calci-prediction-market

Kalshi prediction markets organize data as Series → Event → Market hierarchies. Prices represent binary Yes/No outcomes; convert bid/ask spreads to implied probabilities by dividing price by 100. The API exposes these structures with real-time pricing, volume, and settlement metadata across public endpoints.

Kalshi prices range 0–100 for binary markets. A Yes price of 65 implies 65% probability; No price of 35 implies 35% probability. Prices sum to 100. Use these conversions to reason about market expectations and translate between Kalshi UI terms and Calci domain concepts.

Kalshi provides public endpoints for market, order book, and trade data, plus WebSocket streaming for real-time updates. Authentication uses API credentials for private endpoints. Map Kalshi API responses to Calci structures; cite specific endpoints and authentication methods when building integrations.

Kalshi supports limit and market orders with standard trading rules. Settlement occurs at event resolution; markets resolve to Yes (100) or No (0) based on event outcome. Understanding order types, trading halts, and settlement workflows is essential for modeling Kalshi-based trading logic.

Yes. Map Kalshi markets to Calci portfolio structures by treating each market as a binary asset. Use implied probabilities from prices for scenario analysis, and leverage order book depth and volume data to assess liquidity and execution risk.

Public endpoints have rate limits and latency; WebSocket streaming mitigates this for live updates. Not all data is available on public endpoints—private data requires authentication. Plan integrations around these constraints and fallback to polling if streaming is unavailable.