Evolving Quantitative Trading with Deep Reinforcement Learning

3 min read

Have you always wished to upgrade your trading with the modern times and develop the confidence to effectively trade in the markets?

Do you want to implement modern technological methods like Machine Learning or Deep Reinforcement Learning in your trading?

Are you not sure if you can do it or wonder how to go about it?

So did Mattia, until he found a way to make all this possible. Here’s how he made it possible.

Mattia Mosolo photo

Hi! I am Mattia Mosolo and I hail from Italy. I am constantly searching for excellence in evolving my trading.

But, I was not always a trader. Some years back, I developed an interest in Financial Markets and studied technical & fundamental analysis. I saw Machine Learning (ML) as the best solution to implement a Quantitative strategy and thus pursued it seriously for the last eight months.

I was introduced to Deep Reinforcement Learning while exploring freely available resources on Youtube and other relevant avenues on the internet. However, I wanted to dig deeper into Quantitative Finance with a structured approach. That was when I came across the 'Deep Reinforcement Learning trading' offered by Quantra and enrolled in it.

At first, I was surprised to see that the videos were short! I almost felt like the content was not good. But as I did the course, I realized it to be a very efficient way of learning! These short (yet crisp) and to-the-point videos are immediately followed up by a series of quizzes to check on your learning.

Jupyter notebooks within the syllabus helped me with the practical application of the topics. Since I now have lifetime access to the course, revisiting it helped me understand the concepts of managing data and the standardization of data in a better manner.

I did the capstone project offered in the course on a different data set, and found it immensely useful. I took Euro-USD data and reduced the sample data size to reduce the time of training.

I changed the learning rate, epsilon rate, Minimum epsilon rate, and related parameters so that it does not take hours to train the data. I downloaded the Euro-USD forex pair because it is an efficient market. As a result, I completed the project without seeing the solution.

The Quantra community also makes this course easy to understand as it helped to clear all my questions and doubts within a day. This made my learning even smoother.

All in all, my experience with the Deep Reinforcement learning course was really good. Now I have an understanding of the right structure for reinforcement learning. And that I have to make more improvements to my trading as well.

Next, I am looking forward to learning about Neural networks while simultaneously trying to make a Quantitative Model using Deep Reinforcement Learning.

By the end of the course, I realised that this was the right way to learn. I am happy with my decision. Thank you for this amazing course, Quantra.

Thank you for the kind words, Mattia. We’re delighted to see your incredible learning and growth with the course. We’re glad that you had a great learning experience at Quantra and benefitted from the countless benefits that our courses offer.

Your journey in trading starting with just a keen interest and developing it to this level is really incredible. We wish you the best in your future endeavours.

Like Mattia, if you too wish to learn to quantitatively analyze the returns and risks, and to apply reinforcement learning to create, backtest, paper trade and live trade a strategy using two deep learning neural networks and replay memory, be sure to check out our Deep Reinforcement Learning in Trading course.

Disclaimer: In order to assist individuals who are considering pursuing a career in algorithmic and quantitative trading, this success story has been collated based on the personal experiences of a student from Quantra. Success stories are for illustrative purposes only and are not meant to be used for investment purposes. The results achieved post completion of the Quantra course(s) may not be uniform for all individuals.

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