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学生对 纽约金融学院 提供的 Reinforcement Learning for Trading Strategies 的评价和反馈

3.6
142 个评分
36 条评论

课程概述

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)....

热门审阅

MS
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.

GS
Mar 6, 2020

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

筛选依据:

26 - Reinforcement Learning for Trading Strategies 的 35 个评论(共 35 个)

创建者 Jakub K

Aug 28, 2020

I learned a few cool things. The main problem with this specialization is that the Machine Learning Stuff and Finance stuff are really separated (Google, NY univ). What I was looking for is the place where two concepts meets. Also i felt like ML stuff went too deeply too fast. Still... Cool Introduction.

创建者 WAI F C

May 10, 2020

The course could be improved if the lab included stock trading related works for both RL and LSTM. I had already learned stock trading with RL and LSTM before I took this class.

创建者 Aadam

Apr 2, 2020

It is geared more towards people who already have an understanding of the stock market and its lingo. Not much information about stock market lingo for a beginner.

创建者 Dmitrievskiy A

Apr 19, 2020

Reinforcement learning tasks are not related to financial domain. Financial topics are superficial. Course for absolute newbies in RL and FinTech

创建者 Oliver P

Aug 4, 2020

While there were a lot of interesting concepts in this course, I didn't feel that I learned a lot from it and certainly was nowhere near implementing what I wanted to. It pushes Google's cloud services so you're on your own if you want to program on your own computer. I've since completed a course by deeplearning.ai (not trading focussed) which I felt was a lot better, I learned a lot of theory to develop an understanding of what they're teaching as well as practical coding assignments that I felt I could actually take the code and apply to my own projects.

Google pushes its ability to learn from BigData but I really don't consider stock data to be BigData, at least if you're processing a single instrument/currency/stock at a time. If you're trying to go down to tick level data then you're going to have more problems with lag and execution making processing that amount of data a bit pointless... unless that's really really what you want/need to be doing.

To be fair to this course, it is good to know what is out there should it be suitable for your challenges and yeah, they can process a massive, huge, gigantic amount of data very quickly.

创建者 David G

Jun 18, 2020

Few financial applications. RL is a complex notions. Exerices are too difficult.

创建者 Novi K

Jul 13, 2020

not really make me statisfied

创建者 Biagio B

May 30, 2020

Most of the course is a generic lecture about RL and LSTM taken from other courses. The rest is mostly advertisement for GoogleCloud, which it is not useful since you could do all exercises on a local laptop. Only a fraction of the course talks about finance and it is so generic that cannot be applied to any real world case.

创建者 Tony H

Oct 24, 2020

The learning curve is broken. It's like teach you 1+1=2 first, then you need to do calculus yourself, and lecturer say "see, it's easy" and move on to deep neural network now......

创建者 Nitin K

Nov 9, 2020

Highly limited information with extremely steep learning curve.