Trading Environment(OpenAI Gym) + DDQN (Keras-RL)

Trading Environment(OpenAI Gym) + PPO(TensorForce)

Deep LSTM Duel DQN Reinforcement Learning Forex EUR/USD Trader

This Reinforcement learning agent uses Policy-Gradient method to trade the market

DQN RL Trading bot for bitcoin

This MLOps project productionizes a Deep Reinforcement Learning agent with a scalable, distributed data streaming infrastructure using Kafka and Ray. A thorough walkthrough of the code is described in this article on medium: https://ryanraymartin.medium.com/deep-reinforcement-learning-for-stock-trading-with-kafka-and-rllib-d738b9634675

Stock investment can be one of the ways to manage one’s asset. Technical analysis is sometimes used in financial markets to assist traders make buying and selling decisions [1]. Many technical analysis trading rules are deterministic trading policies. [2] uses genetic algorithm to find technical trading rule. [3] studies evolutionary algorithms in optimization of technical rules for automated stock trading. [4] proposed a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. [5] studies the selection of the optimal trading model for stock investment in different industries. [6] describes the optimization of trading strategies. The optimization of trading rule using genetic algorithm or evolutionary algorithms belongs to policy-based method, which is a branch of Machine Learning. Policy-based methods try to directly optimize for the optimal policy which is an important branch for domains with continuous action spaces [7]. There are studies focus on how to find a trading strategy via Reinforcement Learning (RL) [8] or using Deep-Q learning for automatic trading algorithm [9]. But in this study we will focus on the policy-based method using Generic Algorithm that directly search for the optimal parameters of a deterministic policy. Yahoo Finance’s stock history data [10] will be used in this study. The reason to choose Yahoo Finance data is because it is free and available for public to assess. The performance of algorithm will be evaluated using different stocks. The purpose of the study is to see the difference between using an agent with optimized policy to manage one’s asset with buy-and-hold strategy, or manage one’s asset with an agent with unoptimized policy.

In this repository I'm going to train an RL agent using metatrader in python!

Deep Reinforcement Learning Trading

Reinforcement Learning trading environment built on gym-mtsim, re-engineered with transformers and PPO

Deep Reinforcement Learning for Trading. Custom Gym Environment. Paper and code available.

Trading system with Reinforcement Learning. (Currently) Runs on Databricks, then pull the weights and use them in Quantopian.

No description available

Trading agent for Minute wise stock prices

A merchant roguelike

RL trader

Uses the reinforcement learning algorithm, PPO, to trade Bitcoin.

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Trading Reinforcement Learning Agent

Train a reinforcement learning model to trade on stock news

RL Trader

實驗用強化學習做比特幣交易

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Reinforcement Learning in Real-Life: A Use Case in Stock Trading

Production-ready RLAIF trading system with multi-agent Claude AI that learns from market outcomes. Features 60+ indicators, foundation models, and serverless deployment.

No description available

Experiments with reinforcement learning - within a FOREX trading environment

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This project involves developing and testing a trading model designed to predict stock prices and evaluate trading strategies. The core of the project includes building and training a LSTM based model for time series forecasting in addition to a RL model, evaluating its performance, and visualizing the results.