CHAPTER 12Reinforcement Learning in Finance

Gordon Ritter


We live in a period characterized by rapid advances in artificial intelligence (AI) and machine learning, which are transforming everyday life in amazing ways. AlphaGo Zero (Silver et al. 2017) showed that superhuman performance can be achieved by pure reinforcement learning, with only very minimal domain knowledge and (amazingly!) no reliance on human data or guidance. AlphaGo Zero learned to play after merely being told the rules of the game, and playing against a simulator (itself, in that case).

The game of Go has many aspects in common with trading. Good traders often use complex strategy and plan several periods ahead. They sometimes make decisions which are ‘long‐term greedy’ and pay the cost of a short‐term temporary loss in order to implement their long‐term plan. In each instant, there is a relatively small, discrete set of actions that the agent can take. In games such as Go and chess, the available actions are dictated by the rules of the game.

In trading, there are also rules of the game. Currently the most widely used trading mechanism in financial markets is the ‘continuous double auction electronic order book with time priority’. With this mechanism, quote arrival and transactions are continuous in time and execution priority is assigned based on the price of quotes and their arrival order. When a buy (respectively, sell) order x is submitted, the exchange's matching engine checks whether ...

Get Big Data and Machine Learning in Quantitative Investment now with the O’Reilly learning platform.

O’Reilly members experience live online training, plus books, videos, and digital content from nearly 200 publishers.