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Jrinne

Yuval, P123 classic is a powerful tool and no one has to use machinelearning to make money here. Furthermore, Marco is working to expand the tools available through the API for whatever methods people want to employ including (but not limited to) machinelearning. So I am not trying to convince anyone to use machine learning. That having been said I have benefitted tremendously from discussing machine learning with Steve Auger, Azooz, you and an others on this forum. I also know that you have been a fan of de Prado in the past. He has this to say about multiple regression (and linear regression): "If the statistical toolbox used to model these observations is linear regression, the researcher will fail to recognize the complexity of the data, and the theories will be awfully simplistic, useless. I have no doubt in my mind, econometrics is a primary reason economics and finance have not experienced meaningful progress over the past decades." Okay, that is a bit too strong. From time to time I review linear regression. I forget the equations. Lose sight of how closely correlation is tied to the slope of the regression line. How important Zscore is to linear regression. Lose sight of how the line is related to the data. Honestly, if one fully understands that then I think the rest is just an extrapolation. Boosting is just linear regression with a curved line and an extra dimension or two. Ultimately the equations are equivalent if you use rootmeansquarederror as your boosting metric. Okay, that’s not right. Boosting is linear regression without any of the limiting assumption—like homoscedasticity, normality and importantly of linearity. But that is all theory one needs to understand. Ultimately however, if one is interested in the equations they are equivalent to the equations for linear regression and with the same purpose. I think authors go out of the way to make this difficult so they can look supersmart. That having been said, Steve Auger and Azooz really are supersmart and do not have to go out of the way to make it seem difficult. Me, I just try to fully understand linear regression and imagine how those equations can be used on a curved line. Copy a little Python code from the internet. Reuse it for my next project. Anyone can do it if they have an interest. And maybewith a little persistenceI will have data to see how close de Prado is to the truth. Best, Jim From time to time you will encounter Luddites, who are beyond redemption. de Prado, Marcos López on the topic of machine learning for financial applications 

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Jrinne
at Jan 14, 2021 5:08:18 AM

yuvaltaylor

Thanks, Jim, that makes a lot of sense to me. It's a nice explanation indeed. I was doing some nonlinear regression yesterday regarding the relationship between portfolio size and buy and sell position ranks (i.e. to get a portfolio of 40 stocks with a buy rule of rankpos <= 10 what does the corresponding sell rule (rankpos > X) have to be?). I found a power equation that came pretty close, but the best thing was just grabbing a bunch of data points and making educated guesses as to the inbetween values. I suppose that's somewhat comparable to what the decisiontree ML algorithms are doing. At some point I should learn Python . . . Yuval Taylor Product Manager, Portfolio123 invest(igations) Any opinions or recommendations in this message are not opinions or recommendations of Portfolio123 Securities LLC. 


