Banger vs Simmer
Simmer is An early product in the prediction-market strategy space, focused on automating bets with risk controls. Banger is a strategy-automation runtime for prediction markets. Here is how they compare, and when each one is the right call.
| Banger | Simmer | |
|---|---|---|
| Primary assets | Prediction markets (Polymarket, Kalshi); equities and crypto as expansion adapters | Prediction markets |
| Prediction markets | Native. The wedge, not an afterthought. | Yes, this is the closest comparison. |
| Strategy authoring | Real Python (banger.Strategy) or clone a marketplace strategy and configure it | Product-defined strategies with risk caps |
| Custody | Never. You bring your own venue keys. | Non-custodial. |
Where Banger is different
- Banger is a full runtime: real-Python authoring plus clone-and-configure, not just preset strategies.
- Developer surface: a Python CLI and MCP server so you can build from Cursor or Claude Code, plus a web dashboard.
- A strategy marketplace with creator economics, so supply compounds over time.
- Multi-venue from day one (Polymarket and Kalshi) with a deterministic risk envelope and paper-first testing.
When Simmer is the better choice
Prediction-market traders who want a simple, guided way to automate with sensible risk defaults.
The verdict
Simmer and Banger target the same wedge. Banger goes wider and deeper: code-or-clone authoring, a CLI/MCP developer surface, a marketplace, and multi-venue support on one engine.
FAQ
- How is Banger different from Simmer?
- Both automate prediction-market strategies. Banger adds real-Python authoring alongside clone-and-configure, a CLI and MCP developer surface, a strategy marketplace, and multi-venue support on a single runtime.
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