Polymarket vs Kalshi Arbitrage Trading Bot: Complete Guide to Automated Prediction Market Arbitrage in 2026

Polymarket vs Kalshi arbitrage looks simple on a spreadsheet.

Buy YES where it is cheaper. Buy NO on the other venue. If the combined executable cost is below $1 after fees, the two complementary positions can create a positive spread.

The difficult part is everything between those two sentences.

Polymarket and Kalshi expose different identifiers, order-book conventions, fee structures, authentication models and market-resolution language. A production arbitrage trading bot has to normalize all of them before it can reliably decide whether a price difference is actually tradeable.

Predictefy is designed to put that fragmented prediction market infrastructure behind one API and SDK. The strategy, position sizing and execution policy remain yours; the cross-venue data and qualification layer does not have to be rebuilt from scratch.

How Polymarket vs Kalshi Arbitrage Actually Works

Binary prediction markets have a simple payoff structure: the winning side resolves to $1 while the losing side resolves to $0.

That creates the basic cross-venue arbitrage condition:

Polymarket YES purchase cost
+ Kalshi NO purchase cost
+ fees
< $1.00

or the reverse:

Kalshi YES purchase cost
+ Polymarket NO purchase cost
+ fees
< $1.00

For example:

Buy YES on Polymarket: $0.42
Buy NO on Kalshi:      $0.53

Combined price:        $0.95
Gross gap:             $0.05

That five-cent gap is only theoretical until the bot checks the full trade.

Both contracts must describe the same economic outcome. A Polymarket question asking whether a candidate wins an election is not necessarily equivalent to a Kalshi contract asking whether the same candidate wins the popular vote.

The trade must work at the position size you want. If only 10 Polymarket contracts are available at 42 cents and the next 500 sit at 47 cents, a 42-cent headline price tells you almost nothing about a 500-contract strategy.

The bot needs the volume-weighted average execution price, or VWAP, across every level required to fill the intended position.

Fees also differ by venue. Polymarket's current CLOB uses market-specific fee parameters and charges taker fees on fee-enabled markets. Kalshi also charges transaction fees and notes that fee schedules can differ across markets, with maker fees applying in some cases.

That means the usable formula is:

YES VWAP
+ NO VWAP
+ Polymarket fees
+ Kalshi fees
+ any applicable settlement costs
< $1.00

A serious Polymarket vs Kalshi arbitrage bot calculates that result from executable books, not from the probability displayed at the top of a market page.

Why Predictefy Is the Easier Infrastructure Layer

You can integrate Polymarket and Kalshi directly.

But even these two venues already expose materially different interfaces.

Kalshi's native order-book API is a good example of why normalization matters. It returns YES bids and NO bids rather than separate ask arrays; an opposing-side bid implies the corresponding ask.

Polymarket's CLOB, by contrast, directly returns bids and asks for each token.

Predictefy normalizes both into the same order-book shape:

{
  bids: [
    { price: 0.44, size: 100 }
  ],
  asks: [
    { price: 0.46, size: 80 }
  ]
}

The advantage is not cosmetic. The arbitrage strategy can now operate on one schema instead of containing Polymarket-specific logic in one branch and Kalshi-specific price reconstruction in another.

The same applies to market matching. Predictefy's matched-market layer can identify cross-venue relationships, while its executable arbitrage endpoint applies the stricter live-book and resolution checks before turning a discrepancy into an arbitrage claim.

If you want to inspect the underlying normalized books yourself:

const polymarketBook =
  await client.polymarket.fetchOrderBook({
    outcomeId: POLYMARKET_OUTCOME_ID
  });

const kalshiBook =
  await client.kalshi.fetchOrderBook({
    outcomeId: KALSHI_OUTCOME_ID
  });

Building the Polymarket vs Kalshi Arbitrage Trading Bot

Start with the official Predictefy TypeScript SDK:

npm install @predictefy/sdk

Create the client:

import Predictefy from '@predictefy/sdk';

const client = new Predictefy({
  apiKey: process.env.PREDICTEFY_API_KEY
});

The core cross-venue request is:

const opportunities =
  await client.router.fetchArbitrage({
    contracts: 100,
    executableOnly: true
  });

Now restrict the all-venue result to the exact Polymarket–Kalshi pair:

function isPolymarketKalshi(row) {
  const venues = [
    row.legs?.buyYes?.venue,
    row.legs?.buyNo?.venue
  ];

  return (
    venues.includes('polymarket') &&
    venues.includes('kalshi')
  );
}

const polyKalshiArbs =
  opportunities.filter(isPolymarketKalshi);

Add your own strategy rules:

const CONFIG = {
  contracts: 100,
  minRoi: 0.02,
  minNetEdge: 2
};

function passesStrategy(row) {
  return (
    row.label === 'arbitrage' &&
    row.executable === true &&
    row.roi !== null &&
    row.roi >= CONFIG.minRoi &&
    row.netEdge !== null &&
    row.netEdge >= CONFIG.minNetEdge &&
    row.reasons.length === 0
  );
}

The REST equivalent is:

curl --request GET \
  --url 'https://data.predictefy.com/api/router/fetchArbitrage?contracts=100&executableOnly=true&limit=10' \
  --header 'Authorization: Bearer pk_live_YOUR_KEY'

Why contracts matters. Predictefy walks both live ask books at exactly the size you request. An opportunity can qualify for 50 contracts and disappear for 1,000 when either venue runs out of cheap depth.

const small =
  await client.router.fetchArbitrage({
    contracts: 50,
    executableOnly: true
  });

const large =
  await client.router.fetchArbitrage({
    contracts: 1000,
    executableOnly: true
  });

Use the discovery layer when you want to see near-misses too.

const discrepancies =
  await client.fetchDiscrepancies({
    live: true
  });

Matched markets and discrepancies tell you where venues disagree. fetchArbitrage tells you whether the best complementary pair passes the stricter executable assessment.

