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.

Scoring and typed unavailability

score_run(returns, n_trials, periods_per_year) returns the pinned SharpeBench CompositeScore as JSON. When the kernel cannot score a series (a non-finite observation, fewer than two observations) the composite carries a typed error beside a no-skill floor rather than an estimate: deflation_error (the deflated Sharpe, its bar and its interval are the floor), bootstrap_error (the bootstrap p-value is the conservative 1.0 sentinel) and selection_error (the selection diagnostic is absent). Reading deflated_sharpe past such a key publishes the floor as a score.

sharpearena.kernel_score is the one place a consumer reads the ranked numbers:

• kernel_deflated_sharpe(composite) and kernel_psr(composite) return the finite value or raise KernelScoreUnavailable(reason, errors) naming every typed error key present. Selection, ranking inputs and the paper producers use these and stop.
• kernel_score_or_unavailable(composite, key="deflated_sharpe") returns the float or the reason string unavailable_scoring_kernel_error: <key>: <message> for rows that must be recorded (eval-seed snapshots, generalization and regime rows, the PettingZoo leaderboard, trace meta, the local-field journal). kernel_score_difference makes a gap unavailable when either side is; is_kernel_score_unavailable tests a recorded cell.

Every score_run consumer in this package goes through these helpers; none substitutes a default. The multi-agent ranking places an unscorable agent after every scored one with the reason in its cell.

Other surfaces

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