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SharpeArena for Python

The Python distribution combines SharpeArena's deterministic point-in-time evaluation sandbox with a Gymnasium-compatible environment and optional adapters for multi-agent, RLVR, offline-RL, MCP, and local-model workflows.

Point-in-time observations prevent future-bar access through the environment API. They do not isolate Python code from the host. Run trusted local code here, or use SharpeBench's digest-pinned container path for an untrusted entrant.

Install

pip install sharpearena

The base install includes NumPy, Gymnasium, the Python package, and the compiled pyo3 extension. Optional integrations are explicit:

pip install "sharpearena[pettingzoo]"
pip install "sharpearena[verifiers]"
pip install "sharpearena[minari]"
pip install "sharpearena[mcp]"

Step a Gymnasium environment

from sharpearena import SharpeArenaEnv

env = SharpeArenaEnv(n_symbols=4, n_days=120, seed=7)
observation, info = env.reset(seed=7)

terminated = truncated = False
while not (terminated or truncated):
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)

Actions are signed target-weight vectors in the environment's symbol order. The default range is [-1, 1]; pass allow_short=False to make it [0, 1], or set max_weight to change the per-symbol bound.

Importing sharpearena registers the following Gymnasium IDs:

SharpeArena/Calm-v1          SharpeArena/Calm-Eval-v1
SharpeArena/Hard-v1          SharpeArena/Hard-Eval-v1
SharpeArena/Extreme-v1       SharpeArena/Extreme-Eval-v1

The -Eval-v1 environments draw from the disjoint evaluation seed band. The -v1 suffix freezes the environment semantics; a rule change requires a new versioned ID.

Native boundary

sharpearena.sharpearena_py.TradingEnv is the low-level binding. Its boundary is JSON: reset() returns an observation string, and step(decision_json) returns (observation_json, reward, done, info_json). The Python Gymnasium wrapper converts between that wire format and NumPy spaces.

The wheel ships py.typed and a stub for the compiled extension. Typed boundary exceptions distinguish invalid input, invalid JSON, invalid salt, unavailable data, and engine failures.

Other surfaces

Task Surface
Batched Gymnasium rollout SharpeArenaVectorEnv or gymnasium.make_vec(...)
Multi-agent environment MultiAgentSharpeArenaEnv with the pettingzoo extra
RLVR / Prime RL load_environment() with the verifiers extra
Offline RL export to_minari, to_minari_train_test with the minari extra
External tool server MCP server with the mcp extra
Evidence field sharpearena-local-field and the model transport shims
Benchmark compilation sharpearena-compile-bench
Strategy search sharpearena-strategy-search

See the repository's Gymnasium guide, training guide, and agent contract for the full workflows.

Build from source

python -m maturin develop --manifest-path crates/sharpearena-py/Cargo.toml

License

MIT OR Apache-2.0

Metadata

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