Bitcoin Historical Dataset 3M records from 2017-2023

Context:

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

Dataset Details:

Total Number of Entries: 3.126.000

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

Data Type: csv

Size: 133 MB

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

Content:

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

Inspiration:

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

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

Acknowledgements:

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

Licensing:

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

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

In March 2024 Bitcoin BTC reached a new all-time high with prices exceeding 73000 USD marking a milestone for the cryptocurrency market This surge was due to the approval of Bitcoin exchange-traded funds ETFs in the United States allowing investors to access Bitcoin without directly holding it This development increased Bitcoin’s credibility and brought fresh demand from institutional investors echoing previous price surges in 2021 when Tesla announced its 15 billion investment in Bitcoin and Coinbase was listed on the Nasdaq By the end of 2022 Bitcoin prices dropped sharply to 15000 USD following the collapse of cryptocurrency exchange FTX and its bankruptcy which caused a loss of confidence in the market By August 2024 Bitcoin rebounded to approximately 64178 USD but remained volatile due to inflation and interest rate hikes Unlike fiat currency like the US dollar Bitcoin’s supply is finite with 21 million coins as its maximum supply By September 2024 over 92 percent of Bitcoin had been mined Bitcoin’s value is tied to its scarcity and its mining process is regulated through halving events which cut the reward for mining every four years making it harder and more energy-intensive to mine The next halving event in 2024 will reduce the reward to 3125 BTC from its current 625 BTC The final Bitcoin is expected to be mined around 2140 The energy required to mine Bitcoin has led to criticisms about its environmental impact with estimates in 2021 suggesting that one Bitcoin transaction used as much energy as Argentina Bitcoin’s future price is difficult to predict due to the influence of large holders known as whales who own about 92 percent of all Bitcoin These whales can cause dramatic market swings by making large trades and many retail investors still dominate the market While institutional interest has grown it remains a small fraction compared to retail Bitcoin is vulnerable to external factors like regulatory changes and economic crises leading some to believe it is in a speculative bubble However others argue that Bitcoin is still in its early stages of adoption and will grow further as more institutions and governments recognize its potential as a hedge against inflation and a store of value 2024 has also seen the rise of Bitcoin Layer 2 technologies like the Lightning Network which improve scalability by enabling faster and cheaper transactions These innovations are crucial for Bitcoin’s wider adoption especially for day-to-day use and cross-border remittances At the same time central bank digital currencies CBDCs are gaining traction as several governments including China and the European Union have accelerated the development of their own state-controlled digital currencies while Bitcoin remains decentralized offering financial sovereignty for those who prefer independence from government control The rise of CBDCs is expected to increase interest in Bitcoin as a hedge against these centralized currencies Bitcoin’s journey in 2024 highlights its growing institutional acceptance alongside its inherent market volatility While the approval of Bitcoin ETFs has significantly boosted interest the market remains sensitive to events like exchange collapses and regulatory decisions With the limited supply of Bitcoin and improvements in its transaction efficiency it is expected to remain a key player in the financial world for years to come Whether Bitcoin is currently in a speculative bubble or on a sustainable path to greater adoption will ultimately be revealed over time.

This dataset offers a detailed examination of Bitcoin's price behavior over the last eight years, featuring a range of technical indicators for analyzing its trends. It captures the daily opening, highest, lowest, and closing prices, along with trading volume. The dataset includes momentum indicators such as the 7-day and 14-day Relative Strength Index (RSI) to determine if the asset is overbought or oversold. It also contains the 7-day and 14-day Commodity Channel Index (CCI), which compares the current price to the historical average to spot short- and medium-term trends. Additionally, it encompasses moving averages like the 50-day and 100-day Simple Moving Average (SMA) and Exponential Moving Average (EMA), which shed light on the asset's trend direction. Other essential indicators in the dataset are the Moving Average Convergence Divergence (MACD), Bollinger Bands for assessing price volatility, the True Range, and the 7-day and 14-day Average True Range (ATR) that provide insights into market volatility. This dataset's primary goal is to forecast the closing price for the next day, making it a crucial tool for predicting future market movements.

Dataset

This dataset was created by PurplePhoenix

Released under CC0: Public Domain

Contents

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

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

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

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

Dataset Presentation:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

df = df.drop(550341)

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

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

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

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

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

Subtitle:

Comprehensive Bitcoin Hourly Trading Dataset: 2017–2024

Description:

This dataset offers detailed hourly trading data for Bitcoin (BTC-USD), covering the period from August 17, 2017, to December 25, 2024. It is sourced from Binance, one of the most prominent cryptocurrency exchanges, and is designed to provide a granular view of Bitcoin's market activity over several years.

