[Ranger]

Hello

I searched the forum and found limited information on the topics of Neural Networks and Genetic Optimizers. My friend and I have started reviewing this topic with the objective of selecting a software; designing signals for active trading. We both program; we're both engineers. In the past, I studied linear programming and operations research and I also have advanced mathematics at the masters level.

If the topic interests you; you're a day trader or very active trader; and you think that you can contribute at a higher level to our team, we'd love the hear from you.

Also, any forum members with constructive comments, we'd love to hear from you too.

Happy Trading

RANGER

[waveslider]

I too am looking at NN technology. I trade end of day successfully using proprietary algorithms. My systems are somewhat adaptive, but I am looking for multiple and diverse levels of confirmations. I don't know if NN is a path I should be following.
Can anyone recommend a good starting point?

thanks

[natedredd10]

This book is the only one I've found that I'm starting to actually make some sense of NN.
Amazon.com: Machine Learning: An Algorithmic Perspective (Chapman & Hall/Crc Machine Learning & Pattern Recognition)…

Unfortunately, It doesn't get as deep into other machine learning algorithms as it does NN.

Ranger, you should check out hidden markov models. I don't know the math to make heads or tails of them but Rentech hired the Baum in this algo awhile back:
http://en.wikipedia.org/wiki/Baum-Welch_algorithm

As far as other books there are a lot of machine learning bioinformatics books that I think are easier to follow than straight ML textbooks. Also has the nice feature that the problem domain is a bit similar as far as large data sets and not totally sure what it is you are looking for.

[MseTrap]

IMO the Marsland machine learning book linked above is pretty awful. Just skip the book and download the source code from the website. Although it's fairly obfuscated python.

[waveslider]

Originally Posted by natedredd10 »

As far as other books there are a lot of machine learning bioinformatics books that I think are easier to follow than straight ML textbooks.

could you recommend some which are easier to follow? I do not have a higher mathematics background and I fell asleep in class most of calculus.

thanks

[Ranger]

Happy to see this posting getting some interest and not because it's mine.

@natedredd10 - thanks for the posting and links. I'll check them.

@waveslider - NN are intended to be predictive. So, let's say you build a NN based on a few momentum indicators; and another based on few Osc; and another based on Price Action. You can then create a committee of those nueral networks and base your trade signals on your criteria - short answer YES.

I just started playing with Tradecision. Why Tradecision - most of the NN packages are around 2.5K. TD Ameritrade offers Tradecision lease 109/month as an add-on. I thought this was a reasonable price for a training tool and possibly more.

If I get additional interest in this, I don't mind posting my progress but it takes a few hours to make screenshots and document, so there needs to be an interest in the topic. Skype screenshare session is also possible.

pages 1-2: show performance on TRAINING DATA; TEST RANGE DATA. (Particulars of model are unimportant but for example this could be MA x Over NN.

Note the performance between the TRAINING DATA & TEST RANGE DATA.

pages 3-4: Shows predictive line (page 4 is close-up scale).

pages 5-6: NN Signals applied to chart

pages 7 - 8: simulation applied to all data; equity curve and results.

Apologize for the quality of the documentation!



RANGER

[BlowFish]

I'd say just get some tools and start 'playing' you don't really need to know too much about exactly how neural networks work the real skill (and art) comes in picking what inputs to feed in and what you look for in the output. (predicting price, particularly a long way in to the future is about as tough a task as one could give a net). Genetic algorithms are a much more straight forward beast, they are pretty good at determining suitable inputs or ranking outputs of nets incidently. A lot of the heartache is in pre and post processing the data. In fact as far as I can tell that is the number one issue, normalising the inputs appropriately and trying to predict sensible things. I am not sure it's stuff you can learn from a book (though I would be first in line for a book on practical applications, tips & tricks) you just need to try and see what kinda 'works' and what does not.

I have had a copy of NeuroShell Daytrader for years (I used to be a bit of a software junky). If I am honest I never got past the 'dabbling with intent' phase though I learnt much more through that than anything I read. I must say I was impressed by the vast array of tools in a single package (some are pay extra modules but the basic package has loads). It has a couple of types of NN's, genetic optimisation, a vast amount of maths stuff, regular TA stuff, plus various 'exotic' bits and pieces. Edit: unlike Ranger I prefer to buy an out and out license.

If you want to place an order you learn how to use your brokers platform rather than learn the FIX protocol. Of course with the brokers platform it has a pretty specific task so it is much easier to see that it 'works'.

Does anyone have recommendations for resources that cover the practical application of these sorts of tool? I do have a few older tomes though not sure any are worthy of an out and out recommendation.

[natedredd10]

Originally Posted by waveslider »

could you recommend some which are easier to follow? I do not have a higher mathematics background and I fell asleep in class most of calculus.

thanks

That book I posted above is by the far the easiest to follow I've found, its kind of a machine learning for dummies book which may be why MseTrap thinks its crap.
I don't have the background to gain much of anything from the most highly recommended books in the field and I do think you have to be open to the idea that if you don't have the background this may be a huge waste of time.
I also have Bioinformatics: The Machine Learning Approach by Pierre Baldi..Maybe the only thing I find easier about it though is that I find bioinformatics interesting in and of itself so the concepts are not so abstract as with a straight ML textbook.
I think without the background its important to find text that give real world examples on real datasets that interest you since there is no book on ML with financial datasets at its base that I know of. A straight up ML textbook is as much fun to read as your highschool calc book.