Sparse12C is different from Sparse12 in that the output is a binary condition (true or false) instead of a signal from –1 to 1.  Therefore Sparse12C can be used in a Trading Strategy without the necessity of inserting it into an A>B indicator.  Sparse12C was created from Sparse12 by inserting it as A in the A>B indicator with B set to 0.

Sparse12C is a Sparse Net with 12 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 input12  -  neural network inputs. Indicators such as Price Momentum indicators are recommended. Let the optimizer find the parameters for the indicators.

w1 to w15 – the weights in the neural network. These are similar to coefficients in regression analysis. Recommended optimizer range is –1 to 1.