Sparse10C is different from Sparse10 in that the output is a binary condition (true or false) instead of a signal from –1 to 1.  Therefore Sparse10C can be used in a Trading Strategy without the necessity of inserting it into an A>B indicator.  Sparse10C was created from Sparse10 by inserting it as A in the A>B indicator with B set to 0.

Sparse10C is a Sparse Net with 10 inputs and 3 hidden neurons. The hidden neurons and the output neuron all use the hyperbolic tangent activation function.  Inputs are connected to only one hidden neuron, but all hidden neurons are connected to the output neuron.

scale – the number of past bars over which input scaling will take place. Recommended optimizer range is 10 to 200.

input1 to input10  -  neural network inputs. Indicators such as Price Momentum indicators are recommended. Let the optimizer find the parameters for the indicators.

w1 to w13 – the weights in the neural network. These are similar to coefficients in regression analysis. Recommended optimizer range is –1 to 1.