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
Get Instant Download this course at Shoppycourses
Sale Page: https://quantra.quantinsti.com/course/deep-reinforcement-learning-trading
Proof Download
