  Deep Reinforcement Learning (DRL) algorithms can scale to previously
intractable problems. The automation of profit generation in the stock market
is possible using DRL, by combining the financial assets price "prediction"
step and the "allocation" step of the portfolio in one unified process to
produce fully autonomous systems capable of interacting with their environment
to make optimal decisions through trial and error. This work represents a DRL
model to generate profitable trades in the stock market, effectively overcoming
the limitations of supervised learning approaches. We formulate the trading
problem as a Partially Observed Markov Decision Process (POMDP) model,
considering the constraints imposed by the stock market, such as liquidity and
transaction costs. We then solve the formulated POMDP problem using the Twin
Delayed Deep Deterministic Policy Gradient (TD3) algorithm reporting a 2.68
Sharpe Ratio on unseen data set (test data). From the point of view of stock
market forecasting and the intelligent decision-making mechanism, this paper
demonstrates the superiority of DRL in financial markets over other types of
machine learning and proves its credibility and advantages of strategic
decision-making.
