Real-Time Cryptocurrency Prices Dataset (Top 200 Coins)

This dataset contains real-time cryptocurrency market data fetched from the Crypto News Mini API (via RapidAPI). The dataset includes detailed price and market information for the top cryptocurrencies, ranked by market capitalization. Each row represents one cryptocurrency with the following attributes:

Features

rank – Global market cap ranking symbol – Trading symbol (e.g., BTC, ETH, SOL) name – Full coin name slug – API-friendly unique identifier id – Internal API ID price – Current price in USD image – Logo image URL market_cap – Total market capitalization in USD change_24h_percent – 24-hour price movement (%)

How This Dataset Was Collected :-

Source: Crypto-News51 Mini Crypto Prices API API Provider: RapidAPI Base Currency: USD Page Size: 20 coins per request Pages scraped: multiple (up to 200 coins total)

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

Find my notebook : Advanced EDA & Data Wrangling - Crypto Market Data where I cover the full EDA and advanced data wrangling to get beautiful dataset ready for analysis.

Find my Deep Reinforcement Learning v1 notebook: "https://www.kaggle.com/code/franoisgeorgesjulien/deep-reinforcement-learning-for-trading">Deep Reinforcement Learning for Trading

Find my Quant Analysis notebook:"https://www.kaggle.com/code/franoisgeorgesjulien/quant-analysis-visualization-btc-v1">💎 Quant Analysis & Visualization | BTC V1

Dataset Presentation:

This dataset provides a comprehensive collection of hourly price data for 34 major cryptocurrencies, covering a time span from January 2017 to the present day. The dataset includes Open, High, Low, Close, Volume (OHLCV), and the number of trades for each cryptocurrency for each hour (row).

Making it a valuable resource for cryptocurrency market analysis, research, and trading strategies. Whether you are interested in historical trends or real-time market dynamics, this dataset offers insights into the price movements of a diverse range of cryptocurrencies.

This is a pure gold mine, for all kind of analysis and predictive models. The granularity of the dataset offers a wide range of possibilities. Have Fun!

Ready to Use - Cleaned and arranged dataset less than 0.015% of missing data hour: crypto_data.csv

First Draft - Before External Sources Merge (to cover missing data points): crypto_force.csv

Original dataset merged from all individual token datasets: cryptotoken_full.csv

crypto_data.csv & cryptotoken_full.csv highly challenging wrangling situations: - fix 'Date' formats and inconsistencies - find missing hours and isolate them for each token - import external data source containing targeted missing hours and merge dataframes to fill missing rows

see notebook 'Advanced EDA & Data Wrangling - Crypto Market Data' to follow along and have a look at the EDA, wrangling and cleaning process.

Date Range: From 2017-08-17 04:00:00 to 2023-10-19 23:00:00

Date Format: YYYY-MM-DD HH-MM-SS (raw data to be converted to datetime)

Data Source: Binance API (some missing rows filled using Kraken & Poloniex market data)

Crypto Token in the dataset (also available as independent dataset): - 1INCH - AAVE - ADA (Cardano) - ALGO (Algorand) - ATOM (Cosmos) - AVAX (Avalanche) - BAL (Balancer) - BCH (Bitcoin Cash) - BNB (Binance Coin) - BTC (Bitcoin) - COMP (Compound) - CRV (Curve DAO Token) - DENT - DOGE (Dogecoin) - DOT (Polkadot) - DYDX - ETC (Ethereum Classic) - ETH (Ethereum) - FIL (Filecoin) - HBAR (Hedera Hashgraph) - ICP (Internet Computer) - LINK (Chainlink) - LTC (Litecoin) - MATIC (Polygon) - MKR (Maker) - RVN (Ravencoin) - SHIB (Shiba Inu) - SOL (Solana) - SUSHI (SushiSwap) - TRX (Tron) - UNI (Uniswap) - VET (VeChain) - XLM (Stellar) - XMR (Monero)

