Wickra GymO(1) steps. Deterministic rollouts.

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

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How it works

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Every step is an index

The dataset is precomputed once into a fixed feature tensor. After that `step()` does no indicator work at all — it reads a row. Rollout speed stops depending on how many features you asked for.

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Microstructure-aware

The observation is built from the same market structure the rest of the stack reads, not from a bare price series.

🎯

Deterministic rollouts

The same seed and the same spec replay the identical episode, so a training run is reproducible rather than approximately repeatable.

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514 indicators as features

The feature tensor is drawn from the Wickra core's 514 streaming indicators, so a feature set is a list of names rather than code you maintain.

Install

The same engine from every language — native Rust, Python, Node.js and WASM, plus a C ABI for C, C++, C#, Go, Java and R.

pip install wickra-gym

The API pages carry a quick start per language; the repository README the same in one place.

Built on the Wickra core

Wickra Gym is part of the Wickra ecosystem — one indicator core, twenty-three products, the same ten-language binding surface in every one of them, checked byte-for-byte by a golden corpus in every repository.

Wickra Gym is a software library, not a trading system, and gives no financial advice — its outputs are deterministic transforms of the input data and do not predict future returns. Use it at your own risk.