Datasets:
metadata
license: mit
task_categories:
- time-series-forecasting
tags:
- polymarket
- prediction-markets
- orderbook
- btc
- eth
- trading
- chainlink
- binance
size_categories:
- 1M<n<10M
pretty_name: Polymarket BTC/ETH 15-Minute Market Orderbook Data
Polymarket BTC/ETH 15-Minute Market Orderbook Data
Real-time orderbook snapshots from Polymarket 15-minute BTC and ETH prediction markets, collected via WebSocket with Chainlink oracle and Binance spot prices.
Dataset Description
Each row is a point-in-time snapshot of one side (YES or NO) of a 15-minute binary option market on Polymarket. Markets resolve based on whether BTC/ETH price goes up or down over 15 minutes, as determined by Chainlink oracle.
Collection method: WebSocket connection to Polymarket CLOB via shard manager, sampled at ~1 second intervals per market.
Data Format
Parquet shards (shard_XXXX.parquet) with the following schema:
| Column | Type | Description |
|---|---|---|
ts |
int64 | Timestamp in milliseconds |
progress |
float | Market progress (0.0 = start, 1.0 = settlement) |
outcome_up |
float | 1.0 if this is the UP/YES token |
outcome_down |
float | 1.0 if this is the DOWN/NO token |
best_bid |
float | Best bid price (0.0-1.0) |
best_ask |
float | Best ask price (0.0-1.0) |
best_bid_size |
float | Liquidity at best bid (in dollars) |
best_ask_size |
float | Liquidity at best ask (in dollars) |
oracle_price |
int64 | Chainlink oracle price in cents |
binance_price |
int64 | Binance BTC/ETH price in cents |
target_price |
int64 | Market target/strike price in cents |
imbalance |
float | (bid_size - ask_size) / (bid_size + ask_size) |
Settlement
Markets settle at the 15-minute mark based on Chainlink oracle:
- YES wins if
oracle_price > target_price(price went up) - NO wins if
oracle_price <= target_price(price went down or flat)
The target_price is the oracle price at market open.
Usage
import pandas as pd
# Read a single shard
df = pd.read_parquet("shard_0001.parquet")
# Read all shards
import glob
dfs = [pd.read_parquet(f) for f in sorted(glob.glob("shard_*.parquet"))]
df = pd.concat(dfs, ignore_index=True)
# Filter BTC only (oracle_price > $50,000)
btc = df[df["oracle_price"] > 5_000_000]
# Get YES side only
yes_side = df[df["outcome_up"] == 1.0]
Use Cases
- Backtesting prediction market trading strategies
- Orderbook microstructure analysis
- Price discovery dynamics in binary options
- Oracle vs spot price divergence studies
Collection Details
- Source: Polymarket CLOB (Central Limit Order Book)
- Oracle: Chainlink price feeds on Arbitrum
- Spot: Binance BTC/USDT and ETH/USDT
- Shard rotation: Every 30 minutes
- Update frequency: ~1 second per market side
- Collection start: February 2026