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: stable
Privacy: public market data, no PII

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Dataset Structure

Split

• train — all trades

Features (columns)

name	dtype	description
trade_id	string	Unique trade identifier
ticker	string	Contract/series symbol at the trade level
market_ticker	string	Parent market symbol
count	int64	Matched quantity (units/contracts)
created_time	string	UTC timestamp of trade (ISO-8601 string)
yes_price	int64	Price for YES leg (integer; typically cents or ticks)
no_price	int64	Price for NO leg (integer; typically cents or ticks)
taker_side	string	Aggressor 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).

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Usage

Load with datasets

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)