October 2024
Intermediate to advanced
214 pages
5h 4m
English
Content preview from Reinforcement Learning for Finance
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
Chapter 3. Financial Q-Learning
Today’s algorithmic trading programs are relatively simple and make only limited use of AI. This is sure to change.
Murray Shanahan (2015)
The previous chapter shows that a deep Q-learning (DQL) agent can learn to play the game of CartPole quite well. What about financial applications? As this chapter shows, the agent can also learn to play a financial game that is about predicting the future movement in a financial market. To this end, this chapter implements a Finance environment that mimics the behavior of the CartPole environment and trains the DQL agent from the previous chapter based on the requirements of the Finance environment.
This chapter is brief, but it illustrates an important point: with the appropriate environment, DQL can be applied to financial problems basically in the same way as it is applied to games and in other domains. “Finance Environment” develops step-by-step the Finance class that mimics the behavior of the CartPole class. “DQL Agent” slightly adjusts the DQLAgent class from “CartPole as an Example”. The adjustments are made to reflect the new context. The DQL agent can learn to predict future market movements with a significant margin over the baseline accuracy of 50%. “Where the Analogy Fails” finally discusses the major issues of the modeling approach and the Finance class when compared, for example, to a gaming environment such as the CartPole game.
Finance Environment
The goal in this section is to implement ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access
More than 5,000 organizations count on O’Reilly
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.