Reinforcement Learning for Trading Strategies
- Offered byCoursera
- Public/Government Institute
Reinforcement Learning for Trading Strategies at Coursera Overview
Duration | 12 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Reinforcement Learning for Trading Strategies at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 3 in the Machine Learning for Trading Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
- Approx. 12 hours to complete
- English Subtitles: English
Reinforcement Learning for Trading Strategies at Coursera Course details
- In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data. By the end of the course, you will be able to build trading strategies using reinforcement learning, differentiate between actor-based policies and value-based policies, and incorporate RL into a momentum trading strategy.
- To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
Reinforcement Learning for Trading Strategies at Coursera Curriculum
Introduction to Course and Reinforcement Learning
Introduction to Course
What is Reinforcement Learning?
History Overview
Value Iteration
Policy Iteration
TD Learning
Q Learning
Benefits of Reinforcement Learning in Your Trading Strategy
DRL Advantages for Strategy Efficiency and Performance
Introduction to Qwiklabs
Idiosyncrasies and challenges of data driven learning in electronic trading
Neural Network Based Reinforcement Learning
TD-Gammon
Deep Q Networks - Loss
Deep Q Networks Memory
Deep Q Networks - Code
Policy Gradients
Actor-Critic
What is LSTM?
More on LSTM
Applying LSTM to Time Series Data
Portfolio Optimization
How to Develop a DRL Trading System
Steps Required to Develop a DRL Strategy
Final Checks Before Going Live with Your Strategy
Investment and Trading Risk Management
Trading Strategy Risk Management
Portfolio Risk Reduction
Why AutoML?
AutoML Vision
AutoML NLP
AutoML Tables
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