Reinforcement Learning for Financial Trading

MATLAB example on how to use Reinforcement Learning for developing a financial trading model
Updated 7 Mar 2024

Reinforcement Learning For Financial Trading ?
How to use Reinforcement learning for financial trading using Simulated Stock Data using MATLAB.

To run:

Open RL_trading_demo.prj
Open workflow.mlx
Run workflow.mlx
Environment and Reward can be found in: myStepFunction.m


The goal of the Reinforcement Learning agent is simple. Learn how to trade the financial markets without ever losing money.
Note, this is different from learn how to trade the market and make the most money possible.

The aim of this example was to show:

1. What reinforcement learning is
2. How it can be applied to trading the financial markets
3. Leave a starting point for financial professionals to use and enhance using their own domain expertise.

The example use an environment consisting of 3 stocks, $20000 cash & 15 years of historical data.

Stocks are:
Simulated via Geometric Brownian Motion or
Historical Market data (source: AlphaVantage:

Copyright 2020 The MathWorks, Inc.

Cite As

David Willingham (2024). Reinforcement Learning for Financial Trading (, GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2019b
Compatible with any release
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes

Updated Description


Added MATLAB Live script version


To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.