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Fascinating discussion on results of Neural Networks in world of quant part 2

(Last Updated On: September 20, 2010)

Fascinating discussion on results of Neural Networks in world of quant part 2
We have heard about Nerual Networks in world of quant. The following was found in a Linked In discussion:
OK. Let’s start from the beginning for there is too many things going on. First, we need to filter the market. I’ll use a method of mine call the max-min filter. Look for a formation where the next high is lower than the current high. Make sure that highs before the current high are all at most equal or you find a lower high. Then declare the current high a market maximum. Do the same on an inverted pattern to find the current minimum. Now you have a market with minima and maxima. Make sure they alternate. If you find two consecutive max (or min) look for the lowest low (highest high) between them and mark as min (max). Now the alternate perfectly. By connecting the dots in time you have a perfect characterization of the market. Remember that you must be certain what happen first in each bar the high or the low.

With this set of points we can then characterize a trade by saying within x ticks of a maximum I want to short this market or, within 25% of the previous down leg, I want to short the current leg. Now we have out perfect objective market.

Take each trade from the series and scale. This is done like this, take the close of the market bar at entrypoint and call it zero. Use that value to subtract from all your embedding (OHLC of previous bars), and do the same for you objective, say, 80% of the current leg.
Notice that all you have now is a bunch of unrelated trades with an embedding and a target. And that is all you have on a ANN because they are memoryless systems. I designed ANN with short term memory but never applied to the market.
IEEE Transactions on Neural Networks, volume me 6 (1995) Tom, M.D. and M.F. Tenorio, A neural computation model with short term memory, pp. 387-397. Thathachar, M.A.L. and V.V. Phansalkar, Learning the global …
www.ph.tn.tudelft.nl/…/IEEE.Transactions.on.Neural.Networks.6.html – Em cache –

So back to our problem, we could do straight training, no harm done because ANNs do not car what came first. Except when you use training. Training algorithms in their majority are gradient based. A person training 1988-2000 will get a different result that 1989-2001. That is absurd. But neural network follow same of the same learning done in the brain, and therefore you can make it easy for learning or not.
Tenorio*, M. F., R. G. Schwartz, and M. D. Tom, “Adaptive Network as a Model for Human Speech Development,” Computers in Human Behavior, Pergamon Press, vol. 6, no. 4, pp. 291-313, 1990.

So let’s make it each on our ANN that already is doing a tremendous task. Let’s decrease the discontinuities and the high frequencies and the total bandwidth of the signal (less hidden units) by using a trick we use with children. First the basics then teach it the exception. Cluster all the trades by hamming distance of the multidimensional input point. Sort them by closeness. Then take the sort set and resort them by closeness in output difference. Now all situations that are similar are together. Some thing like:
Who loves you? Daddy.
Who things you are the greatest? Daddy
Who is king? Daddy
Who…? Daddy
You get my drift.

Now we are ready to train. The choice of ANN should align with the task at hand. This is a clustering problem and a RFB ANN will do much better than a sigmoidal one. Perform the training. Now we need an exit strategy. Measure by eye how close to the actual filtered result the ANN is signaling entry, and how many ticks does it indicate for the move. If you are satisfied this is a good strategy, else add trailing stop and the works. Test is on the training set and do a post mortem analysis (in the book I have indicated previously) to see how well you are performing during the trade. Fix it accordingly and try the test set. A couple more adjustments and you are done.

The main thing to take from here is that it is not a blind exercise of raw market learning, neither such things exist. The neural network is always performing a classification of patterns when you use bars as inputs.

Now to the money management scheme. The same way that I can take ANY input signal and make an excellent or a poor systems (discussed in a posting in another discussion in this group) I can also take ANY trading system and make it glorious. It all depends on adjust the exits and then use a non fixed number of contracts. To see if this works, get the set of trade results and place them in the filtered market. Now in that order plot past trade x current trade final equity. You want all the points in the first and 3 quadrant. Histogram the positive trades x past trades, and negative trades x past trades. You should see serial correlations, when you win, you will win for a while, and when you lose it is the same. Transitions between winning and losing are rare. So now if you have streaks of win and lost plan you traders accordingly. This will make a 55% system into a 90% system with low drawdown and very high return.

If you follow the recipes here you should be in good shape for an excellent trade systems. There are many other variants on this specially when dealing with entire portfolio balance.

NOTE I now post my TRADING ALERTS into my personal FACEBOOK ACCOUNT and TWITTER. Don't worry as I don't post stupid cat videos or what I eat!

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