Ripple - or XRP - prices surged in 2021, but went down significantly as 2022 progressed. As of March 26, 2025, one XRP token was worth 2.35 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 oneTechnically 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 on 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 after 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 30 billion U.S. dollars. One year later, this had changed to 120 billion U.S. dollars. The company Ripple faced charges in 2020 from the U.S. Securities and Exchange Commission (SEC), which led to a 125 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.

This dataset provides predicted prices, return on investment (ROI), and sentiment analysis for XRP over a series of dates. The dataset includes predictions for XRP's price, with low, average, and high values, as well as ROI figures for each predicted date. This data helps users forecast market trends for XRP and make informed trading decisions.

Ripple - or XRP - prices surged in 2021, but went down significantly as 2022 progressed. As of March 25, 2025, one XRP token was worth 2.45 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 oneTechnically 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 on 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 after 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.

Prices for XRPUSD Ripple / US Dollar including live quotes, historical charts and news. XRPUSD Ripple / US Dollar was last updated by Trading Economics this March 27 of 2025.

Price predictions and ROI data for XRP on March 26, 2025.

Price predictions and ROI data for XRP on April 02, 2025.

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

Price predictions and ROI data for XRP on March 24, 2025.

Historical price and volatility data for XRP in Euro across different time periods.

Dogecoin (DOGE) price per day from July 1, 2022 to March 26, 2025 (in U.S. cents)

The market cap of the top 10 stablecoin initially multiplied over time, reaching a combined value of over 221 billion USD in March 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 in the wake of 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 in a different way. 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 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 that 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 volality. 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 seen as 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.

Price predictions and ROI data for XRP on April 17, 2025.

Price predictions and ROI data for XRP on April 01, 2025.

Price predictions and ROI data for XRP on April 06, 2025.

Context

Each file contains klines for 1 month period with 1 minute intervals. File name formating looks like mm-yyyy-SMB1SMB2 (e.g. 11-2017-XRPBTC).

This data set contains now only XRP/BTC and ETH/USDT symbol pair now, but it will be expand soon.

Features

• Open time -> timestamp (milliseconds)
• Open price -> float
• High price -> float
• Low price -> float
• Close price -> float
• Volume -> float
• Quote asset volume -> float
• Close time -> timestamp (milliseconds)
• Number of trades -> int
• Taker buy base asset volume -> float
• Taker buy quote asset volume -> float

Acknowledgements

This dataset was collected from Binance Exchange | Worlds Largest Crypto Exchange

Inspiration

This data set could inspire you on most efficient trading algorithms.

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.

Price predictions and ROI data for XRP on April 03, 2025.

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.

Price predictions and ROI data for XRP on March 29, 2025.