Wickra Gym — Python

A deterministic, Gymnasium-compatible backtest environment. The whole candle dataset is precomputed once into a fixed feature tensor, so each step() is a constant-time array index — and the same spec, data, seed and actions produce a byte-identical trajectory across every language binding.

Install

pip install wickra-gym            # RawEnv (no Gymnasium dependency)
pip install "wickra-gym[gym]"     # + the gymnasium.Env subclass

Use as a Gymnasium environment

import numpy as np
from wickra_gym import WickraGymEnv

spec = """{
  "dataset_ref": "demo", "symbol": "BTCUSDT",
  "observation": {"features": [
    {"kind": "price", "field": "close"},
    {"kind": "indicator", "name": "Rsi", "params": [14]}
  ]},
  "action_space": {"type": "discrete", "n": 3},
  "reward": "pnl",
  "episode": {"max_steps": 256, "warmup": 14}
}"""

candles = [
    {"ts": i, "open": 100 + i, "high": 100 + i, "low": 100 + i, "close": 100 + i}
    for i in range(300)
]

env = WickraGymEnv(spec, candles)
obs, info = env.reset(seed=0)
done = False
while not done:
    action = env.action_space.sample()
    obs, reward, terminated, truncated, info = env.step(action)
    done = terminated or truncated

The observation and action spaces are derived from the spec: unbounded observation columns use ±np.inf; a discrete action space becomes spaces.Discrete(n) and a continuous one becomes a 1-D spaces.Box.

Register it under a Gymnasium id:

from wickra_gym import register
register()  # WickraGym-v0

The raw command surface

RawEnv is the thin, dependency-free wrapper over the native command JSON surface — the same boundary every language binding forwards verbatim:

import json
from wickra_gym import RawEnv

env = RawEnv(spec)
env.command(json.dumps({"cmd": "load", "candles": candles}))
reset = json.loads(env.command(json.dumps({"cmd": "reset", "seed": 0})))
step = json.loads(env.command(json.dumps({"cmd": "step", "action": 2})))

Commands: load, reset, step, spec, version. Domain errors come back as {"ok": false, "error": ...}; a bad spec raises ValueError at construction.

License

Dual-licensed under MIT or Apache-2.0, at your option.