Dataset Highlights:

• Timeframe: Over seven years of hourly data for in-depth analysis of market trends.
• OHLC Data: Includes Open, High, Low, and Close prices, enabling detailed candlestick analysis.
• Trading Volume: Captures the total traded volume for each hour, aiding in liquidity analysis.
• Quote Asset Volume: Represents the traded volume in USD equivalent, facilitating fiat comparisons.
• Number of Trades: The count of trades executed within each hour, offering insights into market activity.
• Taker Buy Base/Quote Volumes: Differentiates between aggressive (taker) buy orders in both Bitcoin and USD equivalents.
• Close Time: Marks the precise end of each hourly interval for temporal accuracy.

Applications:

This dataset is an excellent resource for:
- Time-Series Analysis: Understanding long-term and short-term market trends.
- Machine Learning Models: Training models for price prediction, anomaly detection, or volatility analysis.
- Algorithmic Trading: Building and backtesting trading strategies.
- Market Research: Analyzing Bitcoin's market dynamics, trading behavior, and historical performance.

The dataset is particularly useful for crypto enthusiasts, data scientists, and financial analysts seeking to explore the nuances of Bitcoin's price movements and trading activity over time.

Context

This minute by minute historical dataset of bitcoin prices offers a wealth of information for data scientists and analysts. In addition to the OHLC prices for each minute, this dataset also includes the volume of bitcoin traded during that time period. This granular data, going back to 2015, allows for in-depth analysis of the market fluctuations and trends of the world's most popular cryptocurrency.

With this dataset, researchers can study the underlying mechanisms of the bitcoin network, traders can gain a better understanding of market movements, and investors can make more informed decisions about their investments. The open, high, low, and close prices, as well as the volume data, provide a wealth of information for analyzing the market and identifying potential opportunities.

Whether you're looking to gain a competitive edge as a trader, conduct research on the bitcoin market, or simply want to learn more about the world of cryptocurrency, this dataset is a valuable resource. With its rich and detailed data, you'll be able to dive deep into the world of bitcoin and uncover insights that can help you make better decisions.

Content

• Timestamp: This is the UNIX timestamp or the "Epoch Time", number of seconds elapsed since 00:00:00 UTC on 1 January 1970.
• Date: Date and time of price recording.
• Open - This is the opening price of the time period (in US Dollars).
• High - This is the highest price of the time period (in US Dollars).
• Low - This is the lowest price of the time period (in US Dollars).
• Close - This is the closing price of the time period (in US Dollars).
• Volume BTC - This is the volume of ₿ transacted in the time interval.
• Volume USD - This is the volume of $ transacted in the time interval.

OHLCV is an abbreviation for the five critical data points: Open, High, Low, Close, and Volume. It refers to the key points in analyzing an asset such as Bitcoin (BTC) in the market over a specified time. The dataset is important for not only traders and analysts but also for data scientists who work on BTC market prediction using artificial intelligence. The 'Open' and 'Close' prices represent the starting and ending price levels, while the 'High' and 'Low' are the highest and lowest prices during that period (a daily time frame (24h)). The 'Volume' is a measure of the total number of trades. This dataset provides five OHLCV data columns for BTC along with a column called "Next day close price" for regression problems and machine learning applications. The dataset includes daily information from 1/1/2012 to 8/6/2022.

