Pybrain: Completely linear network
Try applying log() to the price-attribute - then scale all inputs and outputs to [-1..1] - of course, when you want to get the price from the network-output you'll have to reverse log() with exp()
I am currently trying to create a Neural Network with pybrain for stock price forecasting. Up to now I have only used Networks with a binary output. For those Networks sigmoid inner layers were sufficient but I don't think this would be the right approach for Forecasting a price.
The problem is, that when I create such a completely linear network I always get an error like

RuntimeWarning: overflow encountered in square while backprop training.

I already scaled down the inputs. Could it be due to the size of my training sets (50000 entries per training set)?
Has anyone done something like this before?