---
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

```python
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