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Reinforcement Learning for Trading Strategies(으)로 돌아가기

New York Institute of Finance의 Reinforcement Learning for Trading Strategies 학습자 리뷰 및 피드백

203개의 평가

강좌 소개

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)....

최상위 리뷰


2020년 3월 5일

It was easy to follow but not easy. I learned a lot and I now have the confidence to implement Reinforcement learning to my own FX trading strategies. Thank you so much.


2021년 2월 2일

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

필터링 기준:

Reinforcement Learning for Trading Strategies의 57개 리뷰 중 51~57

교육 기관: Jakub K

2020년 8월 28일

교육 기관: Aadam

2020년 4월 2일

교육 기관: Oliver P

2020년 8월 4일

교육 기관: VICTOR T

2022년 11월 3일

교육 기관: David G

2020년 6월 18일

교육 기관: Ni P N K

2020년 7월 13일

교육 기관: Red R

2022년 11월 10일