Datasets:
| license: mit | |
| task_categories: | |
| - time-series-forecasting | |
| - tabular-classification | |
| tags: | |
| - prediction-markets | |
| - finance | |
| - politics | |
| - time-series | |
| - ohlc | |
| size_categories: | |
| - 100K<n<1M | |
| # Politics1M Dataset | |
| OHLC price data for political prediction markets, aggregated into 5-minute candles. | |
| ## Dataset Structure | |
| - **Format**: Parquet | |
| - **Time Resolution**: 5-minute intervals | |
| - **Source**: Polymarket prediction markets | |
| - **Scope**: Political markets with resolved outcomes | |
| ## Data Schema | |
| - `condition_id`: Market condition identifier | |
| - `market_id`: Market identifier | |
| - `token_id`: Outcome token identifier | |
| - `bucket_start_ts`: Unix timestamp (5-minute bucket start) | |
| - `open`, `high`, `low`, `close`: Price values (0-1 range) | |
| - `volume`: Total trading volume | |
| - `trade_count`: Number of trades in bucket | |
| - `question`: Market question text | |
| - `theme`: Market category | |
| - `target_resolution`: Ground truth outcome (0 or 1) | |
| - `yes_won`: Binary label indicating if "Yes" outcome won | |
| ## Usage | |
| ```python | |
| import pandas as pd | |
| df = pd.read_parquet("v0.1.0/02_process/ohlc.parquet") | |
| ``` | |