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Reinforcement Learning for Trading Strategies
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).
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Rating | Not enough ratings |
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Length | 4 weeks |
Effort | 4 weeks of study, 5 hours per week |
Starts | Jul 3 (43 weeks ago) |
Cost | $79 |
From | New York Institute of Finance, Google Cloud via Coursera |
Instructors | Jack Farmer, Ram Seshadri |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Business Programming |
Tags | Data Science Business Machine Learning Finance |
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Rating | Not enough ratings |
---|---|
Length | 4 weeks |
Effort | 4 weeks of study, 5 hours per week |
Starts | Jul 3 (43 weeks ago) |
Cost | $79 |
From | New York Institute of Finance, Google Cloud via Coursera |
Instructors | Jack Farmer, Ram Seshadri |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Business Programming |
Tags | Data Science Business Machine Learning Finance |
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