\n\n日本語要約: Polymarketの予測市場におけるバイナリオプションの仕組みを開発者視点で解説する記事です。CLOB（中央指値注文帳）の構造、5分/15分BTCバイナリコントラクトのライフサイクル、APIやWebSocketを使ったデータ取得方法をPythonコード例とともに紹介します。
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I’ve been digging into Polymarket’s binary options infrastructure for the past few weeks, specifically the short-duration BTC contracts. If you’re a developer looking to build trading tools, analytics dashboards, or just want to understand how these contracts actually work under the hood, this post covers what I’ve learned.
\nPolymarket is a prediction market platform built on Polygon. Users trade shares in the outcome of events - political elections, sports, crypto prices, whatever. Each market resolves to either YES or NO, and shares pay out $1 if correct, $0 if wrong.
\nBinary options on Polymarket take this concept and apply it to price movements. Instead of “Will candidate X win the election?”, you get markets like “Will BTC be above $67,500 at 14:05 UTC?” - a simple up/down bet on where price will be at a specific time.
\nThe key insight: these are NOT traditional options with Greeks and continuous payoffs. They’re binary - you either get $1 or $0. The pricing reflects the market’s implied probability of the event occurring. A YES share trading at $0.62 means the market thinks there’s roughly a 62% chance BTC will be above the strike at expiry.
\nPolymarket runs a Central Limit Order Book (CLOB) - not an AMM. This is important because it means you get real bid/ask spreads, limit orders, and proper price discovery.
\nThe CLOB is operated by Polymarket’s exchange operator but settlement happens on-chain via their CTF (Conditional Token Framework) contracts. Here’s the flow:
\nThe order book for each binary option market has two sides: YES tokens and NO tokens. Since YES + NO = $1 always, buying YES at $0.60 is equivalent to selling NO at $0.40. The exchange handles this complementarity automatically.
\nWhat makes this interesting from a market microstructure perspective: the spreads on short-duration BTC binaries are often 2-5 cents wide. Market makers are actively quoting, and you can see the book update in real-time via WebSocket.
\nThe short-duration BTC markets are where things get fun. Polymarket runs rolling binary option contracts on BTC/USDT price with 5-minute and 15-minute durations.
\nHere’s how a typical 5-minute contract works:
\nThe 15-minute contracts work identically but with a longer window, which generally means tighter spreads and more liquidity since there’s more time for price discovery.
\nThe dashboard above shows the relationship between the underlying BTC/USDT mid price and the binary option price curves. You can see how the BO price responds non-linearly to moves in the underlying - this is basically the CDF of the implied distribution compressed into a 5-minute window.
\nOne thing that tripped me up initially: the contract creation is staggered. New 5m contracts launch every 5 minutes, so at any given time you might have an active contract with 3 minutes remaining and a new one that just opened. The overlap creates interesting arbitrage dynamics.
\nEvery binary option contract goes through distinct phases:
\nThe market is created with a condition ID, strike price, and expiry timestamp. The CTF contract mints the initial token positions. At this point, YES and NO tokens exist but no one has traded yet.
\nOrders flow in. Market makers post bids and asks on both sides. As BTC moves relative to the strike, the BO price adjusts. The closer to expiry, the more sensitive the price becomes to small moves in the underlying (gamma increases, if you want to think in options terms).
\nAt expiry, the oracle reports the BTC price. The contract resolves: if BTC >= strike, YES tokens are redeemable for $1 each. NO tokens become worthless. Redemption happens on-chain through the CTF contract.
\nThe fills chart above shows actual trades on a 5m binary market. Notice how trade frequency and size increase as we approach expiry - that’s participants reacting to last-second price moves in BTC.
\nPolymarket exposes two main interfaces for developers:
\nThe CLOB API gives you access to markets, order books, and trade history. Base URL is https://clob.polymarket.com.
