342 lines
12 KiB
Python
342 lines
12 KiB
Python
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#!/usr/bin/env python3
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"""Scan active Polymarket markets for arbitrage edges.
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Detects:
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- Underpriced markets: best-ask YES + best-ask NO < $1.00 (buy both for profit)
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- Overpriced markets: best-bid YES + best-bid NO > $1.00 (sell both for profit)
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- Wide spreads: markets where bid-ask spread creates opportunity
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Uses Gamma API for market discovery and CLOB API for real order book prices.
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Gamma mid-prices always sum to $1.00 by construction, so order book prices are
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needed to find real executable edges.
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"""
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import argparse
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import json
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import sys
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import time
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import requests
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from py_clob_client.client import ClobClient
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GAMMA_API = "https://gamma-api.polymarket.com"
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CLOB_HOST = "https://clob.polymarket.com"
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def fetch_markets(limit: int = 100, offset: int = 0) -> list[dict]:
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"""Fetch active markets from Gamma API."""
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url = (
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f"{GAMMA_API}/markets"
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f"?limit={limit}&offset={offset}&active=true&closed=false"
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)
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resp = requests.get(url, timeout=15)
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resp.raise_for_status()
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return resp.json()
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def parse_token_ids(market: dict) -> tuple[str, str] | None:
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"""Extract YES and NO token IDs from a market dict."""
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raw = market.get("clobTokenIds")
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if not raw:
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return None
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try:
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ids = json.loads(raw)
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if len(ids) < 2:
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return None
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return ids[0], ids[1]
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except (json.JSONDecodeError, ValueError, IndexError):
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return None
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def parse_mid_prices(market: dict) -> tuple[float, float] | None:
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"""Extract mid-prices from Gamma API (for display context)."""
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raw = market.get("outcomePrices")
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if not raw:
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return None
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try:
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prices = json.loads(raw)
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if len(prices) < 2:
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return None
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return float(prices[0]), float(prices[1])
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except (json.JSONDecodeError, ValueError, IndexError):
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return None
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def calculate_fee(price: float, base_rate: float = 0.063) -> float:
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"""Calculate dynamic taker fee rate for fee-bearing markets."""
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return base_rate * min(price, 1.0 - price)
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def get_book_prices(client: ClobClient, token_id: str) -> tuple[float, float] | None:
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"""Get best bid and best ask for a token. Returns (best_bid, best_ask) or None."""
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try:
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book = client.get_order_book(token_id)
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except Exception:
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return None
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bids = [(float(b.price), float(b.size)) for b in (book.bids or [])]
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asks = [(float(a.price), float(a.size)) for a in (book.asks or [])]
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bids.sort(key=lambda x: x[0], reverse=True)
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asks.sort(key=lambda x: x[0])
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best_bid = bids[0][0] if bids else None
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best_ask = asks[0][0] if asks else None
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if best_bid is None or best_ask is None:
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return None
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return best_bid, best_ask
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def scan_edges(
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max_markets: int = 200,
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min_edge: float = 0.005,
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check_orderbooks: bool = True,
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) -> list[dict]:
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"""Scan markets for pricing edges.
