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This course is part of Machine Learning for Trading Specialization
Instructor: Jack Farmer
18,870 already enrolled
Included with
(235 reviews)
Recommended experience
Intermediate level
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
(235 reviews)
Recommended experience
Intermediate level
Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.
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.
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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.
10 videos1 reading1 app item
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.
9 videos2 app items
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.
10 videos1 app item
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
The New York Institute of Finance (NYIF), is a global leader in training for financial services and related industries. Started by the New York Stock Exchange in 1922, it now trains 250,000+ professionals in over 120 countries. NYIF courses cover everything from investment banking, asset pricing, insurance and market structure to financial modeling, treasury operations, and accounting. The institute has a faculty of industry leaders and offers a range of program delivery options, including self-study, online courses, and in-person classes. Its US customers include the SEC, the Treasury, Morgan Stanley, Bank of America and most leading worldwide banks.
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New York Institute of Finance
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Google Cloud
Specialization
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New York University
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235 reviews
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Reviewed on Feb 2, 2021
After the first two courses, this one grabs you into the reinforcement learning spectrum. This topic has been revealing to me and its applications to trading
Reviewed on Jul 12, 2021
A touhg and very advanced course, with an amazing Google Cloud Platform !!!!
Reviewed on Mar 6, 2020
Great introduction to some very interesting concepts. Lots of hands on examples, and plenty to learn
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