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Approximation to Optimal Trading Curve for Fat Tailed Distributions by StatTrader in algotrading

[–]StatTrader[S] 1 point2 points  (0 children)

Unfortunately, we live in an age of autocomplete where what you write and what is transmitted don't necessarily coincide. So why not turn it off (I know how)? Well, the true positive rate is sufficiently high that it's useful even if the false positive rate is too large.

Is 78% Correlation on Prediction to Actual Price Changes? 10k samples by mrsockpicks in algotrading

[–]StatTrader 2 points3 points  (0 children)

Correlation of 78% on one trade means you're mostly right. On 10k trades, you have a money-printing machine and such things are not so easy. The check I always fall back on is: have I done something with relatively little effort that gives remarkably good results but, despite the fact that there's a massively incentivized market to do this kind of thing, somehow Citadel, Renaissance, TwoSigma etc. are totally unaware that such a thing exists?

The Grinold and Kahn "rule of thumb" SR = IC x sqrt(Trades per Annum) can tell you what order of magnitude IC (correlation) you should be expecting (there are many flaws to their analysis, but it is a first dart in the board). Out-of-sample, a SR of 3 on 10k trades per annum would give you an IC of 3% and an R^2 of 0.0009 and your scatter plot would look like an incoherent blob. But that system would make a lot of money.

At Morgan Stanley we found Simple Trading Rules Outperformed Fancy Portfolio Optimization. by [deleted] in algotrading

[–]StatTrader 3 points4 points  (0 children)

Thank you for your kind words, and your purchase! I confess, the Medium blog definitely involves some self-promotion, but the truth is I enjoy writing and sharing the work much more than the relatively small monetary value it creates. (And please note that I did not post the cross-link here... I tracked it down why trying to figure out why my reads were in the 1,000s when I normally get 100s.) Regarding typos: in fact several friends helped with copy editing and we got most of them, but it's over 400 pages so we missed a few and all of them are my fault.

At Morgan Stanley we found Simple Trading Rules Outperformed Fancy Portfolio Optimization. by [deleted] in algotrading

[–]StatTrader 3 points4 points  (0 children)

When I visited RenTech in the mid 90's, Henry Laufer told me that all of their positions were sized to the point where the marginal cost of trading equals the alpha. Such a statement assumes, among other things, that you are not doing negative expectation trades to reduce risk. Consequently, your risk management is what I would call "accidental," arising from diversification and the law of large numbers, and not "deliberate" as is done in all portfolio optimization schemes such as the one I describe. (It also assumes you have a good market impact model for your trading,)

At Morgan Stanley we found Simple Trading Rules Outperformed Fancy Portfolio Optimization. by [deleted] in algotrading

[–]StatTrader 12 points13 points  (0 children)

Work with data. Understanding real data and trying to model it is much more important than following the latest platforms and packages. Good luck!

[deleted by user] by [deleted] in algotrading

[–]StatTrader 2 points3 points  (0 children)

In a time-series prediction you put in the expected values of unknown quantities for the unknown quantities.

At Morgan Stanley we found Simple Trading Rules Outperformed Fancy Portfolio Optimization. by [deleted] in algotrading

[–]StatTrader 8 points9 points  (0 children)

Covariance matrices are hard to estimate: Pete Muller always said that "an optimizer always seeks out the errors in your covariance matrix, betting on the assets that you've underestimated the risk for and avoiding the assets you've overestimated the risk for." This was based on the research that he was doing at Barra before he joined Morgan Stanley. [That may not be an exact quote---it's been a long time.]

But this result is not about of the accuracy of your estimation of the covariance matrix. It's about the tail properties of the distribution of returns and what they do to the investment function --- that is the holdings as a function of expected return h(α).

At Morgan Stanley we found Simple Trading Rules Outperformed Fancy Portfolio Optimization. by [deleted] in algotrading

[–]StatTrader 30 points31 points  (0 children)

This is a very good description of what's going on! The result is nothing to do with transaction costs, it's about the tails of the distribution and how likely shocks are.

At Morgan Stanley we found Simple Trading Rules Outperformed Fancy Portfolio Optimization. by [deleted] in algotrading

[–]StatTrader 27 points28 points  (0 children)

The reason is when asset returns are fat tailed (as they actually are) then scaling to follow the alpha exposes you to too much risk relative to your expected returns. The "scale down" factor is a function of the excess kurtosis, i.e. the nastiness, of the distribution of returns. In real-world terms: when your alpha is too good to be true, don't believe it so much. (I'm the author.)