\n\n日本語要約: Polymarketの5分BTCバイナリオプションのL2オーダーブックを分析した記事です。FastAPI + vanilla JSで構築したビジュアライザーを使い、スプレッドの推移、流動性のパターン、スナップショット間のサイズ変化を可視化。市場効率性への考察も行います。
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I’ve been spending way too much time staring at Polymarket’s 5-minute BTC binary options lately. Not trading them (well, not much), but pulling apart their orderbooks to understand what’s actually happening in these ultra-short-duration prediction markets. Here’s what I found and the tool I built to see it all.
\nMost people look at prediction markets from the price/probability angle: “What does the market think the odds are?” But if you dig into the L2 orderbook, you get a much richer picture. You can see:
\nFor 5-minute binary options on BTC price, this is especially interesting because the entire lifecycle of the market plays out in 300 seconds. You get to watch an orderbook be born, mature, and die in the time it takes to make coffee.
\nPolymarket runs a CLOB (Central Limit Order Book) on their hybrid exchange. For binary options, you have two outcome tokens — let’s call them YES and NO (or in the BTC case, UP and DOWN). Each outcome has its own orderbook with bids and asks.
\nKey things to understand:
\nThe L2 data gives you multiple price levels deep on each side. For these 5-minute markets, you typically see 3-8 meaningful levels before the book gets sparse.
\nI wanted to see how the orderbook evolves over the life of a 5-minute contract. Not just the final state, but snapshots over time, side by side, with changes highlighted. So I built a simple tool.
\nNothing fancy. FastAPI backend that polls Polymarket’s API for orderbook snapshots, stores them, and serves them to a vanilla JS frontend that renders the books visually.
\nThe market selector lets you pick which 5-minute contract to analyze. I filter for markets where there was actual user trading activity (not just market maker quotes sitting there).
\nSince these markets are short-lived, I cache snapshots aggressively. Each market gets polled every 2 seconds during its active window, giving ~150 snapshots per contract.
\nfrom functools import lru_cache\nfrom datetime import datetime\nimport httpx\n\nclass OrderbookSnapshotCache:\n def __init__(self, max_snapshots_per_market=200):\n self.snapshots: dict[str, list[dict]] = {}\n self.max_snapshots = max_snapshots_per_market\n\n async def capture_snapshot(self, market_id: str, token_id: str):\n async with httpx.AsyncClient() as client:\n resp = await client.get(\n f\"https://clob.polymarket.com/book\",\n params={\"token_id\": token_id}\n )\n book = resp.json()\n\n snapshot = {\n \"timestamp\": datetime.utcnow().isoformat(),\n \"bids\": [(float(o[\"price\"]), float(o[\"size\"])) for o in book[\"bids\"]],\n \"asks\": [(float(o[\"price\"]), float(o[\"size\"])) for o in book[\"asks\"]],\n \"best_bid\": float(book[\"bids\"][0][\"price\"]) if book[\"bids\"] else 0,\n \"best_ask\": float(book[\"asks\"][0][\"price\"]) if book[\"asks\"] else 1,\n }\n\n if market_id not in self.snapshots:\n self.snapshots[market_id] = []\n\n snaps = self.snapshots[market_id]\n snaps.append(snapshot)\n if len(snaps) > self.max_snapshots:\n snaps.pop(0)\n\n return snapshot\n\n def get_window(self, market_id: str, start_idx: int, window_size: int = 5):\n \"\"\"Get a window of consecutive snapshots for side-by-side display.\"\"\"\n snaps = self.snapshots.get(market_id, [])\n end_idx = min(start_idx + window_size, len(snaps))\n return snaps[start_idx:end_idx]This is where it gets interesting. I compute the difference between consecutive snapshots so you can see at a glance what changed — did size get added or pulled at a given level?
