> ## Documentation Index
> Fetch the complete documentation index at: https://docs.allium.so/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Comprehensive Kalshi prediction market data.

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](/historical-data/predictions/kalshi/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

<Tabs>
  <Tab title="Recent Trades">
    Query recent Kalshi trades with market details:

    ```sql theme={null}
    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
    ```
  </Tab>

  <Tab title="Daily Volume">
    Calculate daily trading volume for the past month:

    ```sql theme={null}
    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
    ```
  </Tab>

  <Tab title="Daily Fees">
    Sum Kalshi trade fees for the past week:

    ```sql theme={null}
    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
    ```
  </Tab>
</Tabs>