Through the use of blockchain and modern APIs, Ripple enables financial institutions who are part of the network, RippleNet, to send money globally, instantly, reliably and for fractions of a penny. RippleNet is built on top of Ripple’s consensus ledger, XRP Ledger (XRPL).

Ripple is the leading provider of crypto-enabled solutions for businesses. Ripple was founded by Chris Larsen and Jed McCaleb and launched in 2012 (previously OpenCoin).

XRP tokenholders own and govern Ripple's consensus ledger 'XRP Ledger' (XRPL). Ripple's investors include a16z, Pantera Capital, Route 66 Ventures, etc.

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Blockchains (L1)

XRP Ledger

# XRP Ledger Active addresses (monthly) Methodology

## 1. Overview

Active addresses (monthly) measures the number of unique addresses that initiate transactions on XRP Ledger within a rolling 30-day window. This metric captures both general transaction activity and specialized trading activity across XRP Ledger's native exchange infrastructure. The metric is segmented by user type to distinguish transaction senders from DEX traders, and by product to differentiate between the XRPL CLOB (order book) and XRPL AMM (automated market maker) products.

## 2. Data sources

- **Blockchain data**: Transaction records and affected node data from XRP Ledger, capturing account activity for successful transactions

## 3. Step-by-step calculation

### Component A: Transaction sender identification

- **Purpose**: Capture all addresses that initiate any transaction type on XRP Ledger
- **Logic**: Transaction records are parsed to extract the account field from all successful transactions. Each unique account is identified as a transaction sender. Monthly granularity is established using UTC day boundaries.
- **Key fields**: `account` (transaction initiator), `block_timestamp` (timing), `transaction_hash` (deduplication)

### Component B: XRPL CLOB trader identification

- **Purpose**: Identify addresses actively trading on the central limit order book
- **Logic**: Transactions of type `OfferCreate` with successful results are parsed to extract trading accounts. Additionally, `Payment` transactions that modify or delete order book entries are captured by joining transaction records with affected node data where the ledger entry type is `Offer` and previous fields exist. Each unique account is classified as a XRPL CLOB trader.
- **Key fields**: `transaction_type` (identifying order creation and payment flows), `transaction_result` (filtering for success), `ledger_entry_type` (identifying order book interactions), `node_type` (capturing modified or deleted nodes)

### Component C: XRPL AMM trader identification

- **Purpose**: Identify addresses actively trading through automated market maker pools
- **Logic**: XRPL AMM reserve change events are joined with transaction records to identify swap activity. Only `Payment` and `OfferCreate` transaction types with successful results are included, as these represent the transaction types that route through XRPL AMM pools. Each unique account from these transactions is classified as an XRPL AMM trader.
- **Key fields**: `pool_address` (XRPL AMM pool identification), `transaction_type` (swap routing mechanisms), `transaction_result` (success filtering)

### Component D: Deduplication and aggregation

- **Purpose**: Count unique addresses per 30-day window while maintaining dimensional breakdowns
- **Logic**: All identified addresses are deduplicated within each 30-day window. Counts are aggregated by timestamp, user type (transaction sender vs DEX trader), and product (XRPL CLOB vs XRPL AMM where applicable). Project-level and chain-level aggregates are computed alongside user type and product-level breakdowns. Missing days are filled with zeros to maintain continuous time series.

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

Unique addresses active over a rolling 30-day window.

XRP LedgerXRP

Active addresses (monthly)

Active addresses (monthly)

Competitive landscape