About Wickra Gym

A Gymnasium-compatible, microstructure-aware backtest environment: precompute the dataset to a fixed feature tensor once, and every step() becomes a pure array index.

What it does

Wickra Gym exposes a Gymnasium-compatible environment over a deterministic backtest. The expensive part — turning candles into features — happens once, up front, into a fixed tensor. Each step() then indexes that tensor, so the per-step cost is constant no matter how wide the feature set is.

Why it exists

Reinforcement learning needs millions of steps. An environment that recomputes indicators inside step() spends its budget on arithmetic it already did, and a run that is not deterministic cannot be compared with the one before it.

Open source

Released under the MIT OR Apache-2.0 license — permissive, OSI-approved and free for any use, including commercial. Source, issues and releases on GitHub.

Disclaimer

Wickra Gym is software, not a trading system, and is provided as-is with no warranty. It does not give financial advice. Use it at your own risk.