Automating Detection and Revalidation

Polling fetchArbitrage is enough for a basic scanner, but a live trading bot benefits from Predictefy's cross-venue WebSocket arbitrage surface.

The SDK exposes watchArbitrage():

const close = client.watchArbitrage(
  ({ frame }) => {
    const candidates =
      frame.rows
        .filter(isPolymarketKalshi)
        .filter(passesStrategy);

    for (const row of candidates) {
      console.log({
        question: row.question,
        roi: row.roi,
        netEdge: row.netEdge,
        buyYes: row.legs.buyYes.venue,
        buyNo: row.legs.buyNo.venue
      });
    }
  },
  {
    onError: (error) =>
      console.error(
        error.code,
        error.message
      )
  }
);

// later:
// close();

The underlying WebSocket operation is:

{
  "op": "subscribeArbitrage"
}

Predictefy recomputes that shared surface server-side rather than forcing every client to continuously join Polymarket and Kalshi books and rebuild the cross-venue calculation itself.

That does not mean the first qualifying frame should immediately trigger two orders.

A better automated flow is:

A simple revalidation helper looks like:

async function revalidate(candidate) {
  const latest =
    await client.router.fetchArbitrage({
      contracts: CONFIG.contracts,
      executableOnly: true
    });

  return latest.find((row) =>
    row.clusterId === candidate.clusterId &&
    isPolymarketKalshi(row) &&
    passesStrategy(row)
  ) ?? null;
}

If the candidate is gone, the bot should not trade the stale copy.

That single rule eliminates one of the most common mistakes in automated prediction market arbitrage: finding a valid spread and then acting on market data that is already several seconds old.

Execution, Fees and Leg Risk

Predictefy's arbitrage API qualifies the market-data side of the opportunity. Execution is still a separate system.

Predictefy's qualification is intentionally strict. A result only receives the arbitrage label when the required checks pass.

Do not hard-code either venue's fees.

Polymarket uses market-dependent fee parameters on CLOB V2. Its official documentation exposes fee information per market and notes that taker fees apply on fee-enabled markets.

Kalshi also states that fees can vary between markets and that some markets can carry maker fees. Its series API exposes fee type and multiplier metadata.

This is another reason Predictefy's verified-fee gate is useful: the arbitrage engine does not assume one global zero-fee model across two very different venues.

Execution capabilities also change independently from data support. Before depending on Predictefy's hosted execution layer, query:

GET https://exec.predictefy.com/v1/exec/venues

The response is the authoritative runtime list of armed execution lanes and exposes capabilities such as build, submit, cancel and modify.

Predictefy's current trading documentation also distinguishes the venue paths. Kalshi can use either its Predictefy-supported execution path or the native Kalshi account client, while the documented Polymarket architecture uses a caller-direct SDK path so the wallet, CLOB credentials and signed order remain in the caller's process.

Because execution status and venue eligibility can change, the bot should treat execution capability as something to check at runtime rather than a hard-coded assumption.

The biggest remaining risk is leg risk.

Polymarket YES fills at $0.43

Kalshi NO was expected at $0.52
but moves to $0.61 before fill

The original 95-cent arbitrage has disappeared.

The bot now holds a directional Polymarket YES position.

Your automated execution layer needs predefined behavior for:

• one-leg fills
• partial fills
• rejected orders
• venue downtime
• price movement between submissions
• insufficient balances
• timeouts
• cancel failures

For that reason, Polymarket vs Kalshi arbitrage should not be described as automatically risk-free. A successfully completed pair can lock in the modeled payoff relationship, but getting both legs into that state introduces operational and venue risk.

Frequently Asked Questions

Conclusion

A Polymarket vs Kalshi arbitrage trading bot is not mainly a two-price calculator.

The real engineering work is identifying equivalent contracts, normalizing two different venue APIs, walking both order books at the intended size, modeling the correct fees and making sure the opportunity still exists when the bot is ready to execute.

You can build each of those layers yourself.

Predictefy gives you a shorter path.

Use its normalized Polymarket and Kalshi data clients when you need individual market information. Use matched markets and discrepancies to find cross-venue pricing differences. Use client.router.fetchArbitrage() to qualify the pair at the exact trade size. Use watchArbitrage() for automated monitoring.

Then keep your actual competitive logic — sizing, risk limits, capital deployment and execution policy — inside your own bot.

Predictefy handles the fragmented prediction market infrastructure. Your strategy decides which opportunities are worth trading.

This guide is for general informational and engineering purposes and is not investment, financial or legal advice. Prediction market access and venue eligibility vary by jurisdiction.