polymarket-paper-trader MCP Server
agent-next/polymarket-paper-trader
[](https://agentrank-ai.com/tool/agent-next--polymarket-paper-trader/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge) Paper trading simulator for Polymarket
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
agent-next/polymarket-paper-trader is a Python MCP server licensed under MIT. Paper trading simulator for Polymarket Topics: ai-agent, algorithmic-trading, backtesting, clawhub, mcp, openclaw, paper-trading, polymarket, prediction-markets, python, sqlite, trading-bot, benchmark, forecasting, leaderboard, llm, mcp-server, model-context-protocol.
Ranked #5203 out of 24846 indexed tools.
Ecosystem
Score Breakdown
498 stars → growing community
Last commit 17d ago → recently updated
2/7 issues closed → many open issues
2 contributors → solo project
No dependents → no downstream usage
Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →
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From the README
# polymarket-paper-trader **A zero-risk gym for AI agents on real prediction markets — real order books, official fees, verified fill fidelity. Practice, evaluate, and benchmark decision intelligence.** Agents make probability judgments all day. Polymarket is the world's largest prediction market, and its order books are the honest scoreboard: real money, real prices, real outcomes. But you cannot hand an agent a wallet to learn with. So this project gives every agent what SWE-bench gave coders — a faithful environment where judgment has consequences and gets scored: - **Practice** — your agent trades $10k of paper money against live Polymarket order books, with the same fee model and fill mechanics as the real exchange - **Evaluate** — the `polymarket-benchmark` harness in this repository (installed separately) scores any model on prediction-market decision sets (Brier score, calibration, alpha) - **Compare** — multi-account battles and leaderboards rank agents against each other -Read full README on GitHub →
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