Trades

The common.predictions.kalshi_trades table provides comprehensive trade data from Kalshi prediction markets. Each row represents a single trade with details about the market ticker, contract quantity, pricing, and trade sides. Use this table to analyze trading patterns, market volumes, price discovery, and participant behavior across Kalshi prediction markets.

Table Columns

Data Precision Notes:

Prices are stored as floats on a 0-1 scale (e.g., 0.65 = $0.65)
Each contract is worth $1 at settlement

Unique Key: trade_id

Column Name	Data Type	Description
project	VARCHAR	Project identifier, always ‘kalshi’.
protocol	VARCHAR	Protocol identifier, always ‘kalshi’.
trade_date	DATE	Date of the trade (used for partitioning).
created_time	TIMESTAMP_NTZ(9)	Timestamp when the trade was created.
trade_id	VARCHAR	Unique identifier for the trade.
ticker	VARCHAR	Market ticker identifier for this trade.
num_contracts	NUMBER	Number of contracts traded in this trade.
taker_side	VARCHAR	Side taken by the taker (yes or no).
maker_side	VARCHAR	Side taken by the maker (opposite of taker_side).
yes_price	FLOAT	Price for “yes” outcome as a float (0-1 scale).
no_price	FLOAT	Price for “no” outcome as a float (0-1 scale).
taker_price	FLOAT	Price paid by the taker based on taker_side.
_created_at	TIMESTAMP_NTZ(9)	Timestamp of the entry creation on Allium’s database.
_updated_at	TIMESTAMP_NTZ(9)	Timestamp of the entry update on Allium’s database.

Sample Queries

Recent Trades
Daily Volume

Query recent Kalshi trades with market details:

select
  trade_id,
  ticker,
  num_contracts,
  taker_side,
  yes_price,
  no_price,
  taker_price,
  trade_date
from common.predictions.kalshi_trades
where trade_date >= current_timestamp - interval '7 days'
order by trade_date desc
limit 100

Calculate daily trading volume for the past month:

select
  trade_date as day,
  count(*) as total_trades,
  sum(num_contracts) as total_notional_volume,
  sum(num_contracts * taker_price) as total_trade_volume
from common.predictions.kalshi_trades
where trade_date >= current_timestamp - interval '30 days'
group by day
order by day desc

Understanding Trade Sides

taker_side: The outcome the taker is buying (yes or no)
maker_side: The opposite outcome (if taker buys yes, maker sells yes / buys no)
yes_price + no_price = 1.0 (they should sum to 1)
taker_price: The price paid by the taker, equal to either yes_price or no_price depending on taker_side
notional volume: The full USD volume moved due to the trade. Sum of the face value of contracts at redemption

Overview

Data Verticals

Solana

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Lighter

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EVM

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Other Ecosystems

Table Columns

Sample Queries

Understanding Trade Sides

​Table Columns

​Sample Queries

​Understanding Trade Sides

Table Columns

Sample Queries

Understanding Trade Sides