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).
TD-Gammon
您将学习的技能
Reinforcement Learning Model Development, Reinforcement Learning Trading Algorithm Optimization, Reinforcement Learning Trading Strategy Development, Reinforcement Learning Trading Algo Development
审阅
3.7（198 个评分）
- 5 stars34.84%
- 4 stars26.26%
- 3 stars19.69%
- 2 stars7.07%
- 1 star12.12%
GS
Mar 6, 2020
Great introduction to some very interesting concepts. Lots of hands on examples, and plenty to learn
SP
May 19, 2021
Succinct and great explanation of deep reinforcement learning methods with amazing demo lab scripts
从本节课中
Neural Network Based Reinforcement Learning
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.
教学方
Jack Farmer
Curriculum Director
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