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New York Institute of Finance

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).

状态:Reinforcement Learning
状态:Recurrent Neural Networks (RNNs)
中级课程小时

精选评论

ML

4.0评论日期:Jul 13, 2021

Provide the idea and method of RL for trading, but seems like less practice knowledge for the trading. hope can add more detail for for the trading build up. overall the course are good.

RS

4.0评论日期:Jul 12, 2021

A touhg and very advanced course, with an amazing Google Cloud Platform !!!!

MS

5.0评论日期:Mar 5, 2020

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.

LR

5.0评论日期:Sep 10, 2024

It's Intensive and Inclusive, but please make sure all labs work smoothly.

MS

4.0评论日期:Apr 6, 2020

It has good practical stuff, BUT not any practical RL related to trading.

GY

4.0评论日期:Jul 1, 2022

I look forward to examples of integration of decision based on reinforcement learning and algo-trading logic

PS

5.0评论日期:May 19, 2021

Succinct and great explanation of deep reinforcement learning methods with amazing demo lab scripts

SF

4.0评论日期:Mar 14, 2020

Good course introducing concepts in RL. Wish course provided more examples of using RL in stock prediction.

JM

4.0评论日期:Jul 18, 2020

perhaps an applied trading notebook would have been nice...I understand that liability issues might have arisen, but there might have been a reasonable avenue with repeat disclaimers, etc

RR

5.0评论日期: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

GS

5.0评论日期:Mar 6, 2020

Great introduction to some very interesting concepts. Lots of hands on examples, and plenty to learn

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Yutong Xie
1.0
评论日期:Apr 27, 2020
Jiaheng Zhou
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Nissims s
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Brian Y
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Colin Edwards
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Vlasov Vladimir
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Josef Kerner
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Chaojun Li
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Paolo D'Elia
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Mike Sifanele
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Grigoriy Shmigol
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Manfred Rupp
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Amos Elberg
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评论日期:Jun 27, 2021
Biagio Brattoli
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Valdis Ho
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Jeremy Harris
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Javier Morales
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评论日期:Jul 10, 2024