Chapter 5: Reinforcement Learning in the Real World – Building Stock/Share Trading Agents
Software-based deep reinforcement learning (deep RL) agents have tremendous potential when it comes to executing trading strategies tirelessly and flawlessly without limitations based on memory capacity, speed, efficiency, and emotional disturbances that a human trader is prone to facing. Profitable trading in the stock market involves carefully executing buy/sell trades with stock symbols/tickers while taking into account several market factors such as trading conditions and macro and micro market conditions, in addition to social, political, and company-specific changes. Deep RL agents have a lot of potential when it comes to solving challenging problems in the real world and a lot of opportunities exist.
However, only a few successful stories of using deep RL agents in the real world beyond games exist due to the various challenges associated with real-world deployments of RL agents...