4k]

This page is awesome!

[@dustinyt5223]

Great content as always. 😊

[@6Ligma]

Amazing. Is this colab notebook shared with the patreon members?

[@scottcurry3767]

Trying to replicate from the de Prado paper, where does your calculate_psr_se formula from the repo tie to the paper?

[@combatcritique]

Very in depth

[@Daniel88santos]

Hello great content! ... you made me subscribe to your channel! ... Is it possible you make a series of videos talking about Copulas and their implementations in finance? I know they gained a lot of bad reputation due to the Great Financial Crisis of 2008, but to me all resumes to bad assumptions of the people that used & applied the wrong copulas to model risk. Complex copulas, like Vine copulas or Bayesian Copulas are very effective tools to model multiple dependences. And I know that the industry are trying to use them as statistical arbitrage tools. Unfortunately does not exist too much content about copulas especially in Python in youtube or even in the Web. I'm now trying to enter in the field of Quant (only with electronics/telecoms engineering background) and I want to understand the dependence (dynamics) between several assets and it's hard to find good models that describe it with good confidence. Yes, I understand that market dynamics are very complex (and many times exhibit chaotic behaviour), but amazes me how we, in an time of huge technological advances and with all the technology and knowledge to our disposal, still struggle to understand the markets and their dynamics.

[@QuantPy]

Hi Daniel, thanks for the suggestion- yes I’m happy to cover copulas in the future on the channel. It’s been added to the list . Thanks

[@axe863]

​@@QuantPy Nothing worse then not incorporating non-stationarities in dependencies when performing portfolio optimization especially if the optimization method is very tail sensitive.

[@axe863]

Guiding princple.... Never start with the most flexible methods...... overfitting risk is always maximized.