Reinforcement Learning for Financial Trading

MATLAB example on how to use Reinforcement Learning for developing a financial trading model
1,7K descargas
Actualizado 7 mar 2024

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

Setup
To run:

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

Overview:

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: www.alphavantage.co)

Copyright 2020 The MathWorks, Inc.

Citar como

David Willingham (2024). Reinforcement Learning for Financial Trading (https://github.com/matlab-deep-learning/reinforcement_learning_financial_trading), GitHub. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2019b
Compatible con cualquier versión
Compatibilidad con las plataformas
Windows macOS Linux
Categorías
Más información sobre Financial Toolbox en Help Center y MATLAB Answers.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

MultiAgentLearning

No se pueden descargar versiones que utilicen la rama predeterminada de GitHub

Versión Publicado Notas de la versión
1.0.2

Updated Description

1.0.1

Added MATLAB Live script version

1.0.0

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.