Polymarket & Kalshi Trading Bot: Automate Prediction Market Trading

Prediction markets are the first betting venues designed from the ground up for bots. Traditional sportsbooks ban automated betting, restrict API access, and limit anyone who wins too consistently. Polymarket and Kalshi are the opposite. They provide trading APIs, encourage programmatic participation, and profit from volume regardless of who's winning.

This makes prediction markets the ideal environment for automated trading strategies, from simple arbitrage to sophisticated market making. If you've been thinking about building or using a prediction market trading bot, this guide covers everything you need to know. For the full walkthrough of what we cover across Kalshi, Polymarket, and the tools that sit on top, start from our prediction market strategies playbook.

Why Prediction Markets Are Bot-Friendly

Before diving into the technical details, it's worth understanding why prediction markets work so well for automated trading compared to traditional sportsbooks:

Factor	Sportsbooks	Prediction Markets
API trading	Banned (most) or very limited	Fully supported, encouraged
Account limits	Restrict winning accounts	No restrictions on winners
Revenue model	Profit from losing bettors	Profit from trading fees
Orderbook visibility	Hidden (single price shown)	Full depth visible
Order types	Market only (take what's offered)	Limit, market, FOK, FAK
Bot detection	Active detection + bans	No bot restrictions
Data feeds	Screen scraping required	WebSocket + REST APIs

This is why the prediction market bot ecosystem is growing rapidly. The legal framework supports it, the technology enables it, and the platforms want it.

Polymarket Trading Bot: How It Works

Polymarket operates a Central Limit Order Book (CLOB) on the Polygon blockchain. A Polymarket trading bot interacts with this system through several layers:

Architecture

• Polygon L2 blockchain: settlement layer. All trades settle on-chain in USDC. Your wallet holds your funds and positions
• CLOB (off-chain matching): Polymarket's order matching engine. Orders are signed off-chain and matched by the CLOB operator, then settled on-chain
• WebSocket feed: real-time orderbook updates, trade notifications, and price changes. Your bot subscribes to markets it cares about
• REST API: market discovery, order placement, position queries, historical data

What a Polymarket Bot Does

1. Connects to the Polymarket WebSocket to receive real-time orderbook updates for target markets
2. Maintains a local copy of the orderbook (bids and asks at each price level with depth)
3. Evaluates each price update against its strategy (arbitrage check, spread analysis, signal model)
4. Signs orders with your Polygon wallet private key (no gas required for CLOB orders, only on settlement)
5. Submits orders to the CLOB via REST API
6. Monitors fills and manages positions

Key Technical Details

• Token IDs: each market outcome has a unique token ID (a long hex string). Your bot uses these to subscribe to specific orderbooks
• Prices: quoted as 0.00 to 1.00 (probability). A YES token at 0.65 means the market prices the event at 65% likely
• Minimum order size: varies by market, typically $1-5 USDC
• Fees: maker/taker fee structure. Makers (limit orders that add liquidity) often pay lower fees than takers (market orders that remove liquidity)
• Settlement: when an event resolves, winning tokens are redeemable for $1.00 USDC each. Losing tokens become worthless

Kalshi Trading Bot: How It Works

Kalshi is a CFTC-regulated designated contract market (DCM). It operates more like a traditional financial exchange than a crypto platform.

Architecture

• Centralized exchange: Kalshi operates the matching engine, custody, and settlement. No blockchain involved
• REST API: full trading API for order placement, market discovery, portfolio management, and account operations
• WebSocket feed: real-time orderbook deltas (incremental updates) and ticker data (last trade price, volume)
• Authentication: API key + secret pair. Standard HMAC signing for authenticated endpoints

What a Kalshi Bot Does

1. Authenticates via API key credentials
2. Subscribes to WebSocket channels for target event tickers (e.g., orderbook_delta and ticker channels)
3. Maintains local orderbook state by applying delta updates to a base snapshot
4. Evaluates opportunities against its strategy
5. Places orders via REST API with specified price (in cents, 1-99), quantity, and side (yes/no)
6. Manages fills, cancellations, and position lifecycle

