Chevron Left
Back to Reinforcement Learning for Trading Strategies

Learner Reviews & Feedback for Reinforcement Learning for Trading Strategies by New York Institute of Finance

3.6
stars
220 ratings

About the Course

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)....

Top reviews

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.

RA

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

Filter by:

26 - 50 of 64 Reviews for Reinforcement Learning for Trading Strategies

By Andrew C

Oct 10, 2020

There are some lectures on RL and some on Trading. But there aren't enough materials on the application of RL to Trading. It just talks about some high level concepts on how it could be used. We could get this from any basic article on RL and Trading. Even the last exercise is not RL on Trading. It's just a machine learning exercise to predict S&P500's direction. Basically there is zero example and exercise on RL for Trading Strategies, which is the main topic.

By lonnie

Sep 12, 2022

This course could have been better if a full trading strategy project using Reinforcement Learning is provided, but it just provides some theories and sample Reinforcement Learning code instead.

By 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.

By Alexander 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

By Terence Y

Sep 23, 2022

Good for begginer, but this course was heavy binded with GCP which might not practical in some country.

By Dimitar B

Mar 15, 2023

The labs need an update, otherwise interesting mix of content. It was useful, but can be better

By Sushil V

Mar 24, 2021

no actual model on stock prediction using RL

By VICTOR T

Nov 3, 2022

Good and bad:

Good: Jack Farmer's explanations about the financial parts of the course were great. Very well explained and useful.

Bad: The labs and technical part of the course were horrible... I would say half of the labs did not work properly, I couldn't finish them or sometimes even go beyond the 3rd cell of code because there were many errors that I did not know how to fix (we are not supposed to do that). The final project is supposed to let us create our own model but we haven't been taught enough to use the different tools since the labs do not teach you enough.

All in all, I only give the 2 stars as an average because Jack's part was on point, you really learn useful insights, 5/5, but the labs were beyond bad (0/5).

By Simone B

May 4, 2022

There is no real application of RL in trading in this course. They just first skim quickly to the basics of RL, quite superficially, then they explain the basics of portfolio management. These two rails go parallel and never touch each other. Moreover, the part covering RL, MDP, TD and Q Learning is illustrated too fast to understand any subtle points, with too many details (equations quickly explained, code fragments gone through in a minute or too) put together roughly to be a qualitative introduction.

By Tullio B

Nov 9, 2023

I appreciated the general theoretical components, exposed in a clear and concise way. However, I found the total lack of in-depth AI applications on real cases, which are essential for using the notions in practice. From a concrete point of view, therefore, the course was of no use to me.

By David G

Jun 28, 2022

A few interesting nuggets buried in a mess of cobbled together material, dodgy slide decks with poorly formatted code snippets, all combined with the annoying "QwikLabs" that takes about 3 minutes to start for every single assessment. This could be so much better.

By David A

Sep 26, 2022

Giving it two stars because the material was interesting. HOWEVER, it does not cover how to use reinforcement learning for trading strategies. It discusses reinforcement learning, and trading, but fails to connect the dots. This course feel incomplete.

By Hyder A A

Jan 1, 2022

Way below expectations!

By Peter D

May 13, 2023

The specialization needs a major revamp. It almost useless as it stands. The topics covered are important but the labs are horrific. The Aquan toolbox used is not maintained by anyone anymore and had I had to fix it myself. The RL topics and the LSTM are covered in haste, like going through the motions.

By Ben T

Jan 3, 2023

80% of the course is generic DL without any connection to RL in trading, NLP / image processing and more unrelated topics.

It's just a group of unrelated lectures aggregated in to a "course". its the first coursera course that I took and the last one for sure.

By Erfan A

Aug 22, 2022

No useful information regarding the RL for trading was shared. It does not provide any information regarding the feature engineering, understaing the required input features, and so on. The RL parst were also at a very beginner level

By Ilia K

Apr 22, 2023

The labs are junk. Half of them are broken, some of them are missing. Those that are working, are not good enough. Week 3, AutoML part doesn't mention trading even once. The time you will spend on this course doesn't worth it.

By Lloyd P

May 11, 2021

Too general to pursue any meaningful work with RL for trading. The class is trying to cover too much material from too many different angles to be useful.

By Jeremy H

Oct 23, 2023

Cannot understand the girl in https://www.coursera.org/learn/trading-strategies-reinforcement-learning. Had to leave on subtitles. Very distracting.

By Nitin K

Nov 10, 2020

Highly limited information with extremely steep learning curve.

By Fabister

Jul 19, 2021

Bozo education at its best!

By Red R

Nov 10, 2022

Labs Malfunction

By Zachary

Apr 8, 2023

你对得起我们吗?日内瓦退钱

By Antony J

Nov 24, 2020

It's an exceptionally difficult task to predict financial time series, and even harder to design an automated trading methodology that can take into account those forecasts while monitoring the trading environment (trading costs, other traders, sentiment). This final course is an ambitious attempt to expose learners to the most advanced concepts in the field.

To be able to comprehend the Reinforcement Learning materials appears to require expertise in deep learning far beyond sequential models, and also appears to need the volume and integrity of data only available to high-frequency trading firms. Thumbs up to the specialization curators for providing a non-trivial introduction.

The module that rescued the course (and lifted my rating to 5 stars) was the AutoML demonstration. I was reassured to see that Gradient Boosted Trees were chosen as the appropriate methodology, as this is what I have casually observed as being the most effective methodology in use today for end-of-day data problems. Looks like an amazing product, if you have the money!

By Peter S

May 20, 2021

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