Hands-On Machine Learning for Algorithmic Trading

How to design an OpenAI trading environment

The OpenAI Gym allows for the design, registration, and utilization of environments that adhere to its architecture, as described in its documentation (https://github.com/openai/gym/tree/master/gym/envs#how-to-create-new-environments-for-gym). The trading_env.py file implements an example that illustrates how to create a class that implements the requisite step() and reset() methods.

The trading environment consists of three classes that interact to facilitate the agent's activities. The DataSource class loads a time series, generates a few features, and provides the latest observation to the agent at each time step. TradingSimulator tracks the positions, trades and cost, and the performance. It ...