Date column presents some inconsistencies that need to be cleaned before formatting to datetime: - For column 'Symbol' and 'ETCUSDT' = '23-07-27': it is missing all hours (no data, no hourly rows for this day). I fixed it by using the only one row available for that day and duplicated the values for each hour. Can be fixed using this code:

start_timestamp = pd.Timestamp('2023-07-27 00:00:00')
end_timestamp = pd.Timestamp('2023-07-27 23:00:00')

hourly_timestamps = pd.date_range(start=start_timestamp, end=end_timestamp, freq='H')

hourly_data = {
  'Date': hourly_timestamps,
  'Symbol': 'ETCUSDT',
  'Open': 18.29,
  'High': 18.3,
  'Low': 18.17,
  'Close': 18.22,
  'Volume USDT': 127468,
  'tradecount': 623,
  'Token': 'ETC'
}

hourly_df = pd.DataFrame(hourly_data)
df = pd.concat([df, hourly_df], ignore_index=True)

df = df.drop(550341)

• Some rows for 'Date' have extra digits '.000' '.874' etc.. instead of the right format YYYY-MM-DD HH-MM-SS. To clean it you can use the following code:

# Count the occurrences of the pattern '.xxx' in the 'Date' column
count_occurrences_before = df['Date'].str.count(r'\.\d{3}')
print("Occurrences before cleaning:", count_occurrences_before.sum()) 

# Remove '.xxx' pattern from the 'Date' column
df['Date'] = df['Date'].str.replace(r'\.\d{3}', '', regex=True)

# Count the occurrences of the pattern '.xxx' in the 'Date' column after cleaning
count_occurrences_after = df['Date'].str.count(r'\.\d{3}')
print("Occurrences after cleaning:", count_occurrences_after.sum()) 

**Disclaimer: Any individual or entity choosing to engage in market analysis, develop predictive models, or utilize data for trading purposes must do so at their own discretion and risk. It is important to understand that trading involves potential financial loss, and decisions made in the financial mar...

Bitcoin Historical Dataset 3M records from 2017-2023

Context:

Bitcoin, the pioneering cryptocurrency, has captured the world's attention as a decentralized digital asset with a fluctuating market value. This dataset offers a comprehensive record of Bitcoin's price evolution, spanning from August 2017 to July 2023. The data has been meticulously collected from the Binance API, with price data captured at one-minute intervals. Each record includes essential information such as the open, high, low, and close prices, alongside associated trading volume. This dataset provides an invaluable resource for those interested in studying Bitcoin's price trends and market dynamics.

Dataset Details:

Total Number of Entries: 3.126.000

Attributes: Timestamp, Open Price, High Price, Low Price, Close Price, Volume , Quote asset volume, Number of trades, Taker buy base asset volume, Taker buy quote asset volume.

Data Type: csv

Size: 133 MB

Date ranges: 2023/08/17 till 2023/07/31

Content:

This dataset provides granular insights into the price history of Bitcoin, allowing users to explore minute-by-minute changes in its market value. The dataset includes attributes such as the open price, high price, low price, close price, trading volume, and the timestamp of each recorded interval. The data is presented in CSV format, making it easily accessible for analysis and visualization.

Inspiration:

The Bitcoin Price Dataset opens up numerous avenues for exploration and analysis, driven by the availability of high-frequency data. Potential research directions include:

Intraday Price Patterns: How do Bitcoin prices vary within a single day? Are there recurring patterns or trends during specific hours? Volatility Analysis: What are the periods of heightened volatility in Bitcoin's price history, and how do they correlate with external events or market developments? Correlation with Events: Can you identify instances where significant price movements coincide with notable events in the cryptocurrency space or broader financial markets? Long-Term Trends: How has the average price of Bitcoin evolved over different years? Are there multi-year trends that stand out? Trading Volume Impact: Is there a relationship between trading volume and price movement? How does trading activity affect short-term price fluctuations?

Acknowledgements:

The dataset has been sourced directly from the Binance API, a prominent cryptocurrency exchange platform. The collaboration with Binance ensures the dataset's accuracy and reliability, offering users a trustworthy foundation for conducting analyses and research related to Bitcoin's price movements.