Key Technical Details

• Tickers: markets are identified by ticker strings (e.g., KXBTC-26MAR21-T50000 for a Bitcoin price contract)
• Prices: quoted in cents (1-99). Buying YES at 45¢ means you pay $0.45 and receive $1.00 if the event occurs
• Order types: limit orders with fill-or-kill (FOK) or fill-and-kill (FAK) time-in-force options. FOK means the entire order fills at your price or cancels completely
• Fees: tiered fee structure based on monthly volume. New accounts start at 7% of profit. High-volume traders can reach lower tiers
• Depth: the WebSocket feed provides full orderbook depth (price levels with quantities), allowing precise slippage calculation before trading

SX Bet Trading Bot: How It Works

SX Bet is the third major venue worth integrating into a serious prediction market bot stack. It's the largest decentralized peer-to-peer sports betting exchange (over $780M in lifetime volume), built on the SX Network blockchain with Arbitrum support. What makes it structurally appealing for a trading bot:

• Public REST API with no key required for reading markets, odds, and orderbook depth. Anyone can pull data in real time.
• EIP-712 wallet signing for placement. Any Ethereum-compatible wallet works. No KYC, no API approval process, no rate-limit gating beyond standard fairness limits.
• 0% fees on single bets, no account limits. SX Bet has never limited or banned a winning trader, because the protocol has no house position to protect. This makes it a permanent home for high-volume arbitrage strategies that get gubbed everywhere else.
• Officially welcomes bots. SX Bet publishes a Python bot tutorial on their developer blog and treats algorithmic trading as a first-class use case.
• Best-in-class US sports pregame odds on NFL, NBA, MLB, and NHL, with deep liquidity that rivals or exceeds Polymarket on those categories.

For prediction market bot operators, SX Bet pairs especially well with Kalshi and Polymarket as a third venue: it adds a no-vig market-set reference price (useful for value betting), it expands the universe of cross-venue arb pairs, and it provides a venue where winning is structurally unrestricted. For the architectural details and a worked example, see our dedicated SX Bet arbitrage guide.

Bot Strategies for Prediction Markets

What should your prediction market bot actually do? Here are the most common and profitable strategies:

1. Cross-Venue Arbitrage

The most straightforward strategy: find the same event priced differently on Kalshi and Polymarket, and bet both sides to lock in a guaranteed profit.

Example: "Will Bitcoin exceed $100K by March 31?"

Platform	YES Price	NO Price
Kalshi	42¢	58¢
Polymarket	0.47	0.53

Buy YES on Kalshi at 42¢ + buy NO on Polymarket at 0.53. Total cost: $0.95. Guaranteed payout: $1.00. Profit: $0.05 per contract (5.3% edge before fees).

Your bot monitors both orderbooks via WebSocket, computes the arb percentage on every price update, and executes when the edge exceeds your minimum threshold after fees. You can model these edges manually with our prediction market calculator. Read our full guide on Kalshi vs Polymarket arbitrage for more detail on this strategy.

2. Cross-Book Arbitrage (Sharp vs Prediction Market)

This strategy goes beyond just Kalshi-vs-Polymarket. It compares sharp sportsbook odds (Pinnacle, PS3838) against prediction market prices on the same events.

Example: Pinnacle prices Lakers ML at 2.10 (implied 47.6%). Kalshi has "Lakers to win" YES at 42¢ (implied 42%). The sharp line says the true probability is ~47.6%, but you can buy it on Kalshi at 42%. That's a 5.6% edge backed by the most efficient odds in the market.

This isn't pure arbitrage (you're taking directional risk), but when the sharp line consistently outperforms soft pricing, the expected value is strongly positive over many trades. This approach sits between pure arbing and value betting -- you're betting with the sharp side rather than hedging both sides. A bot running this strategy:

1. Polls sharp sportsbook odds every 200ms
2. Compares implied probabilities against Kalshi/Polymarket prices
3. Executes when the edge exceeds a configurable threshold (e.g., 3%+)
4. Sizes positions based on Kelly criterion or fixed-fraction bankroll management

3. Market Making

Market making bots provide liquidity by placing both buy and sell orders around the fair price. They profit from the spread between their bid and ask prices.

