Our extensive historical database captures every significant market movement, from the earliest Bitcoin trades through today's crypto ecosystem, across 350+ global exchanges.

This rich historical dataset serves multiple critical functions: from enabling sophisticated strategy backtesting and long-term trend analysis to supporting academic research and trading pattern identification. Whether analyzing market volatility, studying price correlations, or conducting deep market research, our historical data provides the reliable foundation needed for meaningful cryptocurrency market analysis.

Why work with us?

Market Coverage & Data Types: - Real-time and historical data since 2010 (for chosen assets) - Full order book depth (L2/L3) - Tick-by-tick data - OHLCV across multiple timeframes - Market indexes (VWAP, PRIMKT) - Exchange rates with fiat pairs - Spot, futures, options, and perpetual contracts - Coverage of 90%+ global trading volume - Full Cryptocurrency Investor Data

Technical Excellence: - 99% uptime guarantee - Multiple delivery methods: REST, WebSocket, FIX, S3 - Standardized data format across exchanges - Ultra-low latency data streaming - Detailed documentation - Custom integration assistance

CoinAPI serves hundreds of institutions worldwide, from trading firms and hedge funds to research organizations and technology providers. Our commitment to data quality and technical excellence makes us the trusted choice for cryptocurrency market data needs.

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

CoinAPI delivers enterprise-grade data infrastructure specifically designed for quantitative trading, providing real-time and historical data feeds from over 350+ exchanges through unified, scalable APIs.

Our platform serves sophisticated quant trading operations with microsecond-precision data delivery, enabling everything from statistical arbitrage to long-term systematic strategies through comprehensive market coverage.

✅ Availability - over 800 cryptocurrencies.

➡️ Why choose us?

📊 Market Coverage & Data Types: ◦ Real-time and historical data since 2010 (for chosen assets) ◦ Full order book depth (L2/L3) ◦ Trade-by-trade data ◦ OHLCV across multiple timeframes ◦ Market indexes (VWAP, PRIMKT) ◦ Exchange rates with fiat pairs ◦ Spot, futures, options, and perpetual contracts ◦ Coverage of 90%+ global trading volume

🔧 Technical Excellence: ◦ 99% uptime guarantee ◦ Multiple delivery methods: REST, WebSocket, FIX, S3 ◦ Standardized data format across exchanges ◦ Ultra-low latency data streaming ◦ Detailed documentation ◦ Custom integration assistance

CoinAPI serves hundreds of institutions worldwide, from trading firms and hedge funds to research organizations and technology providers. Our commitment to data quality and technical excellence makes us the trusted choice for the cryptocurrency market's data needs.

Our Market Data covers historical and real-time data. For CEXs, our data spans back to 2015, and for DEXs, we cover since the genesis trade. We cover every instrument on any exchange, so if it's traded, we cover it.

We understand you need to access the data you want, when and where you need it. With this in mind, we built our Market Data with several delivery options, including a robust streaming service offering the most advanced live data distribution in the cryptocurrency industry, as well as REST API, CSV via cloud services, and BigQuery.

Our Market Data empowers traders, analysts, and financial institutions with the insights needed to navigate the complex derivatives market effectively.

| Use Cases | Backtesting Hedging Trading Strategies Risk Management Regulatory Compliance

| Why work with us? |

A proven enterprise-grade solution We prioritize the needs of enterprises in our product development, ensuring our solutions meet the requirements of larger organizations seeking best-in-class crypto data.

A UI-free approach to crypto data We recognize the importance of flexibility when it comes to crypto data, and so we offer you complete freedom by taking a UI-free approach to data delivery. This gives you total control over how you use and interpret the data, reducing friction and streamlining workflows.

Flexible to meet your needs Flexibility lies at the heart of our product and is fundamental to how crypto data can deliver value across industries and use cases. Living this philosophy, we’re always building custom options that can help you achieve your specific objectives. Whether it’s tailoring a package to meet your requirements, or adapting infrastructure to support your use case, our data and product teams are on-hand to help you find the best way to achieve your priority outcomes.

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).

We monitor and process Currency data, all major Forex pairs, 10K+ Cryptocurrencies, and Bitcoin Prices data with seamless integration. for flexible market analysis or custom use cases.