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Two modes:
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1. Fast scan (check_orderbooks=False): Uses Gamma mid-prices (always sum to 1.0,
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so only finds spread-based opportunities via CLOB spot check)
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2. Deep scan (check_orderbooks=True): Fetches actual order book for each market
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to find real executable edges (slower, rate-limited)
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"""
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client = ClobClient(CLOB_HOST) if check_orderbooks else None
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edges = []
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offset = 0
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batch_size = 100
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fetched = 0
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checked_books = 0
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while fetched < max_markets:
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batch = fetch_markets(limit=batch_size, offset=offset)
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if not batch:
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break
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for market in batch:
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token_ids = parse_token_ids(market)
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mid_prices = parse_mid_prices(market)
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if token_ids is None or mid_prices is None:
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continue
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yes_token_id, no_token_id = token_ids
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yes_mid, no_mid = mid_prices
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# Skip very low-liquidity markets
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liquidity = float(market.get("liquidityNum", 0) or 0)
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if liquidity < 100:
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continue
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if not check_orderbooks:
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continue
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# Fetch real order book prices
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yes_book = get_book_prices(client, yes_token_id)
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no_book = get_book_prices(client, no_token_id)
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checked_books += 1
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if yes_book is None or no_book is None:
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continue
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yes_bid, yes_ask = yes_book
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no_bid, no_ask = no_book
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# Check underpriced: buy YES at ask + buy NO at ask < $1.00
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buy_both_cost = yes_ask + no_ask
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if buy_both_cost < (1.0 - min_edge):
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raw_edge = 1.0 - buy_both_cost
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yes_fee = calculate_fee(yes_ask)
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no_fee = calculate_fee(no_ask)
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total_fee = yes_fee + no_fee
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net = raw_edge - total_fee
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edges.append({
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"question": market.get("question", "Unknown"),
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"slug": market.get("slug", ""),
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"type": "UNDERPRICED",
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"yes_ask": yes_ask,
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"no_ask": no_ask,
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"cost_sum": round(buy_both_cost, 6),
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"raw_edge": round(raw_edge, 6),
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"fee_impact": round(total_fee, 6),
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"net_profit_per_share": round(net, 6),
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"profitable_after_fees": net > 0,
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"yes_mid": yes_mid,
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"no_mid": no_mid,
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"volume_24h": market.get("volume24hr", 0) or 0,
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"liquidity": liquidity,
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})
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# Check overpriced: sell YES at bid + sell NO at bid > $1.00
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sell_both_value = yes_bid + no_bid
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if sell_both_value > (1.0 + max(min_edge, 0.005)):
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raw_edge = sell_both_value - 1.0
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yes_fee = calculate_fee(yes_bid)
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no_fee = calculate_fee(no_bid)
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total_fee = yes_fee + no_fee
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net = raw_edge - total_fee
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edges.append({
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"question": market.get("question", "Unknown"),
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"slug": market.get("slug", ""),
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"type": "OVERPRICED",
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"yes_bid": yes_bid,
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"no_bid": no_bid,
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"cost_sum": round(sell_both_value, 6),
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"raw_edge": round(raw_edge, 6),
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"fee_impact": round(total_fee, 6),
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"net_profit_per_share": round(net, 6),
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"profitable_after_fees": net > 0,
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"yes_mid": yes_mid,
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"no_mid": no_mid,
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"volume_24h": market.get("volume24hr", 0) or 0,
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"liquidity": liquidity,
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})
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# Also report wide spreads (opportunity for market making)
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yes_spread = yes_ask - yes_bid
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no_spread = no_ask - no_bid
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max_spread = max(yes_spread, no_spread)
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if max_spread >= 0.03: # 3 cent spread or wider
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edges.append({
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"question": market.get("question", "Unknown"),
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"slug": market.get("slug", ""),
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"type": "WIDE_SPREAD",
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"yes_bid": yes_bid,
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"yes_ask": yes_ask,
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"yes_spread": round(yes_spread, 6),
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"no_bid": no_bid,
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"no_ask": no_ask,
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"no_spread": round(no_spread, 6),
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"max_spread": round(max_spread, 6),
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"raw_edge": round(max_spread, 6),
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"fee_impact": 0.0,
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"net_profit_per_share": round(max_spread, 6),
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"profitable_after_fees": True,
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"yes_mid": yes_mid,
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"no_mid": no_mid,
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"volume_24h": market.get("volume24hr", 0) or 0,
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"liquidity": liquidity,
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})
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# Rate limit: avoid hammering the CLOB API
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if checked_books % 5 == 0:
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time.sleep(0.2)
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fetched += len(batch)
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offset += batch_size
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if len(batch) < batch_size:
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break
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# Sort by raw edge descending
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edges.sort(key=lambda x: x["raw_edge"], reverse=True)
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return edges
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def format_output(edges: list[dict]) -> str:
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"""Format edges for display."""
