Learning Financial Asset-Specific Trading Rules via Deep Reinforcement
  Learning
  Generating asset-specific trading signals based on the financial conditions
of the assets is one of the challenging problems in automated trading. Various
asset trading rules are proposed experimentally based on different technical
analysis techniques. However, these kind of trading strategies are profitable,
extracting new asset-specific trading rules from vast historical data to
increase total return and decrease the risk of portfolios is difficult for
human experts. Recently, various deep reinforcement learning (DRL) methods are
employed to learn the new trading rules for each asset. In this paper, a novel
DRL model with various feature extraction modules is proposed. The effect of
different input representations on the performance of the models is
investigated and the performance of DRL-based models in different markets and
asset situations is studied. The proposed model in this work outperformed the
other state-of-the-art models in learning single asset-specific trading rules
and obtained a total return of almost 262% in two years on a specific asset
while the best state-of-the-art model get 78% on the same asset in the same
time period.
