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Lawrence,
Thank you for an excellent summary on Neural Nets.
I first worked with these in 1991, and my opinion was,
and still is, that you need a lot of non-correlated inputs
to predict time series data. Perhaps a better use would
be working from chart patterns, even P&F.
Recently, however, there was a post to the Omega-list
claiming to have found the Holy Grail trading the S&P
with NNs.
I would now like to do some testing with genetic algorithms.
If you have any suggestions for software, let me know.
Neal
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> From: Lawrence Lewis <lel@xxxxxxxxxxxxxxxx>
> To: realtraders@xxxxxxxxxxxxxx
> Subject: GEN Neural Networks are not magic
> Date: Friday, April 24, 1998 10:00 AM
>
> I have spent the last several years studying neural networks and their
> use in predicting prices and developing trading systems. This includes a
> couple of graduate courses at University of Washington, purchase of a
> commercial neural network package, several hand written programs, and
> subscription to a neural network trading service. For anyone interested,
> I thought I'd give you a quick summary of what I've found so far. The
> following is not mathematically rigorous.
>
> 1. The typical neural network used in trading systems has nothing to do
> with simulating the way humans think. It doesn't adapt or learn on the
> fly. It simply computes a non-linear mathematical function of its inputs
> that produces a predicted output. The neat part is you don't have to know
> what function you need to compute, There are algorithms which find a
> function that fits a set of training examples that are given to it during
> a training phase. For those of you who hate curve fitting, neural
> networks aren't for you. That's exactly what they're intended to do.
> 2. Neural network fitting algorithms can approximate non-linear and
> chaotic functions, and are tolerant to noise in the training and test
> data. This makes them suited to price series, since I think most traders
> believe that prices are chaotic functions with a noise component. Chaotic
> doesn't mean random - chaotic functions are completely deterministic. It
> typically means a function that is dependent on prior values of itself
> and is extremely sensitive to its initial value. I believe SP500 prices
> are chaotic with noise because I believe they are dependent on some of
> their prior prices and have noise. Typical chaotic functions can look
> random.
> 3. Training a neural net involves optimizing a set of parameters (called
> weights) in order to generate a function which closely matches the
> training data. For those of you who shrink at the dreaded thought of
> over-optimization, neural nets may be the ultimate evil. A neural net
> that takes 5 inputs (say price, yesterdays price, price 2 days ago, price
> 10 days ago, MACD), will typically have a minimum of 20 weights to
> optimize (maybe many more). How many of you like trading systems with 20
> optimized parameters? If you have 10 inputs, you might have 50 or more
> weights. A great deal of research in neural networks tries to address
> this problem. In the end, you train on a set of training data, which
> calculates the weights, then test on a different set of testing data
> using the previously calculated weights. The theory is, if it does well
> on the test data, that what you learned by "fitting" was the underlying
> mechanism of the market.
> 4. One way to feel better about all this curve fitting is to have a good
> understanding of some reasonably advanced statistics. This is probably
> not a bad idea for trading systems developers of any kind. You need to
> know whether your results could have been achieved by chance or not. You
> need to know whether your system is still working if it has 5 straight
> losing trades. Remember, completely random coin tossing will usually get
> a run of 5 consecutive heads or tails 3 times during 100 tosses.
> 5. Neural networks are not self designing! To create a neural network
> trading system you, the human, have to a) select a set of inputs, b)
> select a network architecture, c) decide how to initialize the weights,
> d) select a set of activation functions, e) select a learning algorithm,
> f) select an error function, g) decide how to pre-process the data, and
> h) decide how to use the output to generate buy/sell signals. Oh...first
> you have to spend the time to learn how to do all that. A number of
> people have tried to use genetic algorithms that randomly generate a
> whole bunch of different combinations of these things and then select out
> the ones which work bes
> t. Talk about more optimization! It also takes a
> ton of time.
> 6. Neural networks clearly work for some applications. They are used
> everyday in radar processing, chemical processing, optical character
> recognition, physics labs, etc. That is, in places where there is no
> question that they were able to learn an underlying mechanism from a set
> of examples. In some cases, neural networks have solved problems that had
> not been solved (at least in a reasonable amount of time) previously.
> They are also use very successfully in a variety of financial
> applications like credit scoring.
> 7. On the other hand, most kids are pretty good at character
> recognition. I'm not sure whether financial forecasting is more like
> character recognition or more like radar processing. Humans are awfully
> good at certain types of pattern recognition and awfully bad at others.
> 8. I bought Biocomp Systems Neurogenetic Optimizer (NGO) and their
> Profit add-on package (www.biocompsystems.com). NGO is a general purpose
> neural network development system. Profit is really still a work in
> progress, although it seems to demonstrate that it is possible to develop
> profitable neural network based trading systems. Good quality software,
> but I found that it was just a stop along my own personal journey. I
> wanted to try things that were not yet supported in the software and I
> didn't want to wait. On the other hand, it's always nice to have a known
> good test system to verify some ideas. I think most traders would benefit
> from software like this if they're interested and willing to put in the
> work to understand how it works (not just its operation, but some theory
> behind it).
> 9. I subscribed to Joseph Prewitt's TrendySystems
> (www.trendysystems.com) neural net based trading system. I've watched it
> since December 1997. It appears to just plain work as long as you are a
> short term trader (1-2 days per trade), can accept 15 point stops (SP),
> can accept being out of the market 2/3 of the time, and can accept not
> knowing why it's doing what it's doing. But, it's still a short track
> record in the best bull market of all time. I don't think I can stick to
> a system when I don't understand the details of how it works.
> 10. I subscribe to the Journal of Computational Intelligence in Finance
> (formerly Neurovest Journal). Great journal, but not for the
> mathematically challenged. Need at least undergraduate level math and
> statistics. I have a math degree, but not a lot of formal statistics, and
> it's tough for me. Try on Generalized Autoregressive Conditional
> Heteroskedasticity for size.
>
> Good Trading.
>
> Lawrence E. Lewis (lel@xxxxxxxxxxxxxxxx)
> Director of Engineering
> Thrustmaster, Inc. (http://www.thrustmaster.com)
>
>
> ----------
>
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