Apply reinforcement learning to create, backtest, paper trade and live trade a strategy using two deep learning neural networks and replay memory. Learn to quantitatively analyze the returns and risks. Hands-on course in Python with implementable techniques and a capstone project in financial markets.
- List and explain the need for reinforcement learning to tackle the delayed gratification experiment
- Describe states, actions, double Q-learning, policy, experience replay and rewards.
- Explain exploitation vs exploration tradeoff
- Create and backtest a reinforcement learning model
- Analyse returns and risk using different performance measures
- Practice the concepts on real market data through a capstone project
- Explain the challenges faced in live trading and list the solutions for them
- Deploy the RL model for paper and live trading