Reinforcement Learning for Trading Strategies
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Reinforcement Learning for Trading Strategies
This course is part of Machine Learning for Trading Specialization
Instructor: Jack Farmer
21,019 already enrolled
Included with
251 reviews
Recommended experience
251 reviews
Recommended experience
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
- Markov Model
- Model Training
- Machine Learning Software
- Securities Trading
- Financial Trading
- Machine Learning Methods
- Portfolio Management
- Recurrent Neural Networks (RNNs)
- Reinforcement Learning
- Artificial Intelligence and Machine Learning (AI/ML)
- Model Optimization
- Deep Learning
- Applied Machine Learning
- Artificial Neural Networks
- Risk Management
Tools you'll learn
Details to know
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There are 3 modules in this course
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).
In this module, reinforcement learning is introduced at a high level. The history and evolution of reinforcement learning is presented, including key concepts like value and policy iteration. Also, the benefits and examples of using reinforcement learning in trading strategies is described. We also introduce LSTM and AutoML as additional tools in your toolkit to use in implementing trading strategies.
What's included
10 videos1 reading1 app item
10 videos•Total 64 minutes
- Introduction to Course•2 minutes
- What is Reinforcement Learning?•9 minutes
- History Overview•3 minutes
- Value Iteration•10 minutes
- Policy Iteration•7 minutes
- TD Learning•8 minutes
- Q Learning•7 minutes
- Benefits of Reinforcement Learning in Your Trading Strategy•6 minutes
- DRL Advantages for Strategy Efficiency and Performance•8 minutes
- Introduction to Qwiklabs•4 minutes
1 reading•Total 10 minutes
- Idiosyncrasies and challenges of data driven learning in electronic trading•10 minutes
1 app item•Total 120 minutes
- Early Reinforcement Learning•120 minutes
In the previous module, reinforcement learning was discussed before neural networks were introduced. In this module, we look at how reinforcement learning has been integrated with neural networks. We also look at LSTMs and how they can be applied to time series data.
What's included
9 videos2 app items
9 videos•Total 39 minutes
- TD-Gammon•4 minutes
- Deep Q Networks - Loss•3 minutes
- Deep Q Networks Memory•2 minutes
- Deep Q Networks - Code•3 minutes
- Policy Gradients•5 minutes
- Actor-Critic•3 minutes
- What is LSTM?•7 minutes
- More on LSTM•4 minutes
- Applying LSTM to Time Series Data•8 minutes
2 app items•Total 270 minutes
- Reinforcement Learning DQN•120 minutes
- Policy Gradients and Actor-to-Critic•150 minutes
In this module we discuss the practical steps required to create a reinforcement learning trading system. Also, we introduce AutoML, a powerful service on Google Cloud Platform for training machine learning models with minimal coding.
What's included
10 videos1 app item
10 videos•Total 54 minutes
- How to Develop a DRL Trading System•2 minutes
- Steps Required to Develop a DRL Strategy•7 minutes
- Final Checks Before Going Live with Your Strategy•5 minutes
- Investment and Trading Risk Management•5 minutes
- Trading Strategy Risk Management•5 minutes
- Portfolio Risk Reduction•4 minutes
- Why AutoML?•13 minutes
- AutoML Vision•3 minutes
- AutoML NLP•3 minutes
- AutoML Tables•7 minutes
1 app item•Total 180 minutes
- Machine Learning for Finance Freestyle•180 minutes
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Showing 3 of 251
Reviewed on Mar 14, 2020
Good course introducing concepts in RL. Wish course provided more examples of using RL in stock prediction.
Reviewed on Sep 10, 2024
It's Intensive and Inclusive, but please make sure all labs work smoothly.
Reviewed on Jul 12, 2021
A touhg and very advanced course, with an amazing Google Cloud Platform !!!!
Frequently asked questions
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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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