\nfrom dataclasses import dataclass\nfrom typing import Optional\n\n@dataclass\nclass LevelDelta:\n price: float\n prev_size: float\n curr_size: float\n delta: float\n is_new: bool\n is_removed: bool\n\n @property\n def direction(self) -> str:\n if self.is_new:\n return \"new\"\n if self.is_removed:\n return \"removed\"\n if self.delta > 0:\n return \"increased\"\n if self.delta < 0:\n return \"decreased\"\n return \"unchanged\"\n\n\ndef compute_book_deltas(\n prev_snapshot: dict,\n curr_snapshot: dict,\n side: str = \"bids\"\n) -> list[LevelDelta]:\n \"\"\"\n Compare two orderbook snapshots and return per-level deltas.\n Highlights new levels, removed levels, and size changes.\n \"\"\"\n prev_levels = {price: size for price, size in prev_snapshot[side]}\n curr_levels = {price: size for price, size in curr_snapshot[side]}\n\n all_prices = sorted(\n set(prev_levels.keys()) | set(curr_levels.keys()),\n reverse=(side == \"bids\")\n )\n\n deltas = []\n for price in all_prices:\n prev_size = prev_levels.get(price, 0.0)\n curr_size = curr_levels.get(price, 0.0)\n\n deltas.append(LevelDelta(\n price=price,\n prev_size=prev_size,\n curr_size=curr_size,\n delta=curr_size - prev_size,\n is_new=(price not in prev_levels),\n is_removed=(price not in curr_levels),\n ))\n\n return deltas\n\n\ndef compute_spread(snapshot: dict) -> float:\n \"\"\"Spread as percentage of mid price.\"\"\"\n if not snapshot[\"bids\"] or not snapshot[\"asks\"]:\n return float(\"inf\")\n best_bid = snapshot[\"bids\"][0][0]\n best_ask = snapshot[\"asks\"][0][0]\n mid = (best_bid + best_ask) / 2\n return (best_ask - best_bid) / mid * 100The frontend displays 5 consecutive snapshots side by side. Each level is color-coded based on whether size increased (green), decreased (red), is new (blue), or was removed (gray strikethrough).
\n<div id=\"orderbook-window\" class=\"snapshots-container\"></div>\n\n<style>\n .level { display: flex; font-family: monospace; font-size: 12px; padding: 2px 4px; }\n .level.increased { background: rgba(0, 255, 0, 0.1); }\n .level.decreased { background: rgba(255, 0, 0, 0.1); }\n .level.new { background: rgba(0, 100, 255, 0.15); border-left: 2px solid #06f; }\n .level.removed { opacity: 0.4; text-decoration: line-through; }\n .price { width: 60px; text-align: right; margin-right: 8px; }\n .size { width: 80px; }\n .delta { width: 60px; color: #666; font-size: 11px; }\n</style>function renderOrderbookLevel(level, side) {\n const el = document.createElement('div');\n el.className = `level ${level.direction}`;\n\n const priceEl = document.createElement('span');\n priceEl.className = 'price';\n priceEl.textContent = level.price.toFixed(2);\n\n const sizeEl = document.createElement('span');\n sizeEl.className = 'size';\n sizeEl.textContent = `$${level.curr_size.toFixed(0)}`;\n\n const deltaEl = document.createElement('span');\n deltaEl.className = 'delta';\n if (level.delta !== 0 && !level.is_new && !level.is_removed) {\n const sign = level.delta > 0 ? '+' : '';\n deltaEl.textContent = `${sign}${level.delta.toFixed(0)}`;\n deltaEl.style.color = level.delta > 0 ? '#0a0' : '#c00';\n }\n\n if (side === 'bid') {\n el.append(deltaEl, sizeEl, priceEl);\n } else {\n el.append(priceEl, sizeEl, deltaEl);\n }\n\n return el;\n}\n\nfunction renderSnapshot(snapshot, deltas, container) {\n const header = document.createElement('div');\n header.className = 'snapshot-header';\n const ts = new Date(snapshot.timestamp);\n header.textContent = `${ts.getMinutes()}:${ts.getSeconds().toString().padStart(2, '0')}`;\n container.appendChild(header);\n\n const spread = ((snapshot.best_ask - snapshot.best_bid) * 100).toFixed(1);\n const spreadEl = document.createElement('div');\n spreadEl.className = 'spread-indicator';\n spreadEl.textContent = `spread: ${spread}c`;\n container.appendChild(spreadEl);\n\n // Render asks (reversed so lowest ask is closest to spread)\n const askDeltas = deltas.asks.slice().reverse();\n askDeltas.forEach(level => {\n container.appendChild(renderOrderbookLevel(level, 'ask'));\n });\n\n // Render bids\n deltas.bids.forEach(level => {\n container.appendChild(renderOrderbookLevel(level, 'bid'));\n });\n}The visualization above shows 5 consecutive L2 orderbook snapshots for both UP and DOWN outcomes. You can clearly see the book evolving — levels appearing and disappearing, sizes adjusting, spreads tightening and widening.
