Jump2C is different from Jump2 in that the output is a binary condition (true or false) instead of a signal from –1 to 1.  Therefore Jump2C can be used in a Trading Strategy without the necessity of inserting it into an A>B indicator.  Jump2C was created from Jump2 by inserting it as A in the A>B indicator with B set to 0.

Jump2C is a net with 2 inputs and two hidden neurons. The hidden neurons and the output neuron all use the hyperbolic tangent activation function. There are extra (jump) connections from the input neurons to the output neuron, as well as a connection from the first hidden neuron to the second.

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

input1 to input2  -  neural network inputs. Indicators such as Price Momentum indicators are recommended. Let the optimizer find the parameters for the indicators.

w1 to w9 – the weights in the neural network. These are similar to coefficients in regression analysis. Recommended optimizer range is –1 to 1.