Neural Networks don't work for trading

One exercise I went through that might help someone else was to create a file that had random binary inputs and an output that was some logical combination of the inputs. I ran that file through an NN program and some other statistical analysis programs to see that they could predict the output. I then introduced varying amounts of noise to the output to see how strong the correlation between the inputs and the output needed to be for the predictions to still have value.

Not long after doing all that I started looking for better inputs.
 
Quote from dtrader98:

Thanks. Are you using the GAs primarily for NN weight optimization? If not, do you mind if I ask what your GA fitness function is optimizing?
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We use the GA to tune the weights of the NN (our hybrid models may also include other parameterized stuff). The output from the NN is used in the signal generation. The signals are then used to generate trades.

Somewhat simplified the GA goal function looks at these trades and see how their performance evolve over the time after the signal and generates a fitness measure, typically based on the Sharpe Ratio or Profit Factor.

One benefit of using this type of model is that you don't require the NN to create a complete classification for all inputs in the training set since the GA goal function will not punish the NN for not including all good trades in its "good trade" classification, which we risk doing (maybe unknowingly) when a simple error measure is used. In fact our classifications only includes the two classes "good trade" and "don't care".

One major drawback with our set-up is that training becomes very slow.

/Hugin
 
Quote from Hugin:

Specific issues we have found with NN:s are how to create a good error measurement (at least for back-propagation networks)
/Hugin
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What is the good "cost function" for NN? I want to try something different then MSE... Thanks in advance.
 
Ok. So, essentially you are optimizing the weights via GA, not so much to minimize RMSE faster, but using fitness functions (sharpe, etc., I read your other post somewhere) as a type of criteria.

Thanks,
dt



Quote from Hugin:

I have read and I like this thread and agree with what is said above. Which technology to use in order to optimize your model is only one part, and not the most important one.

Since we use both Genetic Algorithms (GA) and Neural Networks (NN) in our trading systems I may have some input on the issues with using NN:s. The major problem with all optimization for trading is, as been stated many times, the problem of overfitting the model parameters to the data.

Specific issues we have found with NN:s are how to create a good error measurement (at least for back-propagation networks), how to create a goal function that makes sense for trading and how to select a training algorithm.

If all of these are not created carefully you can be sure that the optimization algorithm, whatever technology you are using, will find ways to exploit any problem with your model in order to satisfy your goal function, often leading to bad results.

One simple example is that the optimization algorithm could become obsessed with including a few very good trades in the output even though the results are purely news-driven. This means that the NN training will try to isolate a single super trade by tweaking the parameters of the solution space no matter what. This will in all likelihood not provide a good generalization of what constitutes a good trading opportunity.

All in all, it may not be the NN itself that is the problem, but the complete optimization set-up. We found that some working non-NN trading models we created could easily be converted into a standard feed-forward NN. Now, when we use NN in our hybrid models we use the GA for training it. GA:s are bad at training a NN, but it gives us larger freedom in creating our goal functions. GA:s have problems of their own, but that's another story...

/Hugin
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Quote from dtrader98:

Ok. So, essentially you are optimizing the weights via GA, not so much to minimize RMSE faster, but using fitness functions (sharpe, etc., I read your other post somewhere) as a type of criteria.

Thanks,
dt
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That's correct, the reason for us to do it this way is to get away from the limitations of the standard textbook NN training algorithms. There are probably other ways to do it, e.g. by using other network topologies (e.g. non-feedforward) and other training algorithms than gradient based.

We have also looked into other types of classification algorithms like Support Vector/Kernel regression but so far this works OK for us. Also, the feedforward network integrates well into our models.

/Hugin
 
Quote from RedRat:

What is the good "cost function" for NN? I want to try something different then MSE... Thanks in advance.
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What I say here comes from my experience working with NN some 15 years ago so it may be outdated. Also, our focus then was on feedforward networks trained by the back propagation training algorithm so my answer is biased by that.

The main problem we found with changing the goal function is that it may have impact on the training algorithm so the training might just stop working.

Maybe somebody with experience with other types of networks have some input?

Anyway, this is why we use a GA to optimize network weights. The goal function for a GA can be more flexible, since the only requirement is that it can tell which of two network configurations is the better one. The GA also makes it possible for us to use the same algorithm for optimizing all parameters in the trading model, including the weights of the NN.

/Hugin
 
H1N1 virus can evolve and accumulate strength as it passes back and forth between different animals and people; it's adaptive and dynamic nature seems like a genetically engineered algorithm...a role model for trading systems .i.e. changing grid spacing and position sizing. :)
 
Thank you Hugin,
I am familiar with GA. But unfortunately my basic NN has 16 input neurons + hidden layer, say 24 neurons, so I suppose it will take enormous time to train with GA :(. One output.

How many neurons or connections do you have?

I am also trying SVM and RVM but do not have results so far.

Are you familiar with reinforcement learning? Is it better then GA optimization?

RR
 
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