How to Build a Prediction Market Trading Bot With Claude (2026)

Search "how to build a prediction market trading bot" and most tutorials start with a Polymarket API call, then a Kalshi API call, then another exchange integration.

That works if you are building for one venue. It becomes a much harder engineering problem when you want to build across prediction markets.

A real cross-venue trading bot needs to answer questions such as: Which markets represent the same underlying event? Are their resolution rules actually compatible? Is the displayed price executable at the size you want? How much liquidity sits behind the best quote? What happens after venue fees? Can both legs be filled? Is there an execution lane for that venue?

Those are infrastructure problems before they are AI problems.

The cleaner architecture is to use Predictefy as the prediction market data and execution layer, Claude as the reasoning layer, and deterministic code as the strategy and risk layer.

What You Are Actually Building

A prediction market trading bot is not one program. It is several layers working together.

The first layer is market discovery. The system needs to know which markets exist across supported venues and how those markets are represented.

The second layer is market data. The bot needs current prices, order books, liquidity, trades and historical data where available.

The third layer is cross-venue intelligence. A prediction market arbitrage bot must determine whether two contracts can actually be compared before calculating a spread.

The fourth layer is strategy. The system calculates whether the opportunity is worth trading at the requested size.

The fifth layer is reasoning. This is where Claude can help: inspecting context, requesting the right tools, comparing candidates and explaining why a trade deserves further attention.

The final layers are risk and execution. They should remain constrained by code.

Why Use Predictefy Instead of Integrating Every Venue Yourself?

You can integrate prediction markets directly. For a single-venue bot, that can be the right choice.

For a cross-venue system, every additional prediction market adds another data model, identifier scheme, authentication method, order-book format, capability set and execution flow.

Predictefy's unified API is designed to normalize that layer. Its API reference uses an {exchange} path segment for venue-specific requests and a router surface for cross-venue or all-venue operations.

That means the bot can reason about prediction markets instead of spending most of its engineering time translating exchange schemas.

The Basic Architecture

The simplest architecture looks like this:

This prevents the system from becoming a black box. Claude can recommend an action, but it does not have to be trusted with arithmetic, account limits or unrestricted execution.

Step 1: Install the Predictefy SDK

Predictefy's official TypeScript SDK is @predictefy/sdk.

npm install @predictefy/sdk

Create the client with your API key:

import Predictefy from '@predictefy/sdk'; const client = new Predictefy({ apiKey: process.env.PREDICTEFY_API_KEY });

The API key is sent as a Bearer token. Keep it on the server and do not expose it in browser-side code.

Step 2: Search Prediction Markets

The first useful operation is market discovery.

The documented SDK can fetch markets for a specific venue:

const markets = await client.polymarket.fetchMarkets({ limit: 5, query: 'fed' }); console.log(markets.map((market) => market.title));

If you want to search across the unified market layer instead, use the router:

const markets = await client.router.fetchMarkets({ query: 'election', status: 'active' });

The REST equivalent uses the canonical reads API and an exchange path segment:

curl --request GET \ --url 'https://data.predictefy.com/api/polymarket/fetchMarkets?limit=5&status=active&query=fed' \ --header 'Authorization: Bearer pk_live_YOUR_KEY'

This is already a significant improvement over asking Claude to search several prediction market APIs itself. Claude can receive normalized results with consistent market fields rather than learning a new schema for each venue.

Step 3: Read a Live Order Book

Headline probabilities are useful for discovery, but trading decisions usually require order-book depth.

The SDK exposes fetchOrderBook. For example:

const book = await client.kalshi.fetchOrderBook({ outcomeId: 'KXFED-26MAR-T4.00' }); console.log(book);

The REST operation is:

GET /api/{exchange}/fetchOrderBook

and it takes an outcomeId.

This distinction matters because the price at the top of an order book may only be available for a tiny amount of size. A prediction market arbitrage bot should care about the average executable price for the intended position, not just the best displayed quote.

Step 4: Understand Matched Markets Before Arbitrage

The biggest mistake in prediction market arbitrage is assuming that similarly worded contracts are automatically equivalent.

Imagine one venue lists:

Will the Fed cut interest rates at the September meeting?

Another lists:

Federal Reserve lowers target rate in September 2026?

They may represent the same economic outcome, but wording alone is not enough to prove it.

Predictefy exposes matched-market and cross-venue intelligence routes specifically for this problem.

One documented route is:

GET /api/router/fetchMatchedMarkets

The important distinction is that a matched-market price gap remains an indicative price discrepancy. It should not automatically be treated as executable arbitrage.

That distinction is important enough to build directly into the bot.

Step 5: Scan for Prediction Market Arbitrage

This is where Predictefy's arbitrage API becomes particularly useful.

The basic theoretical binary arbitrage equation is:

YES purchase price + NO purchase price < $1.00

For example:

Buy YES: $0.41 Buy NO: $0.56 Combined cost: $0.97 Settlement payout: $1.00

But a production arbitrage engine needs to know much more than that.