How it works: If you estimate the true probability of an event at 50%, you might place a buy order at 48¢ and a sell order at 52¢. When both sides fill, you've earned 4¢ per contract regardless of the outcome.

Market making on prediction markets is viable because:

• Many markets have wide spreads (low competition for liquidity)
• Maker fees are lower than taker fees (you're paid to add liquidity)
• New events constantly create fresh markets with no existing liquidity

The risk: adverse selection. If a material news event occurs, informed traders will hit your stale quotes before you can cancel them. Good market-making bots incorporate news feeds and rapid cancellation logic to minimize this risk.

4. Bracket Arbitrage

Some prediction markets offer multiple related contracts that should sum to 100%. For example, "Which team will win the NBA Championship?" might have 30 contracts (one per team). If the YES prices across all teams sum to more or less than $1.00, there's an arbitrage opportunity.

A bracket arb bot:

1. Monitors all contracts in a bracket simultaneously
2. Calculates the sum of best ask prices (to buy all YES) or best bid prices (to sell all YES)
3. If the sum of best asks is less than $1.00, buys all outcomes for guaranteed profit
4. If the sum of best bids exceeds $1.00, sells all outcomes for guaranteed profit

5. Event-Driven Trading

Some bots trade on information speed rather than price discrepancies. They consume external data feeds (sports scores, election results, economic data releases) and trade prediction market contracts before human traders can react.

This is how live in-play prediction market trading works. A bot watching a live NBA game score feed can submit a trade on Kalshi's "Lakers to win" contract within milliseconds of a momentum-shifting play, before the orderbook reflects the new game state.

Building vs Buying: DIY Bot or Use a Platform?

You have two paths to automated prediction market trading:

Build Your Own

Pros:

• Full control over strategy, execution, and risk parameters
• No dependency on third-party software
• Can implement custom, proprietary strategies
• No subscription fees

Cons:

• Significant development time (weeks to months for a production-quality bot)
• Need to handle WebSocket reconnection, orderbook management, error recovery, and edge cases
• Ongoing maintenance as APIs change
• Must build your own risk management (kill switch, exposure limits, naked position tracking)
• No support community or documentation beyond the platform APIs

What you'll need:

• Python (asyncio for concurrent WebSocket handling) or Node.js
• Kalshi API credentials (apply at kalshi.com)
• Polymarket CLOB SDK or direct smart contract interaction
• A Polygon wallet with USDC for Polymarket
• A server running 24/7 (cloud VPS recommended for consistent latency)

Use Claw Arbs

Claw Arbs is a ready-built prediction market trading bot that handles the entire pipeline (data ingestion, opportunity detection, execution, and risk management) across Kalshi, Polymarket, and sharp sportsbooks (PS3838, BetInAsia).

Pros:

• Production-ready with WebSocket feeds for Kalshi and Polymarket (25ms debounce)
• Cross-venue arb engine (Kalshi vs Polymarket) and cross-book engine (sharp sportsbooks vs prediction markets) built in
• Full risk management: kill switch, naked position tracking, daily loss limits, per-pair cooldowns, execution deduplication
• Paper trading mode to validate strategies before risking capital
• Real-time dashboard showing live opportunities, executions, and P&L
• Configurable fee tiers, slippage buffers, and minimum edge thresholds
• Free during alpha

Cons:

• Currently in alpha (limited access)
• Self-hosted, so you run it on your own server
• Strategy is focused on arbitrage and cross-book value betting (not market making)

For most people, starting with a platform like Claw Arbs and customizing from there is faster than building from scratch. You can always build your own bot later once you understand the market dynamics from running an existing system.

