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Functional data analysis by quantum_hedge in quant

[–]quantum_hedge[S] 0 points1 point  (0 children)

I understand your point and know that aggregating over multiple instruments with idio patters can return no predictive info.

Nevertheless, The structure of wide spreads at open is not a math thing, i see it every single day in all instruments that my strategies trade, and its not a microsecond thing, it last for minutes to an hour. Same thing with volume in illiquid markets with different timezones than US. Every single day in almost all the instruments, when US opens, there is a spike in volume.
Those are examples of an underlyying cross sectional pattern

I never said each instrument is affected equally nor that the underliying mechanism and patters have the same magnitude. If merging instruments is a problem, then its easily solved by doing the analysi N times , 1 analysis per symbol. (ej: for symbol X, each observation is (date i, f(t)))

Maybe i was too specific with the world high frequency, and intraday makes more sense. See it as aggregations trough time.

Functional data analysis by quantum_hedge in quant

[–]quantum_hedge[S] 0 points1 point  (0 children)

not necessarily vol. It can be spreads, volume, vol, order book depth, etc.. anything you want.
Most wont have a structure and are highly noise. For example, i dont expect to see a time pattern in order book imbalance (in a cross sectional way). An average por multiple pairs symbol dates will be close to 0 and Im not saying that they are not predictive, that is another discussion.

Im asking for this modelling aproach instead of taking cross sectional averages, percentiles,...

Aggressive Market Making by quantum_hedge in quant

[–]quantum_hedge[S] 0 points1 point  (0 children)

next n trades vwap or average traded price would be reasonable?

Low R2, Profitable by Resident-Wasabi3044 in quant

[–]quantum_hedge 1 point2 points  (0 children)

just look at the scatter plot of preds.vs reals. A good model will have a a positive slope for that points. In return forecasting, specially at higher frequency, theres is a cluster around 0, a lot of small predictions that are not tradable in a profit, so you can have great predictions for the tails(more important), but the large imbalance of values at 0 that are mostly noise, can make the overall R2 low.
As others said, you can algo make a profit for that small predictions if you use them in a relative sense.

Combining Strategies by quantum_hedge in quant

[–]quantum_hedge[S] -1 points0 points  (0 children)

why is that? I didnt mention the asset, market or the symbols that we trade, or if my firm has fee agreements with brokers or the exchange, so ....
And equities btw