Ripple - or XRP - prices surged in 2021, but went down significantly as 2022 progressed. As of June 30, 2025, one XRP token was worth 2.19 U.S. dollars. Ethereum's price, for example, kept on reaching new all-time highs, a feat not performed by XRP. Indeed, XRP's more price spikes followed relatively late - only occurring in early 2021, against late 2020 for most other cryptos - after the US SEC filed a legal complaint against Ripple in November 2020. This legal action caused the XRP price to plummet from around 0.70 U.S. dollars to 0.20 U.S. dollars. Ripple versus XRP: two become one Technically speaking, Ripple is not a cryptocurrency. Renamed from a protocol called OpenCoin in 2013, Ripple facilitates open-source payments. XRP, on the other hand, is the cryptocurrency that runs on this network. In that sense, Ripple and XRP have a similar symbiosis to each other like the Ethereum network and its cryptocurrency Ether. Unlike Ethereum - whose price changes are connected to the world of Decentralized Finance or DeFI - Ripple/XRP mostly looks at developments in cross-border payments for companies. In 2020, companies worldwide began to favor fintech solutions for future B2B solutions and, in a way, Ripple is an extension of that. What affects the price of Ripple? Ripple is mostly active in Southeast Asia - a region with a splintered payment landscape and that heavily investigates its own types of state-issued cryptocurrency to make cross-border payments a lot easier. Price spikes tend to follow news on this topic in this specific region. In 2019, for example, the XRP price grew after Japan and South Korea began testing to reduce time and costs for transferring international funds between the two countries. In March 2021, Ripple announced that it had agreed to acquire 40 percent of Malaysian cross-border payments firm Tranglo to meet growing demand in Southeast Asia.

The market cap of Ripple, or XRP, grew substantially in November 2024, after the results of the United States elections. At the beginning of 2024, the cryptocurrency had a market capitalization of around ** billion U.S. dollars. One year later, this had changed to *** billion U.S. dollars. The company Ripple faced charges in 2020 from the U.S. Securities and Exchange Commission (SEC), which led to a *** million U.S. dollar fine in August 2024 as the company was sentenced for violating investor-protection laws. The SEC appealed this decision, deeming the sentence too low. The results of the U.S. elections in November 2024, however, and the announced changes to the leadership of the SEC, made crypto investors believe that the case against Ripple Labs might be dropped in January 2025.

Prices for XRPUSD Ripple / US Dollar including live quotes, historical charts and news. XRPUSD Ripple / US Dollar was last updated by Trading Economics this July 13 of 2025.

The price of the cryptocurrency based on the famous internet meme broke its price decline in early November 2022 - as people started buying the coin after FTX's collapse. This rally only lasted for a few days, however, as a Dogecoin was worth roughly 0.16 U.S. dollars on June 30, 2025. This is a different development than in 2021 - when the crypto became very popular in a short amount of time. Between January 28 and January 29, 2021, Dogecoin's value grew by around 216 percent to 0.023535 U.S. dollars after comments from Tesla CEO Elon Musk. The digital coin quickly grew to become the most talked-about cryptocurrency available: not necessarily for its price - the prices of Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) and several other virtual currencies were much higher than that of DOGE - but for its growth.

Context

Real and up to date stock market exchange of cryptocurrencies can be quite expensive and are hard to get. However, historical financial data are the starting point to develop algorithm(s) to analyze market trend and why not beat the market by predicting market movement.

Content

Data provided in this dataset are historical data from the beginning of XRP-XBT pair market on Kraken exchange up to the present (2021 December). This data comes frome real trades on one of the most popular cryptocurrencies exchange.

Trading history

Historical market data, also known as trading history, time and sales or tick data, provides a detailed record of every trade that happens on Kraken exchange, and includes the following information: - Timestamp - The exact date and time of each trade. - Price - The price at which each trade occurred. - Volume - The amount of volume that was traded.

OHLCVT

In addition, OHLCVT data are provided for the most common period interval: 1 min, 5 min, 15 min, 1 hour, 12 hours and 1 day. OHLCVT stands for Open, High, Low, Close, Volume and Trades and represents the following trading information for each time period: - Open - The first traded price - High - The highest traded price - Low - The lowest traded price - Close - The final traded price - Volume - The total volume traded by all trades - Trades - The number of individual trades

Don't hesitate to tell me if you need other period interval 😉 ...

Update

This dataset will be updated every quarter to add new and up to date market trend. Let me know if you need an update more frequently.

Inspiration

Can you beat the market? Let see what you can do with these data!

