Hello guys,
just another newbie question. I'm sorry if it ends up being daft :)
For my project (integrating HTM inference, prediction and anomalies
detection into a OTC high liquidity trading system) swarming (even with
few inputs/sensors) could be very expensive in computational costs.
The first idea that I had in order to speed up this process was to
create a set of ANNs or RNNs in order to <<statically>> "guess" the best
parameters for a good OPF model. Initially this would be really time
consuming but after, if I succeed, I could compute a good enough
swarming model in few seconds.
The way I have in mind now is this: I would compare the swarming real
outputs for different kinds of datasets (lots of baby datasets with
known functions) as real outputs and adjust my predicted ones to these.
In this case:
- Do you think that this could be a feasible approach?
- Which inputs should I consider to eventually build these neural
networks? I've read the pages about the swarming algorithm and I though
that a couple of statistical indicators, but I'd probably also add
principal component analysis, ZCA, standard deviation, Pearson index
against time, etc. What else would you suggest?
The second idea is about using the CUDA libraries.
Do you think that CUDA could be something beneficial for the swarming
process? Do you plan to add its support anytime soon?
Thanks a lot for your time and keep going the great job :)
--
Raf
www.madraf.com/algotrading
reply to: [email protected]
skype: algotrading_madraf