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if not edges:
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return (
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"No arbitrage edges found in current markets.\n"
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"This is normal -- Polymarket is well-arbitraged, with most\n"
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"opportunities lasting only ~2.7 seconds (median) in 2026."
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)
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lines = []
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# Group by type
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underpriced = [e for e in edges if e["type"] == "UNDERPRICED"]
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overpriced = [e for e in edges if e["type"] == "OVERPRICED"]
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wide_spread = [e for e in edges if e["type"] == "WIDE_SPREAD"]
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if underpriced:
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lines.append(f"\n=== UNDERPRICED ({len(underpriced)}) - Buy both sides for guaranteed profit ===\n")
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lines.append(f" {'YES ask':>8} {'NO ask':>8} {'Sum':>8} {'Edge':>7} {'Net':>7} {'Vol24h':>10} Question")
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lines.append(" " + "-" * 100)
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for e in underpriced:
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marker = " *" if e["profitable_after_fees"] else ""
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lines.append(
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f" ${e['yes_ask']:<7.4f} ${e['no_ask']:<7.4f} "
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f"${e['cost_sum']:<7.4f} ${e['raw_edge']:<6.4f} "
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f"${e['net_profit_per_share']:<+6.4f}{marker} "
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f"${e['volume_24h']:>9,.0f} {e['question'][:55]}"
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)
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if overpriced:
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lines.append(f"\n=== OVERPRICED ({len(overpriced)}) - Sell both sides ===\n")
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lines.append(f" {'YES bid':>8} {'NO bid':>8} {'Sum':>8} {'Edge':>7} {'Net':>7} {'Vol24h':>10} Question")
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lines.append(" " + "-" * 100)
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for e in overpriced:
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marker = " *" if e["profitable_after_fees"] else ""
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lines.append(
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f" ${e['yes_bid']:<7.4f} ${e['no_bid']:<7.4f} "
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f"${e['cost_sum']:<7.4f} ${e['raw_edge']:<6.4f} "
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f"${e['net_profit_per_share']:<+6.4f}{marker} "
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f"${e['volume_24h']:>9,.0f} {e['question'][:55]}"
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)
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if wide_spread:
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lines.append(f"\n=== WIDE SPREADS ({len(wide_spread)}) - Market-making opportunities ===\n")
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lines.append(f" {'Y Spread':>8} {'N Spread':>8} {'Max':>7} {'Vol24h':>10} {'Liq':>10} Question")
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lines.append(" " + "-" * 100)
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for e in wide_spread:
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lines.append(
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f" ${e.get('yes_spread', 0):<7.4f} ${e.get('no_spread', 0):<7.4f} "
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f"${e.get('max_spread', 0):<6.4f} "
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f"${e['volume_24h']:>9,.0f} "
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f"${e['liquidity']:>9,.0f} "
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f"{e['question'][:55]}"
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)
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lines.append("")
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lines.append("* = profitable even on fee-bearing markets (most markets are fee-free)")
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return "\n".join(lines)
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def main():
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parser = argparse.ArgumentParser(
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description="Scan Polymarket for arbitrage edges using real order book data"
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)
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parser.add_argument(
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"--min-edge",
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type=float,
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default=0.005,
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help="Minimum edge to report (default: 0.005 = $0.005/share)",
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)
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parser.add_argument(
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"--limit",
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type=int,
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default=200,
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help="Maximum markets to scan (default: 200, each requires 2 API calls)",
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)
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parser.add_argument(
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"--json",
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action="store_true",
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help="Output results as JSON",
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)
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args = parser.parse_args()
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print(f"Scanning up to {args.limit} markets (2 order book lookups each)...",
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file=sys.stderr)
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try:
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edges = scan_edges(
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max_markets=args.limit,
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min_edge=args.min_edge,
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)
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except requests.RequestException as e:
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print(f"Error fetching data: {e}", file=sys.stderr)
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sys.exit(1)
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if args.json:
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print(json.dumps(edges, indent=2))
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else:
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print(format_output(edges))
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if __name__ == "__main__":
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main()
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