Reinforcement Learning for Financial Trading

Versione 1.0.2 (4,79 MB) da David Willingham

MATLAB example on how to use Reinforcement Learning for developing a financial trading model

https://github.com/matlab-deep-learning/reinforcement_learning_financial_trading

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Aggiornato 7 mar 2024

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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)

Cita come

David Willingham (2024). Reinforcement Learning for Financial Trading (https://github.com/matlab-deep-learning/reinforcement_learning_financial_trading), GitHub. Recuperato agosto 25, 2024.