Ward2C is different from Ward2 in that the output is a binary condition (true or false) instead of a signal from –1 to 1.  Therefore Ward2C can be used in a Trading Strategy without the necessity of inserting it into an A>B indicator.  Ward2C was created from Ward2 by inserting it as A in the A>B indicator with B set to 0.

Ward2C is a Ward Net with 2 inputs and two hidden neurons. The hidden neurons may have either the hyperbolic tangent activation function or the Gaussian activation function, depending on the setting of parameters h1 and h2. The output activation function is the hyperbolic tangent.

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

h1 and h2 – the parameter which determines the activation function used in the respective hidden neurons. Set to 0 for hyperbolic tangent. Set to 1 for Gaussian. Recommended optimizer range is 0 to 1.

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 w6 – the weights in the neural network. These are similar to coefficients in regression analysis. Recommended optimizer range is –1 to 1.