Algorithmic Stock Trading Strategies

Automated trading strategies can provide an edge over human trading but are challenging to develop and monitor. In this study, two strategies are investigated and developed, namely a rule-based momentum strategy and reinforcement learning driven strategy. The rule-based momentum strategy uses multiple technical indicators to define profitable entry and exit criteria over time, whereas the reinforcement learning driven strategy combines market and sentiment data to train a machine learning model on how to profit from stock trading. A scalable framework is then constructed to enable the automation of these algorithmic trading strategies by streaming real time data and integrating with a stockbroker API to manage order and accounts.