- Introduction to Course
- What is Reinforcement Learning?
- History Overview
- Value Iteration
- Policy Iteration
- TD Learning
- Q Learning
- Benefits of Reinforcement Learning in Your Trading Strategy6m
- DRL Advantages for Strategy Efficiency and Performance7m
- Introduction to Qwiklabs
Reinforcement Learning for Trading Strategies
Gain a comprehension of the benefits of employing reinforcement learning in trading methods and RL's integration with neural networks.
Intermediate
Online
3 Weeks
Quick facts
particular | details | |
---|---|---|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Course overview
An understanding of Reinforcement learning (RL) is crucial in machine learning that looks at how gradient boosting should act in an environment to get the most rewards over time. Reinforcement learning is one of the three basic ways that machines can learn, along with supervised learning and unsupervised learning. The Reinforcement Learning for trading Strategies certification course is designed by the New York Institute of Finance along with Google Cloud and taught by Jack Farmer - specializing in training and consulting solutions, which is presented by Coursera.
Reinforcement Learning for Trading Strategies online classes offer 12 hours of digital lessons that are intended to provide students with a comprehensive understanding of reinforcement learning (RL) and the benefits of using RL in trading strategies, as well as how RL has been used with neural networks and review LSTMs.
With Reinforcement Learning for Trading Strategies online training students will learn more about how they can be used to look at data over time. Aspirants will be able to use reinforcement learning to build trading strategies, recognize the distinction between actor-based policies and value-based policies, and use RL as part of a momentum trading strategy.
The highlights
- Shareable Certificate of Completion
- Self-Paced Course
- 12 Hours of Effort
- 100% Online Content
- Course Videos and Readings
Program offerings
- Shareable certificates
- Self-paced learning option
- Course videos
- Readings
- Practice quizzes
- Graded assignments
- Graded quizzes
- Graded programming assignments.
Course and certificate fees
Reinforcement Learning for Trading Strategies Fee Structure:
Description | Amount |
1 month, 16 hours per week | Rs. 4,051 |
3 months, 5 hours per week | Rs. 8,102 |
6 months, 3 hours per week | Rs. 12,154 |
certificate availability
certificate providing authority
What you will learn
After completing the Reinforcement Learning for Trading Strategies online certification, students will explore the structure and principles of reinforcement learning, as well as the advantages of utilizing RL over alternative learning approaches. Aspirants will obtain an understanding of the steps necessary to create and test an RL trading strategy which covers optimization techniques for RL trading strategies also. Participants will develop their expertise in learning model development, skill development, trading algorithms, optimization trading strategy development, and trading algo development.
The syllabus
Week 1: Introduction to Course and Reinforcement Learning
Videos
Reading
- Idiosyncrasies and challenges of data driven learning in electronic trading
Week 2: Neural Network Based Reinforcement Learning
Videos
- 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
Week 3: Portfolio Optimization
Videos
- 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
Instructors
Mr Jack Farmer
Instructor
New York Institute o...
Other Bachelors, Other Masters, Ph.D, MBA
Articles
Popular Articles
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