Reinforcement Learning for Financial Trading
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.
Citation pour cette source
David Willingham (2024). Reinforcement Learning for Financial Trading (https://github.com/matlab-deep-learning/reinforcement_learning_financial_trading), GitHub. Récupéré le .
Compatibilité avec les versions de MATLAB
Plateformes compatibles
Windows macOS LinuxCatégories
Tags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Découvrir Live Editor
Créez des scripts avec du code, des résultats et du texte formaté dans un même document exécutable.
MultiAgentLearning
data
docs
MultiAgentLearning
Les versions qui utilisent la branche GitHub par défaut ne peuvent pas être téléchargées
Version | Publié le | Notes de version | |
---|---|---|---|
1.0.2 | Updated Description |
|
|
1.0.1 | Added MATLAB Live script version |
|
|
1.0.0 |
|