Key Technical Considerations

Whether you build or buy, these technical factors determine whether your bot profits or bleeds:

Latency

In prediction market trading, latency is the time between a price change occurring and your order reaching the matching engine. Every millisecond matters:

• WebSocket connection: colocate your bot near the exchange's servers. Kalshi operates from AWS US-East. A VPS in the same region gives you single-digit-ms round trips
• Processing time: your arb calculation should take microseconds, not milliseconds. Pre-compute everything possible
• Order submission: use persistent HTTP connections (connection pooling) to avoid TCP handshake overhead on every order

Orderbook Management

Maintaining an accurate local orderbook is critical. WebSocket feeds send incremental deltas (price level changes), not full snapshots. Your bot must:

• Request an initial orderbook snapshot via REST API
• Apply each WebSocket delta in order (handle sequence numbers to detect missed messages)
• Periodically resync the full snapshot to correct any drift
• Track staleness. If your WebSocket disconnects, your local orderbook is stale and you must stop trading until it's resynced

Error Handling and Recovery

Production bots need to handle:

• WebSocket disconnections: auto-reconnect with exponential backoff, resync orderbook state
• Order rejections: price moved, insufficient balance, market suspended. Each requires a different response
• Partial fills: you ordered 100 contracts but only 73 filled. Your hedge sizing is now wrong. Adjust or cancel
• Naked positions: leg 1 filled but leg 2 failed. You need immediate alerting and a decision: hedge at worse price, hold, or close at loss
• Rate limits: both platforms rate-limit API calls. Your bot must respect these or risk temporary bans

Capital Efficiency

Prediction market contracts lock up capital until resolution. A $100 position on a contract that resolves in 30 days ties up that $100 for a month. Smart bots optimize capital by:

• Targeting shorter-duration events (resolves in hours/days, not months)
• Closing positions early by selling on the orderbook if the edge has been captured
• Balancing position sizing against available capital across multiple simultaneous opportunities

Getting Started: Step by Step

Ready to start automated prediction market trading? Here's the practical path:

Step 1: Set Up Accounts

• Kalshi: create an account, complete KYC verification, fund with USD. Generate API credentials from your account settings
• Polymarket: set up a Polygon wallet, bridge USDC to Polygon, connect to Polymarket. Your wallet's private key is your API credential

Step 2: Paper Trade First

Never go live with real capital on day one. Run your bot (or Claw Arbs) in paper trading mode. Track:

• How many opportunities are detected per day
• What the theoretical P&L would be
• How often fills would succeed at the displayed prices (simulated slippage)
• Maximum drawdown and naked exposure frequency

Step 3: Start Small

Fund each platform with a small amount ($500-$1,000) and go live. Monitor closely for:

• Fill rate: are your orders actually executing at expected prices?
• Slippage: how much worse are actual fills vs. displayed prices?
• Execution timing: how many milliseconds between detection and fill?

Step 4: Optimize and Scale

Once you have real P&L data:

• Adjust minimum edge thresholds based on observed slippage
• Tune position sizing based on available liquidity at each venue
• Add more market pairs as you identify consistently profitable categories
• Scale capital allocation to venues where your edge is strongest

Common Mistakes to Avoid

• Ignoring fees in your calculations. A 2% arb with 3% combined fees is a losing trade. Your bot must compute net edge after all platform fees. Use the arb calculator formulas with fee adjustments
• No kill switch. If your bot malfunctions, it can lose your entire bankroll in minutes. Always have a kill switch that halts all trading instantly
• Trading on stale data. If your WebSocket disconnects for even a few seconds, your prices are wrong. Never trade on data older than a few seconds
• Over-sizing positions. Thin orderbooks mean large orders move prices against you. Always check available depth before sizing
• Not tracking naked exposure. In arb strategies, a failed leg 2 leaves you unhedged. Track every naked position and have a protocol for resolving them
• Assuming historical edges persist. As more bots enter the market, edges compress. What worked last month may not work next month. Monitor your realized edge continuously

The Bottom Line

Prediction markets are purpose-built for trading bots. Kalshi's CFTC-regulated API and Polymarket's CLOB provide the infrastructure. The strategies are well-defined: cross-venue arbitrage, sharp-line value betting, market making, and bracket arbs. And unlike traditional sportsbooks, nobody will limit your account for being too profitable.

The question isn't whether prediction market bots work. The volume data proves they do. It's whether you build the infrastructure yourself or use a platform that's already solved the hard problems: real-time data ingestion, sub-second execution, risk management, and multi-venue orchestration.

Either way, now is the best time to start. Prediction markets are still young, pricing is still inefficient, and the edge opportunities are larger than they'll ever be again. Every month, more participants enter and spreads tighten. The money goes to whoever gets there first with the best tools.