Polymarket Wallet Intelligence

Polymarket Wallet Intelligence

An MCP server and REST API that classifies Polymarket wallets as human or bot, scores their trading edge from 0–10, and streams their current open positions. Built for AI agents on copy-trading and signal-following stacks.

# Use it from any MCP client (Claude Desktop, Cursor, etc.)

pip install polymarket-intel-mcp polymarket-intel-mcp

Or call the hosted REST API directly

curl https://polymarket-intel-production.up.railway.app/wallet/0xf1528f12e645462c344799b62b1b421a6a4c64aa

## How this fits with other Polymarket MCP servers

Status

Latest: v1.2 (May 2026) — classifier improved to distinguish active human grinders from HFT bots. See release notes and v1.3 backlog issue for what's next.

A daily snapshot job runs at 08:00 UTC and re-scores the top 50 leaderboard wallets, building a historical dataset of classification stability over time.

There are several MCP servers covering Polymarket, each at a different layer:

These complement each other. A copy-trading agent would use polymarket-intel to filter wallets worth following, then graph-polymarket-mcp to read the markets those wallets are betting on, then whitmorelabs/polymarket-mcp to size its own entries.

What it answers

• "Is this trader a human or a bot?" — score_polymarket_wallet(wallet_address) → returns classification ∈ {human, bot, insufficient_data} plus a confidence score and reason codes.
• "Do they actually have an edge?" — edge_score from 0–10, gated on net realised PnL so distributed-but-losing wallets don't get false positives.
• "What are they betting on right now?" — get_open_positions(wallet_address) returns live positions sorted by size, refreshed every 30s.
• "How has their edge changed over time?" — /wallet/{address}/history returns the score time series from the daily snapshots.

Why this exists

The Polymarket leaderboard is misleading. It includes unrealised PnL marked-to-current-price, so the names at the top are dominated by bots running structural arb plus a few wallets sitting on huge open positions that may never resolve in their favour. Agents that copy-trade naively from the leaderboard get burned.

This service runs every leaderboard wallet through behavioural fingerprinting (focus ratio, holding period, timing regularity, category concentration) plus PnL reconstruction from raw activity, and only surfaces traders that look like genuine humans with a real edge.

The dataset grows more valuable over time — every day the snapshot job runs, historical signals accumulate. Wallets that have been consistently above edge 7 for 90 days are a stronger signal than any single point-in-time score.

Distributed as both a REST API and an MCP server

Architecture

┌──────────────────────────────────────────────┐

│ core/ │ │ client.py — Polymarket data API client │ │ signals.py — pure signal calculators │ │ scorer.py — classifier + edge score │ │ models.py — Pydantic response schemas │ ├──────────────────────────────────────────────┤ │ db/ │ │ schema.sql — Postgres tables + indexes │ │ repository.py — Repository protocol + │ │ InMemoryRepository │ │ supabase_repo.py — Supabase impl │ │ converters.py — ScoreResult ↔ records │ ├──────────────────────────────────────────────┤ │ api/main.py — FastAPI HTTP server │ │ mcp_server/ — MCP server (stdio) │ │ scripts/ │ │ analyze_wallet.py — CLI │ │ snapshot_job.py — daily cron entry │ │ tests/ │ └──────────────────────────────────────────────┘

Core has no idea persistence exists. The API and snapshot job depend on the Repository protocol — Supabase in production, in-memory in tests and when env vars are unset. This is what makes the suite run without a database and what lets you swap Supabase for Neon, RDS, or anything else later by adding one file.

Quickstart

git clone <repo> && cd polymarket-intel

python -m venv .venv && source .venv/bin/activate pip install -r requirements-dev.txt pytest # 19 tests, all green

CLI

python scripts/analyze_wallet.py phonesculptor

python scripts/analyze_wallet.py 0xf1528f12e645462c344799b62b1b421a6a4c64aa --json

REST API

uvicorn api.main:app --reload --port 8000

open http://localhost:8000/docs

The API is split into a slow tier (cached aggressively, cheap, ideal for one-off discovery) and a fast tier (short cache, ideal for live copy-trading agents). The split exists because the underlying data has different freshness needs — a wallet's classification doesn't change minute-to-minute, but their open positions do.

