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The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)
| Date | Stars |
|---|---|
| 2026-07-24 | 2379 |
| 2026-07-25 | 2379 |
| 2026-07-28 | 2379 |
| 2026-07-30 | 2379 |
| 2026-08-06 | 2379 |
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# gym-anytrading `AnyTrading` is a collection of [OpenAI Gym](https://github.com/openai/gym) environments for reinforcement learning-based trading algorithms. Trading algorithms are mostly implemented in two markets: [FOREX](https://en.wikipedia.org/wiki/Foreign_exchange_market) and [Stock](https://en.wikipedia.org/wiki/Stock). AnyTrading aims to provide some Gym environments to improve and facilitate the procedure of developing and testing RL-based algorithms in this area. This purpose is obtained by implementing three Gym environments: **TradingEnv**, **ForexEnv**, and **StocksEnv**. TradingEnv is an abstract environment which is defined to support all kinds of trading environments. ForexEnv and StocksEnv are simply two environments that inherit and extend TradingEnv. In the future sections, more explanations will be given about them but before that, some environment properties should be discussed. **Note:** For experts, it is recommended to check out the [gym-mtsim](https://github.com/AminHP/gym-mtsim) project. ## Installation ### Via PIP ```bash pip install gym-anytrading ``` ### From Repository ```bash git clone https://github.com/AminHP/gym-anytrading cd gym-anytrading pip install -e . ## or pip install --upgrade --no-deps --force-reinstall https://github.com/AminHP/gym-anytrading/archive/master.zip ``` ## Environment Properties First of all, **you can't simply expect an RL agent to do everything for you and just sit back on your chair in such complex trading markets!** Things need to be simplified as much as possible in order to let the agent learn in a faster and more efficient way. In all trading algorithms, the first thing that should be done is to define **actions** and **positions**. In the two following subsections, I will explain these actions and positions and how to simplify them. ### Trading Actions If you search on the Internet for trading algorithms, you will find them using numerous actions such as **Buy**, **Sell**, **Hold**, **Enter**, **Exit**, etc. Referring to the first statement of this section, a typical RL agent can only solve a part of the main problem in this area. If you work in trading markets you will learn that deciding whether to hold, enter, or exit a pair (in FOREX) or stock (in Stocks) is a statistical decision depending on many parameters such as your budget, pairs or stocks you trade, your money distribution policy in multiple markets, etc. It's a massive burden for an RL agent to consider all these parameters and may take years to develop such an agent! In this case, you certainly will not use this environment but you will extend your own. So after months of work, I finally found out that these actions just make things complicated with no real positive impact. In fact, they just increase the learning time and an action like **Hold** will be barely used by a well-trained agent because it doesn't want to miss a single penny. Therefore there is no need to have such numerous actions and only `Sell=0` and `Buy=1` actions are adequate to train an agent just as well. ### Trading Positions If you're not familiar with trading positions, refer [here](https://en.wikipedia.org/wiki/Position_\(finance\)). It's a very important concept and you should learn it as soon as possible. In a simple vision: **Long** position wants to buy shares when prices are low and profit by sticking with them while their value is going up, and **Short** position wants to sell shares with high value and use this value to buy shares at a lower value, keeping the difference as profit. Again, in some trading algorithms, you may find numerous positions such as **Short**, **Long**, **Flat**, etc. As discussed earlier, I use only `Short=0` and `Long=1` positions. ## Trading Environments As I noticed earlier, now it's time to introduce the three environments. Before creating this project, I spent so much time to search for a simple and flexible Gym environment for any trading market but didn't find one. They were
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Mohammad Amin Haghpanah · @koala-team
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d66b07ac78fa6a94, topic:reinforcement-learning, topic:openai-gym, readme:reinforcement learning
matched fp:d66b07ac78fa6a94, topic:trading