Licensing:

Users are welcome to utilize this dataset for personal, educational, and research purposes, with attribution to the Binance API as the source of the data.

Hope you enjoy this dataset as much as I enjoyed putting it together. Can't wait to see what you can come up with :)

This Dataset contains historical price data for 10 cryptocurrencies spanning from 2021 to 2024, in three different time frames: 1 day, 4 hours, and 1 hour. The data is sourced from the Binance API and stored in CSV (Comma Separated Values) format for easy accessibility and analysis.

Usage

You can use this data for various purposes such as backtesting trading strategies, conducting statistical analysis, or building predictive models related to cryptocurrency markets.

Note

• All timestamps are in UTC timezone.
• Prices are quoted in USDT (Tether).

This dataset contains information about ethereum prices at hourly intervals. It cover between 2019-09 to 2023-05. I get this data with using Binance API. Here are the features of dataset:

• open_time: Kline Open time in unix time format
• open: Open Price
• high: High Price
• low: Low Price
• close: Close Price
• volume: Volume
• close_time: Kline Close time in unix time format
• quote_volume: Quote Asset Volume
• count: Number of Trades
• taker_buy_volume: Taker buy quote asset volume during this period
• taker_buy_quote_volume: Taker buy base asset volume during this period
• ignore : Ignore(you can drop this feature)

MT Newswires’ team of highly experienced financial reporters produces timely and actionable commentary throughout the day to keep readers abreast of all the latest happenings in the digital marketplace: price spikes and price plunges in popular virtual coins, DeFi and NFT price action, regulatory updates, corporate adoption announcements, overarching industry trends, and more. Live Briefs Crypto News & Insights additionally incorporates educational “explainer” guides and longer form technical analysis to ensure that the content and crypto discovery is accessible to everyone – whether individual investors and traders entirely new to the concept or professional wealth managers looking for in-depth industry coverage to guide informed decision making on behalf of their clients. 

Every story includes relevant symbols and is category-coded to allow for seamless platform integration.

·       Top News – The most significant drivers of digital assets every day;  ·       Breaking News – real-time coverage of the events most likely to affect prices and adoption of cryptocurrencies and actively traded NFTs at any given moment; ·       Crypto Market Summaries – daily summaries covering major price action and regulatory developments globally; ·       Influencers & Social Buzz – objective coverage of the most talked about cryptocurrencies on social media and related sentiment indications; ·       Top Movers - intra-day updates on major price moves among the most popular cryptocurrencies; ·       Policy & Regulation - timely news on the rapidly evolving Digital Central Bank Currency policies with country specific regulatory developments; ·       Crypto Explainer - educational pieces to help investors understand the complex world of digital assets; ·       Get Digital - The Weekend Crypto Report, wrapping up the biggest digital currency news from the prior week and looking ahead to what may drive pricing in the week to come

Authors, through Twitter API, collected this database over eight months. These data are tweets of over 50 experts regarding market analysis of 40 cryptocurrencies. These experts are known as influencers on social networks such as Twitter. The theory of Behavioral economics shows that the opinions of people, especially experts, can impact the stock market trend (here, cryptocurrencies). Existing databases often cover tweets related to one or more cryptocurrencies. Also, in these databases, no attention is paid to the user's expertise, and most of the data is extracted using hashtags. Failure to pay attention to the user's expertise causes the irrelevant volume to increase and the neutral polarity to increase considerably. This database has a main table named "Tweets1" with 11 columns and 40 tables to separate comments related to each cryptocurrency. The columns of the main table and the cryptocurrency tables are explained in the attached document. Researchers can use this dataset in various machine learning tasks, such as sentiment analysis and deep transfer learning with sentiment analysis. Also, this data can be used to check the impact of influencers' opinions on the cryptocurrency market trend. The use of this database is allowed by mentioning the source. Also, in this version, we have added the excel version of the database and Python code to extract the names of influencers and tweets. in Version(3): In the new version, three datasets related to historical prices and sentiments related to Bitcoin, Ethereum, and Binance have been added as Excel files from January 1, 2023, to June 12, 2023. Also, two datasets of 52 influential tweets in cryptocurrencies have been published, along with the score and polarity of sentiments regarding more than 300 cryptocurrencies from February 2021 to June 2023. Also, two Python codes related to the sentiment analysis algorithm of tweets with Python have been published. This algorithm combines RoBERTa pre-trained deep neural network and BiGRU deep neural network with an attention layer (see code Preprocessing_and_sentiment_analysis with python).

