Question 1

How to load custom CSV data into TradingGym?

Accepted Answer

TradingGym uses pandas DataFrames, so you can read your CSV into a DataFrame and ensure columns match the expected format, such as 'Price' and 'Volume', similar to the HDF5 example in the README. Then pass it to the `make` function with appropriate parameters.

Question 2

TradingGym vs Backtrader for backtesting?

Accepted Answer

TradingGym is focused on reinforcement learning integration and customizable simulations, while Backtrader is a more mature library with extensive backtesting features and community support. Choose TradingGym if you need RL environments, but opt for Backtrader for traditional, rule-based backtesting with more built-in indicators.

Question 3

Can TradingGym handle multiple assets or portfolio strategies?

Accepted Answer

The README examples show single-asset simulations, and there's no explicit support for multi-asset or portfolio-level backtesting. You would need to extend the environment manually, which could be complex without documentation.

Question 4

How to implement a custom reinforcement learning agent in TradingGym?

Accepted Answer

Define a class with a `choice_action` method that takes the state and returns an action (0, 1, or 2), as shown in the agent example. Then integrate it with the environment's `step` method during training or backtesting.

Question 5

Does TradingGym support real-time trading yet?

Accepted Answer

No, real-time trading is planned via Interactive Brokers API but not implemented, as stated in the README. Currently, it only supports historical data simulations for training and backtesting.

Question 6

What data formats does TradingGym accept?

Accepted Answer

It primarily uses pandas DataFrames, with examples in HDF5 format, but it can work with any DataFrame containing required columns like 'Price'. Support includes tick and OHLC data, but you need to preprocess your data to fit the expected structure.

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