I'm using around 130 technical indicators for 100 different companies. Each company's stock price moves in a different range, see FTSE 100. In addition, each technical indicator moves in a different range as well, ie some goes between 0-1, other 0-100 and others move with the price. This is the list I'm using http://ta-lib.org/function.html
I'd like to feed this into a machine learning algorithm where I predict the relative price movement of the stock price the next day. I use a logistic loss for profit optimization and two regularizer terms, one between companies, and the other between time periods. This is unimportant for now.
Rather, what I'm asking about is how to normalize the input data? I've tried zscore, rate of change, absolute differences, and various combination of these, but I'm not sure which is the right approach. Also, I assume that first I need to calculate the indicators and then normalize that data. or would these indicators make sense on normalized data?