I have also started to become disillusioned with my attempts at creating a market classifier neural net; I think it may very well be too large a project to complete satisfactorily, so I am going to try something simpler in concept at least. In large part this change of heart is due to some recent reading I have been doing, particularly this article in which I was struck by the phrase
"As an aside, the ideal "trades" formed by the turning points might make a good training set of trades for a neural network-based system. Rather than scanning a chart manually to come up with trades to feed into the neural network, the method described here could be used to automatically find the training set."
Also over on the Mechanical Forex site there is a whole series of articles on neural nets which I have found useful and have given me a new insight. I am now going to ask readers to suspend their disbelief for a few moments while I explain.
Imagine it is a few minutes before the trading open and you have to decide whether to enter long, short or remain out of the market when the market opens. However, unlike any other market participant, the Gods have given you a gift - the ability to see the near future - which means you know the OHLC for today, tomorrow and the next trading day. But, as with all gifts from the Gods, there is a catch - you can only enter and exit trades at the market open; there is no buying the low and selling the high of a candlestick bar or intraday trading permitted by the Gods. What would such a blessed but responsible trader do? How would they make their trade decision? Being responsible s/he might consider the reward to risk ratio and only take a trade if this ratio is greater than one: on the long side the maximum open of tomorrow or the day after minus today's open divided by today's open minus the minimum low of today, tomorrow or the day after: and a similar reasoning for the short side. If neither of these ratios is greater than one, no new trade will be entered today.
The upper pane in the video below shows the coding of such logic. The decision to go long is rendered in blue, short in red and neutral in green. The colour of the bar indicates the action to be taken at the open of the next bar. However, it might be that when a neutral signal is given there is already be a position held, in which case the existing position is held for the duration of the neutral signal. This is effectively an always in the market, stop and reverse signal, and this is shown in the lower pane of the video. To make things slightly easier to see the entry/exit action occurs at the open of a new colour bar, i.e. if the bars change from blue to red the long is exited and the short initiated at the open of the first red bar.
Even with a cursory viewing it can be seen what a great "system" this would be, and using neural nets to create such a "system" is now my ambition.
The plan is simple: roll a moving window along the price series and use the known relationships between bars and indicators within this window to train a locally optimised neural net. The purpose of the training will be to classify the bars as long entry, short entry or neutral as in the chart in the upper pane of the above video. At the hard right edge of the chart the last three bars will be unavailable to the neural net for training purposes, but the hope is that the neural net, sufficiently well trained on all data in the window immediately prior to these three bars, will have predictive ability for them. After all, in the main, market dynamics slowly evolve over a few bars rather than dramatically leap.
Before I embark on this new work there are a few optimisation tests I would like to conduct, and these tests will form the subject matter of my next few posts.