This dataset contains historical price data for the top global cryptocurrencies, sourced from Yahoo Finance. The data spans the following time frames for each cryptocurrency:

BTC-USD (Bitcoin): From 2014 to December 2024 ETH-USD (Ethereum): From 2017 to December 2024 XRP-USD (Ripple): From 2017 to December 2024 USDT-USD (Tether): From 2017 to December 2024 SOL-USD (Solana): From 2020 to December 2024 BNB-USD (Binance Coin): From 2017 to December 2024 DOGE-USD (Dogecoin): From 2017 to December 2024 USDC-USD (USD Coin): From 2018 to December 2024 ADA-USD (Cardano): From 2017 to December 2024 STETH-USD (Staked Ethereum): From 2020 to December 2024

Key Features:

Date: The date of the record. Open: The opening price of the cryptocurrency on that day. High: The highest price during the day. Low: The lowest price during the day. Close: The closing price of the cryptocurrency on that day. Adj Close: The adjusted closing price, factoring in stock splits or dividends (for stablecoins like USDT and USDC, this value should be the same as the closing price). Volume: The trading volume for that day.

Data Source:

The dataset is sourced from Yahoo Finance and spans daily data from 2014 to December 2024, offering a rich set of data points for cryptocurrency analysis.

Use Cases:

Market Analysis: Analyze price trends and historical market behavior of leading cryptocurrencies. Price Prediction: Use the data to build predictive models, such as time-series forecasting for future price movements. Backtesting: Test trading strategies and financial models on historical data. Volatility Analysis: Assess the volatility of top cryptocurrencies to gauge market risk. Overview of the Cryptocurrencies in the Dataset: Bitcoin (BTC): The pioneer cryptocurrency, often referred to as digital gold and used as a store of value. Ethereum (ETH): A decentralized platform for building smart contracts and decentralized applications (DApps). Ripple (XRP): A payment protocol focused on enabling fast and low-cost international transfers. Tether (USDT): A popular stablecoin pegged to the US Dollar, providing price stability for trading and transactions. Solana (SOL): A high-speed blockchain known for low transaction fees and scalability, often seen as a competitor to Ethereum. Binance Coin (BNB): The native token of Binance, the world's largest cryptocurrency exchange, used for various purposes within the Binance ecosystem. Dogecoin (DOGE): Initially a meme-inspired coin, Dogecoin has gained a strong community and mainstream popularity. USD Coin (USDC): A fully-backed stablecoin pegged to the US Dollar, commonly used in decentralized finance (DeFi) applications. Cardano (ADA): A proof-of-stake blockchain focused on scalability, sustainability, and security. Staked Ethereum (STETH): A token representing Ethereum staked in the Ethereum 2.0 network, earning staking rewards.

This dataset provides a comprehensive overview of key cryptocurrencies that have shaped and continue to influence the digital asset market. Whether you're conducting research, building prediction models, or analyzing trends, this dataset is an essential resource for understanding the evolution of cryptocurrencies from 2014 to December 2024.

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

XRP 2 Ang pagsubaybay sa kasaysayan ng presyo ay nagbibigay-daan sa mga crypto investor na madaling masubaybayan ang performance ng kanilang pamumuhunan. Maginhawa mong masusubaybayan ang opening value, high, at close sa XRP 2 sa paglipas ng panahon, pati na rin ang trade volume. Bukod pa rito, maaari mong agad na tingnan ang pang-araw-araw na pagbabago bilang isang porsyento, na ginagawang effortless na tukuyin ang mga araw na may significant fluctuations. Ayon sa aming data ng history ng presyo ng XRP 2, tumaas ang halaga nito sa hindi pa naganap na peak sa 2023-10-24, na lumampas sa $0.{5}6315 USD. Sa kabilang banda, ang pinakamababang punto sa trajectory ng presyo ni XRP 2, na karaniwang tinutukoy bilang "XRP 2 all-time low", ay naganap noong 2023-11-29. Kung ang isa ay bumili ng XRP 2 sa panahong iyon, kasalukuyan silang masisiyahan sa isang kahanga-hangang kita na -100%. Sa pamamagitan ng disenyo, ang 100B XRP 2 ay malilikha. Sa ngayon, ang circulating supply ng XRP 2 ay tinatayang 0. Ang lahat ng mga presyong nakalista sa pahinang ito ay nakuha mula sa Bitget, galing sa isang reliable source. Napakahalagang umasa sa iisang pinagmulan upang suriin ang iyong mga investment, dahil maaaring mag-iba ang mga halaga sa iba't ibang nagbebenta. Kasama sa aming makasaysayang XRP 2 dataset ng presyo ang data sa pagitan ng 1 minuto, 1 araw, 1 linggo, at 1 buwan (bukas/mataas/mababa/close/volume). Ang mga dataset na ito ay sumailalim sa mahigpit na pagsubok upang matiyak ang consistency, pagkakumpleto, at accurancy. Ang mga ito ay partikular na idinisenyo para sa trade simulation at mga layunin ng backtesting, madaling magagamit para sa libreng pag-download, at na-update sa real-time.

