In this work we present a data-driven end-to-end Deep Learning approach for time series prediction,
applied to financial time series. A Deep Learning scheme is derived to predict the temporal
trends of stocks and ETFs in NYSE or NASDAQ. Our approach is based on a neural network (NN)
that is applied to raw financial data inputs, and is trained to predict the temporal trends of 
stocks and ETFs. In order to handle commission-based trading, we derive an investment strategy 
that utilizes the probabilistic outputs of the NN, and optimizes the average return. 
The proposed scheme is shown to provide statistically significant accurate predictions of 
financial market trends, and the investment strategy is shown to be profitable under 
this challenging setup. The performance compares favorably with contemporary benchmarks 
along two-years of back-testing.