I just started learning deep learning a few weeks ago. Currently I am working on a deep learning project that uses tensorflow to predict stock prices. I think I currently lack a bigger picture of how DP can be applied to this type of problem due to the insufficent data set and features. I will post my thoughts and what I've done and would like to hear people's opionions on this.
Here is what I've done so far.
Let's say I pick one stock and its history prices of past 30 years, and price is given in a montly basis. And I use that as my Y.
For X, I use features like company revenues, profits, growth rate, etc for that company as well as interest rate, inflation rate. Also that data is spread in a monthly basis.
So the total sample size is about ~550 (80% used as training set and 20% as testing set) and currently I am just using various shallow layers (1-3) and with various hidden units in each layer (2-30) along with combinations of different learning rate, optimizers, batch sizes etc. So far I couldn't get any good results.
Am I doing something completely off the chart here? I would like to make sure I am at least on the right track.