Introduction: The "Cryptocurrency Price Analysis Dataset: BTC, ETH, XRP, LTC (2018-2023)" is a comprehensive dataset that captures the daily price movements of six popular cryptocurrencies. It covers a period from January 1, 2018, to May 31, 2023, providing a valuable resource for researchers, analysts, and enthusiasts interested in studying the historical price behavior of these digital assets.

Description: This dataset contains a wealth of information for six major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC). The data spans a time frame of over five years, enabling users to explore long-term trends, analyze volatility patterns, and gain insights into market dynamics.

Columns:

1. Crypto: This column specifies the name of the cryptocurrency (e.g., BTC, ETH, XRP, LTC).
2. Date: The date on which the price data was recorded.
3. Open: The opening price of the cryptocurrency at the beginning of the day.
4. High: The highest price reached by the cryptocurrency during the day.
5. Low: The lowest price reached by the cryptocurrency during the day.
6. Close: The closing price of the cryptocurrency at the end of the day.

Use Cases: The dataset offers numerous possibilities for analysis and research within the field of cryptocurrencies. Here are a few potential use cases:

1. Price Analysis: Researchers can investigate the historical price movements of each cryptocurrency to identify trends, patterns, and potential correlations between different assets.
2. Volatility Study: The dataset enables the study of volatility in cryptocurrency markets, helping users understand the frequency and magnitude of price fluctuations.
3. Market Performance: Analysts can analyze the performance of individual cryptocurrencies over time, comparing returns and risk measures to assess their investment potential.
4. Trading Strategies: Traders can utilize the dataset to develop and backtest trading strategies based on technical indicators, price patterns, or machine learning algorithms.
5. Sentiment Analysis: Combine this dataset with external sentiment data to explore the relationship between market sentiment and cryptocurrency price movements. By sharing this dataset on Kaggle, you are providing a valuable resource to the data science community, encouraging collaborative research, and enabling the development of innovative models and solutions within the cryptocurrency domain.

Please note that this dataset is for educational and research purposes only and should not be used for making financial decisions without thorough analysis and consultation with financial professionals.

We present a high-frequency dataset of algorithmic trading. Given that, the dataset contains different time intervals depending on the timestamp when an arbitrage opportunity occurred. Our dataset has 9,799,130 tick-level records of the Bitcoin-to-Euro exchange rate starting from 2019-01-01 00:00:31 until 2020-03-30 23:59:48. Data covered information about different cryptocurrency pairs from 18 cryptocurrency exchanges. These pairs contained information about exchanges in which it was possible to buy and sell simultaneously. Each row presented the amount of arbitrage that it was possible to earn if a transaction would have been executed. The dataset contains information about the amount of arbitrage that could be earned after executing a transaction in given cryptocurrency exchanges, the quantity which had to be bought to earn arbitrage, the best sell, and the best buy prices, the balance of fiat currency in “Exchange 1” and the balance of cryptocurrency in “Exchange 2”. If there was enough fiat currency in “Exchange 1” and enough cryptocurrency in “Exchange 2” it means that the transaction was successfully executed and given arbitrage amount was earned. This information could be used by investors to discover potential earning capabilities, and create effective arbitrage trading strategies. Moreover, this dataset could serve academics for deeper analysis of efficiency and liquidity questions as well as it could be used to spot and evaluate risks in the market, identify patterns in the market. Short description of the dataset: ID - Unique ID arb_timestamp - timestamp of arbitrage opportunity arb_exch1 - presents exchanges where one was able to successfully buy Bitcoin arb_exch2 - presents exchanges where one was able to successfully sell Bitcoin arb_ticker - BTCEUR exchange rate arb_prc - percentage earned compared to the invested amount arb_amount - the amount of arbitrage that would be earned if a transaction had been executed arb_quantity - Bitcoin quantity that needed to be bought in order to execute a transaction and to earn arbitrage best_sell_price - best price at which it was possible to sell Bitcoin in "Exchange 2" best_buy_price - best price at which it was possible to buy Bitcoin in "Exchange 1" balance_fiat - the amount of Euros available in “Exchange 1” balance_crypto - the amount of Bitcoin available in “Exchange 2”

