Description

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.


LIVE TRADING

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

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