Dr. Thomas Starke – Deep Reinforcement Learning in Trading

Description

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Dr. Thomas Starke – Deep Reinforcement Learning in 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

SKILLS COVERED

Finance and Math Skills

• Sharpe ratio
• Returns & Maximum drawdowns
• Stochastic gradient descnet
• Mean squared error

Python

• Pandas, Numpy
• Matplotlib
• Datetime, TA-lib
• For loops
• Tensorflow, Keras, SGD

Reinforcement Learning

• Double Q-learning
• Artificial Neural Networks
• State, Rewards, Actions
• Experience Replay
• Exploration vs Exploitation

LEARNING TRACK

Machine Learning Strategy Development and Live Trading

INTERMEDIATE

• Data & Feature Engineering for Trading
• Portfolio Management using Machine Learning: Hierachical Risk Parity

ADVANCED

• Neural Networks in Trading
• Natural Language Processing in Trading
• Deep Reinforcement Learning in Trading

COURSE FEATURES

• Interactive Coding Practice
• Capstone Project Using Real Market Data

PREREQUISITES

This course requires a basic understanding of financial markets such as buying and selling of securities. To implement the strategies covered, the basic knowledge of “pandas dataframe”, “Keras”  and “matplotlib” is required. The required skills are covered in the free course, ‘Python for Trading: Basic’, ‘Introduction to Machine Learning for Trading’ on Quantra. To gain an in-depth understanding of Neural Networks, you can enroll in the ‘Neural Networks in Trading’ course which is recommended but optional.

SYLLABUS

1. Introduction
2. Need for Reinforcement Learning
3. State, Actions and Rewards
4. Q Learning
5. State Construction
6. Policies in Reinforcement Learning
7. Challenges in Reinforcement Learning
8. Initialise Game Class
9. Positions and Rewards
10. Input Features
11. Construct and Assemble State
12. Game Class
13. Experience Replay
14. Artificial Neural Network Concepts
15. Artificial Neural Network Implementation
16. Backtesting Logic
17. Backtesting Implementation
18. Performance Analysis: Synthetic Date
19. Performance Analysis: Real World Price Data
20. Automated Trading Strategy
21. Paper and Live Trading
22. Capstone Project
23. Future Enhancements
24. Run Codes Locally on Your Machine
25. Course Summary
    

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