For this test there was no optimisation whatsoever; I simply chose a model order of 3, 50 hidden units for both the Gaussian and binary units, the top 500 training examples from my Cauchy-Schwarz matching algorithm, 100 training epochs and 3 sets of features each to model the next day's open, the 3 day maximum high and the 3 day minimum low. These training targets are different from the targets I presented earlier, where I modelled the next 3 OHLC bars individually, because of the results of a very simple analysis of what I will be trying to predict with the CRBM.
The video below presents the results of this visual test. It shows a sliding window of 9 bars moved from left to right over the bars shown in the chart above. In this window the first 6 bars are used to initialise the CRBM, with the 6th bar being the most recent, and the 7th, 8th and 9th bars are the "future" bars for which the CRBM models the open price of the 7th bar and the 3 bar max high and min low of the 7th, 8th and 9th bars. The open price level is shown by the horizontal black line, the max high by the blue line and the min low by the red line.
In viewing this readers should bear in mind that these levels will be the inputs to my MFEMAE indicator and so the accuracy of the absolute levels is not as important as the ratios between them. However, that said, I am quite impressed with this unoptimised performance and I am certain than this can be improved with cross validated optimisation of the various parameters. This will be my focus for the nearest future.
with this code you can do a regression for simple multiple back propagation NN and compare to your CRBM to see if CRBM is really superior.
ReplyDeletehttp://sourceforge.net/projects/mbp/
From my experience RBMs are not superior at all for financial TS