Prediction market research data for Kalshi and quant workflows

A prediction market price is one input, not a complete forecast. A researcher also needs the contract's exact settlement rule, the event deadline, the underlying official data release, and a record of when each observation became available. Pronto's signal wire supplies observations and source links across domains such as macroeconomics, weather, seismic events, public health, news attention and research. Its Fusion catalogue describes derived indicators and their methods.

Pronto does not currently provide a Kalshi order book or place Kalshi trades. Read Kalshi prices and contract terms from Kalshi's official API and documentation. The Polymarket connector is disabled while data redistribution rights are verified. A Manifold market feed is configured, but its probabilities should not be treated as cash-market prices or independent ground truth.

1. Choose one contract. Save the market ticker, question, exact resolution source, cutoff time and exclusions from Kalshi. A similar headline may resolve differently.
2. Form a prior probability. Write down the base rate, sample window and assumptions before checking the current price. Record how uncertain the estimate is.
3. Collect evidence. Use Pronto's MCP tools to search signals for the event, fetch the relevant records and follow their original source links. Keep observation and publication times separate.
4. Update the estimate. Explain which observation changes the probability and why. Check whether the observation was already known before the market moved.
5. Compare with an executable quote. Use the exchange's current bid and ask, fees, liquidity and contract size. A last traded price is not necessarily a price you can obtain.
6. Review the outcome. Archive the evidence available at decision time, final resolution, slippage and fees. Score forecasts over many contracts; a profitable example alone proves little.

Economic releases: compare official macro releases, revisions and central-bank observations with the contract's stated resolution source. Weather and climate: track forecasts, alerts and observed conditions, taking care not to substitute an alert for a final measurement. News and attention: detect that coverage is accelerating, then verify the primary event independently. Research and science: inspect source-linked preprints and indicators when a contract hinges on a scientific development. Cross-domain events: use the source catalogue to understand which feeds are configured and whether an observation is current.

An AI research agent can reduce the time needed to collect and compare evidence. That alone does not establish an edge. For a binary contract, the rough starting comparison is your calibrated probability against the exchange's executable price. Include fees, spread, position limits, capital lockup, settlement ambiguity and the chance your data arrives late or is revised. Backtest on timestamps that reflect when information was actually available; otherwise the result can contain look-ahead bias. Paper-test the full workflow before relying on it. Pronto provides research inputs, not a profit estimate, betting advice, a backtesting engine or an exchange execution service.

Start with the MCP setup guide. Ask the agent for a bounded evidence brief: the market question, matching observations, source URLs, observation times, conflicting evidence, missing evidence and a reasoned probability range. Require it to say when the feed has no relevant record. If you are building an AI prediction market bot or Kalshi trading bot, combine that brief with exchange data, risk controls and execution in your own application. See 15 commercial uses for live data and AI for adjacent quant, risk, news and research workflows.