Crypto trader CoinUp.io ranked among the largest cryptocurrency exchangers in the world in 2025, with trading volume that was about four times as high as Picol or Pionex. Binance was the second leading exchanger in the ranking, with trading volume over 16 billion U.S. dollars as of November 27, 2025. It should be noted that these figures are separate from the platforms Binance.US, Binance TR, or Binance.KR. The platform from the Cayman Islands faced investigations from the U.S. SEC, which came to a head in November 2023. Binance did not rank as the most used cryptocurrency exchange used by consumers in the United States. Binance's settlement with the U.S. In November 2023, Binance agreed to pay a four billion U.S. dollar settlement with United States agencies — one of the biggest corporate fines in U.S. history. The U.S. Department of Justice investigated the platform for years for failure to prevent money laundering and growing crypto theft. The company's founder and CEO Changpeng Zhao pleaded guilty to the charges, agreeing to step down. Zhao would remain as the company's majority shareholder. The U.S. Treasury announced Binance will be subject to five years of monitoring and “significant compliance undertakings, including to ensure Binance’s complete exit from the United States.” Mixed signals from crypto companies The Binance settlement occurred in a month when overall crypto trading volume recorded its highest numbers for all of 2023. One of the main causes is the sudden popularity of FTT, a token released by FTX — the company founded by Sam Bankman-Fried. The developments surrounding Binance caused investors to move away from Binance's stablecoin BNB to the stablecoin from FTX. Earlier in November 2023, however, Coinbase saw its shares fall after announcing its quarterly performance figures.

Dataset

This dataset was created by Dmitriy Klinkov

Released under CC0: Public Domain

Contents

Context:

I gathered around 4k data by extracting information from Coingecko and created a CSV file. If anyone is interested, they can review the file on this GitHub link.

Objective & Inspiration:

Analyzing cryptocurrencies is essential for making informed decisions in a rapidly evolving and complex ecosystem. It allows stakeholders to navigate risks, seize opportunities, and contribute to the responsible development of this innovative space. Later i utilized the scraped data to understand the following Trends and relations using Tableau Dashboard:

Performance Trends: Visualize and compare the performance trends (1h, 24h, 7d, 30d) of different cryptocurrencies based on their respective Coin Names.

Market Metrics Overview: Create comprehensive visualizations comparing Top Coin Names against essential metrics, including Price, 24-hour Volume, Circulating Supply, and Market Cap.

Aggregate Metrics: Calculate and visualize aggregate metrics such as Total Market Cap, Total 24-hour Volume, and Total Circulating Supply across all cryptocurrencies.

Columns Name of the data :

Rank: The "Rank" column indicates the ranking of each cryptocurrency based on certain criteria. It helps users understand the relative standing of each coin in comparison to others.

Coin Name: The "Coin Name" column contains the names of various cryptocurrencies. Each row represents a different digital asset, such as Bitcoin, Ethereum, or other altcoins.

Symbol: The "Symbol" column typically represents the shorthand symbol or abbreviation associated with each cryptocurrency. For example, the symbol for Bitcoin is "BTC," and for Ethereum, it's "ETH."

Price: The "Price" column shows the current or latest market price of each cryptocurrency. It is the value at which the coin is traded on the market.

1h, 24h, 7d, 30d: These columns ("1h," "24h," "7d," "30d") represent the percentage change in the price of each cryptocurrency over different time intervals. They provide insights into short-term and long-term price fluctuations.

24h Volume: The "24h Volume" column indicates the total trading volume (in terms of the cryptocurrency) over the last 24 hours. It reflects the total value of all transactions within that time frame.

Circulating Supply: The "Circulating Supply" column specifies the number of units of a cryptocurrency that are currently available and in circulation. It helps assess the liquidity and availability of the cryptocurrency.

Total Supply: The "Total Supply" column represents the total number of units of a cryptocurrency that will ever exist. It provides information about the maximum supply limit of the cryptocurrency.

Market Cap: The "Market Cap" column represents the total market capitalization of each cryptocurrency. It is calculated by multiplying the current price by the circulating supply and provides an overall valuation of the cryptocurrency in the market.

Visualization

Tableau visualization of this dataset can also be found in this link

This dissertation comprises four essays contributing to a deeper understanding of cryptocurrency exchange rates. The first essay proposes a model for cryptocur- rencies with flexible exchange rates. The model integrates an asset-flow approach, modeling speculative trader behavior, into an adapted version of the quantity equa- tions of money. Among others, the essay finds in simulations that a higher fraction of tokens held by investors (rather than circulated by users) leads to increased levels of exchange rate volatility. The second essay conceptualizes a novel technique for measuring the velocity of money for cryptocurrencies based on money in effective circulation. The measure follows recent models on cryptocurrency pricing, argu- ing that tokens held by investors and tokens used as a medium of exchange (MOE) should be differentiated. The third essay systematically explores the landscape of cryptocurrency projects that actively stabilize their exchange rates—so-called stablecoins. It proposes a framework, which can serve as a tool for transferring knowl- edge from monetary economics to the novel field of cryptocurrency research: The study highlights inherently vulnerable stablecoin designs and open design spaces for more sustainable approaches. The thesis concludes with an essay contribut- ing empirical insights on the formation of stablecoin exchange rates, applying the Caginalp & Balenovic (1999) model for asset flow dynamics to fully collateralized stablecoins. The essay offers insights into how trend-reversion and reactions to de- viations from the peg work together to keep stablecoin exchange rates close to their target.

