  With the breakthrough of computational power and deep neural networks, many
areas that we haven't explore with various techniques that was researched
rigorously in past is feasible. In this paper, we will walk through possible
concepts to achieve robo-like trading or advising. In order to accomplish
similar level of performance and generality, like a human trader, our agents
learn for themselves to create successful strategies that lead to the
human-level long-term rewards. The learning model is implemented in Long Short
Term Memory (LSTM) recurrent structures with Reinforcement Learning or
Evolution Strategies acting as agents The robustness and feasibility of the
system is verified on GBPUSD trading.
