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
• Compete — Forecast Arena: the live public leaderboard where AI models and simple baselines forecast the same Polymarket questions daily and are scored by real outcomes (Brier score). Runs from this repo (enter your model · workflow · raw data).

Part of agent-next — building an agentic world.

60-second demo

npx clawhub install polymarket-paper-trader    # install via ClawHub
pm-trader init --balance 10000                 # $10k paper money
pm-trader markets search "bitcoin"             # find markets
pm-trader buy will-bitcoin-hit-100k yes 500    # buy $500 of YES
pm-trader stats --card                         # shareable stats card

That's it. Your AI agent is now trading Polymarket with zero risk.

Install

# via pip
pip install polymarket-paper-trader

# via ClawHub (for OpenClaw agents)
npx clawhub install polymarket-paper-trader

# from source (development)
uv pip install -e ".[dev]"

Requires Python 3.10+.

Works with

Runtime	One-line install
Claude Code	/plugin marketplace add agent-next/polymarket-paper-trader
Codex CLI	codex mcp add polymarket-paper-trader -- uvx --from polymarket-paper-trader pm-trader-mcp
Cursor
Gemini CLI	gemini extensions install https://github.com/agent-next/polymarket-paper-trader
OpenCode / Goose / Cline / Windsurf / Copilot	add pm-trader-mcp to the client's MCP config
OpenClaw / ClawHub	npx clawhub install polymarket-paper-trader
Hermes Agent / LangChain / OpenAI Agents SDK / CrewAI	wrap uvx --from polymarket-paper-trader pm-trader-mcp

Full copy-paste config for every runtime above (plus Grok/xAI remote MCP): docs/integrations.md.

Quick start

# Initialize with $10k paper balance
pm-trader init --balance 10000

# Browse markets
pm-trader markets list --sort liquidity
pm-trader markets search "bitcoin"

# Trade
pm-trader buy will-bitcoin-hit-100k yes 100      # buy $100 of YES
pm-trader sell will-bitcoin-hit-100k yes 50       # sell 50 shares

# Check portfolio and P&L
pm-trader portfolio
pm-trader stats

How it works — and why to trust it

• Your order walks the real book. A buy consumes live ask levels from the lowest price upward, exactly like a real taker order — slippage is real and reported in basis points
• Fees follow the official per-match curve — fee = C × rate × p × (1-p), rounded to 5 decimals, makers exempt (exact spec in the CHANGELOG) — charged per filled level from each market's published fee schedule, not an approximation
• Paper cash, real discipline. Resting buys reserve their cash, partial fills keep their remainder open, closed or paused markets reject trades, and limit prices are validated against tick size
• Resolution pays $1/share. Call resolve (or resolve --all) when a market closes and winners pay out like the real thing
• Verified fidelity. The live test suite asserts that simulated fills land inside the band of prices the market actually quoted (Data API v2 price history) and that fees match the official curve exactly — run against real APIs on CI
• Upstream-aligned. The client tracks the current Gamma / CLOB / Data API surface, contract-verified by live probes
• Multi-outcome markets — any number of outcomes, not just YES/NO
• 100% coverage gate on the core package, plus end-to-end tests against the live API

CLI commands

Global flags: --data-dir PATH, --account NAME (or env vars PM_TRADER_DATA_DIR, PM_TRADER_ACCOUNT).

MCP server — what your agent can do

Your agent gets the following tools via the Model Context Protocol. The server also carries the full trading playbook with it — as MCP server instructions, as a trading_playbook prompt, and as a skill://trading-playbook resource — so MCP-only clients (Cursor, Claude.ai connectors, Grok API remote MCP, ChatGPT apps) get the same guidance skill-aware agents get from skill/polymarket-paper-trader/SKILL.md.

pm-trader-mcp  # starts on stdio

# or, with no local install:
uvx --from polymarket-paper-trader pm-trader-mcp

Add to your Claude Code config:

{
  "mcpServers": {
    "polymarket-paper-trader": {
      "command": "pm-trader-mcp"
    }
  }
}

Remote transport (streamable-http)

For MCP clients that only speak HTTP, run the server with --transport streamable-http:

pm-trader-mcp --transport streamable-http --host 127.0.0.1 --port 8000
# or: pm-trader mcp --transport streamable-http --host 127.0.0.1 --port 8000

The MCP endpoint is then http://<host>:<port>/mcp. There is no isolation between callers — everyone who reaches the server shares all of its paper accounts; it is self-host-only, not a public multi-user service. There is no authentication on this transport; anyone who can reach the port can call every exposed tool. backtest and pk_battle (local file reads + local strategy-module execution) are stdio-only and are not registered when serving over streamable-http.

Docker

docker build -t pm-trader-mcp .
docker run -p 127.0.0.1:8000:8000 -v pm-trader-data:/root/.pm-trader pm-trader-mcp

Publish the port to 127.0.0.1 only (as above) unless you put a real authenticating proxy in front of it — the container has no auth of its own.

MCP tools