This is the dataset fetched from the API, it has a total of 21 columns explaining the different features of various cryptocurrencies. Binance is a crypto trading platform, so this data will be good practice to test your skills.

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After a peak in **************, Litecoin saw its transaction volume decrease by nearly ** percent in the early months of 2021. The cryptocurrency ranks among the oldest digital coins available, and is consistently one of the most traded virtual currencies on the market. Nevertheless, Litecoin is not as popular as Bitcoin (BTC), Ethereum (ETH), or Ripple (XRP).

Bitcoin's transaction volume was at its highest in December 2023, when the network processed over ******* coins on the same day. Bitcoin generally has a higher transaction activity than other cryptocurrencies, except Ethereum. This cryptocurrency is often processed more than *********** times per day. Note that the transaction volume here refers to transactions registered within the Bitcoin blockchain. It should not be confused with Bitcoin's 24-hour trade volume, a metric associated with crypto exchanges. The more Bitcoin transactions, the more it is used in B2C payments? A Bitcoin transaction recorded in the blockchain can be any transaction, including B2C but also P2P. While it is possible to see in the blockchain which address sent Bitcoin to whom, details on who this person is and where they are from are typically missing. Bitcoin was designed to go against monetary authorities and prides itself on being anonymous. An important argument against Bitcoin replacing cash or cards in payments is that the cryptocurrency was not allowed for such a task: Bitcoin ranks among the slowest cryptocurrencies in terms of transaction speed. Are cryptocurrencies taking over payments? Cryptocurrency payments are set to grow at a CAGR of nearly ** percent between 2022 and 2029, although the market is relatively small. The forecast is according to a market estimate made in early 2023, based on various conditions and sources available at that time. Research across ** countries during the same time suggested that the market share of cryptocurrency in e-commerce transactions was "less than *** percent" in all surveyed countries, with predictions being this would not change in the future.

ExactOne delivers unparalleled consumer transaction insights to help investors and corporate clients uncover market opportunities, analyze trends, and drive better decisions.

Dataset Highlights - Source: Debit and credit card transactions from 600K+ active users and 2M accounts connected via Open Banking. Scale: Covers 250M+ annual transactions, mapped to 1,800+ merchants and 330+ tickers. Historical Depth: Over 6 years of transaction data. Flexibility: Analyse transactions by merchant/ticker, category/industry, or timeframe (daily, weekly, monthly, or quarterly).

ExactOne data offers visibility into key consumer industries, including: Airlines - Regional / Budget Airlines - Cargo Airlines - Full Service Autos - OEMs Communication Services - Cable & Satellite Communication Services - Integrated Telecommunications Communication Services - Wireless Telecom Consumer - Services Consumer - Health & Fitness Consumer Staples - Household Supplies Energy - Utilities Energy - Integrated Oil & Gas Financial Services - Insurance Grocers - Traditional Hotels - C-corp Industrial - Tools And Hardware Internet - E-commerce Internet - B2B Services Internet - Ride Hailing & Delivery Leisure - Online Gambling Media - Digital Subscription Real Estate - Brokerage Restaurants - Quick Service Restaurants - Fast Casual Restaurants - Pubs Restaurants - Specialty Retail - Softlines Retail - Mass Merchants Retail - European Luxury Retail - Specialty Retail - Sports & Athletics Retail - Footwear Retail - Dept Stores Retail - Luxury Retail - Convenience Stores Retail - Hardlines Technology - Enterprise Software Technology - Electronics & Appliances Technology - Computer Hardware Utilities - Water Utilities

Use Cases

For Private Equity & Venture Capital Firms: - Deal Sourcing: Identify high-growth opportunities. - Due Diligence: Leverage transaction data to evaluate investment potential. - Portfolio Monitoring: Track performance post-investment with real-time data.

For Consumer Insights & Strategy Teams: - Market Dynamics: Compare sales trends, average transaction size, and customer loyalty. - Competitive Analysis: Benchmark market share and identify emerging competitors. - E-commerce vs. Brick & Mortar Trends: Assess channel performance and strategic opportunities. - Demographic & Geographic Insights: Uncover growth drivers by demo and geo segments.

