crypto-rl
Unverified ML strategy on Crypto by sadighian. BotFinder score 18 out of 100.
Deep Reinforcement Learning toolkit: record and replay cryptocurrency limit order book data & train a DDQN agent
Source: github
BotFinder analysis pending.
crypto-rl
Deep Reinforcement Learning Toolkit for Cryptocurrencies Table of contents: 1. Purpose 2. Scope 3. Dependencies 4. Project structure 5. Design patterns 6. Getting started 7. Citing this project 8. Appendix 1. Purpose The purpose of this application is to provide a toolkit to: - Record full limit order book and trade tick data from two exchanges (Coinbase Pro and Bitfinex) into an Arctic Tickstore database (i.e., MongoDB), - Replay recorded historical data to derive feature sets for training - Train an agent to trade cryptocurrencies using the DQN algorithm (note: this agent implementation is intended to be an example for users to reference) 2. Scope - Research only: there is no capability for live-trading at exchanges. - Reproducing RL paper results: the dataset used for this article is available on Kaggle Datasets. 3. Dependencies See requirements.txt Note: to run and train the DQN Agent (./agent/dqn.py) tensorflow and Keras-RL need to be installed manually and are not listed in the requirements.txt in order to keep this project compatible with other open sourced reinforcement learn
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