Christopher Dietrich (chrisd12)

chrisd12

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Location:London

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Christopher Dietrich 's starred repositories

crypto-rl

Deep Reinforcement Learning toolkit: record and replay cryptocurrency limit order book data & train a DDQN agent

Language:PythonStargazers:827Issues:0Issues:0

PGPortfolio

PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).

Language:PythonLicense:GPL-3.0Stargazers:1716Issues:0Issues:0

quickEC2

A simple app to deploy and connect to AWS EC2 instances

Language:SvelteStargazers:1Issues:0Issues:0

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

Language:PythonLicense:MITStargazers:13354Issues:0Issues:0

Deep-Reinforcement-Stock-Trading

A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.

Language:Jupyter NotebookLicense:GPL-3.0Stargazers:538Issues:0Issues:0

Deep-Reinforcement-Learning-Hands-On

Hands-on Deep Reinforcement Learning, published by Packt

Language:PythonLicense:MITStargazers:2807Issues:0Issues:0

Tensorflow-2-Reinforcement-Learning-Cookbook

Tensorflow 2 Reinforcement Learning Cookbook, published by Packt

Language:Jupyter NotebookLicense:MITStargazers:183Issues:0Issues:0

RLTrader

A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym

Language:PythonLicense:GPL-3.0Stargazers:1710Issues:0Issues:0

Stock_Analysis_For_Quant

Various Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau

Language:Jupyter NotebookLicense:MITStargazers:1621Issues:0Issues:0

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

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