1. Global Coverage: Track all major forex pairs, 10K+ cryptocurrencies, Bitcoin prices, and digital assets from across the globe.
2. Rick Data Set: Leverage 15+ years of historical trends, and EOD refreshes for in-depth market insights and trend analysis.
3. Tailored Insights: On profiles, references, and tailored financial data feeds.
4. Seamless Integration: Retrieve data via API/SFTP/CSV with fully customizable fields and formats, ensuring flexibility for diverse analysis needs.
5. Trusted by Professionals: Used by financial institutions, traders, investors, hedge funds, asset managers, and researchers in both traditional and digital markets.
6. Secure & Compliant: Ensure risk-free integration with globally compliant data security protocols.

Empower smarter decisions across currency, forex, and cryptocurrency markets with InfoTrie’s global data solutions.

Book a meeting here: https://calendar.app.google/4UEQVKsuSiTM4JxB8 to gain immediate access to refreshed and reliable currency data APIs today.

Context:

This dataset is an extra updating dataset for the G-Research Crypto Forecasting competition.

Introduction

This is a daily updated dataset, automaticlly collecting market data for G-Research crypto forecasting competition. The data is of the 1-minute resolution, collected for all competition assets and both retrieval and uploading are fully automated. see discussion topic.

The Data

For every asset in the competition, the following fields from Binance's official API endpoint for historical candlestick data are collected, saved, and processed.

1. **timestamp** - A timestamp for the minute covered by the row.
2. **Asset_ID** - An ID code for the cryptoasset.
3. **Count** - The number of trades that took place this minute.
4. **Open** - The USD price at the beginning of the minute.
5. **High** - The highest USD price during the minute.
6. **Low** - The lowest USD price during the minute.
7. **Close** - The USD price at the end of the minute.
8. **Volume** - The number of cryptoasset u units traded during the minute.
9. **VWAP** - The volume-weighted average price for the minute.
10. **Target** - 15 minute residualized returns. See the 'Prediction and Evaluation section of this notebook for details of how the target is calculated.
11. **Weight** - Weight, defined by the competition hosts [here](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)
12. **Asset_Name** - Human readable Asset name.

Indexing

The dataframe is indexed by timestamp and sorted from oldest to newest. The first row starts at the first timestamp available on the exchange, which is July 2017 for the longest-running pairs.

Usage Example

The following is a collection of simple starter notebooks for Kaggle's Crypto Comp showing PurgedTimeSeries in use with the collected dataset. Purged TimesSeries is explained here. There are many configuration variables below to allow you to experiment. Use either GPU or TPU. You can control which years are loaded, which neural networks are used, and whether to use feature engineering. You can experiment with different data preprocessing, model architecture, loss, optimizers, and learning rate schedules. The extra datasets contain the full history of the assets in the same format as the competition, so you can input that into your model too.

Baseline Example Notebooks:

• Neural Network Starter
• LightGBM Starter
• Catboost Starter
• XGBoost Starter
• TabNet Starter
• Reinforcement Learning (PPO) Starter

These notebooks follow the ideas presented in my "Initial Thoughts" here. Some code sections have been reused from Chris' great (great) notebook series on SIIM ISIC melanoma detection competition here

Loose-ends:

This is a work in progress and will be updated constantly throughout the competition. At the moment, there are some known issues that still needed to be addressed:

• VWAP: - At the moment VWAP calculation formula is still unclear. Currently the dataset uses an approximation calculated from the Open, High, Low, Close, Volume candlesticks. [Waiting for competition hosts input]
• Target Labeling: There exist some mismatches to the original target provided by the hosts at some time intervals. On all the others - it is the same. The labeling code can be seen here. [Waiting for competition hosts] input]
• Filtering: No filtration of 0 volume data is taken place.

Example Visualisations

Opening price with an added indicator (MA50): https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fb8664e6f26dc84e9a40d5a3d915c9640%2Fdownload.png?generation=1582053879538546&alt=media" alt="">

Volume and number of trades: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fcd04ed586b08c1576a7b67d163ad9889%2Fdownload-1.png?generation=1582053899082078&alt=media" alt="">

License

This data is being collected automatically from the crypto exchange Binance.

CoinAPI provides institutional-grade cryptocurrency market data, delivering real-time and historical information from 350+ global exchanges through a unified API infrastructure. Our comprehensive coverage includes Bitcoin price data, Ethereum metrics, and detailed market information for over 800 digital assets across both centralized (CEX) and decentralized exchanges (DEX).

Our system captures market movements down to the microsecond, so you never miss important price action. We standardize all this data to make it easy to use, no matter which exchanges you're watching. For clients seeking deeper insights, we also provide alternative data that reveals market dynamics beyond traditional price and volume metrics.

Why work with us?

Market Coverage & Data Types: - Real-time and historical data since 2010 (for chosen assets) - Full order book depth (L2/L3) - Trade-by-trade data - OHLCV across multiple timeframes - Market indexes (VWAP, PRIMKT) - Exchange rates with fiat pairs - Spot, futures, options, and perpetual contracts - Coverage of 90%+ global trading volume

Technical Excellence: - 99,9% uptime guarantee - Multiple delivery methods: REST, WebSocket, FIX, S3 - Standardized data format across exchanges - Ultra-low latency data streaming - Detailed documentation - Custom integration assistance

Banks, trading companies, and market researchers rely on our Bitcoin, Ethereum, and altcoin feeds for their trading strategies and market analysis. Whether you need detailed market depth information, historical price data, or real-time trading signals, we provide the solid foundation you need to participate confidently in the crypto markets.

Context:

This dataset is an extra updating dataset for the G-Research Crypto Forecasting competition.

Introduction

This is a daily updated dataset, automaticlly collecting market data for G-Research crypto forecasting competition. The data is of the 1-minute resolution, collected for all competition assets and both retrieval and uploading are fully automated. see discussion topic.

The Data

For every asset in the competition, the following fields from Binance's official API endpoint for historical candlestick data are collected, saved, and processed.

1. **timestamp** - A timestamp for the minute covered by the row.
2. **Asset_ID** - An ID code for the cryptoasset.
3. **Count** - The number of trades that took place this minute.
4. **Open** - The USD price at the beginning of the minute.
5. **High** - The highest USD price during the minute.
6. **Low** - The lowest USD price during the minute.
7. **Close** - The USD price at the end of the minute.
8. **Volume** - The number of cryptoasset u units traded during the minute.
9. **VWAP** - The volume-weighted average price for the minute.
10. **Target** - 15 minute residualized returns. See the 'Prediction and Evaluation section of this notebook for details of how the target is calculated.
11. **Weight** - Weight, defined by the competition hosts [here](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)
12. **Asset_Name** - Human readable Asset name.

Indexing

The dataframe is indexed by timestamp and sorted from oldest to newest. The first row starts at the first timestamp available on the exchange, which is July 2017 for the longest-running pairs.

Usage Example

The following is a collection of simple starter notebooks for Kaggle's Crypto Comp showing PurgedTimeSeries in use with the collected dataset. Purged TimesSeries is explained here. There are many configuration variables below to allow you to experiment. Use either GPU or TPU. You can control which years are loaded, which neural networks are used, and whether to use feature engineering. You can experiment with different data preprocessing, model architecture, loss, optimizers, and learning rate schedules. The extra datasets contain the full history of the assets in the same format as the competition, so you can input that into your model too.

Baseline Example Notebooks:

• Neural Network Starter
• LightGBM Starter
• Catboost Starter
• XGBoost Starter
• TabNet Starter
• Reinforcement Learning (PPO) Starter

These notebooks follow the ideas presented in my "Initial Thoughts" here. Some code sections have been reused from Chris' great (great) notebook series on SIIM ISIC melanoma detection competition here

Loose-ends:

This is a work in progress and will be updated constantly throughout the competition. At the moment, there are some known issues that still needed to be addressed:

• VWAP: - At the moment VWAP calculation formula is still unclear. Currently the dataset uses an approximation calculated from the Open, High, Low, Close, Volume candlesticks. [Waiting for competition hosts input]
• Target Labeling: There exist some mismatches to the original target provided by the hosts at some time intervals. On all the others - it is the same. The labeling code can be seen here. [Waiting for competition hosts] input]
• Filtering: No filtration of 0 volume data is taken place.

Example Visualisations

Opening price with an added indicator (MA50): https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fb8664e6f26dc84e9a40d5a3d915c9640%2Fdownload.png?generation=1582053879538546&alt=media" alt="">

Volume and number of trades: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fcd04ed586b08c1576a7b67d163ad9889%2Fdownload-1.png?generation=1582053899082078&alt=media" alt="">

License

This data is being collected automatically from the crypto exchange Binance.

CoinAPI provides reliable and top-quality solutions for cryptocurrency custodians, delivering crypto market data, and seamless trading connectivity across 350+ global exchanges through a unified API system.

Our platform enables you to access centralized exchanges (CEX) and decentralized exchanges (DEX), offering comprehensive market coverage with real-time order execution and microsecond-precision price feeds.

➡️ Why choose us?

📊 Market Coverage & Data Types: ◦ Real-time and historical data since 2010 (for chosen assets) ◦ Full order book depth (L2/L3) ◦ Trade-by-trade data ◦ OHLCV across multiple timeframes ◦ Market indexes (VWAP, PRIMKT) ◦ Exchange rates with fiat pairs ◦ Spot, futures, options, and perpetual contracts ◦ Indices data available ◦ Coverage of 90%+ global trading volume

🔧 Technical Excellence: ◦ 99% uptime guarantee ◦ Multiple delivery methods: REST, WebSocket, FIX, S3 ◦ Standardized data format across exchanges ◦ Ultra-low latency data streaming ◦ Detailed documentation ◦ Custom integration assistance

CoinAPI serves hundreds of institutions worldwide, from trading firms and hedge funds to research organizations and technology providers. Our commitment to data quality and technical excellence makes us the trusted choice for the cryptocurrency market's data needs.

Algorithmic trading demands data that's both comprehensive and precise. CoinAPI delivers exactly this - institutional-grade cryptocurrency data spanning 350+ global exchanges through a unified API infrastructure that scales with your trading operation.

For high-frequency strategies where microseconds matter, our trade feeds provide the timestamp precision and delivery consistency required for effective execution. Our platform captures Bitcoin price data alongside 800+ other cryptocurrencies, ensuring complete market coverage for both established and emerging digital assets.

➡️ Why choose us?

📊 Market Coverage & Data Types: ◦ Real-time and historical data since 2010 (for chosen assets) ◦ Full order book depth (L2/L3) ◦ Trade-by-trade data ◦ OHLCV across multiple timeframes ◦ Market indexes (VWAP, PRIMKT) ◦ Exchange rates with fiat pairs ◦ Spot, futures, options, and perpetual contracts ◦ Coverage of 90%+ global trading volume

🔧 Technical Excellence: ◦ 99,9% uptime guarantee ◦ Multiple delivery methods: REST, WebSocket, FIX, S3 ◦ Standardized data format across exchanges ◦ Ultra-low latency data streaming ◦ Detailed documentation ◦ Custom integration assistance

Whether you're deploying latency-sensitive algorithms or developing longer-term systematic strategies, CoinAPI provides the reliable data foundation that professional cryptocurrency trading requires. From market microstructure analysis to strategy backtesting, our unified historical and real-time feeds support the complete algorithmic trading lifecycle.

CoinAPI's Level 1 Crypto Quote Data delivers essential crypto market data, capturing real-time best bid/ask prices and sizes across 350+ exchanges. This data stream offers a precise market snapshot with microseconds-accurate timestamps, ideal for applications requiring rapid price discovery and market monitoring.

Designed for minimal latency and high update frequency, this feed powers everything from trading algorithms and price widgets to market analysis tools. Access data through WebSocket for real-time streaming or REST API for historical analysis.

Why work with us?

Market Coverage & Data Types: - Real-time and historical data since 2010 (for chosen assets) - Full order book depth (L2/L3) - Tick-by-tick data - OHLCV across multiple timeframes - Market indexes (VWAP, PRIMKT) - Exchange rates with fiat pairs - Spot, futures, options, and perpetual contracts - Coverage of 90%+ global trading volume - Full Cryptocurrency Investor Data

Technical Excellence: - 99% uptime guarantee - Multiple delivery methods: REST, WebSocket, FIX, S3 - Standardized data format across exchanges - Ultra-low latency data streaming - Detailed documentation - Custom integration assistance

CoinAPI serves hundreds of institutions worldwide, from trading firms and hedge funds to research organizations and technology providers. Our commitment to data quality and technical excellence makes us the trusted choice for cryptocurrency market data needs.