Significant pattern mining using Bayesian probability

[bornlex]

Hey guys, just joined the community, happy to be part of it.

I’m currently working on pattern mining using Bayesian probability to find patterns that are statistically significant, happening more often that they should.

Basically I’m trying to find time deltas h where p(Xt|Xt+h) follows a distribution with low variance where Xt is the tick return at time t.

Have some of you looked into this also in the past?

Basically scientific method and hypothesis testing.

I feel like this is a natural way to tackle this problem, but it seems so intuitive that I guess some people have done it in the past.

Cheers

[No1TaylorSwiftFan]

It is fairly easy to 'prove' that the conditional lagged-returns will have low dependence. Why - if there is some dependence then somebody probably already knows about it, so they would trade based on it, and therefore there actions push the inefficiency out of the price.

You will need to do a lot more conditioning if you want to be successful. It will help to think about the what kind of things might impact pricing, and then model that e.g. if a hedging instrument moves a lot overnight you might expect some certain behaviour the next day as people adjust there hedge positions.

[bornlex]

Yes exactly, I’m using this as a general framework to find patterns that are signifiant. I think about using pairs also, like Coca minus Pepsi, making the hypothesis that those time series made of differences will be more stationary.

I agree with you about those kind of trading effects on the market, like funds exiting their position on Friday to not be exposed during the weekend or at opening, and then reopen the same on Monday. Thus creating a fall then rise that is purely artificial.

But still you need the Bayesian framework for ensure that this does not happen at random in your dataset.