Predictefy's documented fetchArbitrage operation prices discrepancy clusters against live order books at a requested contract size. It checks whether the legs have executable depth and whether the opportunity survives the relevant gates rather than automatically calling every spread arbitrage.

Using the SDK:

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

Using the REST API:

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

The contracts value matters. The assessment is performed at that size, so a trade that qualifies for 100 contracts may fail at 10,000 because the available order-book depth is different.

What Predictefy Checks Before Calling It Arbitrage

This is one of the most important differences between an arbitrage calculator and an executable prediction market arbitrage system.

Predictefy's arbitrage qualification can account for factors including:

• live non-synthetic asks on both legs
• open market status
• enough depth at the requested size
• verified venue fee models
• resolution compatibility
• resolution equivalence
• a positive net edge after the relevant costs

If those conditions do not pass, the row remains an indicative discrepancy rather than being promoted to executable arbitrage.

That is exactly the kind of distinction you want before Claude ever sees the opportunity.

Step 6: Let Claude Evaluate Qualified Opportunities

Claude's role starts after structured market data exists.

Anthropic's tool-use system allows Claude to request functions defined by your application. For a prediction market trading agent, those tools can wrap Predictefy operations.

Claude can then reason in a loop:

Claude is orchestrating tools. It is not inventing the market state.

Step 7: Connect Predictefy Directly to Claude With MCP

You can also skip much of the custom tool-wrapper work by using Predictefy's MCP server.

The official package is:

@predictefy/mcp

For Claude Code, the documented setup is:

claude mcp add predictefy \ -e PREDICTEFY_API_KEY=pk_live_your_key_here \ -- npx -y @predictefy/mcp

This gives Claude access to Predictefy tools for market search, market data, order books, matched markets, cross-venue intelligence, arbitrage assessment and other supported capabilities.

For a research-only setup, trading tools can be disabled:

MCP_ENABLE_TRADE=false

This is useful when you want Claude to research and analyze prediction markets without exposing an execution-capable tool surface.

Useful Predictefy MCP Tools for a Trading Agent

Step 8: Use WebSockets for Live Order Books

A REST request gives you the market at a moment in time. Arbitrage is usually more time-sensitive than that.

Predictefy also documents a WebSocket streaming surface for live order books.

The connection uses /v1/stream on the WebSocket origin shown in the developer dashboard and authenticates with the same API key.

const ws = new WebSocket(`${WS_ORIGIN}/v1/stream`, { headers: { Authorization: `Bearer ${process.env.PREDICTEFY_API_KEY}` } }); ws.on('open', () => { ws.send(JSON.stringify({ op: 'subscribe', channel: 'orderbook', venue: 'polymarket', marketId: OUTCOME_ID })); });

For Polymarket, the identifier passed to the stream is the venue-native CLOB asset or token ID, which the unified API exposes as the outcome's outcomeId.

One implementation detail matters: Predictefy's documented order-book snapshot and update frames carry the full book. Replace your local book state rather than merging the frame as though it were a delta.

Do Not Send Every Market Tick to Claude

Just because Claude can inspect market data does not mean every price update should become an LLM request.

Use code for fast deterministic filtering:

function shouldEvaluate(opportunity) { return ( opportunity.executable === true && opportunity.netEdge !== null && opportunity.netEdge > MIN_NET_EDGE ); }

Only send meaningful candidates to Claude.

This reduces latency, model cost and unnecessary context while keeping the actual trading calculations deterministic.

Step 9: Keep Risk Management Outside the Model

Claude should not be able to decide that one opportunity looks so good that the account's normal rules no longer apply.

Define fixed limits in code:

const MAX_TRADE_SIZE = 500; const MAX_EVENT_EXPOSURE = 2000; const MIN_EXPECTED_ROI = 0.02; const MAX_SLIPPAGE = 0.01;

Then reject anything outside those limits regardless of Claude's recommendation.

if (tradeSize > MAX_TRADE_SIZE) rejectTrade(); if (eventExposure + tradeSize > MAX_EVENT_EXPOSURE) rejectTrade(); if (expectedRoi < MIN_EXPECTED_ROI) rejectTrade(); if (expectedSlippage > MAX_SLIPPAGE) rejectTrade();

The strongest architecture is:

Claude recommends. Code decides what is allowed.

Step 10: Revalidate Immediately Before Execution

An arbitrage opportunity is a snapshot.

Prices can move. Order-book depth can disappear. One venue can close the market. A trade that qualified seconds ago may no longer qualify now.

Before placing anything, re-read the market state and re-run the relevant checks.

How Predictefy Execution Actually Works

Predictefy separates market-data reads from execution.

The execution service uses a separate execution origin, and supported trading follows a build, sign and submit flow.

The documented order lifecycle is:

This is important: good market data on a venue does not automatically mean that venue currently has an active hosted execution lane. Check GET /v1/exec/venues rather than assuming.