For Investor Relations Teams: - Shareholder Insights: Monitor brand performance relative to competitors. - Real-Time Intelligence: Analyse sales and market dynamics for public and private companies. - M&A Opportunities: Evaluate market share and growth potential for strategic investments.

Key Benefits of ExactOne - Understand Market Share: Benchmark against competitors and uncover emerging players. - Analyse Customer Loyalty: Evaluate repeat purchase behavior and retention rates. - Track Growth Trends: Identify key drivers of sales by geography, demographic, and channel. - Granular Insights: Drill into transaction-level data or aggregated summaries for in-depth analysis.

With ExactOne, investors and corporate leaders gain actionable, real-time insights into consumer behaviour and market dynamics, enabling smarter decisions and sustained growth.

Although the number of transactions in Dogecoin increased in early 2021, there were roughly ****** of these on a single day. This figure is significantly lower when compared to the transaction volumes of other cryptocurrencies. Between January 28 and January 29, Dogecoin's value grew by around *** percent to ******** U.S. dollars after comments from Tesla CEO Elon Musk. The digital coin quickly grew to become the most talked-about cryptocurrency available.

This statistic shows the daily value of payments in France in 2018, by type of payment and in billion euros. In 2018, the daily value of payment transactions by checks amounted to approximately * billion euros. The total value of daily payments reached **** billion euros.

This comprehensive dataset offers a thorough and meticulous analysis of Dash transactions, providing a detailed and all-encompassing view. It delves into crucial metrics such as transaction volume, fees, and the overall activity of the network, shedding light on the pulse of the cryptocurrency world. The daily updates not only reflect the dynamic nature of this digital landscape but also make this dataset an essential tool for a diverse range of individuals. Whether you're an astute financial expert conducting in-depth market analyses, a curious researcher unraveling the complexities of the blockchain, or simply a passionate cryptocurrency enthusiast eager to stay informed, this dataset caters to your needs.

If you require further insights or have any inquiries regarding this dataset, please don't hesitate to contact us at info@blockchair.com. Our team is dedicated to assisting you and ensuring you maximize the value of the information provided.

Daily cryptocurrency data (transaction count, on-chain transaction volume, value of created coins, price, market cap, and exchange volume) in CSV format. The data sample stretches back to December 2013. Daily on-chain transaction volume is calculated as the sum of all transaction outputs belonging to the blocks mined on the given day. “Change” outputs are not included. Transaction count figure doesn’t include coinbase transactions. Zcash figures for on-chain volume and transaction count reflect data collected for transparent transactions only. In the last month, 10.5% (11/18/17) of ZEC transactions were shielded, and these are excluded from the analysis due to their private nature. Thus transaction volume figures in reality are higher than the estimate presented here, and NVT and exchange to transaction value lower. Data on shielded and transparent transactions can be found here and here. Decred data doesn’t include tickets and voting transactions. Monero transaction volume is impossible to calculate due to RingCT which hides transaction amounts.

Monero was processed on the blockchain roughly ****** times per day in February 2025. Monero or XMR focuses on digital payments on the blockchain, but ranks as a mid-cap cryptocurrency. Its overall market position is deemed much smaller than the likes of Bitcoin or Ethereum.

Bahrain Number of Transactions: BHB: Daily Average data was reported at 60.000 Unit in Mar 2025. This records a decrease from the previous number of 79.000 Unit for Feb 2025. Bahrain Number of Transactions: BHB: Daily Average data is updated monthly, averaging 71.550 Unit from Dec 2004 (Median) to Mar 2025, with 244 observations. The data reached an all-time high of 247.000 Unit in Mar 2008 and a record low of 27.000 Unit in Jul 2012. Bahrain Number of Transactions: BHB: Daily Average data remains active status in CEIC and is reported by Bahrain Bourse. The data is categorized under Global Database’s Bahrain – Table BH.Z010: Bahrain Bourse: Number of Transactions.

The historical data in this table are sourced from Becker C and M Sinclair (2004), :Profitability of Reserve Bank Foreign Exchange Operations: Twenty Years After the Float-C/, RB Research Discussion Paper No 2004-06. Updates to the data are published annually with a one-year lag.

