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Someone will go after your stop and take your money

Ok I understand that for some mysterious reason people hate it when you tell them it was you who took their money.... so forgetting about the overall strategy, what is your target profit and stop loss for each one of these short term trades? Do you use trailing stops?

Good job... what is your average holding period and return per trade? I would say keep doing what you are doing

LOL ?? Economists? I believe you are thinking of the wrong R-Squared . Check it out. Anyway, From the top or your wisdom, what sharpe and sortino are you looking for?

In summary, that would pretty much mean a max loss of 5% for an average running gain of 50% of traded value. Definitely interesting for anybody. May I ask where do you test these metrics? Tradestation or which other setup?

Or what would you say is a suitable metric and its threshold value out of sample ?

R-squared out of sample then?

When would you say an algo strategy , on a liquid instrument, on daily timeframe, would become interesting? R-squared = 0.1? 0.2? 0.3? 0.4? 0.5 is probably good enough..

Ok, but what kind of thing would you need to know? I agree with the out of sample easily becoming in sample, but what if I did not optimize again after trying the out of sample? Something like 1000 points in sample, 200 points out of sample? Perhaps to improve I would randomize the selection of...

Sorry, I added text inside the quote. Damn iphones

All right so thanks everyone.. Now, the key question is.. do you need to make sure parameters introduced in random forest or neural networks or whatever ML algorithm are statistically relevant in driving the output function? By themselves or as an interaction with others? Or is ML going to...

Thank you! Good , helpful reply. Any chance this can be implemented in Matlab? Or is Python more advanced and required for this level of detail?

Thank you Robert. Are these spot prices though? What I have seen is futures quotes at those links, not EOD spot, which is what I was looking for, ideally

For a range of commodities. Preferably, free..

thanks. I am referring to actual alpha factors. Indeed, high risk, higher than with Decision trees / SVM / KNN ? and is there a difference between regression / classification wrt overfitting risk?

What about the overfitting risk though?

If provided with good training data, and not overtrained, are they capable of “muting” features that are not statistically relevant? In this way they would stand a better chance once tried out of sample. Whereas non-statistically relevant features mess up the statistical basis of decision trees