StockPredictor: Adding Past Values to Training Data

In my training data for the predictions, I use the following data to predict the changes in stock prices:

Stock Price, P/E, P/C, Yield, Price Margin, RSI and the number of days to dividend. 

I want to be able to add historic key numbers as well and I'll specify that using bit masks,

	Price	P/E	P/C	Yield	RSI	PM	RSI	Days to Dividend
Today	x	x	x	x	x	x	x	x
One week ago	x				x			x
Two weeks ago	x	x		x
Three weeks ago	x		x	x	x
Code (binary)	1111	0101	1001	1101	1011	0001	0001	0011
Code (Hex)	F	5	9	D	B	1	1	3

For simplicity, if one parameter is required two weeks ago, the script that generates the training data will add all parameters two weeks ago. The selection of parameters will happen in the prediction script. 

There won't be matching historic dates for all stock records in the database. For the historic dates, I'll try the calculated date +/- one day.

If the current stock record date is on the 27th, I'll search for the one-week-old record on the 20th. If that date can't be found, I'll search for the 19th and 21th. If neither can be found, the program will give up fetching records for that date.

This means that the training set will be some 50% smaller. Removing half of the training set in order to train the neural network on what happened to stocks over the last weeks can, in theory, bias the underlying data. Assumption: I assume that the performance of the recorded stocks are independent on when I recorded the training data.

In the network trainer, the data that isn't used will be removed from the training set. The script will drop the columns that I don't need according to the bitmask.

The next step is to evaluate some machine learning algorithms on the data.