As expected the NN achieved 100 % accuracy and my prediction of 20 % to 30 % accuracy for my current Naive Bayesian Classifier was more or less right - in various runs of sample sizes up to 50,000 it achieved accuracy rates of 30 % to 33 %. A screen shot of both classifiers applied to the last 200 days worth of S & P futures prices is shown below, with the Naive Bayesian in the upper pane and the NN in the lower pane.
However, despite it vastly superior performance in the tests, I don't really like the look of the NN on real data - it appears to be more erratic or noisier than the Bayesian classifier. I suspect that the NN may be overly complex, with 54 nodes in its one hidden layer. I shall try to improve the NN by reducing the number of hidden layer nodes to 25, and then seeing how that looks on real data.
"Trading is statistics and time series analysis." This blog details my progress in developing a systematic trading system for use on the futures and forex markets, with discussion of the various indicators and other inputs used in the creation of the system. Also discussed are some of the issues/problems encountered during this development process. Within the blog posts there are links to other web pages that are/have been useful to me.
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2 comments:
Thank you for sharing lots of brilliant information...love the posts and your attention to detail. Really excited to hear about your NN progress....and out of sample accuracy...I know this is a big commitment so have avoided using any ML so far.
@vectorman - Thanks for your kind words - it's nice to hear that my blog posts are appreciated.
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