  Solving portfolio management problems using deep reinforcement learning has
been getting much attention in finance for a few years. We have proposed a new
method using experts signals and historical price data to feed into our
reinforcement learning framework. Although experts signals have been used in
previous works in the field of finance, as far as we know, it is the first time
this method, in tandem with deep RL, is used to solve the financial portfolio
management problem. Our proposed framework consists of a convolutional network
for aggregating signals, another convolutional network for historical price
data, and a vanilla network. We used the Proximal Policy Optimization algorithm
as the agent to process the reward and take action in the environment. The
results suggested that, on average, our framework could gain 90 percent of the
profit earned by the best expert.
