I hope I this will help you out
The recurrent network structure
A few tips
Choosing your recurrent network
The more mature Long Short Time Memory (LSTM) neural network is a great fit for this kind of task. LSTM is able to detect common "shapes" and "variations" in the stock value "graph", and there is A LOT of research which tries to prove that such shapes actually occur in real life! See this link for an example.
Accuracy
If you want the network to achieve higher accuracy, I would recommend you to also feed the network the stock values from the previous year (at the exact same date), so that the number of inputs doubles from 50 to 100. Though the network might be well optimised on your dataset, it will never be able to predict the unpredictable behaviour of the future ;)