Reinforcement Learning for Financial Trading
Actualizada 14 Dec 2021
Reinforcement Learning For Financial Trading ?
How to use Reinforcement learning for financial trading using Simulated Stock Data using MATLAB.
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.
Simulated via Geometric Brownian Motion or
Historical Market data (source: AlphaVantage: www.alphavantage.co)
Copyright 2020 The MathWorks, Inc.
David Willingham (2022). Reinforcement Learning for Financial Trading (https://github.com/matlab-deep-learning/reinforcement_learning_financial_trading), GitHub. Recuperado .
Compatibilidad con la versión de MATLAB
Compatibilidad con las plataformasWindows macOS Linux
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