--- url: "https://predictiontoolkit.com/tools/simmer-sdk/" publisher: "PredictionToolkit" dateModified: "2026-11-03" --- [← SDKs & libraries]() ![Simmer SDK logo]() SDK # Simmer SDK: choose the simulation and client mode explicitly Simmer SDK is a Python agent-development harness with CLI access, market and portfolio methods, sizing tools and supported live-venue integrations. Simulation options use different market and fill models. [**3.5**/ 5Editorial rating]() [Editorial assessment]() [Open Simmer SDK repository]() github.com **Available on:** CLI **Pricing:** [See plans and costs ↓]() [Screenshots]() [Explore alternatives]() [SDKs & libraries]() [AI agents]() +2 more [Paper trading]() [Backtesting]() ## Screenshots and interface Product preview1 / 2 Repository introduction to the SDK. ## What is Simmer SDK? Virtual SIM markets use an AMM. A separate paper path uses real-venue prices with live=False, including a Polymarket spread model. Historical replay runs a skill over a frozen trade tape, while live operation requires its own venue setup and permissions. These results should be compared with their model differences intact. Client construction matters: a regular live client can process pending stop-loss or take-profit exits when a signer is configured. SimmerClient.readonly is the documented choice for status and validation without that constructor behavior. ### Who it is for - Developers building reusable agent skills. - Comparing virtual, real-price paper and live modes. - Adding position and risk controls around a strategy. ## Clients and supported operations - **Markets and portfolios** Discover markets, inspect positions and history, and query portfolio buckets across venues. - **Paper modes** Use virtual SIM or real-venue prices with live=False. - **Tape replay** The optional backtest runner reports P&L, hit rate, drawdown, baselines and a configuration hash. - **Sizing helpers** Fractional Kelly and expected-value utilities support bounded allocations. - **Orders and monitors** Manage supported orders, stop/take-profit monitors and redemption. - **Skill resources** Use documented skill bundles and integration material. - **Readonly client** Avoid constructor-triggered risk exits in validation and status code. ## Development workflows ### Develop before live Start with virtual SIM, then compare Polymarket live=False and its spread model before adding a live signer. ### Backtest a skill Use the bundled demo or an available bounded tape. Review realism gaps and compare the reported baselines. ### Build a status check Use SimmerClient.readonly instead of constructing a live client capable of processing pending exits. ## Pricing and total cost Choose the hosted/live plan separately from installing the MIT-licensed SDK. The README assigns Kalshi access to Pro and dedicated per-agent wallets to Elite, so the code license alone does not establish that either hosted entitlement is included. ## Installation and configuration 1. Install simmer-sdk and obtain the required Simmer API access. 2. Choose venue and live/paper mode explicitly. 3. Use a readonly client for status or validation. 4. Run a bounded paper strategy or backtest demo. 5. Set venue credentials and approvals separately before live use. With a configured signer, a regular live constructor can process pending stop-loss or take-profit exits. [Read Simmer documentation]() ## Useful links - [Simmer Telegram community]() ### What to check - Virtual SIM uses an AMM; real-price paper and live books have different behavior. - Historical replay uses trade-tape prices rather than full books. - Tape coverage has a finite end date: inspect the available window. - Client construction can have live exit-processing effects; select readonly for observation. A closer look ## Ratings & reviews Editorial rating **3.5**/ 5 0 user reviews PredictionToolkit editorial ### Our take on Simmer SDK Distinct paper modes, replay and live integrations give agent developers useful components. Constructor effects, different simulation models and hosted-plan requirements need deliberate configuration. **Assessment criteria:** Task usefulness, documented scope, setup clarity, and access and cost transparency. **Assessment date:** 2026-10-01 **Documentation reviewed:** 2026-10-01 Documentation-based assessment. [How we rate]() ### User reviews 0 The main score switches to user ratings after 5 published reviews. **Be the first to share your experience** A specific example helps the next person choose the right tool. ## Related tools [Browse category]() ![Marketlens Python installation and Strategy quickstart]() ![Marketlens Python SDK logo]()[Marketlens Python SDK]() SDK**4.0**Editorial Compare fast bar experiments with detailed L2 replay, reusing downloaded history between strategy changes. - Language Python Historical L2 replay with execution assumptions. ![Numinous Tools agent-skill overview and installation paths]() [Numinous Tools]() SDK**3.5**Editorial Request forecasts and market evidence from an agent while tracking separately metered API usage. - Language Python Use the Numinous skill package for forecasts and sourced signals in a compatible research agent. ![PMXT repository overview and supported venue families]() ![PMXT open-source SDK logo]()[PMXT open-source SDK]() SDK**3.5**Editorial Use one Python or TypeScript interface across supported prediction venues, with an explicit hosted or local backend choice. - Language Python · TypeScript A general client layer across venues. ## Frequently asked questions about Simmer SDK ### Can I simulate Polymarket without funding a wallet? Yes. The documented Polymarket live=False mode simulates trades. ### Why choose readonly? It avoids constructor-time risk-exit processing during status and validation. ### Does replay reconstruct the full book? No. The runner uses trade-tape prices and describes the resulting realism gaps. In this profile