How to Build a Market-Making Bot for Prediction Markets (2026)

To build a market-making bot for prediction markets, connect to a platform's CLOB API, implement discovery and quoting logic, manage inventory risk, and deploy with monitoring. Prediction markets like those on Polymarket allow bots to provide liquidity by posting two-sided quotes and profiting from spreads while handling binary outcome resolution.

Market making in this space differs from traditional crypto or equities because contracts resolve to 0 or 1 based on real-world events, requiring careful position management near resolution dates. Bots typically scan markets, score opportunities using historical data or signals, and maintain quotes with dynamic spreads adjusted for volatility and inventory.

What Is Market Making in Prediction Markets

Market making involves continuously offering to buy and sell outcome shares at prices that reflect your fair value estimate plus a spread. In prediction markets, this means quoting Yes and No tokens for events in politics, sports, crypto, and news. Successful bots capture the bid-ask spread repeatedly while avoiding large directional bets that could lead to losses upon resolution.

According to official documentation, platforms provide CLOB APIs for posting limit orders that rest on the book until filled or canceled. Key challenges include handling resolution risk, managing multi-market portfolios, and dealing with thin liquidity in niche events. Bots often focus on high-volume markets where spreads are tighter but volume supports frequent turnover.

Zanlo offers built-in analytics, historical stats, live data, and AI-powered forecasts across 18 categories, making it an ideal environment to test forecast accuracy and refine bot signals before deploying capital. Readers can explore current events and validate ideas using Zanlo's tools at https://new.zanlo.com/.

Prerequisites and Tools Needed

Before coding, prepare the following:

• A funded wallet with USDC on Polygon (for Polymarket) or equivalent on supported chains, plus gas tokens.
• API access via wallet signature; no traditional KYC for many international platforms.
• Python 3.10+ environment with libraries including py-clob-client, web3.py, pandas for data handling, and websockets for real-time feeds.
• A strategy for market selection, such as focusing on events with clear resolution criteria and sufficient liquidity.
• Backtesting data from public APIs or repositories to simulate performance.

GitHub repositories provide open-source examples of market-making bots tailored for Polymarket's CLOB, including modules for discovery, quoting, and inventory management.

Step 1: Set Up Authentication and Client Connection

Begin by creating a secure client using your private key. For Polymarket, derive API credentials through EIP-712 signing and initialize the ClobClient with host, chain ID 137, and signer details. Store keys in environment variables only.

Test the connection by fetching market metadata or order books. This step ensures your bot can authenticate without exposing keys in code. Always use dedicated wallets for bots to limit exposure.

Step 2: Implement Market Discovery

Use Gamma API or similar endpoints to scan active markets, filter by volume, resolution date, and category. Score markets based on liquidity, implied volatility, and your edge model. Bots often prioritize events 12-48 hours from resolution for tighter spreads.

Store market data in a local database like SQLite for quick lookups. Include filters to avoid low-liquidity or ambiguous resolution markets that increase risk.

Step 3: Build the Quoting Engine

Calculate fair value using external signals or historical accuracy. Post two-sided limit orders around this value with a configurable spread. Use batch order submission where supported to reduce latency.

Adjust quotes dynamically based on order book depth, your current inventory, and time to resolution. Implement GTC for resting orders and GTD for time-bound quotes near events.

Step 4: Add Risk and Inventory Management

Monitor positions across markets and enforce limits on net exposure. Use rebalancing logic to flatten inventory when skew becomes too large. Include stop-loss style rules for rapid momentum shifts.

Track performance metrics like spread capture, fill rates, and PnL per market. Paper trade extensively before live deployment.

Step 5: Deploy, Monitor, and Iterate

Run the bot in a containerized environment with logging and alerts. Set up reconciliation loops to verify fills and cancel stale orders. Continuously backtest and refine the probability model using new data.

Common pitfalls include over-leveraging on single events, ignoring rate limits, and failing to handle API changes. Start small and scale based on verified results.

How Much Does It Cost to Run a Market-Making Bot

Operational costs include gas fees for on-chain interactions (typically low on Polygon), potential taker fees on fills, and any platform subscription for premium data. Initial capital of a few hundred to several thousand USDC suffices for testing, but meaningful profits require scale and consistent edge. Expect ongoing expenses for hosting and API access if using paid data feeds.

Is It Safe to Run Automated Bots on Prediction Markets

Automation introduces smart contract, API, and key management risks. Use hardware wallets or multisig where possible, never hardcode keys, and implement kill switches. Prediction markets carry event-specific risks like ambiguous resolutions or low liquidity traps. Always verify contract addresses and monitor for platform updates that could break integrations.

Common Mistakes and Troubleshooting

• Hardcoding credentials leads to key compromise; always use env vars.
• Ignoring inventory skew results in large directional losses on resolution.
• Poor market selection wastes capital on illiquid events.
• Failing to cancel orders near resolution causes unwanted fills.
• Over-optimizing on historical data without live validation leads to overfitting.

Troubleshoot by reviewing logs for signature errors, checking rate limits, and testing individual modules in isolation. Use community GitHub issues for platform-specific bugs.

Market making bots thrive on consistent execution and disciplined risk rules rather than perfect predictions. Platforms evolve quickly, so review official docs regularly for API updates in 2026.

This guide is for educational purposes only and does not constitute financial, investment, or trading advice. Past performance does not guarantee future results, and trading prediction markets involves substantial risk of loss.