This dataset contains a comprehensive collection of historical price records for the top 1000 cryptocurrencies. The data in this dataset is updated daily, providing a reliable and up-to-date source of information for cryptocurrency traders, researchers, and enthusiasts.

Each file in the dataset includes the following columns: date, open price, high price, low price, closing price, adjusted closing price, and trading volume. These columns provide a detailed picture of the daily price movements and trading activity of each cryptocurrency in the dataset.

The "date" column indicates the day on which the price data was recorded, while the "open" column provides the opening price of the cryptocurrency for that day. The "high" and "low" columns indicate the highest and lowest prices of the cryptocurrency on that day, respectively. The "close" column represents the closing price of the cryptocurrency on that day, while the "adjusted close" column takes into account any dividends or other corporate actions that may have affected the price. Finally, the "volume" column shows the trading volume of the cryptocurrency on that day.

With this dataset, users can analyze and visualize the performance of individual cryptocurrencies, compare them to one another, and track trends over time. The data is ideal for use in machine learning models, predictive analytics, and other data-driven applications.

It is estimated that the cumulative market cap of cryptocurrencies increased in early 2023 after the downfall in November 2022 due to FTX. That value declined in the summer of 2023, however, as international uncertainty grew over a potential recession. Bitcoin's market cap comprised the majority of the overall market capitalization. What is market cap? Market capitalization is a financial measure typically used for publicly traded firms, computed by multiplying the share price by the number of outstanding shares. However, cryptocurrency analysts calculate it as the price of the virtual currencies times the number of coins in the market. This gives cryptocurrency investors an idea of the overall market size, and watching the evolution of the measure tells how much money is flowing in or out of each cryptocurrency. Cryptocurrency as an investment The price of Bitcoin has been erratic, and most other cryptocurrencies follow its larger price swings. This volatility attracts investors who hope to buy when the price is low and sell at its peak, turning a profit. However, this does little for price stability. As such, few firms accept payment in cryptocurrencies. As of October 01, 2025, the cumulative market cap of cryptocurrencies reached a value of *******.

RDA Exchange Rate is part of the RDA Index Data Suite. The RDA Exchange Rate clarifies the intrinsic exchange rates between cryptoassets based on their underlying attributes.

RDA exchange rates enable consumers to identify the intrinsic value parity behind the market rates for every cryptoasset pair.

Explore Crypto Exchange Statistics to uncover market share, trading volume, user trends, and growth drivers that fuel smarter decisions

Prices for BTCUSD Bitcoin US Dollar including live quotes, historical charts and news. BTCUSD Bitcoin US Dollar was last updated by Trading Economics this March 18 of 2026.

FTX's collapse in November 2022 meant that the market share of Binance and other leading crypto exchanges changed significantly from one month to the next. Binance, for instance, regained some of the market share it had lost between September and October 2022, growing by *** percentage points in the month of November. Kraken, especially, was affected as the increase of *** percentage point is the largest it had seen since 2021. The strong market position of Binance can also be observed when investigating the trading for crypto pairs on such exchanges, such as for Bitcoin - with trades on Binance that involve both Bitcoin and stablecoins being common. News that Binance was to take over FTX in 2022 initially led to a crypto trading volume that was *** to **** times higher than it was in the previous days. As of September 2025, Binance's market share stands at **** percentage points, reflecting its ongoing dominance in the crypto exchange market.

Description

This dataset, titled "Cryptocurrency Market Sentiment & Prediction," is a synthetic collection of real-time crypto market data designed for advanced analysis and predictive modeling. It captures a comprehensive range of features including price movements, social sentiment, news impact, and trading patterns for 10 major cryptocurrencies. Tailored for data scientists and analysts, this dataset is ideal for exploring market volatility, sentiment analysis, and price prediction, particularly in the context of significant events like the Bitcoin halving in 2024 and increasing institutional adoption.

Key Features Overview: - Price Movements: Tracks current prices and 24-hour price change percentages to reflect market dynamics. - Social Sentiment: Measures sentiment scores from social media platforms, ranging from -1 (negative) to 1 (positive), to gauge public perception. - News Sentiment and Impact: Evaluates sentiment from news sources and quantifies their potential impact on market behavior. - Trading Patterns: Includes data on 24-hour trading volumes and market capitalization, crucial for understanding market activity. - Technical Indicators: Features metrics like the Relative Strength Index (RSI), volatility index, and fear/greed index for in-depth technical analysis. - Prediction Confidence: Provides a confidence score for predictive models, aiding in assessing forecast reliability.