Context

This data was collected for a dissertation project titled, "Forecasting cryptocurrency prices using machine learning".

Content

The three csv files contain the daily price data for Bitcoin, Ether and Ripple. The data was collected from https://coinmarketcap.com/
The datasets contain the following features:
* Open * Close * High * Low * Volume * Market Capitalisation * EMA 10 (Exponential moving average of 10 timesteps) * EMA 30 (Exponential moving average of 30 timesteps) * ATR (Average true range)

This dataset tracks annual reduced-price lunch eligibility from 2002 to 2023 for Broad Ripple Mgnt High School For Prfm Arts vs. Indiana and Indianapolis Public Schools School District

Bitcoin ranked as one of the most expensive cryptocurrencies existing by April 2024 - although values changed noticeably. Bitcoin had the most expensive cryptocurrency for a while, but Ethereum was significantly cheaper, with a price that was roughly 30 times less than that of the most well-known digital currency. However, Bitcoin is in a unique position. Ethereum is one of several cryptocurrencies, for instance, that come from blockchains that focus on making financial applications possible. Bitcoin, or a digital equivalent of gold When one categorizes the different types of cryptocurrencies, Bitcoin stands out as it is one of the few that are essentially meant to store digital value. Some describe Bitcoin as a digital version of gold, purely designed to hold or possibly purchasing power over time. It has no other applications built around it, and is considered too slow to perform financial transactions. Stablecoins, the less volatile cryptocurrency Many coins in this ranking stand out as their price seemingly has not changed as much as others. This is because these are stablecoins - cryptocurrencies pegged to the price development of an external asset. This group of digital assets comprise an increasing share within the overall crypto market. Some see these coins as the future of retail payments, whereas others view these coins as a "safe" addition to their crypto investments.

The market cap of the top 10 stablecoin initially multiplied over time, reaching a combined value of over *** billion USD in May 2025. Note this value does not include TerraUSD (UST), the algorithmic stablecoin tied to the LUNA crypto which declined severely in May 2022. Up to then, estimates reveal that the market cap had more than tripled within five months - likely following growing interest worldwide in cryptocurrencies, after sudden price spikes in a coin like Dogecoin (DOGE). Stability above all, or what does a stablecoin do? Stablecoins are cryptocurrencies - like the commonly known Bitcoin (BTC) and Ethereum (ETH) - but their value is determined differently. Whilst the price of Bitcoin mainly follows supply - how many coins are being mined or are available to purchase - and demand - how many investors want to buy the coin - stablecoins are synthetically connected to the price of an altogether different asset. Tether's USDT, for instance, is connected to the price development of the U.S. dollar (USD): if the U.S. dollar falls in the FX market, so does the USDT. Compare this to the "regular" price history of a cryptocurrency like Ripple (XRP) and stablecoins reveal themselves to be a relatively less volatile digital currency to either use or invest in than their counterparts in the free market. A test ground for digital payments This stability of these particular cryptocurrencies is important for two areas in digital payments that do not prefer volatility. For instance, these coins are a popular choice within the world of Decentralized Finance or DeFi - an online financial market without the supervision of central bank that relies on cryptocurrencies for payments and loans. Because of that reliance, it is a market that can rapidly change in size due to price fluctuations or changing transaction fees of certain cryptocurrencies - something that is less likely to occur when using stablecoins. Additionally, stablecoins are considered the inspiration for so-called CBDC or Central Bank Digital Currencies - such as China's e-CNY currency or the "digital euro" that is being researched in the EU-27. In terms of how advanced countries worldwide are into researching their own cryptocurrency, China ranked third in 2020, behind Cambodia, and The Bahamas.

RationaleHigh frequency oscillations (HFO; ripples = 80–200, fast ripples 200–500 Hz) are promising epileptic biomarkers in patients with epilepsy. However, especially in temporal epilepsies differentiation of epileptic and physiological HFO activity still remains a challenge. Physiological sleep-spindle-ripple formations are known to play a role in slow-wave-sleep memory consolidation. This study aimed to find out if higher rates of mesial-temporal spindle-ripples correlate with good memory performance in epilepsy patients and if surgical removal of spindle-ripple-generating brain tissue correlates with a decline in memory performance. In contrast, we hypothesized that higher rates of overall ripples or ripples associated with interictal epileptic spikes correlate with poor memory performance.MethodsPatients with epilepsy implanted with electrodes in mesial-temporal structures, neuropsychological memory testing and subsequent epilepsy surgery were included. Ripples and epileptic spikes were automatically detected in intracranial EEG and sleep-spindles in scalp EEG. The coupling of ripples to spindles was automatically analyzed. Mesial-temporal spindle-ripple rates in the speech-dominant-hemisphere (left in all patients) were correlated with verbal memory test results, whereas ripple rates in the non-speech-dominant hemisphere were correlated with non-verbal memory test performance, using Spearman correlation).ResultsIntracranial EEG and memory test results from 25 patients could be included. All ripple rates were significantly higher in seizure onset zone channels (p < 0.001). Patients with pre-surgical verbal memory impairment had significantly higher overall ripple rates in left mesial-temporal channels than patients with intact verbal memory (Mann–Whitney-U-Test: p = 0.039). Spearman correlations showed highly significant negative correlations of the pre-surgical verbal memory performance with left mesial-temporal spike associated ripples (rs = −0.458; p = 0.007) and overall ripples (rs = −0.475; p = 0.006). All three ripple types in right-sided mesial-temporal channels did not correlate with pre-surgical nonverbal memory. No correlation for the difference between post- and pre-surgical memory and pre-surgical spindle-ripple rates was seen in patients with left-sided temporal or mesial-temporal surgery.DiscussionThis study fails to establish a clear link between memory performance and spindle ripples. This highly suggests that spindle-ripples are only a small portion of physiological ripples contributing to memory performance. More importantly, this study indicates that spindle-ripples do not necessarily compromise the predictive value of ripples in patients with temporal epilepsy. The majority of ripples were clearly linked to areas with poor memory function.

Overview:

The attached spreadsheet, "AeolianRippleMigration_ShermanEtAl2019.csv," summarizes the ripple migration and related data acquired from the wind tunnel and field experiment literature and from the field experiments at Jericoacoara, Ceará, Brazil (2008) and Oceano, California, USA (2015), associated with the article "Aeolian Ripple Migration and Associated Creep Transport Rates" by Douglas J. Sherman, Pei Zhang, Raleigh L. Martin, Jean T. Ellis, Jasper F. Kok, Eugene J. Farrell, and Bailiang Li.

Key to data sources:

* indicates that the data from a particular study were included in our final analyses

§ indicates an estimate of threshold shear velocity (calculated as per Lorenz et al., 2011) with A = 0.1

† the value for ripple height in this study is the average of about 200 measurements for ripples in equilibrium or near-equilibrium with the wind field

‡ the data from this study were digitized as depicted in terms of ust/ust_th and u_r/(gd)^0.5 (see "Key to variables" below)

¶ Shear velocity (ust) values are from Martin & Kok, 2017. Median grain diameter (d) and threshold shear velocity (ust_th) values are from Martin & Kok, 2018 (see Table 2: "Date interval")

Key to variables [units]:

Source - literature origin of previous studies or field location of observations for this study

StudyType - classified as "field" or "wind tunnel"

Date - date of observation for observations at Jericoacoara and Oceano ("N/A" for other sites)

StartTime - start time of observation window (local time) for observations at Jericoacoara and Oceano ("N/A" for other sites)

EndTime - end time of observation window (local time) for observations at Jericoacoara and Oceano ( "N/A" for other sites)

u_r [mm/s] - calculated ripple migration speed ( "N/A" for Zhu et al, 2011, see "u_r_alt" below)

sigma_u_r [mm/s] - uncertainty in ripple migration speed. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.

u_r_alt - dimensionless proxy values for ripple migration speed for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as u_r/(gd)^1/2, where "g" is gravitational acceleration and "d" is median surface grain diameter

ust [m/s] - shear velocity ( "N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)

d [mm] - median surface grain diameter ("N/A" if not reported for literature studies)

ust_th [m/s] - threshold shear velocity ("N/A" for Zhu et al, 2011, see "ust_over_ust_th" below)

ust_over_ust_th - dimensionless proxy values for shear velocity for Zhu et al, 2011 (marked as "N/A" for other sites) calculated as ust/ust_th

length [mm] - ripple wavelength ("N/A" if not reported or measured)

height [mm] - ripple amplitude ("N/A" if not reported or measured)

sigma_height [mm] - uncertainty in ripple amplitude. Calculated as fixed percentage for Jericoacoara and as standard error for Oceano. For literare-derived values, "N/A" indicates lack of uncertainty estimates. For Oceano, "N/A" indicates inability to calculate standard error for certain measurement intervals containing only a single observation.

