What inputs would you choose for an intraday neural network? : Forex

When you build a neural network one of the first things you need to decide is which values will be the inputs and which values will be the outputs of your network. The outputs are the values you want to predict — to make profit within a trading system — and the inputs are the values which will allow you to predict the outputs with enough accuracy as to constitute an inefficiency. Now choosing inputs and outputs is no trivial task as this constitutes the majority of the success or failure of a trading neural network.

A neural network is formed by a set of function layers which turn a certain set of input variables the input layer into a given set of output values the output layer.

Between the input and output layers you will find a varied amount of layers and functions neurons — depending on what you choose — which will attempt to transform inputs into outputs with the least possible error.

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The hope of course is that the network will hold at least the same predictive power within a non-trained set. The first thing you need to choose when you build a neural network for trading is exactly what values you want to predict.

The next support or resistance level?

Once you choose the output you want the next logical step is to build inputs that you believe are predictive towards this output. Certainly before building the network there is no way of telling if one input will be better than another but you can obviously reduce the amount of variables you will use by doing a PCA Principal Component Analysis to filter out those variables which are evidently and heavily correlated.

The PCA technique allows us to eliminate those variables which might be redundant within the network and therefore only increase complexity without increasing the quality of the results. A very interesting thing I have found out in the networks I have developed for Sunqu is that the use of absolute price values is very good since the network learns about support and resistance levels as they develop, actually trading around them in a certain way.

Since price is what you want to capture then taking into account absolute price values and basing predictors on them is a more straighforward way to system building than attempting to develop indicator output based networks.

Predicting things like RSI extremes will allow you to take advantage of rapid price movements before they happen and predicting moving average shifts in the long term will allow you to take long term trend following positions.

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Nonetheless whatever you want to predict your output choice needs to be accompanied by a very judicious decision of which inputs you will use, reinforced by an adequate PCA analysis which can show you the quality of your inputs and how well separated they are from one another. I hope you enjoyed this article! Is there an overview or table on your page or somewhere about such correlations between currencies and other indices?

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Home About Me Atinalla FE OpenKantu System Generator. Neural Networks in Trading: Choosing Inputs and Outputs March 28th, 1 Comment. Posted in Articles Tags: Investigating the Distribution of Return of Trading System Portfolios.

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