It is not appropriate to use the :Market-C/ series as a proxy for foreign exchange market intervention. The RB engages in spot or forward transactions with dealers in the market virtually every day. Most of these transactions are not intended to influence the exchange rate. Rather, they occur to cover orders for foreign exchange from clients such as the Australian Government. When the RB sells foreign exchange to a client, it has the choice of meeting this out of its holdings of foreign exchange or buying the equivalent amount of foreign exchange in the market. Most of the time it does the latter, though even then the timing of the sale and purchase may not coincide precisely. The RB can also engage in foreign exchange transactions with counterparties other than dealers as a means of covering client orders.

Daily net foreign exchange transactions, net sales (-) and purchases (+), are reported according to the date on which the trade took place. This is in contrast to the monthly transactions data in Table A.4, which are reported according to the day on which settlement took place. Another difference to Table A.4 is that interest received on holdings of foreign assets is not included.

aMarketa transactions are foreign exchange transactions against the Australian dollar (excluding foreign exchange swaps) undertaken by the RB with authorised foreign exchange dealers in Australia or banks overseas.

aGovernment and other counterpartiesa transactions include the RBAas foreign exchange transactions with the Australian Government, outright transactions with other central banks and international financial institutions that are not intended to affect the exchange rate, and transactions with clients other than the Australian Government.

Analysis of ‘OpenSea Daily Ethereum Transactions’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ankanhore545/opensea-daily-transactions on 13 February 2022.

--- Dataset description provided by original source is as follows ---

This all-time data represents the raw on-chain activity of the tracked smart contracts.

I am thankful that we could collect the data from the dapprader platform: https://dappradar.com/ethereum/marketplaces/opensea These are for 5 ETH Smart Contracts as mentioned in the above site.

--- Original source retains full ownership of the source dataset ---

High Frequency Indicator: The dataset contains year-, month- and bank-wise compiled data from the year 2021 to till date on the transactions performed (responses) and failed (returns) by debit sponsor banks through National Automated Clearing House (NACH) system

Notes:

1. NACH Credit is an electronic payment service used by an institution for affording credits to a large number of beneficiaries in their bank accounts for the payment of dividend, interest, salary, pension etc. by raising a single debit to the bank account of the user institution
2. Business Declines (BD) are declined transactions due to a customer entering an invalid pin, incorrect beneficiary account etc. or due to other business reasons such as exceeding per transaction limit, exceeding permitted count of transactions per day, exceeding amount limit for the day etc.
3. Technical Declines (TD) transactions are those transactions are declined due to any technical reasons such as bank ID is empty or not in correct format or exception code not in Database or not in correct format, etc

The dataset contains year- and month-wise compiled data from the year 2020 to till date on the number of digital payment transactions done by location and through through the systems such as Aadhar Enabled Payment System (AEPS), Cheque Truncation System (CTS), National Automated Clearing System (NACH), National Electronic Toll Collection (NETC), Real Time Gross Settlement (RTGS), Immediate Payment Service (IMPS), National Electronic Fund Transactions (NEFT) and other modes of payment

Notes: i) The data published is only for RBI, NPCI-operated systems and Card Networks (domestic Off-Us transactions). ii) RTGS data includes only Customer and Interbank transactions. iii) AePS data under Payment transactions include AePS Fund Transfers and BHIM Aadhaar Pay transactions. iv) UPI data includes BHIM-UPI and USSD transactions. v) NACH Credit data includes Aadhaar Payment Bridge System (APBS) transactions. vi) NETC figures are for FASTags linked with all instruments and hence would be different from the monthly bulletin which only includes NETC linked to bank accounts vii) BBPS data is not included in the monthly bulletin as the data is captured under other systems. viii) Data on Prepaid cards are only those that are processed by card networks. ix) Blanks in the dataset represent holiday

