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.
Reinforcement Learning for Trading Strategies
Reinforcement Learning for Trading Strategies
This course is part of Machine Learning for Trading Specialization
Instructor: Jack Farmer
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Intermediate level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
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What you'll learn
Understand the structure and techniques used in reinforcement learning (RL) strategies.
Understand the benefits of using RL vs. other learning methods.
Describe the steps required to develop and test an RL trading strategy.
Describe the methods used to optimize an RL trading strategy.
Skills you'll gain
- Risk Management
- Artificial Neural Networks
- Markov Model
- Machine Learning Software
- Securities Trading
- Recurrent Neural Networks (RNNs)
- Model Optimization
- Deep Learning
- Artificial Intelligence and Machine Learning (AI/ML)
- Applied Machine Learning
- Model Training
- Financial Trading
- Machine Learning Methods
- Portfolio Management
- Reinforcement Learning
Tools you'll learn
Details to know
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Taught in English
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This course is part of the Machine Learning for Trading Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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