Key endpoints:
\nGET /markets - list available markets (filterable by tag, status)GET /book?token_id={id} - current order book snapshotGET /trades?market={id} - recent trade historyGET /prices?token_id={id} - price history with OHLC dataFor real-time data, you connect to the WebSocket feed at wss://ws-subscriptions-clob.polymarket.com/ws/market. You subscribe to channels per market and get:
Here’s a basic Python script to fetch market data and inspect a BTC binary option:
\nimport requests\nimport json\nfrom datetime import datetime\n\nCLOB_BASE = \"https://clob.polymarket.com\"\n\ndef get_btc_binary_markets():\n \"\"\"Fetch active BTC binary option markets.\"\"\"\n resp = requests.get(\n f\"{CLOB_BASE}/markets\",\n params={\"tag\": \"btc-binary\", \"active\": \"true\"}\n )\n resp.raise_for_status()\n markets = resp.json()\n return markets\n\ndef get_order_book(token_id: str):\n \"\"\"Get current order book for a binary option token.\"\"\"\n resp = requests.get(\n f\"{CLOB_BASE}/book\",\n params={\"token_id\": token_id}\n )\n resp.raise_for_status()\n return resp.json()\n\ndef get_recent_trades(condition_id: str, limit: int = 50):\n \"\"\"Fetch recent trades for a market.\"\"\"\n resp = requests.get(\n f\"{CLOB_BASE}/trades\",\n params={\"market\": condition_id, \"limit\": limit}\n )\n resp.raise_for_status()\n return resp.json()\n\n# Fetch active BTC binary markets\nmarkets = get_btc_binary_markets()\n\nfor market in markets[:3]:\n print(f\"Market: {market['question']}\")\n print(f\" Condition ID: {market['condition_id']}\")\n print(f\" End Date: {market['end_date_iso']}\")\n print(f\" Tokens: YES={market['tokens'][0]['token_id'][:12]}...\")\n print(f\" NO={market['tokens'][1]['token_id'][:12]}...\")\n\n # Get the order book for YES token\n yes_token_id = market['tokens'][0]['token_id']\n book = get_order_book(yes_token_id)\n\n if book.get('bids') and book.get('asks'):\n best_bid = float(book['bids'][0]['price'])\n best_ask = float(book['asks'][0]['price'])\n print(f\" Best Bid: ${best_bid:.3f}\")\n print(f\" Best Ask: ${best_ask:.3f}\")\n print(f\" Spread: ${best_ask - best_bid:.3f}\")\n print()Once you have trade data, you usually want to reconstruct what happened - who was aggressive, what the fill rate looked like, how price evolved. Here’s how I parse trade data from the API:
\nimport pandas as pd\nfrom datetime import datetime, timezone\n\ndef parse_trades_to_df(trades: list) -> pd.DataFrame:\n \"\"\"Parse raw trade data into a DataFrame with derived fields.\"\"\"\n records = []\n for trade in trades:\n ts = datetime.fromisoformat(\n trade['timestamp'].replace('Z', '+00:00')\n )\n records.append({\n 'timestamp': ts,\n 'price': float(trade['price']),\n 'size': float(trade['size']),\n 'side': trade['side'], # 'BUY' or 'SELL'\n 'fee_rate': float(trade.get('fee_rate_bps', 0)) / 10000,\n 'notional': float(trade['price']) * float(trade['size']),\n })\n\n df = pd.DataFrame(records)\n if df.empty:\n return df\n\n df = df.sort_values('timestamp').reset_index(drop=True)\n\n # Compute running VWAP\n df['cum_notional'] = df['notional'].cumsum()\n df['cum_size'] = df['size'].cumsum()\n df['vwap'] = df['cum_notional'] / df['cum_size']\n\n # Tag aggressive side (taker)\n df['is_buyer_taker'] = df['side'] == 'BUY'\n\n # Time until expiry (assuming market_end is known)\n return df\n\n# Example usage\ntrades = get_recent_trades(markets[0]['condition_id'], limit=100)\ndf = parse_trades_to_df(trades)\n\nprint(f\"Total trades: {len(df)}\")\nprint(f\"Total volume: ${df['notional'].sum():.2f}\")\nprint(f\"VWAP: ${df['vwap'].iloc[-1]:.4f}\")\nprint(f\"Buyer-taker ratio: {df['is_buyer_taker'].mean():.1%}\")\nprint(f\"\\nLast 5 trades:\")\nprint(df[['timestamp', 'price', 'size', 'side']].tail())A few things I noticed when working with this data:
\nsize field is in shares (not USD) - multiply by price to get notionalA few gotchas if you’re building on this:
\nRate limits: The CLOB API has rate limits that aren’t always clearly documented. I found ~100 requests/minute to be safe for the REST endpoints. Use WebSocket for anything real-time.
\nToken ID vs Condition ID: Markets have a condition_id (the overall market) and individual token_ids for YES and NO positions. Some endpoints want one, some want the other. The order book endpoint takes token_id, while trades takes the market’s condition_id.
Timing: For 5-minute binaries, even 500ms of latency matters. If you’re building a trading bot, you need to account for the time between your price observation and order arrival at the exchange.
\nResolution edge cases: What happens if the oracle price is exactly at the strike? In Polymarket’s implementation, >= resolves to YES. But double-check the specific market’s resolution rules - they’re in the market description.
\nPolymarket’s binary options are a surprisingly clean environment for building trading tools. The CLOB gives you proper market microstructure to analyze, the contracts are simple enough to model (binary payoff + time decay), and the API access is decent for a crypto platform.
\nIf you’re coming from TradFi options or DeFi AMMs, the mental model shift is: think of these as extremely short-dated digital options with discrete settlement. The pricing behavior near expiry is where all the action happens - and where the data gets interesting.
\nI’m working on a follow-up post covering how to model the implied probability surface across multiple strikes and durations. If you’re building something similar, feel free to reach out.
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