Decentralized Finance (DeFi) is revolutionizing traditional financial services by enabling direct, intermediary-free transactions, thereby generating a substantial volume of open-source transaction data. This evolving DeFi landscape is particularly influenced by the emergence of Layer 2 (L2) solutions, which are poised to enhance network efficiency and scalability significantly, surpassing the existing capabilities of Layer 1 (L1) infrastructures. However, the detailed impact of these L2 solutions has been somewhat obscured due to a dearth of transaction data indices that can provide in-depth economic insights for empirical research. This study seeks to address this critical gap by conducting a comprehensive analysis of raw transactions sourced from Uniswap, a central decentralized exchange (DEX) within the DeFi ecosystem. The dataset encompasses an extensive collection of over 50 million transactions from both L1 and L2 networks. Additionally, we have curated a wide-ranging repository of daily indices derived from transaction trading data across prominent blockchain networks, including Ethereum, Optimism, Arbitrum, and Polygon. These indices shed light on crucial network dynamics, such as adoption trends, evaluations of scalability, decentralization metrics, wealth distribution patterns, and other key aspects of the DeFi landscape. This rich dataset serves as an invaluable tool, enabling researchers to dissect the complex interplay between DeFi and Layer 2 solutions, thus enhancing our collective understanding of this rapidly evolving ecosystem. Its notable contribution to the data science pipeline includes the implementation of a flexible, open-source Python framework, enabling the dynamic calculation of decentralization indices, customizable to specific research requirements. This adaptability makes the dataset particularly suitable for advanced machine learning applications, including deep learning, thereby solidifying its role as a critical asset in shaping Blockchain as the foundational infrastructure for the intelligent Web3 ecosystem. GitHub: https://github.com/sunshineluyao/uniswap

Daily cryptocurrency data (transaction count, on-chain transaction volume, value of created coins, price, market cap, and exchange volume) in CSV format. The data sample stretches back to December 2013. Daily on-chain transaction volume is calculated as the sum of all transaction outputs belonging to the blocks mined on the given day. “Change” outputs are not included. Transaction count figure doesn’t include coinbase transactions. Zcash figures for on-chain volume and transaction count reflect data collected for transparent transactions only. In the last month, 10.5% (11/18/17) of ZEC transactions were shielded, and these are excluded from the analysis due to their private nature. Thus transaction volume figures in reality are higher than the estimate presented here, and NVT and exchange to transaction value lower. Data on shielded and transparent transactions can be found here and here. Decred data doesn’t include tickets and voting transactions. Monero transaction volume is impossible to calculate due to RingCT which hides transaction amounts.

A. SUMMARY Transactions from FPPC Forms 460, 461, 496, 497, and 450. This dataset combines all schedules, pages, and includes unitemized totals. Only transactions from the "most recent" version of a filing (original/amendment) appear here.

B. HOW THE DATASET IS CREATED Committees file campaign statements with the Ethics Commission on a periodic basis. Those statements are stored with the Commission's data provider. Data is generally presented as-filed by committees.

If a committee files an amendment, the data from that filing completely replaces the original and any prior amendments in the filing sequence.

C. UPDATE PROCESS Each night starting at midnight Pacific time a script runs to check for new filings with the Commission's database, and updates this dataset with transactions from new filings. The update process can take a variable amount of time to complete. Viewing or downloading this dataset while the update is running may result in incomplete data, therefore it is highly recommended to view or download this data before midnight or after 8am.

During the update, some fields are copied from the Filings dataset into this dataset for viewing convenience. The copy process may occasionally fail for some transactions due to timing issues but should self-correct the following day. Transactions with a blank 'Filing Id Number' or 'Filing Date' field are such transactions, but can be joined with the appropriate record using the 'Filing Activity Nid' field shared between Filing and Transaction datasets.

D. HOW TO USE THIS DATASET
Transactions from rejected filings are not included in this dataset. Transactions from many different FPPC forms and schedules are combined in this dataset, refer to the column "Form Type" to differentiate transaction types. Properties suffixed with "-nid" can be used to join the data between Filers, Filings, and Transaction datasets. Refer to the Ethics Commission's webpage for more information. Fppc Form460 is organized into Schedules as follows:

• A: Monetary Contributions Received
• B1: Loans Received
• B2: Loan Guarantors
• C: Nonmonetary Contributions Received
• D: Summary of Expenditures Supporting/Opposing Other Candidates, Measures and Committees
• E: Payments Made
• F: Accrued Expenses (Unpaid Bills)
• G: Payments Made by an Agent or Independent Contractor (on Behalf of This Committee)
• H: Loans Made to Others
• I: Miscellaneous Increases to Cash

RELATED DATASETS

• San Francisco Campaign Filers
• Filings Received by SFEC
• Summary Totals
• Transactions

BitcoinHeist Ransomware Dataset

Akcora, C.G., Li, Y., Gel, Y.R. and Kantarcioglu, M., 2019. BitcoinHeist: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchain. IJCAI-PRICAI 2020.

