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Nonius Unbound
Total Posts: 12723
Joined: Mar 2004
Posted: 2016-12-04 15:38
Thoughts, memes, links and bags of themes?

Chiral is Tyler Durden


Total Posts: 441
Joined: Jun 2005
Posted: 2017-04-08 15:50

no need for thanks


Total Posts: 1123
Joined: Feb 2007
Posted: 2017-04-09 12:29
I bet you could get something alpha-like using simple PGMs.

Some nincompoop at two sigma tried to convince me dweeb learning was the future. I happen to know one of the god-fathers of this subject (accident; I'm not interested, mostly because I have good inside information about it); he figured two sigma guy was trolling me.

"Learning, n. The kind of ignorance distinguishing the studious."


Total Posts: 303
Joined: Jan 2015
Posted: 2017-04-09 23:13
Seems like engineering a good feature set is much more important than model selection here. I'd just start with a random forest. RF's are pretty much the closest thing to a free lunch in machine learning. It might not be the optimal model, but if your features work at all, they'll almost definitely work in an RF right out of the box. If not, back to the drawing board.

Good questions outrank easy answers. -Paul Samuelson


Total Posts: 13
Joined: Nov 2016
Posted: 2017-04-10 08:15
On the face of it, PGM's seems like they were designed to deal with a lot of things that pop up in finance.

This seems like a good MOOC to learn more, which I wouldn't mind doing.

Pretty dubious of 'deep' learning in this area. Maybe something worth looking at for spacial structure in the order book, but probably overkill.


Total Posts: 443
Joined: Jul 2008
Posted: 2017-05-01 22:56
Deep learning (no need to mangle the name) makes sense if your model can benefit from being non-linear (in feature space). If yes, try it. If no, stick to linear models.


Total Posts: 443
Joined: Jul 2008
Posted: 2017-05-02 22:36
The problem with deep neural networks is that they're not robus to noise (
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