SharpeArena for Python
The Python distribution combines the native SharpeArena engine 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
1 maintainer
Metadata
1 maintainer
Release files for sharpearena 0.24.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| sharpearena-0.24.0.tar.gz | 609.3 kB | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| sharpearena-0.24.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.13 | CPython 3.13 | Linux glibc 2.17+ x86-64 | Details |
| sharpearena-0.24.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.17+ x86-64 | Details |
| sharpearena-0.24.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.11 | CPython 3.11 | Linux glibc 2.17+ x86-64 | Details |
| sharpearena-0.24.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.10 | CPython 3.10 | Linux glibc 2.17+ x86-64 | Details |
Total release size: 4.9 MB
Release files / sharpearena-0.24.0.tar.gz
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