We have downloaded and parsed the entire Bitcoin transaction graph from 2009 January to 2018 December. Using a time interval of 24 hours, we extracted daily transactions on the network and formed the Bitcoin graph. We filtered out the network edges that transfer less than B0.3, since ransom amounts are rarely below this threshold.

Ransomware addresses are taken from three widely adopted studies: Montreal, Princeton and Padua. Please see the BitcoinHeist article for references.

On the heterogeneous Bitcoin network, in each 24-hour snapshot we extract the following six features for an address: income, neighbors, weight, length, count, loop.

In 24 ransomware families, at least one address appears in more than one 24-hour time window. CryptoLocker has 13 addresses that appear more than 100 times each. The CryptoLocker address 1LXrSb67EaH1LGc6d6kWHq8rgv4ZBQAcpU appears for a maximum of 420 times. Four addresses have conflicting ransomware labels between Montreal and Padua datasets. APT (Montreal) and Jigsaw (Padua) ransomware families have two and one P2SH addresses (that start with '3'), respectively. All other addresses are ordinary addresses that start with ’1’.

Features

address: String. Bitcoin address. year: Integer. Year. day: Integer. Day of the year. 1 is the first day, 365 is the last day. length: Integer. weight: Float. count: Integer. looped: Integer. neighbors: Integer. income: Integer. Satoshi amount (1 bitcoin = 100 million satoshis). label: Category String. Name of the ransomware family (e.g., Cryptxxx, cryptolocker etc) or white (i.e., not known to be ransomware).

Our graph features are designed to quantify specific transaction patterns. Loop is intended to count how many transaction i) split their coins; ii) move these coins in the network by using different paths and finally, and iii) merge them in a single address. Coins at this final address can then be sold and converted to fiat currency. Weight quantifies the merge behavior (i.e., the transaction has more input addresses than output addresses), where coins in multiple addresses are each passed through a succession of merging transactions and accumulated in a final address. Similar to weight, the count feature is designed to quantify the merging pattern. However, the count feature represents information on the number of transactions, whereas the weight feature represents information on the amount (what percent of these transactions’ output?) of transactions. Length is designed to quantify mixing rounds on Bitcoin, where transactions receive and distribute similar amounts of coins in multiple rounds with newly created addresses to hide the coin origin.

White Bitcoin addresses are capped at 1K per day (Bitcoin has 800K addresses daily).

Note that although we are certain about ransomware labels, we do not know if all white addresses are in fact not related to ransomware.

When compared to non-ransomware addresses, ransomware addresses exhibit more profound right skewness in distributions of feature values.

High Frequency Indicator: The dataset contains day-wise compiled data from the year 2008 to till date on total short-term electricity transactions performed and total power generated in India

Notes:

1. The Short Term Transactions of Electricity refers to the contracts of less than one year period for electricity transacted (inter-State) through inter-State Trading Licensees and also directly by the Distribution Licensees, Power Exchanges such as Indian Energy Exchange Ltd (IEX), Power Exchanges (PXs), Power Exchange India Ltd (PXIL), and Deviation Settlement Mechanism (DSM) and their subsidiaries
2. Bilateral Transaction means a transaction for exchange of energy (MWh) between a specified buyer and a specified seller, directly or through a trading licensee, from a specified point of injection to a specified point of drawal for a fixed or varying quantum of power (MW)
3. The Day-Ahead Energy Market (day-ahead market) is a financial market where market participants purchase and sell electric energy at financially binding day-ahead prices for the following day
4. The Green Day ahead market is a financial marked which enables participants to purchase electricity for the same day through intra-day contracts, for the next day through day-ahead contingency, on a daily basis for rolling seven days through daily contracts

Daily Trading Information of Listed Stocks (Taiwan Stock Exchange)