Crypto Coin Historical Data (2018-2025)

A dataset containing cryptocurrency historical price data across multiple timeframes. Designed to provide a standardized, easily accessible dataset for cryptocurrency research and algorithmic trading development. This dataset is automatically updated daily using the Binance API, ensuring that it remains current and relevant for users. Last updated on 2025-12-03 00:21:19.

  Usage

from datasets import load_dataset dataset =… See the full description on the dataset page: https://huggingface.co/datasets/linxy/CryptoCoin.

Crypto Data Platform Market Outlook

According to our latest research, the global Crypto Data Platform market size reached USD 1.85 billion in 2024, reflecting robust adoption across institutional and retail segments. The market is expected to expand at a CAGR of 18.2% during the forecast period, with revenues projected to reach USD 9.25 billion by 2033. This growth is primarily fueled by the increasing demand for real-time data analytics, advanced trading solutions, and regulatory compliance tools in the rapidly evolving cryptocurrency industry. The surge in digital asset adoption, coupled with heightened institutional participation and technological advancements, is driving the need for comprehensive, scalable, and secure crypto data platforms worldwide.

A significant growth factor for the Crypto Data Platform market is the exponential rise in crypto trading volumes and the proliferation of digital assets. As institutional investors, hedge funds, and family offices continue to increase their exposure to cryptocurrencies, the requirement for accurate, timely, and actionable data has become paramount. Crypto data platforms are now pivotal in providing market participants with historical and real-time price feeds, blockchain analytics, on-chain indicators, and sentiment analysis. These platforms also enable seamless integration with trading systems and portfolio management tools, empowering users to make informed investment decisions. The ongoing innovation in decentralized finance (DeFi) and the emergence of new digital asset classes further intensify the demand for robust data solutions, positioning crypto data platforms as a critical infrastructure layer in the digital economy.

Another key driver is the growing emphasis on regulatory compliance and risk management across the crypto ecosystem. As governments and regulatory bodies worldwide introduce stricter frameworks for anti-money laundering (AML), know-your-customer (KYC), and market surveillance, enterprises and exchanges are increasingly leveraging crypto data platforms to ensure adherence to these mandates. These platforms offer advanced compliance modules, transaction monitoring, and risk analytics, enabling stakeholders to mitigate operational and reputational risks. The integration of artificial intelligence (AI) and machine learning (ML) into these solutions further enhances their capability to detect anomalies, prevent fraud, and deliver predictive insights, thereby fostering trust and transparency in the market.

The rapid advancement in cloud computing, API-driven architectures, and interoperability standards is also propelling the Crypto Data Platform market forward. As digital asset markets operate around the clock and across geographies, there is a pressing need for scalable, resilient, and highly available data infrastructure. Cloud-based deployment models facilitate seamless access to vast datasets, while API integrations enable real-time connectivity with trading platforms, wallets, and external data sources. This technological evolution is enabling both established financial institutions and emerging fintech startups to harness the power of crypto data without significant upfront investments in hardware or IT resources. As a result, the market is witnessing accelerated product innovation, ecosystem collaboration, and the entry of new players offering specialized data services.

Regionally, North America continues to dominate the Crypto Data Platform market, accounting for the largest revenue share in 2024. The region’s leadership is underpinned by the presence of major crypto exchanges, institutional investors, and a mature regulatory landscape. Europe and Asia Pacific are also witnessing rapid adoption, driven by progressive regulatory initiatives, growing fintech ecosystems, and increasing retail investor participation. Latin America and the Middle East & Africa are emerging as promising markets, supported by rising digital asset adoption and government-led blockchain initiatives. However, regional disparities in regulatory clarity, technological infrastructure, and capital market maturity present both opportunities and challenges for market participants.

Component Analysis

The Crypto Data Platform market by component is segmented into Solutions and Services, each playing a vital role in the industry’s value chain. Solutions encompass the core software platforms that aggregate, normali

This dataset aims to facilitate NLP sentiment analysis by investigating the correlation between news headlines and cryptocurrency prices. Cryptocurrencies were chosen as they have fewer external variables than traditional stocks, allowing for more accurate analysis.

The dataset was collected by web scraping headlines from the first page of Google News for various cryptocurrencies by date for several years. While headlines for many cryptocurrencies are included, the best results were obtained by limiting models to data from 2021 to the present, when cryptocurrencies became more mainstream.

Pricing data was gathered using the unofficial Robinhood API robin-stocks, and the begins_at column contains the corresponding date for each market price and headline. Note that the headlines are from that date, so to make predictions, the articles column needs to be shifted.

The dataset was collected for the period spanning between 01/07/2019 and 31/12/2022.The historical Twitter volume were retrieved using ‘‘Bitcoin’’ (case insensitive) as the keyword from bitinfocharts.com. Google search volume was retrieved using library Gtrends. 2000 tweets per day using 4 times interval were crawled by employing Twitter API with the keyword “Bitcoin. The daily closing prices of Bitcoin, oil price, gold price, and U.S stock market indexes (S&P 500, NASDAQ, and Dow Jones Industrial Average) were collected using R libraries either Quantmod or Quandl.

🟦 What this is Synthetic, lineage-verified OHLC bars computed from decoded DEX swaps and pool states. Each row is a time bucket for a specific pool and token direction (token_in → token_out), with open/high/low/close, volumes, and trade counts.

Key traits • Schema-stable, versioned, audit-ready • Real-time (WSS) and historical/EOD delivery • Verifiable lineage to pools, tokens, swaps/logs

🌐 Chains / Coverage ETH, BSC, Base, Arbitrum, Unichain, Avalanche, Polygon, Celo, Linea, Optimism (others on request). Full history from chain genesis; reorg-aware real-time ingestion and updates. Coverage includes: • Uniswap V2, V3, V4 • Balancer V2, PancakeSwap, Solidly, Maverick, Aerodrome, and others

📑 Schema Columns as delivered (stable names/types): • id BIGINT - surrogate row id (PK) • pool_uid BIGINT NOT NULL - FK → liquidity_pools(uid) Lineage (ids): • tracing_id BYTEA NOT NULL - row identity (proof-of-derivation) • parent_tracing_ids BYTEA NOT NULL - immediate sources (packed hashes) • genesis_tracing_ids BYTEA NOT NULL - ultimate on-chain sources (packed hashes) Lineage (chain position, window anchors): • first_genesis_block_number BIGINT NOT NULL - first event in bucket • first_genesis_tx_index INTEGER NOT NULL • first_genesis_log_index INTEGER NOT NULL • last_genesis_block_number BIGINT NOT NULL - last event in bucket • last_genesis_tx_index INTEGER NOT NULL • last_genesis_log_index INTEGER NOT NULL Bucket definition: • bucket_start TIMESTAMPTZ NOT NULL - inclusive bucket start (UTC) • bucket_seconds INTEGER NOT NULL - one of {60,300,900,1800,3600,14400,86400} for 1m,5m,15m,30m,1h,4h,1d Pair & mid snapshot: • token_in BYTEA NOT NULL - 20B (FK → erc20_tokens) • token_out BYTEA NOT NULL - 20B (FK → erc20_tokens) OHLC (prices are decimals-adjusted; token_out per 1 token_in): • open NUMERIC(78,18) NOT NULL • high NUMERIC(78,18) NOT NULL • low NUMERIC(78,18) NOT NULL • close NUMERIC(78,18) NOT NULL Volumes (token units are decimals-adjusted): • volume_in NUMERIC(78, 18) NOT NULL - sum of amount_in within bucket • volume_out NUMERIC(78, 18) NOT NULL - sum of amount_out within bucket • trades_count BIGINT NOT NULL - swap count in bucket

Notes • Prices are decimals-adjusted (token_out per 1 token_in). • Volumes are decimals-adjusted • Direction is implied by token_in → token_out. For the reverse, a separate row exists with tokens swapped.

🔑 Keys & Joins • Primary key: id • Idempotency: (pool_uid, token_in, token_out, bucket_start, bucket_seconds) • Foreign keys: • pool_uid → liquidity_pools(uid) • token_in/token_out → erc20_tokens(contract_address) • first_genesis_ and last_genesis_ triples → logs(block_number, tx_index, log_index)

🔗 Joins to Dependency Products • Liquidity Pools Catalog (liquidity_pools) - pool metadata (fee tier, type, tokens). • ERC-20 Tokens Catalog (erc20_tokens) - symbol, decimals, names. • Swaps / Logs - provenance checks and drill-downs.

🧬 Lineage & Reproducibility Every bar’s lineage is cryptographically linked to its inputs: • tracing_id - deterministic identity of this OHLC row • parent_tracing_ids - contributing swaps/states used in the bucket • genesis_tracing_ids - ultimate raw on-chain sources Anchors to the first and last events in the bucket enable exact replay and audit.

📈 Common uses • Charting & analytics (1m → 1d); volatility, and signal engineering • Backtesting and factor research with stable, reproducible bars • Routing heuristics and execution scheduling by time of day • Monitoring: liquidity/price regime shifts at multiple horizons

🚚 Delivery By default • WebSocket (WSS) reorg-aware live emissions when a new update is available; <140 ms median latency on ETH streams (7-day). • SFTP server for archives and daily End-of-Day (EOD) snapshots. • Model Context Protocol (MCP) for AI workflows (pull slices, schemas, lineage). Optional • Integrations to Amazon S3, Azure Blob Storage, Snowflake, and other enterprise platforms on request.

🗂️ Files (time-partitioned in UTC, compressed) • Parquet • CSV • XLS • JSON

💡 Quality and operations • Reorg-aware ingestion. • 99.95% uptime target SLA. • Backfills to chain genesis. • Versioned, schema-stable datasets; changes are additive and announced.

🔄 Change policy Schema is stable. Any breaking change ships as a new version (e.g., token_to_token_prices_ohlc_v2) with migration notes. Content updates are additive; types aren’t changed in place.

Content

The dataset has one csv file for each of the top 50 crypto coins by Market Capitalization.

Price history is available on a daily basis from Jan 1, 2015.

Column Information

• date : date of observation - the price is taken at 00:00:00 hours
• price : Price at the given date and time
• total_volume : volume of transactions on the given day
• market_cap : Market capitalization in USD

Acknowledgements

This data is taken from CoinGecko API and so please check with their terms of usage for using it in your projects.

Photo Credit:

Photo by Pierre Borthiry - Peiobty on Unsplash

Crypto-Native Payroll Market Outlook

According to our latest research, the global crypto-native payroll market size in 2024 reached USD 1.14 billion, reflecting a robust adoption curve across diverse industries. The market is expected to grow at a CAGR of 22.7% from 2025 to 2033, reaching an anticipated value of USD 8.41 billion by 2033. This impressive growth trajectory is primarily driven by the increasing acceptance of cryptocurrencies as legitimate payment instruments, the demand for borderless payroll solutions, and the proliferation of blockchain-enabled financial services. As per our latest research, the market is witnessing a surge in both enterprise and SME adoption, underpinned by the promise of faster settlements, reduced transaction costs, and enhanced transparency.