Predictefy builds the order artifact and relays supported orders, while client-side signing keeps the signing material in your process.

Start With Dry Runs

Before enabling real execution, use preview and dry-run behavior where supported.

For example, the execution build surface supports dryRun: true for documented lanes. That lets the application test authentication, market resolution and order construction without creating a live execution.

A production rollout should usually move through three stages:

1. Read-only market research.
2. Paper or dry-run trade generation.
3. Restricted live execution with small caps.

The Biggest Arbitrage Risk: One Leg Fills and the Other Does Not

Prediction market arbitrage can lock in a payoff only if both intended positions are actually obtained under the assumptions used in the calculation.

Suppose a bot wants to buy:

YES on Venue A: $0.43 NO on Venue B: $0.53 Combined cost: $0.96

If the YES order fills but the NO price moves to $0.61 before the second leg fills, the original arbitrage is gone.

The system is now holding directional exposure.

A production trading bot therefore needs explicit handling for:

• partial fills
• rejected orders
• cancelled orders
• stale books
• price movement
• venue outages
• insufficient balances
• execution timeouts

Claude can help explain an abnormal situation. The execution engine should already know its allowed responses before the situation happens.

Give Claude a Narrow Trading Mandate

A weak system instruction looks like:

You are a prediction market trading bot. Find profitable trades.

A stronger instruction is much more constrained:

You are the reasoning layer for a prediction market trading system. Use Predictefy tools as the source of market data. Never invent: - market prices - liquidity - order-book depth - market status - account positions For arbitrage: 1. Request the qualified arbitrage assessment. 2. Review executable status and reasons. 3. Check the requested trade size. 4. Reject candidates that fail the configured strategy. 5. Return a structured trade proposal. Never bypass deterministic risk rules. Never increase position limits. Never assume a trade executed until the execution system confirms it.

Claude now has one job: reason over trustworthy structured data.

A Simple Claude + Predictefy Agent Loop

The application logic can stay surprisingly simple:

const opportunities = await client.router.fetchArbitrage({ contracts: 100, executableOnly: true }); for (const opportunity of opportunities) { if (!strategy.precheck(opportunity)) continue; const decision = await evaluateWithClaude(opportunity); if (decision.action !== 'TRADE') continue; const refreshed = await client.router.fetchArbitrage({ contracts: 100, executableOnly: true }); const current = findSameOpportunity(refreshed, opportunity); if (!current) continue; if (!risk.approve(current)) continue; await execution.prepare(current); }

The exact strategy is yours. The important part is where responsibility sits.

Predictefy supplies the market state and qualification layer. Claude reasons over it. Your application makes the final risk decision.

Why This Is Better Than Building a Polymarket-Only Bot

A Polymarket bot can only see Polymarket.

A Kalshi bot can only see Kalshi.

A prediction market arbitrage system needs to reason across the fragmented market.

The more venues that list the same economic event, the more useful a unified market layer becomes.

That means the better abstraction is not:

"Build a Polymarket bot."

It is:

"Build a prediction market trading bot, then decide which venue provides the best opportunity."

What Else Can You Build With Claude and Predictefy?

The same infrastructure can power much more than arbitrage.

Where Claude Adds the Most Value

Claude is most useful when the task involves interpretation rather than arithmetic.

Good uses include:

• deciding which market-data tool to call next
• reviewing resolution wording
• ranking several qualified opportunities
• summarizing the context behind a market
• turning natural-language instructions into structured filters
• explaining why an opportunity was accepted or rejected

Bad uses include:

• calculating every order-book tick
• storing your portfolio in model memory
• enforcing maximum position sizes
• inventing missing market data
• assuming a submitted order filled
• overriding a failed risk check

What a Natural-Language Trading Agent Could Look Like

Once the tools are connected, a trader could ask:

Find executable prediction market arbitrage opportunities for 500 contracts, rank them by ROI, exclude anything that settles more than 30 days from now, and do not propose a trade if it would take venue exposure above my configured limit.

Claude can interpret the request.

Predictefy supplies the market and arbitrage data.

Your application enforces the portfolio constraints.

That is a much safer architecture than giving an LLM an exchange key and asking it to trade.

Frequently Asked Questions

Conclusion

The hardest part of building a prediction market trading bot with Claude is not writing the Claude prompt.

It is building everything Claude needs underneath it.

The system needs normalized markets, order books, cross-venue matching, executable arbitrage qualification, historical data, account state, risk controls and a safe execution flow.

Predictefy provides that prediction market infrastructure through its API, SDK, WebSocket and MCP surfaces. Claude can then operate above it as the reasoning layer.

The resulting architecture is simple:

Predictefy provides the prediction market infrastructure.

Claude provides the reasoning.

Your strategy and risk engine control what actually gets traded.

That is a much stronger foundation than building another isolated Polymarket bot or Kalshi bot.

Build once around a normalized prediction market layer, then let the agent operate across the market.