\nAfter watching a few hundred of these 5-minute contracts, some patterns become very clear.
\nAt market open (T+0), spreads are typically 2-4 cents. Market makers are happy to provide liquidity because there’s plenty of time for the underlying (BTC) to move and create trading opportunities.
\nAs expiry approaches (T+4:30 onwards), spreads blow out to 8-15 cents. This makes total sense — the market maker’s gamma risk is enormous in the last few seconds. A $10 move in BTC can flip the outcome, and you don’t want to be caught on the wrong side with tight quotes.
\nUnsurprisingly, the thickest liquidity clusters around:
\nThe deltas reveal classic market-making behavior:
\nThe final 10 seconds are wild. Here’s what consistently happens:
\nThe delta visualization makes this collapse very visible — you see a cascade of “removed” levels (gray strikethrough) rippling through the book in the final snapshots.
\nA few takeaways about market efficiency in these ultra-short contracts:
\nThese markets are surprisingly efficient mid-life. Between T+30s and T+4:00, the prices track BTC movements well. The orderbook adjusts within 2-4 seconds of BTC price changes.
\nBut they’re inefficient at the edges. In the first 10 seconds and last 10 seconds, there are clear mispricings. At open, it takes time for the book to populate and find fair value. At close, the liquidity vacuum creates slippage that benefits anyone with a fast feed.
\nThe spread IS the market’s uncertainty measure. Wider spreads = more disagreement about the outcome. You can actually use the spread time series as a proxy for realized volatility of the underlying during that 5-minute window.
\nSize matters more than price. A level with $5,000 behind it at 0.48 is much more informative than a level with $20 at 0.52. The orderbook depth weighted by size gives you a better probability estimate than the mid-price alone.
\nI’m planning to:
\nThe visualizer has been a great tool for building intuition about these markets. If you’re interested in prediction market microstructure, I’d highly recommend building something similar — there’s no substitute for watching the books move in real time.
\nThe full code is a mess of FastAPI routes and vanilla JS spaghetti, but the core logic above should be enough to get you started. The Polymarket CLOB API is well-documented and rate limits are generous enough for 2-second polling on a handful of markets.
","tableOfContents":"","frontmatter":{"title":"Analyzing Bid-Ask Spreads and Liquidity Shifts in Polymarket's 5-Minute Binary Option Orderbooks","date":"March 18, 2026","description":"A deep dive into the microstructure of Polymarket's 5-minute BTC binary option orderbooks, examining spread dynamics, liquidity patterns, and building a visualization tool."}}},"pageContext":{"slug":"/polymarket-orderbook-analysis/","previous":{"fields":{"slug":"/crypto-data-pipeline/"},"frontmatter":{"title":"Processing 155 Million Orderbook Records: Building a Fast Data Pipeline for Crypto Microstructure Analysis"}},"next":{"fields":{"slug":"/volatility-oracle-prototype/"},"frontmatter":{"title":"Building a Volatility Oracle Prototype: Computing Realized Volatility from Orderbook Data and Serving It On-Chain"}}}}}