Why 30s on positions and not faster? Polygon block time is ~2s and Polymarket's activity index lags a few seconds. Polling below 10s gets you no fresher data, just rate-limit errors. 30s is the sweet spot for cost/freshness/upstream-friendliness.

Why debounced DB writes? A trading agent may hit /wallet/{address} thousands of times an hour. Writing a row per call would bloat history with near-duplicate snapshots. The score endpoint persists at most once per wallet per hour. The daily snapshot job guarantees coverage of the top 50 regardless of API traffic.

MCP server (Claude Desktop, Cursor, Continue)

python mcp_server/server.py

Then drop mcp_server/claude_desktop_config.example.json into your Claude Desktop config and edit the absolute path.

The server exposes four tools:

• score_polymarket_wallet(wallet_address) — full score
• score_polymarket_user(username) — lookup by display name
• get_polymarket_leaderboard(limit) — raw leaderboard
• get_open_positions(wallet_address) — fast snapshot of live bets

Scoring methodology

Bot triggers (any one fires → bot)

Soft signals stack: crypto-market-maker pattern, > 200 trades/day, etc.

Edge score (0–10) for humans

Hard gate: net realised PnL ≤ 0  →  capped at 2.0

Hard gate: < 10 winning markets → capped at 3.0

35% PnL magnitude (log-scaled) 25% win rate (capped at 70%) 15% PnL distribution (penalises top-1 concentration) 15% sample size (winning markets, capped at 50) 10% win/loss ratio (capped at 3x)

Net PnL is the hard gate so wallets like neutralwave23 — many distributed tiny wins masking $375k of losses — are correctly flagged as poor.

PnL reconstruction

money_in   = sum(BUY usdcSize per conditionId)

money_out = sum(SELL usdcSize) + sum(REDEEM usdcSize) pnl = money_out - money_in

status: REDEEM exists → won SELL exists, no REDEEM → exited no SELL, no REDEEM, last trade > 7 days old → lost no SELL, no REDEEM, last trade within 7 days → open

Why activity rather than the positions endpoint: positions vanish from the API after redeem, so any naive analysis using /positions undercounts wins. Always reconstruct from /activity?type=TRADE + /activity?type=REDEEM (separate calls — comma-joined types return 400).

Persistence (Supabase)

The historical dataset is the moat. Every day the snapshot job pulls the leaderboard, scores the top N wallets, and persists three things: the score itself (wallet_scores), the wallet's open positions at that moment (open_position_snapshots), and the leaderboard as it stood (leaderboard_snapshots). After 90 days you can answer questions no one else can: "who has been consistently above edge 7 for the last quarter?", "which wallets just entered the top 50?", "show me everyone who held YES on this market three days before resolution."

Setup

# 1. Create a Supabase project, get the URL and service_role key

cp .env.example .env # fill in SUPABASE_URL and SUPABASE_KEY

2. Apply the schema (Supabase dashboard → SQL editor → paste db/schema.sql → run)

Or via psql:

psql "$DATABASE_URL" -f db/schema.sql

3. Run the snapshot job once to verify it writes:

python scripts/snapshot_job.py --top 10

If SUPABASE_URL and SUPABASE_KEY are unset, both the API and the snapshot job fall back to an in-memory repository — the suite still passes, the API still serves live scoring, but history endpoints will be empty until you wire up Supabase.

Daily snapshot job

Schedule python scripts/snapshot_job.py --top 50 daily (Railway cron, GitHub Actions, or Supabase pg_cron triggering an edge function — your call). The job is idempotent: running twice creates two snapshots, which is fine — history queries pick the closest one.

python scripts/snapshot_job.py --top 50              # production

python scripts/snapshot_job.py --top 5 --dry-run # local testing, no writes

Each run records an audit row in snapshot_runs with start/finish times, wallets scored, and error count.

Schema