pskrunner14 / trading-bot
Stock Trading Bot using Deep Q-Learning
☆948Updated 9 months ago
Related projects: ⓘ
- Deep Q-learning driven stock trader bot☆832Updated 9 months ago
- A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep rei…☆556Updated last month
- Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo.☆1,546Updated 7 months ago
- For trading. Please star.☆2,006Updated 2 months ago
- List of awesome resources for machine learning-based algorithmic trading☆1,481Updated last year
- Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.☆1,198Updated 6 months ago
- Deep Reinforcement Learning toolkit: record and replay cryptocurrency limit order book data & train a DDQN agent☆845Updated 2 years ago
- Quantitative analysis, strategies and backtests☆1,918Updated last year
- Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Ti…☆1,910Updated 2 years ago
- A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.☆948Updated 5 months ago
- Algorithmic trading framework for cryptocurrencies.☆1,082Updated 2 months ago
- Trading Environment(OpenAI Gym) + DDQN (Keras-RL)☆408Updated last year
- A stock trading bot that uses machine learning to make price predictions.☆598Updated 2 years ago
- The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)☆2,112Updated 6 months ago
- Framework for algorithmic trading☆745Updated last year
- Portfolio optimization with deep learning.☆896Updated 7 months ago
- Common financial technical indicators implemented in Pandas.☆2,112Updated 2 years ago
- Scalable, event-driven, deep-learning-friendly backtesting library☆985Updated 3 years ago
- Machine Learning for Algorithmic Trading, Second Edition - published by Packt