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About Algorithmic Trading Using Reinforcement Learning Augmented With Hidden Markov Model
This document presents a novel algorithmic trading system based on reinforcement learning. The trading problem is formulated as a Markov Decision Process (MDP) that is solved using Q-Learning. To improve performance, the MDP states are augmented with trend information estimated using a Hidden Markov Model. The proposed approach achieves an average profit of $6,379 on a $10,000 investment, outperforming baseline profits. The system trains an AI agent to learn optimal trading strategies from historical data without human intervention.
Detailed Information
| Author: | ['Tutor World Online'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 10 |
| Language: | English |
| Format: | |
| Price: | FREE |
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