Ripple’s mission is to enable payments for everyone, everywhere. One of the ways we look to achieve this mission is through building a product called RippleNet’s On-Demand Liquidity service, or ODL. Traditionally, businesses that facilitate international payments need to hold pre-funded accounts in destination currencies, an expensive and inefficient process. As an alternative solution, ODL leverages the digital asset XRP to source destination currencies right at the time of payment.
Liquidity is the ability to buy and sell desired quantities of an asset without impacting the price significantly. ODL relies on XRP liquidity, and liquidity is one factor that determines the size and frequency of payments supported by a particular corridor. Understanding the liquidity dynamics in different markets is important to have a successful product; that understanding primarily comes from analyzing public order book data.
What Are Order Books?
An order book is a record of all outstanding orders to buy or sell an asset, called “bids” and “asks”, respectively. Orders to buy and sell are matched with one another as soon as their requirements are met, which results in a trade occurring between two parties.
There are many different types of orders, but to keep things simple, we’ll focus primarily on two main types: market orders and limit orders. A market order is an instruction to buy or sell an asset immediately for the best price. A limit order is an instruction to buy or sell a specific quantity at a specific price or better.
As time goes on, limit orders are recorded in the order book, and other orders (limit or market) match with these orders and remove them from the book. You can see examples of this occurring in the Bitso XRP/MXN order book below. The area plots at the top represent the total cumulative size of XRP that is available to buy or sell at each price.
Order book dynamics determine the prices of different destination currencies and influence the overall cost of a payment. For example, take the two order books below. With the denser book on the left, a market participant can sell 200k units of an asset and receive a better price than with the thinner book on the right.
Implied FX Rates: An Early Measure of Liquidity
Armed with the order book data, which gives the prices for different payment sizes, we as a data science team can calculate the foreign exchange rate (FX rate) that customers would receive when using an intermediary currency like XRP. For example, if a customer wants to send a payment from the United States to Mexico, we would need a few components to determine the FX rate:
- The price of selling USD and buying XRP, i.e., the XRP/USD ask price, for the payment size after transaction fees, and
- The price of selling XRP and buying MXN, i.e., the XRP/MXN bid price, for the amount of XRP acquired in step #1 above after transaction fees
To calculate the implied FX rate, or the fiat-to-fiat FX rate achieved by bridging with an intermediary currency, the second price is divided by the first price. The implied FX rate was initially leveraged as our primary measure of liquidity because it is operationally helpful: it was one measure that was easy for customers to understand, as this rate can be directly compared to traditional spot FX rates (in other words, the current exchange rate for a currency pair to be bought or sold, which is what most traders refer to when trading foreign currencies). It is a better measure than daily exchange volume, which is often used as a proxy for liquidity but has a number of challenges, including potentially misreported or inflated volumes.
However, as ODL and our analytics grew more sophisticated, we determined that this metric falls short in a few important ways. Our product managers often sought to understand how implied FX rates differed from spot FX rates, and the implied FX rate metric did not allow simple identification of the primary drivers: whether it had to do with originating exchange liquidity, destination exchange liquidity, or differences between particular exchange XRP prices and prevailing market prices, as examples. An upcoming post in this series will discuss how we came to rely on a new, multi-dimensional visualization of liquidity to decompose these different effects.
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