# Kalshi Notional trading volume Methodology

## 1. Overview

This metric measures the total daily notional trading volume across all prediction markets on the Kalshi platform. Kalshi is a regulated prediction market exchange where users can trade on the outcomes of real-world events. The metric aggregates the dollar volume of all trades executed across all active markets on a given day, providing insight into overall platform activity and liquidity.

The calculation sums the daily trading volume reported for each individual market and presents it as a daily time series with any missing dates filled with zero values to maintain continuity.

## 2. Data sources

The calculation relies on a single external data source:

- **Kalshi market data**: Daily market statistics scraped from Kalshi's API or data feeds, containing trading volume, open interest, pricing data, and market metadata for all prediction markets on the platform
- **Market information**: Each record contains market identifiers (`ticker_name`, `report_ticker`), payout structure (`payout_type`), trading metrics (`daily_volume`, `block_volume`), pricing data (`high`, `low`), and market status information

## 3. Step-by-step calculation

### Component A: Market data extraction and standardization
- **Purpose**: Extract and clean daily market data from all Kalshi prediction markets
- **Logic**: The system pulls daily market statistics from Kalshi's data feeds, which include volume and open interest data for each active prediction market. All data fields are cast to appropriate types (dates to timestamps, volume fields to numeric values, text fields to strings) to ensure consistent processing. A distinct operation removes any duplicate records that might exist in the source data.
- **Key fields**: Important fields include `date` (converted to `timestamp`), `daily_volume` (the core volume metric), `ticker_name` and `report_ticker` (market identifiers), `open_interest`, `block_volume`, pricing data (`high`, `low`), and `status`

### Final aggregation
- **Combining components**: The system groups all market data by date and sums the `daily_volume` values across all markets to calculate total platform trading volume for each day
- **Metadata assignment**: Each aggregated record is tagged with standardized metadata: `timestamp_grain` set to 'day', `chain_id` set to 'offchain' (since Kalshi operates off-chain), and `product_id` set to 'kalshi'
- **Time series standardization**: The finalize metric macro applies a 'fill_zeros' strategy, which ensures any missing dates in the time series are filled with zero trading volume rather than null values, creating a continuous daily time series
- **Final calculation**: The result is a daily time series showing the total notional trading volume across all Kalshi prediction markets, with consistent formatting and no gaps in the date sequence

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Total USD value of derivative contracts executed on the protocol.

Notional trading volume

Kalshi

Kalshi is a CFTC-regulated event derivatives exchange where users trade binary contracts (yes/no) based on the outcome of real-world events

Kalshi

Notional trading volume

Notional trading volume

Competitive landscape