zhy0/dmarket_rl
Fast single unit, double auction market for reinforcement learning
This project helps researchers and developers simulate fast-paced trading environments to test and train automated trading strategies. It takes in trading rules and agent behaviors and simulates their interactions within a double auction market, outputting performance metrics and strategic outcomes. Market design researchers and algorithmic traders can use this to understand market dynamics and optimize trading agents.
No commits in the last 6 months.
Use this if you need to rapidly simulate double auction markets to train reinforcement learning agents or analyze market behavior.
Not ideal if you are looking for a real-time trading platform or a tool for financial market prediction.
Stars
7
Forks
1
Language
Python
License
MIT
Category
Last pushed
Dec 05, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/agents/zhy0/dmarket_rl"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Toni-SM/skrl
Modular Reinforcement Learning (RL) library (implemented in PyTorch, JAX, and NVIDIA Warp) with...
facebookresearch/BenchMARL
BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL...
utiasDSL/gym-pybullet-drones
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
datamllab/rlcard
Reinforcement Learning / AI Bots in Card (Poker) Games - Blackjack, Leduc, Texas, DouDizhu, Mahjong, UNO.
gtri/scrimmage
Multi-Agent Robotics Simulator