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