Implementing a stock-exchange environment in OPEN-AI's gym environment.

To get started, you’ll need to have Python 3.5+ installed. Simply install gym using pip:

pip install gym

Once gym is installed, clone this repository, then run

python3 demo_exchange.py

There are two versions, discrete and continuous action space - agents may require one or the other - DDPG vs naive DQN, for example. env = gym.make('game-stock-exchange-v0') is for a discrete environment. env = gym.make('game-stock-exchange-continuous-v0') is for a continuous environment.

Code for continuous and discrete are found here.

If you want to integrate reinforcement learning agents, I recommend using baselines, or stable baselines. These are based on tensorflow.

If you're a fan of pytorch, stock-exchange-pytorch supports implementations for reinforcement learning and supervised learning. This repository is actually an offshoot of that project.

With a random agent, an output of demo_stock_exchange.py is like so:

With stable-baselines, Advantage Actor Critic (A2C) is used in the demo_exchange.py to train the model. An output of such is like so: