Cryptocurrencies

Finage offers you more than 1700+ cryptocurrency data in real time.

With Finage, you can react to the cryptocurrency data in Real-Time via WebSocket or unlimited API calls. Also, we offer you a 7-year historical data API.

You can view the full Cryptocurrency market coverage with the link given below. https://finage.s3.eu-west-2.amazonaws.com/Finage_Crypto_Coverage.pdf

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

This dataset was collected from CoinGecko public API https://www.coingecko.com/api/documentation with the currency of usd. The json file includes marketcap, price and 24hr volume, and the csv file is a result after data wrangling process.

Daily data (00:00 UTC)

Dataset Overview: This dataset is a high-precision financial archive covering the Top 500 Cryptocurrencies by market capitalization. It captures historical price action and market metadata from late 2024 through 2025. Engineered for data scientists and quant analysts, the data has been strictly validated to ensure zero missing values, zero duplicates, and zero negative prices.

It allows for deep-dive analysis into the crypto market's structure, offering both granular daily price data (OHLC) for time-series forecasting and comprehensive metadata (Supply, Volume, ATH) for fundamental valuation.

Data Science Applications: * Price Prediction: Train LSTM/GRU models using open, close, high, and low features. * Market Sentiment Analysis: Correlate volume and market_cap changes with price action. * Portfolio Optimization: Analyze covariance matrices between top assets like Bitcoin (BTC) and emerging altcoins. * Clustering: Group coins by market_cap_rank or circulating_supply to identify asset classes. * Technical Analysis: Compute RSI, MACD, and Bollinger Bands using the clean OHLC history.

Column Descriptors:

top_500_metadata.csv

Contains snapshot data for the top 500 coins. * id: Unique CoinGecko identifier (e.g., bitcoin, solana). * symbol: Ticker symbol (e.g., btc, sol). * name: Full name of the asset. * image: URL to the coin's logo. * current_price: Latest market price in USD. * market_cap: Total market capitalization in USD. * market_cap_rank: Global rank by market cap. * fully_diluted_valuation: Theoretical market cap if max supply was in circulation. * total_volume: 24-hour trading volume. * high_24h / low_24h: Highest and lowest price in the last 24 hours. * price_change_24h: Absolute price change in the last 24 hours. * price_change_percentage_24h: Percentage price change in the last 24 hours. * circulating_supply: Amount of coins currently in the market. * total_supply: Total amount of coins created. * max_supply: Maximum amount of coins that will ever exist. * ath: All-Time High price. * atl: All-Time Low price. * last_updated: Timestamp of the metadata snapshot.

crypto_ohlc_checkpoint.csv

Contains the historical time-series data. | Column Name | Data Type | Description | | :--- | :--- | :--- | | coin_id | String | Unique identifier (matches id in metadata). | | symbol | String | Ticker symbol (e.g., btc). | | timestamp | Integer | Unix timestamp (ms) of the record. | | date | String | Date in YYYY-MM-DD format. | | open | Float | Opening price (USD). | | high | Float | Highest price (USD) during the day. | | low | Float | Lowest price (USD) during the day. | | close | Float | Closing price (USD). |

Ethically Obtained Data: This dataset was constructed using the CoinGecko Public API (v3) in strict adherence to their terms of service. * Rate Limits: Data extraction respected the 30 calls/minute limit using exponential backoff algorithms. * Public Domain: All data points are publicly accessible market information. * No Personal Data: Contains only aggregated financial market metrics.

Acknowledgements: Data provided by the CoinGecko API.

Description

This dataset contains minute-level price data for 50 popular cryptocurrencies, obtained using the Binance API. It is a valuable resource for analyzing cryptocurrency markets, developing trading strategies, and creating financial models.

Dataset Summary

This dataset includes minute-level time series data with the following variables:

• timestamp: Date and minute (UTC)
• open: Price at the beginning of the minute
• high: Highest price within the minute
• low: Lowest price within the minute
• close: Price at the end of the minute
• volume: Trading volume (amount)
• close_time: Closing timestamp
• quote_asset_volume: Trading volume (value)
• number_of_trades: Number of trades
• taker_buy_base_asset_volume: Taker buy volume (amount)
• taker_buy_quote_asset_volume: Taker buy volume (value)
• ignore: Unused field

Use Cases

This dataset can be used for:

• Market Analysis: Analyzing the dynamics of the cryptocurrency market.
• Trading Strategies: Developing and testing algorithmic trading strategies.
• Financial Modeling: Building price prediction models.
• Education and Research: Conducting academic research and educational projects.

Acknowledgements

Thanks to the Binance API team for providing the data used in this dataset. For more information about the Binance API, you can click here.

Access up to 100 years of historical price data for stocks, ETFs, and crypto via our JSON REST API. Clean, adjusted close data from 50+ global exchanges.

📈 Top 100 Cryptocurrencies - 365 Days Historical Price Data

Overview

Comprehensive time-series dataset containing 365 days of daily historical data for the top 100 cryptocurrencies by market capitalization. Collected from CoinGecko API in December 2024, this dataset includes daily prices, trading volumes, market caps, calculated returns, moving averages, and volatility metrics. Perfect for time-series analysis, forecasting models, backtesting strategies, and trend analysis.

What's Inside

📊 Dataset Files

1. crypto_historical_365days.csv (Main Time-Series Dataset) - 36,500+ rows of daily data (100 coins × ~365 days) - 15+ columns including prices, volume, calculated metrics - Daily OHLC-style data (price snapshots) - Derived features: daily returns, moving averages, volatility - Perfect for machine learning and forecasting models

2. crypto_monthly_summary.csv (Monthly Aggregates) - Monthly aggregated statistics - Average prices, total volumes, average returns per month - Last 12 months of market trends - Ideal for identifying seasonal patterns

3. crypto_yearly_performance.csv (Performance Rankings) - Year-over-year performance for each cryptocurrency - Start price, end price, total return percentage - Ranked from best to worst performers - Perfect for investment strategy analysis

📋 Column Details

Main Time-Series Dataset (crypto_historical_365days.csv):

Identification: - coin_id - CoinGecko unique identifier - coin_name - Full cryptocurrency name - symbol - Trading symbol (BTC, ETH, etc.) - market_cap_rank - Ranking by market capitalization

Time Information: - timestamp - Full timestamp with time (ISO format) - date - Date only (YYYY-MM-DD) - month - Month period for aggregation

Price & Volume Data: - price - Daily closing price in USD - market_cap - Daily market capitalization in USD - volume - Daily trading volume in USD

Calculated Metrics: - daily_return - Daily percentage return (% change from previous day) - price_ma7 - 7-day moving average of price - price_ma30 - 30-day moving average of price - volatility_7d - 7-day rolling standard deviation of returns - cumulative_return - Cumulative return from start of period (%)

Monthly Summary (crypto_monthly_summary.csv):

Columns: - month - Month period (YYYY-MM) - avg_price - Average price across all coins for the month - total_volume - Total trading volume for the month - avg_daily_return - Average daily return for the month

Yearly Performance (crypto_yearly_performance.csv):

Columns: - coin_id - CoinGecko identifier - coin_name - Cryptocurrency name - symbol - Trading symbol - start_price - Price at start of period - end_price - Price at end of period - total_return - Total percentage return over the year

🎯 Use Cases

Machine Learning & AI:

• Price prediction models using LSTM, Prophet, ARIMA
• Time-series forecasting with historical patterns
• Feature engineering for trading algorithms
• Anomaly detection in price movements
• Classification models (will price go up/down?)
• Regression analysis for price targets

Trading & Investment:

• Backtesting trading strategies with historical data
• Moving average crossover strategies
• Momentum trading based on cumulative returns
• Risk assessment using volatility metrics
• Portfolio optimization with historical correlations
• Dollar-cost averaging simulations

Technical Analysis:

• Trend identification using moving averages
• Volatility analysis for risk management
• Support/resistance levels from historical prices
• Pattern recognition (head & shoulders, double tops, etc.)
• Volume analysis for confirmation signals

Statistical Analysis:

• Correlation studies between cryptocurrencies
• Distribution analysis of returns
• Statistical arbitrage opportunities
• Mean reversion strategies
• Variance and covariance analysis

Research & Academic:

• Market efficiency studies
• Risk-return tradeoff analysis
• Behavioral finance patterns
• Market microstructure research
• Impact of events on prices

📊 Data Highlights

Coverage: - 100 cryptocurrencies (top by market cap) - 365 days of daily data (December 2023 - December 2024) - 36,500+ data points total - Includes major coins: Bitcoin, Ethereum, BNB, XRP, Cardano, Solana, etc.

Date Range: - Start: ~December 2023 - End: December 2024 - Full year of market cycles - Captures bull runs, corrections, and consolidations

Price Range: - From $0.000001 to $100,000+ - All market cap tiers represented - Various volatility profiles

Returns Analysis: - Daily returns: -99% to +1000%+ (extreme outliers) - Average daily return: ~0-2% - Typical volatility: 5-15%...

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

Get real-time and historical rates for thousands of currency pairs, crypto, and commodities via REST API. 100k requests/day for just €80/month.

This dataset provides comprehensive cryptocurrency market data collected from the CoinGecko API, covering key financial and performance indicators for a wide range of digital assets. It captures real-time market conditions, making it suitable for both academic research and practical data science applications.

The dataset includes essential metrics such as current price in USD, market capitalization, trading volume, circulating supply, market rank, and historical performance indicators like 24-hour and 7-day percentage price changes. These features allow users to analyze short-term and medium-term price movements, identify high-volume assets, compare market dominance, and study volatility patterns across different cryptocurrencies.

Because the data is sourced directly from a trusted public API, it reflects realistic market behavior and is ideal for exploratory data analysis (EDA), financial visualization, statistical analysis, and machine learning experiments. Users can apply this dataset to tasks such as price trend analysis, ranking-based insights, clustering cryptocurrencies based on performance metrics, and building predictive models for market movement.

This dataset is especially useful for: - Beginners learning financial data analysis and APIs - Data science and machine learning practice - Cryptocurrency market research and comparison - Dashboard creation and real-time analytics simulations

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...

snapshot-tab-pane Crypto Wallet Market Size 2025-2029The crypto wallet market size is forecast to increase by USD 631.2 million, at a CAGR of 20.6% between 2024 and 2029.The market is experiencing significant growth, driven by the increasing popularity of digital currencies and the expanding availability of crypto wallets. The inclination towards digital currencies, offering benefits such as decentralization, anonymity, and faster transactions, is fueling market expansion. However, challenges persist, with misuse and security attacks posing significant obstacles to widespread adoption. As the crypto market continues to evolve, it presents both opportunities and risks for businesses. Companies seeking to capitalize on this market can focus on enhancing security measures, ensuring user-friendly interfaces, and expanding their offerings to cater to diverse user needs. Navigating the challenges requires continuous innovation and a commitment to addressing security concerns, ensuring trust and confidence among users.In summary, the market is characterized by robust growth, driven by the shift towards digital currencies, while grappling with challenges related to security and misuse. Companies must seize opportunities to provide secure, user-friendly solutions to capitalize on this dynamic market.What will be the Size of the Crypto Wallet Market during the forecast period?Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report. Request Free SampleThe market continues to evolve, with various types of wallets catering to diverse user needs. Hardware wallets provide offline storage for private keys, enhancing security. Non-fungible tokens (NFTs) integration enables users to store and manage unique digital assets. Multi-currency wallets support various cryptocurrencies, while decentralized applications (dApps) integration offers seamless access to decentralized finance (DeFi) services. Transaction history and asset management are essential features for effective portfolio tracking. Wallet providers offer biometric authentication for enhanced security, while open-source wallets ensure transparency and community-driven development. Development kits (SDKs) enable customizable wallet solutions, catering to specific business requirements. Regulatory compliance is crucial, with Anti-Money Laundering (AML) and Know Your Customer (KYC) integrations becoming standard.Network fees, transaction fees, and gas fees are ongoing considerations for users, necessitating efficient wallet management. Security audits, import/export functions, and backup and restore capabilities are essential for maintaining wallet security. Cross-chain compatibility, seed phrases, staking rewards, and smart contract integrations are emerging trends, offering users more flexibility and opportunities. User experience (UX) and privacy coins prioritize user privacy and convenience. Payment gateways, merchant services, and wallet integrations facilitate seamless transactions. Key management and wallet recovery solutions ensure users maintain control over their assets. Threshold signatures and multi-signature wallets offer enhanced security through collective approval mechanisms. Blockchain integration, cryptocurrency exchange integration, and API integrations streamline user experience.The market's continuous dynamism underscores the importance of staying informed and adaptable to evolving trends and user needs.How is this Crypto Wallet Industry segmented?The crypto wallet industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductSoftware-basedHardware-basedOSAndroidiOSOthersApplicationTradingPeer-to-peer paymentsRemittanceOthersEnd-userIndividualCommercialGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyThe NetherlandsUKAPACChinaIndiaJapanRest of World (ROW)By Product InsightsThe software-based segment is estimated to witness significant growth during the forecast period.Crypto wallets serve as essential digital vaults for managing various cryptocurrencies and non-fungible tokens (NFTs). These wallets offer users the ability to purchase, swap, lend, and earn digital assets, contributing to the growing recognition of cryptocurrencies as a liquid and broadly held asset class. Software-based wallets, including desktop applications and browser extensions, facilitate transactions online, making them known as hot wallets. Multi-currency wallets support multiple cryptocurrencies and tokens, while decentralized applications (dApps) enable users to access various DeFi services. Wallet providers offer additional features like transact

Dataset Overview Comprehensive suite of synthetic, lineage-verified token-to-token pricing datasets built from raw DEX swaps, reserves, and liquidity states. Each level (L1–L3) represents a distinct resolution of price and liquidity impact: from ±1% micro-depth to full-range tick grids.

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. Each sub-product extends this base schema with specific price-resolution fields. • id BIGINT – Surrogate row id. • pool_uid BIGINT NOT NULL – Pool identifier (FK → liquidity_pools(uid)). • block_number BIGINT - first block where the token was recognized • block_time TIMESTAMPTZ - UTC timestamp when the block was mined • tx_index INTEGER - tx index for that event • log_index INTEGER - log index for that event • token_in BYTEA NOT NULL – 20-byte address of input token (FK → erc20_tokens(contract_address)). • token_out BYTEA NOT NULL – 20-byte address of output token (FK → erc20_tokens(contract_address)). • current_price NUMERIC(78,18) NOT NULL – Mid price at snapshot (token_out per 1 token_in, decimals-adjusted). • _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 • Additional columns depend on level: • L1: ±1% impact, offset_bps, size_in, size_out, target_price. • L2: granular price grids (e.g., every 10 bps within ±10%). • L3: full-range tick-level prices across entire Uniswap V3-style pools. • OHLC: time-bucketed open-high-low-close values with volume.

Notes: • 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 • Execution sizing & price impact analysis at micro (L1) and full-range (L3) depth. • Routing and arbitrage detection across chains and pools. • Market structure analytics and slippage modeling. • Backtesting and ML feature generation (depth, liquidity, volatility, impact). • Protocol health monitoring — liquidity degradation, fee sensitivity, depth decay.

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.

Dataset Overview 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.

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

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 • 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

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.

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.

Aggregated over 1m, 5m, 15m, 30m, 1h, 4h, 1d buckets Weighted by liquidity depth across DEX pools within ±radius bps (default ±1% or ±10%)

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.

snapshot-tab-pane Blockchain AI Market Size 2025-2029The blockchain AI market size is forecast to increase by USD 2.27 billion at a CAGR of 28.6% between 2024 and 2029.The market is experiencing significant growth due to the increasing demand for transparency and auditability in artificial intelligence systems. The integration of AI agents on blockchain networks is a key trend driving this market, as it offers enhanced security and trust in AI applications. However, scalability and performance bottlenecks pose a significant challenge to the widespread adoption of Blockchain AI. Neural networks and machine translation have revolutionized the education sector, providing personalized learning experiences and improving language translation services.Companies seeking to capitalize on this market must address these challenges through innovative solutions and collaborations. The potential for increased efficiency, security, and trust in AI systems through blockchain integration is vast, making it an attractive area for strategic investment and operational planning. Quantum computing and cognitive computing are emerging trends, offering faster processing power and advanced reasoning capabilities.What will be the Size of the Blockchain AI Market during the forecast period? Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report. Request Free SampleThe market continues to evolve, with ongoing activities shaping its applications across various sectors. Public key cryptography and data versioning are essential components in ensuring secure and transparent data exchange. Feature engineering methods and semantic web technologies enhance data interpretation, while data visualization tools facilitate better understanding of complex data sets. Security protocols and decentralized finance are driving innovation in the market, with permissioned blockchain networks providing a balance between security and accessibility. Algorithm performance metrics and bias mitigation strategies are critical for improving model accuracy and fairness. Cryptocurrency integration and token economics design offer new revenue streams and business models.Industry growth is expected to reach 70% annually, with blockchain infrastructure, smart contract development, and risk management strategies playing significant roles. Data preprocessing techniques, transaction costs, and model accuracy evaluation are essential considerations for businesses adopting AI on the blockchain. Compliance audits, anomaly detection algorithms, data integrity validation, API integration, network latency, and ontology design are also crucial aspects of this dynamic market. Moreover, the development of hybrid cloud solutions, which can access videos from both the internet and digital video broadcasting, is a significant innovation.How is this Blockchain AI Industry segmented?The blockchain AI industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.Component PlatformServicesTechnology Natural language processingContext-aware computingComputer visionDeployment Cloud-basedOn-premisesGeography North America USCanadaEurope FranceGermanyThe NetherlandsUKAPAC ChinaIndiaJapanSouth America BrazilRest of World (ROW) By Component InsightsThe Platform segment is estimated to witness significant growth during the forecast period. The market is witnessing significant activity and evolving patterns as decentralized technologies intersect with artificial intelligence. Blockchain's data provenance tracking and immutability assurance are essential for AI applications, ensuring trust and reliability. Transaction verification speed and decentralized data storage offer advantages for real-time analysis and decision-making. Blockchain network security, scalability solutions, and access control mechanisms are crucial for enterprise-grade applications. Decentralized applications (DApps) and identity management systems are revolutionizing industries, while natural language processing enhances user experience. Blockchain energy consumption is a concern, but ai-powered blockchain analytics and risk assessment models mitigate potential issues. Deep learning applications, predictive modeling techniques, and data governance policies are driving innovation.Fraud detection systems and audit trail generation are essential for regulatory compliance, with cryptographic hashing algorithms and distributed ledger technology ensuring security. Quantum computing resistance, tokenization platforms, and interoperability standards are key considerations for future-proofing solutions. Data encryp

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).

global B2B cross border payments market size was $31.85 trillion in 2024 and is grow to $55.45 trillion by 2034, a CAGR of 5.70% between 2025 and 2034.

This dataset provides historical daily price data for major global financial markets, including the NASDAQ Composite Index, S&P 500 Index, Bitcoin (BTC-USD), and Ethereum (ETH-USD). The dataset is automatically updated through a scheduled pipeline that retrieves market data using the Yahoo Finance API.

The objective of this dataset is to provide a simple and continuously updated source of financial time-series data suitable for research, machine learning experiments, financial modelling, and data analysis.

Each dataset version represents a snapshot of the market data at the time of the update. Historical versions remain accessible to ensure reproducibility of experiments and analyses.

The dataset currently includes the following instruments:

NASDAQ Composite Index S&P 500 Index Bitcoin (BTC-USD) Ethereum (ETH-USD)

All data is stored in CSV format and contains standard OHLC financial fields:

Date Open High Low Close Adj Close Volume

The dataset is updated automatically through a scheduled Kaggle notebook which downloads the latest available market data and publishes a new dataset version.

This dataset can be used for:

time-series forecasting financial machine learning models portfolio analysis algorithmic trading experiments educational demonstrations of financial data analysis