> ## 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>