After you have installed the Adaptive Net Indicators, you will have a new custom indicator category called "Adaptive Net Indicators" with 18 custom indicators in it. They will be named Predict2, Classify2, Predict3, Classify3, etc. corresponding to the 18 Nets.

Each of the Nets will have the following parameters in the custom indicators:

Lookback – the number of bars in the training set. The training set contains the known patterns in the inputs. The training set starts at the current bar (the bar on which the indicator will be plotted) and goes back the number of bars specified in the Lookback parameter minus 1. If Lookback is 100, there will be a total of 100 known patterns in the training set. Remember that this Lookback window moves forward with the indicator output. The neural net is essentially "retraining" with each new bar.

Lookahead – the number of bars ahead to make the prediction. If Lookahead is 2, then the Actual value two bars ahead of the current bar will be the value the Net is trained to predict. Suppose the Actual value is the momentum of the close. Then we are telling the net to predict the momentum of the close 2 days ahead. The prediction of the momentum will show up as the output of the indicator on the current bar.

Actual – this is the time series you want the Net to learn to predict (or actually classify in the case of a Classifier Net).

Input1, 2, etc. – each of these inputs is a series you want to feed the Net as an input to its prediction or classification. The known patterns are found in the inputs. If there are 4 inputs, then the known pattern for a particular bar consists of the 4 values in the inputs at that particular bar. Depending on the Net you choose, you can have from 2 to 6 inputs.

Contrib1, 2, etc. – these are the contribution values for the inputs with the corresponding number. Contrib1 is the weighting you want the net to assign to input 1. The contribution is a single real number (not a time series) from zero (meaning ignore this input) to 100 (which means this input is very, very important.) Numbers greater than 100 may be used, but will probably have little more effect than 100. You can use numbers less than 1, like .6, if you want.

The defaults for the parameters of the custom indicators do not make any sense in the case of the Classifier Nets. The default Actual (to be predicted) is the Close, which is not positive and negative (or zero) as the Actual should be for Classifier Nets. However, the NeuroShell Trader does not allow indicators like momentum as the default parameter, so we just used close.

In addition to the above parameters, the lagged types also have the following parameter:

Lag – this parameter determines how many lags of each input are also used as inputs.  If lag=0, then there are no lags, and the Net functions just like the unlagged counterpart.  If lag=1, then only one lag of the input is added, etc.  The default is 5, which means that for each specified input, there will be a total of 6 input values all together.