Purpose and Applications: - Perfect for machine learning tasks such as price prediction, sentiment-price correlation studies, and volatility classification. - Supports time series analysis for forecasting price movements and identifying volatility clusters. - Valuable for research into the influence of social media and news on cryptocurrency markets, especially during high-impact events.

Dataset Scope: - Covers a simulated 30-day period, offering a snapshot of market behavior under varying conditions. - Focuses on major cryptocurrencies including Bitcoin, Ethereum, Cardano, Solana, and others, ensuring relevance to current market trends.

Dataset Structure Table:

Dataset Statistics Table:

This dataset is a powerful resource for machine learning projects, sentiment analysis, and crypto market research, providing a robust foundation for AI/ML model development and testing.

At the end of November 2025, the Ethereum cryptocurrency had been processed nearly ***** million times on-chain that month. This was about ***** that of the more commonly known rival Bitcoin, which saw a total of ***** million transactions that month. Other leading cryptocurrencies also saw significantly less transaction activity. What kind of transactions were these? Cryptocurrencies are digital currencies which owe their credibility to their technology rather than a central bank. Many of the transactions in this statistic involve cryptocurrency exchanges which exchange these coins for other currencies, including traditional currencies such as U.S. dollars or euros. In selected countries, Bitcoin ATMs also dispense the local currency in exchange for Bitcoin. However, few retailers accept that or any other cryptocurrency on a large scale. Cryptocurrency as an investment Many cryptocurrency enthusiasts point to the high market capitalization of their favorite cryptocurrencies. Moreover, the currency price is an important factor. The price volatility of Bitcoin and others attracts investors, hoping to buy low and sell high.

We compared actual trading fees across 15 major crypto exchanges to find the cheapest options for different trading volumes.

The global user base of cryptocurrencies increased by nearly *** percent between 2018 and 2020, only to accelerate further in 2022. This is according to calculations from various sources, based on information from trading platforms and on-chain wallets. Increasing demographics might initially be attributed to a rise in the number of accounts and improvements in identification. In 2021, however, crypto adoption continued as companies like Tesla and Mastercard announced their interest in cryptocurrency. Consumers in Africa, Asia, and South America were most likely to be owners of cryptocurrencies, such as Bitcoin, in 2022. How many of these users have Bitcoin? User figures for individual cryptocurrencies are unavailable. Bitcoin, for instance, was created not to be tracked by banks and governments. What comes closest is the trading volume of Bitcoin against domestic fiat currencies. The source assumed, however, that UK residents were the most likely to make Bitcoin transactions with British pounds. This assumption might not be accurate for popular fiat currencies worldwide. Moreover, coins such as Tether or Binance Coin - referred to as "stablecoins"—are" often used to buy and sell Bitcoin. Those coins were not included in that particular statistic. Wallet usage declined Total crypto wallet downloads were significantly lower in 2022 than in 2021. The number of downloads of Coinbase, Blockchain.com, and MetaMask, among others, declined as the market hit a "crypto winter" over the year. The crypto market also suffered bad press when FTX, one of the largest crypto exchanges based on market share, collapsed in November 2022. Binance, on the other hand, regained some of the market share it had lost between September and October 2022, growing by *** percentage points in November. As of 2025, the highest forecast for the global user base of cryptocurrencies is projected to reach *** million.

As of October 2024, Upbit had the highest Bitcoin price among Thailand's leading cryptocurrency exchange platforms. Bitcoin remains one of the most popular cryptocurrencies traded in Thailand, along with other digital currencies such as Ethereum and Tether.

Prices for BTCPKR Bitcoin Pakistani Rupee including live quotes, historical charts and news. BTCPKR Bitcoin Pakistani Rupee was last updated by Trading Economics this March 27 of 2026.

Prices for PABBTC Panamanian Balboa Bitcoin including live quotes, historical charts and news. PABBTC Panamanian Balboa Bitcoin was last updated by Trading Economics this March 27 of 2026.

About this dataset As cryptocurrency markets have gained prominence, individuals and organizations have shown an increased fascination with crafting automated trading strategies. The creation of algorithmic trading approaches, though, necessitates rigorous backtesting to ascertain their profitability. Consequently, the cornerstone of any triumphant algorithmic trading strategy lies in the availability of meticulously detailed historical trading data. This dataset will provide you a deeper understanding of working with this type of financial security, it provides you with open, high, low, close (OHLC) information, recorded at 1-hour intervals (not very high-velocity data), encompassing a multitude of cryptocurrency pairs. This data resource is invaluable for those seeking to devise and refine automated trading systems, data analysis, or predictions.

Content This dataset contains the historical trading data (OHLC) of 14 crypto securities at 1 1-hour resolution. The source of this data is Coindesk. The data in the CSV files is refined and cleaned for easier interpretation.

The data is free to use.