I would say in the context of trading in general (for HFT see my comment above) further developments of recurrent neural networks (RNN), e.g. so called historical consistent neural networks (HCNN) together with forecasting ensembles, are state of the art.
I published an article on that which will be published this month by Springer Verlag (Zimmermann, Grothmann, Tietz, von Jouanne-Diedrich: Market Modeling, Forecasting and Risk Analysis with Historical Consistent Neural Networks)
Just to give you an idea about the new paradigm here is a short excerpt:
In this article, we present a new type
of recurrent NN, called historical
consistent neural network (HCNN).
HCNNs allow the modeling of
highly-interacting non-linear
dynamical systems across multiple time
scales. HCNNs do not draw any
distinction between inputs and
outputs, but model observables
embedded in the dynamics of a large
state space.
[...]
The RNN is used to model and forecast
an open dynamic system using a
non-linear regression approach. Many
real-world technical and economic
applications must however be seen in
the context of large systems in which
various (non-linear) dynamics interact
with each other in time. Projected on
a model, this means that we do not
differentiate between inputs and
outputs but speak about observables.
Due to the partial observability of
large systems, we need hidden states
to be able to explain the dynamics of
the observables. Observables and
hidden variables should be treated by
the model in the same manner. The term
observables embraces the input and
output variables (i. e. Yτ := (yτ , uτ
)). If we are able to implement a
model in which the dynamics of all of
the observables can be described, we
will be in a position to close the
open system.
...and from the conclusion:
The joint modeling of hidden and
observed variables in large recurrent
neural networks provides new prospects
for planning and risk management. The
ensemble approach based on HCNN offers
an alternative approach to forecasting
of future probability distributions.
HCNNs give a perfect description of
the dynamic of the observables in the
past. However, the partial
observability of the world results in
a non-unique reconstruction of the
hidden variables and thus, different
future scenarios. Since the genuine
development of the dynamic is unknown
and all paths have the same
probability, the average of the
ensemble may be regarded as the best
forecast, whereas the bandwidth of the
distribution describes the market
risk. Today, we use HCNN forecasts to
predict prices for energy and precious
metals to optimize the timing of
procurement decisions. Work currently
in progress concerns the analysis of
the properties of the ensemble and the
implementation of these concepts in
practical risk management and financial
market applications.
EDIT
Parts of the paper can now be viewed publicly: Here
