masonJamesWheeler/Active-Trader
Emergent Trading Strategies with DQN in Stock Market Trading This repository contains the implementation of a Deep Q-Network (DQN), applied to the realm of stock market trading. This repository also holds the code for research paper "Emergent Trading Strategies from Deep Reinforcement Learning Models in Stock Market Trading".
This project helps active traders and quantitative analysts develop and test automated trading strategies. It takes historical stock market data and various technical indicators as input. The output is a Deep Reinforcement Learning model that learns to make buy, sell, or hold decisions, aiming to generate profitable trading strategies.
No commits in the last 6 months.
Use this if you are a quantitative analyst or active trader looking to experiment with AI-driven trading models and uncover new, emergent strategies in the stock market.
Not ideal if you are looking for a plug-and-play trading bot without any technical setup or if you prefer traditional, rule-based trading systems.
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Python
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Last pushed
Aug 08, 2023
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