Quantra – Deep Reinforcement Learning in Trading

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.

• Learn applications, effectiveness, need and challenges of using RL models in trading.
• Describe states, actions, double Q-learning, policy, experience replay, positions and rewards. And enhance state, action and reward.
• Explain various input features used in the construction of a state. Assemble the input features to construct a state.
• Create a game class starting with the initialisation of the Game class, updating the position, calculating the reward and assembling the state.
• Explain the basics of ANNs and implement Double Deep Q Learning agents using Keras.
• Create and backtest a reinforcement learning model. Analyse returns and risk using different performance measures.
• Learn about the steps to automate your trading and deploy the RL model for paper and live trading. Implement the concepts on real market data through a capstone project.

What You’ll Learn In Deep Reinforcement Learning in Trading?

• Introduction
• Need for Reinforcement Learning
• State, Actions and Rewards
• Q Learning
• State Construction
• Policies in Reinforcement Learning
• Challenges in Reinforcement Learning
• Initialise Game Class
• Positions and Rewards
• Input Features
• Construct and Assemble State
• Game Class
• Experience Replay
• Artificial Neural Network Concepts
• Artificial Neural Network Implementation
• Backtesting Logic
• Backtesting Implementation
• Performance Analysis: Synthetic Data
• Performance Analysis: Real World Price Data
• Automated Trading Strategy
• Paper and Live Trading
• Capstone Project
• Future Enhancements
• Run Codes Locally on Your Machine
• Course Summary

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Quantra – Deep Reinforcement Learning in Trading

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