One of the primary growth factors for the crypto-native payroll market is the growing globalization of the workforce. With remote and distributed teams becoming the norm, especially in the IT and digital services sectors, traditional payroll systems often struggle with cross-border payments, compliance, and currency conversion complexities. Crypto-native payroll solutions offer seamless, real-time, and cost-effective remuneration options that bypass conventional banking channels, making them particularly attractive to companies with multinational operations. This shift is further supported by the increasing regulatory clarity in major economies, which is encouraging organizations to explore cryptocurrency-based payroll as a viable and compliant alternative.

Another significant driver is the rise of stablecoins and programmable payments, which have addressed many of the volatility and operational risks previously associated with cryptocurrency compensation. Stablecoins, pegged to fiat currencies, offer the stability required for payroll operations, while smart contract-based solutions enable automated, auditable, and conditional payments. This technological evolution is empowering both employers and employees to customize payment schedules, split payments across multiple currencies, and integrate payroll with decentralized finance (DeFi) platforms for additional financial services. The integration of such features is accelerating the adoption of crypto-native payroll, especially among tech-savvy and innovation-driven enterprises.

Additionally, the competitive landscape and the need for talent acquisition in high-growth sectors are pushing organizations to offer crypto-based payroll as a differentiator. Employees, particularly in the technology, blockchain, and creative industries, are increasingly seeking compensation in cryptocurrencies for reasons such as investment potential, financial autonomy, and ease of cross-border transactions. This trend is compelling companies to adopt crypto-native payroll solutions to attract and retain top talent, thereby fueling market expansion. Furthermore, the enhanced security, transparency, and traceability provided by blockchain technology are addressing concerns around fraud, compliance, and auditability, making crypto-native payroll solutions more appealing to enterprises of all sizes.

The integration of Payroll API solutions is becoming increasingly significant in the crypto-native payroll market. These APIs facilitate seamless communication between payroll systems and other financial platforms, enhancing the efficiency and accuracy of payroll processing. By leveraging Payroll API, organizations can automate data exchange, reduce manual errors, and ensure real-time updates across their financial systems. This capability is particularly beneficial for companies operating in multiple jurisdictions, as it simplifies compliance with diverse regulatory requirements and streamlines cross-border transactions. As the demand for integrated financial solutions grows, Payroll API is poised to play a crucial role in the evolution of crypto-native payroll systems, offering organizations a competitive edge in managing their payroll operations.

From a regional perspective, North America currently leads the crypto-native payroll market, accounting for the largest share due to early regulatory advancements, high digital literacy, and the presence of major blockchain innovators. Europe follows closely, driven by progressive regulatory frameworks and the rapid digitization of finan

https://algotrading101.com/learn/wp-content/uploads/2020/06/yahoo-finance-api-guide.png">

This dataset contains real-time prices of various cryptocurrencies that are listed on Yahoo Finance. The data has been collected from Yahoo Finance API and consists of 9,600 rows of data.

Dataset Overview Liquidity-Weighted Average Price (LWAP) per token pair and direction, aggregated in fixed time buckets (1m…1d) and computed from on-chain DEX depth within a configurable ±radius around mid.

By specifying a liquidity radius in basis points (liquidity_radius_bps), LWAP captures the fair-value price implied by executable depth near mid (not just traded volume as VWAP). This is robust to thin-tick noise and better aligned with how large orders actually move through AMMs.

Focused on USD and major fiat-pegged tokens (USDC, USDT, DAI, FRAX, LUSD, crvUSD, etc.) Aggregated over 1m, 5m, 15m, 30m, 1h, 4h, 1d buckets

Chains and Coverage ETH, BSC, Base, Arbitrum, Unichain, Avalanche, Polygon, Celo, Linea, Optimism (others on request). Full history from chain genesis; reorg-aware real-time ingestion and updates. Coverage includes all major DEX protocols holding stablecoin pairs: • Uniswap V2, V3, V4 • Curve, Balancer, Aerodrome, Solidly, Maverick, Pancake, and others

