Overview

Kalshi is a CFTC-regulated prediction market exchange where users can trade on the outcome of real-world events. Unlike decentralized prediction markets on blockchains, Kalshi operates as a centralized exchange with regulatory oversight and compliance. Allium provides comprehensive Kalshi prediction market data, enabling analysis of trading activity, market dynamics, and participant behavior across various event categories.

Kalshi Data Coverage

Protocol	Type	Data Coverage
Kalshi	Centralized	Trades, enriched trades, markets, events, series, series fee changes, hourly candlesticks, daily open interest, market orderbook

All Kalshi data is available in the common.predictions schema.

Data Schemas

Schema	Description	Use Cases
Trades	Complete trade data including ticker, contracts, and pricing	Trading volume analysis, market sentiment, price discovery
Trades Enriched	Trades with market metadata, series tags, and estimated fees	Advanced analytics, tag filtering, fee analysis
Markets	Comprehensive market metadata with settlement rules and outcomes	Market discovery, outcome analysis, settlement tracking
Events	Event-level metadata grouping related markets	Event categorization, series analysis, multi-market tracking
Series	Series metadata including fee type, fee multiplier, and tags	Tag filtering, current fee lookup
Series Fee Changes	Historical scheduled fee updates per series	Reconstruct the fee schedule in force on a given day
Candlesticks Hourly	Hourly OHLCV (Open, High, Low, Close, Volume, OI) data	Price trend analysis, technical analysis, market visualization
Open Interest Daily	Daily end-of-day open interest snapshots	Market liquidity analysis, total value locked tracking
Market Orderbook	Orderbook snapshots with bid/ask price levels and quantities	Liquidity analysis, spread monitoring, market depth assessment

Available Tables

Table	Schema	Description
`common.predictions.kalshi_trades`	`common.predictions`	Core trade data for all Kalshi markets
`common.predictions.kalshi_trades_enriched`	`common.predictions`	Trades with market metadata, series tags, and estimated fees
`common.predictions.kalshi_markets`	`common.predictions`	Comprehensive market metadata with settlement rules and outcomes
`common.predictions.kalshi_events`	`common.predictions`	Event-level metadata grouping related markets
`common.predictions.kalshi_series`	`common.predictions`	Series metadata including fee type, fee multiplier, and tags
`common.predictions.kalshi_series_fee_changes`	`common.predictions`	Historical scheduled fee updates per series
`common.predictions.kalshi_markets_candlesticks_hourly`	`common.predictions`	Hourly price candlestick data with volume and open interest
`common.predictions.kalshi_open_interest_daily`	`common.predictions`	Daily open interest snapshots with market metadata
`common.predictions.kalshi_market_orderbook`	`common.predictions`	Orderbook snapshots with bid/ask price levels

Key Features

Multi-Variable Events (MVE)

Kalshi supports multi-variable events where multiple related markets are grouped together.

Contract Structure

Each contract is worth $1 at settlement
Prices are quoted on a 0-1 scale (e.g., 0.65 = $0.65)
Contracts settle to either $1 (winning outcome) or$ 0 (losing outcome)

Fees

Kalshi’s published trade-fee schedule is:

taker: M_taker × 0.07 × C × P × (1 − P)
maker: M_maker × 0.0175 × C × P × (1 − P)

C is contracts, P is the yes price, and (1 − P) is the no price. Each side is rounded up to the next $0.0001. Per-trade fees are on Trades Enriched as taker_fee_usd, maker_fee_usd, and fee_usd (the sum of the two sides). Kalshi may then add a rounding fee and rebate a penny across fills of the same order; those steps are not in these columns.

Market Categories

Kalshi markets span various categories including:

Politics & Elections
Sports & Entertainment
Economics & Finance
Weather & Climate
Technology & Science

Sample Queries

Recent Trades
Daily Volume
Daily Fees

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

Sum Kalshi trade fees for the past week:

select
  trade_date as day,
  count(*) as fills,
  sum(taker_fee_usd) as taker_fee_usd,
  sum(maker_fee_usd) as maker_fee_usd,
  sum(fee_usd) as fee_usd
from common.predictions.kalshi_trades_enriched
where trade_date >= current_date - 7
group by day
order by day desc

Data Verticals

Solana

Hyperliquid

Lighter

Bitcoin Ecosystem

EVM

Cosmos Ecosystem

Move Ecosystem

Other Ecosystems

Overview

Kalshi Data Coverage

Data Schemas

Available Tables

Key Features

Multi-Variable Events (MVE)

Contract Structure

Fees

Market Categories

Sample Queries

​Kalshi Data Coverage

​Data Schemas

​Available Tables

​Key Features

​Multi-Variable Events (MVE)

​Contract Structure

​Fees

​Market Categories

​Sample Queries

Kalshi Data Coverage

Data Schemas

Available Tables

Key Features

Multi-Variable Events (MVE)

Contract Structure

Fees

Market Categories

Sample Queries