References:

Andreotti, B.; Claudin, P.; Pouliquen, O. Aeolian Sand Ripples : Experimental Study of Fully Developed States. 2006, 028001, 1-4.

Borsy, Z. A homokfodrok. Fldrajzi rtesito 1973, 22, 109-115.

Cheng, H.; Liu, C.; Zou, X.; Li, J.; He, J.; Liu, B.; Wu, Y.; Kang, L.; Fang, Y. Aeolian creeping mass of different grain sizes over sand beds of varying length. Journal of Geophysical Research: Earth Surface 2015, 120, 1404-1417.

Cornish, V. On the formation of sand-dunes. The Geographical Journal 1897, 9, 278-302.

Kindle, E.M. Recent and fossil ripple-mark; Canada Department of Mines, Geological Survey: 1917; pp 9-29.

Ling, Y.-q.; Qu, J.-j.; Li, C.-z. Study on sand ripple movement with close shoot method. Journal of Desert Research 2003, 23, 118-120.

Lorenz, R.D. Observations of wind ripple migration on an Egyptian seif dune using an inexpensive digital timelapse camera. Aeolian Research 2011, 3, 229-234.

Martin, R.L.; Kok, J.F. Aeolian saltation fieldwork 30-minute wind and saltation values (Dataset). Zenodo, https://doi.org/10.5281/zenodo.291798: 2017.

Martin, R.L., Kok, J.F. Distinct Thresholds for the Initiation and Cessation of Aeolian Saltation From Field Measurements. Journal of Geophysical Research - Earth Surface 2018, 123, 1546–1565. https://doi.org/10.1029/2017JF004416

Seppälä, M.; Lindé, K. Wind tunnel studies of ripple formation. Geografiska Annaler: Series A, Physical Geography 1978, 60, 29-42.

Sharp, R.P. Wind ripples. The Journal of Geology 1963, 71, 617-636.

Stone, R.O.; Summers, H.J. Study of Subaqueous and Subaerial Sand Ripples; University of Southern California: Los Angeles, 1972.

Zhu, W. Investigations on the formation and evolution of aeolian sand ripples. Lanzhou University, 2011.

Supplementary files for article "Discontinuity in equilibrium wave-current ripple size and shape and deep cleaning associated with cohesive sand-clay beds"Mixtures of cohesive clay and noncohesive sand are widespread in many aquatic environments. Ripple dynamics in sand-clay mixtures have been studied under current-alone and wave-alone conditions but not combined wave-current conditions, despite their prevalence in estuaries and the coastal zone. The present flume experiments examine the effect of initial clay content, C0, on ripples by considering a single wave-current condition and, for the first time, quantify how changing clay content of substrate impacts ripple dimensions during development. The results show inverse relationships between C0 and ripple growth rates and clay winnowing transport rates out of the bed, which reduce as the ripples develop toward equilibrium. For C0 ≤ 10.6%, higher winnowing rates lead to clay loss, and thus the presence of clean sand, far below the base of equilibrium ripples. This hitherto unquantified “deep-cleaning” of clay does not occur for C0 > 10.6%, where clay-loss rates are much lower. The clay-loss behavior is associated with two distinct types of equilibrium combined flow ripples: (a) Large asymmetric ripples with dimensions and plan geometries comparable to their clean-sand counterparts for C0 ≤ 10.6% and (b) small, flat ripples for C0 > 10.6%. The 10.6% threshold, which may be specific to the experimental conditions, corresponds to a more general 8% threshold found beneath the ripple base, suggesting that clay content here must be

After several fluctuations in earlier years, the price of Tether or USDT since 2020 achieved the stability against the U.S. dollar that it was designed to reach. In 2021, Tether ranks as one of the biggest cryptocurrencies in the world and is regarded as the most well-known "stablecoin", or cryptocurrency that is connected to the price development of another, real-world asset. As Tether's USDT code suggest, the token is, in this case, connected to the U.S. dollar. This effectively means that a single Tether will nearly always be worth one single U.S. dollar. This relative stability, unlike the price development of, say, Ripple (XRP), is what defines a stablecoin and is especially important for decentralized lending and borrowing. This particular segment made up roughly half of the overall value locked in Decentralized Finance or DeFi.