Python Libraries for Prediction-Market Trading: Polymarket and Kalshi

Python clients can simplify access to event-contract data and APIs, but an SDK is not a complete research or execution system. Choose a client for the specific interface you need, inspect its current documentation and preserve the version used by your experiment. Package names and supported surfaces change over time.

Polymarket's current official Python package

As checked on October 14, 2026, Polymarket documents polymarket-client as its official Python SDK. The public and secure client interfaces have different purposes; public clients provide public data access, while secure clients add account and trading access. Its documentation also supplies migration guidance for earlier SDKs.

Do not confuse a repository name such as py-sdk with the package name used by the Python installer. Older articles referring to py-clob-client or intermediate clients need the current migration guide, rather than assuming every old import works unchanged. This article does not verify all prior versions against every live endpoint.

Kalshi's current official packages

Kalshi now documents kalshi_python_sync and kalshi_python_async. Its overview marks the older kalshi-python package deprecated. Choose the interface matching your program's execution model and check the API schema relevant to the method you use. The venue warns that SDKs can lag the API and points to its REST and WebSocket specifications.

Kalshi's current signing documentation supports RSA-PSS and Ed25519 key types, with compatibility dependent on client version. The actual key and supported signer must match. Separate demo and production environments; a demo observation does not prove identical production liquidity or operational behavior.

These are the current official installation names, presented as alternatives for a separate development environment. They do not configure an account, create credentials or authorize orders. After selecting a client, record and lock the resolved version and relevant dependencies rather than assuming future installs are identical.

# Polymarket official Python client
python -m pip install polymarket-client

# Kalshi: choose the interface needed by your application
python -m pip install kalshi_python_sync
# or
python -m pip install kalshi_python_async

Evaluate community clients by demonstrated behavior

Community tools may add conveniences, but a feature list is not proof of reliable retries, complete schema coverage or a correct local book. Check maintenance, licensing, release history and the particular methods needed. Exercise pagination, missing fields and failure handling with bounded fixtures before relying on an advertised capability.

Do not retry a timed-out write indiscriminately. Whether repetition is safe depends on documented idempotency and how you reconcile the prior request. A market-data wrapper, an AI-assistant connector and an authenticated execution client have different authority and failure modes. Avoid granting account access merely to explore public data.

Keep analysis separate from collection

Dataframes and numerical libraries help transform observations, but they do not repair missing history or establish when a feature was knowable. Preserve raw identifiers, price units, source and receipt times, and data gaps before building analysis tables. Price-history points, candles, trades and book depth are different datasets.

A warehouse loader also needs a declared pagination and deduplication policy. A successful load is not evidence of complete outcome coverage. Record rejected or unresolved data and keep later settlement labels separate from earlier decision-time information.

Where Banger fits

Banger's public beta supports paper research through its strategy interface and hosted product. The public bangertrades package provides the actual SDK/runtime/CLI contracts. A local paper run uses banger run strategy.py --paper after account setup and a valid strategy file; the command is not a universal dry run of arbitrary third-party client code.

RiskEnvelope uses actual fields such as max_position_usd and max_daily_loss_usd. These constrain admissions under the implemented accounting, rather than guaranteeing liquidation or every real-world loss bound. Simulated fills do not validate venue execution, and public paper onboarding does not require venue trading keys.

Choose the smallest tool that answers the current research question. Public data exploration, a controlled paper experiment and an account-authorized execution integration are separate projects. Recheck official documentation when migrating, and report the exact versions and tested behavior instead of calling any client a finished trading system.