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DFlow uses the same trading model for all asset types. What changes is what is being traded, not how trading works.

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Trading Crypto

When trading crypto, users swap one SPL token for another.
  • A quote is requested.
  • The user signs and submits a transaction.
  • The trade routes through available onchain liquidity venues.
  • The output token is available in the user’s wallet.
This applies to spot assets like SOL, stablecoins, and other SPL tokens, enabling builders to ship familiar swap experiences, trading UIs, and automated strategies without building execution logic from scratch.

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Trading Prediction Markets

When trading prediction markets, users trade outcome tokens that represent possible results of an event.
  • A quote is requested for an outcome token.
  • The user signs and submits a transaction.
  • The trade executes through Kalshi’s prediction market liquidity.
  • The outcome token is available in the user’s wallet.
All prediction market trades on DFlow execute through Concurrent Liquidity Programs (CLPs) and use multi-transaction async execution. Outcome tokens can be traded again or redeemed after the market resolves, opening up new possibilities for builders such as secondary trading, portfolio tracking, automated strategies, and post-resolution settlement flows.

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How These Trades Are the Same

From a trading perspective, crypto and prediction market trades follow the same flow:
  • Quotes are requested the same way.
  • Users sign and submit transactions the same way.
  • The same routing and execution primitives apply.
DFlow treats both as first-class trading flows.

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Where They Differ

The differences come from asset behavior and liquidity structure, not from the trading model itself.
  • Crypto trades usually complete atomically.
  • Prediction market trades always execute asynchronously through CLPs.
  • Prediction markets eventually resolve, allowing outcome tokens to be redeemed.
Despite these differences, developers interact with both through the same APIs.

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