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thomaswmitch/kalshi-prediction-markets-betting

Kalshi Prediction Markets — Trades High-volume trade-level data from Kalshi prediction markets spanning Aug 2023–Aug 2025, suitable for market microstructure, liquidity, and price-impact analysis. Rows: ~5.08M trades (single split)Schema stability: stablePrivacy: public market data, no PII Dataset Structure Split train — all trades Features (columns) name dtype description trade_id string Unique trade identifier… See the full description on the dataset page: https://huggingface.co/datasets/thomaswmitch/kalshi-prediction-markets-betting.

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Dataset Card

Kalshi Prediction Markets — Trades

High-volume trade-level data from Kalshi prediction markets spanning Aug 2023–Aug 2025, suitable for market microstructure, liquidity, and price-impact analysis.

Rows: ~5.08M trades (single split) Schema stability: stable Privacy: public market data, no PII

Dataset Structure

Split

  • —train — all trades

Features (columns)

namedtypedescription
trade_idstringUnique trade identifier
tickerstringContract/series symbol at the trade level
market_tickerstringParent market symbol
countint64Matched quantity (units/contracts)
created_timestringUTC timestamp of trade (ISO-8601 string)
yes_priceint64Price for YES leg (integer; typically cents or ticks)
no_priceint64Price for NO leg (integer; typically cents or ticks)
taker_sidestringAggressor side for the trade (string enum; e.g., "buy"/"sell" or similar)
Note: yes_price/no_price are stored as integers. If your downstream pipeline expects dollars, convert as needed (e.g., divide by 100 if prices are in cents).

Usage

Load with datasets

python
from datasets import load_dataset

ds = load_dataset("thomaswmitch/kalshi-prediction-markets-trades")  # update to your repo name if different
print(ds)
print(ds["train"].features)