Schema List the columns exactly as delivered. • id BIGINT - surrogate row id (PK) • pool_uid BIGINT NOT NULL - FK → liquidity_pools(uid) • first_block_number BIGINT NOT NULL - first event in bucket • first_tx_index INTEGER NOT NULL • first_log_index INTEGER NOT NULL • last_block_number BIGINT NOT NULL - last event in bucket • last_tx_index INTEGER NOT NULL • last_log_index INTEGER NOT NULL • bucket_start TIMESTAMPTZ NOT NULL - inclusive bucket start (UTC) • bucket_seconds INTEGER NOT NULL - one of {60, 300, 900, 1800, 3600, 14400, 86400} for 1m, 5m, 15m, 30m, 1h, 4h, 1d • liquidity_radius_bps INTEGER NOT NULL DEFAULT 1000 - ±radius around mid used for depth weighting (recommended presets: 100 = ±1%, 1000 = ±10%) • token_in BYTEA NOT NULL - 20B (FK → erc20_tokens) • token_out BYTEA NOT NULL - 20B (FK → erc20_tokens) LWAP (price and liquidity are decimals-adjusted; token_out per 1 token_in): • price_lwap NUMERIC(78,18) NOT NULL - Liquidity-weighted average price • liquidity_token_in NUMERIC(78,18) NOT NULL - total adjusted liquidity of token_in used in aggregation • liquidity_token_out NUMERIC(78,18) NOT NULL - total adjusted liquidity of token_out used in aggregation • pool_count INTEGER NOT NULL - number of pools contributing to the LWAP • _tracing_id BYTEA - deterministic row-level hash • _parent_tracing_ids BYTEA[] - hash(es) of immediate parent rows in the derivation graph • _genesis_tracing_ids BYTEA[] - hash(es) of original sources (genesis of the derivation path) • _created_at TIMESTAMPTZ - Record creation timestamp. • _updated_at TIMESTAMPTZ - Record last update timestamp

Notes • Prices are decimals-adjusted (token_out per 1 token_in). • Liquidities are decimals-adjusted. • Direction is implied by token_in → token_out. For the reverse, a separate row exists with tokens swapped. • Use ±1% (100 bps) for micro-depth baselines; ±10% (1000 bps) for broader fair-value contexts.

Notes • Prices are decimals-adjusted (token_out per 1 token_in). • Volumes are decimals-adjusted. • Direction is implied by token_in → token_out. For the reverse, a separate row exists with tokens swapped. • For hex display: encode(token_in,'hex'), encode(token_out,'hex').

Lineage Every row has a verifiable path back to the originating raw events via the lineage triple and tracing graph: • _tracing_id - this row’s identity • _parent_tracing_ids - immediate sources • _genesis_tracing_ids - original on-chain sources This supports audits and exact reprocessing to source transactions/logs/function calls.

Common Use Cases • Liquidity-based fair-value pricing and index construction • Market health and depth parity analytics across chains • AI/quant feature engineering (liquidity volatility, depth decay) • Stable benchmark for cross-DEX price comparisons

Quality • Each row includes a cryptographic hash linking back to raw on-chain events for auditability. • Tick-level resolution for precision. • Reorg-aware ingestion ensuring data integrity. • Complete backfills to chain genesis for consistency.

Study results and scripts to obtain the results for our paper "Python Crypto Misuses in the Wild" [@akwick @gh0st42 @Breitfelder @miramezini]The archives in this folder contains the following:- evaluations.tar.gz contains the evaluation folder from the GitHub project linked in References. - tools.tar.gz contains the tools folder from the GitHub project linked in References.- repos-py-with-dep-only-src-files.zip contains the source files and their dependencies of the Python projects analyzed.- repos-micropy-with-dep-only-src-files.zip contains the sources files and their depedencies of the MicroPython projects analyzed.

REST API access to thousands of currency pairs, cryptocurrencies and commodities. 100,000 requests/day - €50/month. Real-time quotes and max. available history for all cryptos, currencies and commodities!

Represents the bitcoin rates for the last few days in different currencies. Have been fetched from bitcoinaverage.com API within a Taverna Workbench Workflow