This dataset contains several files:dataset.tar.gz: A compressed PostgreSQL database representing a graph.addresses.csv: A list of approximately 100,000 labeled Bitcoin addresses.BitcoinTemporalGraph (dataset.tar.gz)This dataset represents a graph of value transfers between Bitcoin users. The nodes represent entities/users, and the edges represent value transfers or transactions between these entities. The graph is temporal and directed.Usage:Decompress the archive: "pigz -p 10 -dc dataset.tar.gz | tar -xvf -"Restore the tables into an existing PostgreSQL database using the pg_restore utility: "pg_restore -j number_jobs -Fd -O -U database_username -d database_name dataset"Ensure substantial storage for the database: 40GB for node_features and 80GB for transaction_edges (including indexes)Dataset DescriptionThe database contains two tables: node_features (approximately 252 million rows) and transaction_edges (approximately 785 million rows).Columns for node_features table:alias: Identifier of the nodedegree: Degree of the nodedegree_in: Number of incoming edges to the nodedegree_out: Number of outgoing edges from the nodetotal_transaction_in: Total count of value transfers received by the nodetotal_transaction_out: Total count of value transfers initiated by the nodeAmounts are expressed in satoshis (1 satoshi = 10^-8 Bitcoin):min_sent: Minimum amount sent by the node during a transactionmax_sent: Maximum amount sent by the node during a transactiontotal_sent: Total amount sent by the node during all transactionsmin_received: Minimum amount received by the node during a transactionmax_received: Maximum amount received by the node during a transactiontotal_received: Total amount received by the node during all transactionslabel: Label describing the type of entity represented by the nodeTransactions on the Bitcoin network are stored in the public ledger named the "Bitcoin Blockchain". Each transaction is recorded in a block, with the block index indicating the transaction's position in the blockchain.first_transaction_in: Block index of the first transaction received by the nodelast_transaction_in: Block index of the last transaction received by the nodefirst_transaction_out: Block index of the first transaction sent by the nodelast_transaction_out: Block index of the last transaction sent by the nodeNodes can represent one or more Bitcoin addresses (pseudonyms used by Bitcoin users). A real entity often uses multiple addresses. The dataset contains only transactions between nodes (outer transactions), but provides information about inner transactions (transactions between addresses controlled by the same node).cluster_size: Number of addresses represented by the nodecluster_num_edges: Number of transactions between the addresses represented by the nodecluster_num_cc: Number of connected components in the transaction graph of the addresses represented by the nodecluster_num_nodes_in_cc: Number of non-isolated addresses in the clusterColumns in the transaction_edges table:a: Node alias of the senderb: Node alias of the recipientreveal: Block index of the first transaction from a to blast_seen: Block index of the last transaction from a to btotal: Total number of transactions from a to bmin_sent: Minimum amount sent (in satoshis) in a transaction from a to bmax_sent: Maximum amount sent (in satoshis) in a transaction from a to btotal_sent: Total amount sent (in satoshis) in all transactions from a to bDataset of Bitcoin Labeled Addresses (addresses.csv)This file contains 103,812 labeled Bitcoin addresses with the following columns:address: Bitcoin addressentity: Name of the entitycategory: Type of the entity (e.g., individual, bet, ransomware, gambling, exchange, mining, ponzi, marketplace, faucet, bridge, mixer)source: Source used to label the address

Bitcoin's blockchain size was close to reaching 673.58 gigabytes in September 2025, as the database saw exponential growth by nearly one gigabyte every few days. The Bitcoin blockchain contains a continuously growing and tamper-evident list of all Bitcoin transactions and records since its initial release in January 2009. Bitcoin has a set limit of 21 million coins, the last of which will be mined around 2140, according to a forecast made in 2017. Bitcoin mining: A somewhat uncharted world Despite interest in the topic, there are few accurate figures on how big Bitcoin mining is on a country-by-country basis. Bitcoin's design philosophy is at the heart of this. Created out of protest against governments and central banks, Bitcoin's blockchain effectively hides both the country of origin and the destination country within a (mining) transaction. Research involving IP addresses placed the United States as the world's most Bitcoin mining country in 2022, but the source admits IP addresses can easily be manipulated using VPN. Note that mining figures are different from figures on Bitcoin trading: Africa and Latin America were more interested in buying and selling BTC than some of the world's developed economies. Bitcoin developments Bitcoin's trade volume slowed in the second quarter of 2023 after hitting a noticeable growth at the beginning of the year. The coin outperformed most of the market. Some attribute this to the announcement in June 2023 that BlackRock filed for a Bitcoin ETF. This iShares Bitcoin Trust was to use Coinbase Custody as its custodian. Regulators in the United States had not yet approved any applications for spot ETFs on Bitcoin.

Graph and download economic data for Coinbase Bitcoin (CBBTCUSD) from 2014-12-01 to 2025-12-01 about cryptocurrency and USA.

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.

Content

The dataset has one CSV file. Price history is available on a minute basis from 2017. This dataset has the historical price information of 1 Bitcoin by USDT (equivalent to USD).

• Date : date of observation
• Open : Opening price on the given minute
• High: Highest price on the given minute
• Low : Lowest price on the given minute
• Close : Closing price on the given minute
• Volume : Volume of transactions on the given minute

Acknowledgements

This data is taken from Binance and it is free to use the data.

A currency's essential feature is to be a medium of exchange. We leverage a quasi-natural experiment––El Salvador as the first country to make Bitcoin legal tender––to study a cryptocurrency's potential to be used in daily transactions. The government also launched and provided incentives to download and use a digital wallet named Chivo, which shares features with Central Bank Digital Currencies (CBDCs) and allows users to trade bitcoins and dollars. Were Chivo Wallet and Bitcoin actually adopted after this "big push"? Conducting a representative face-to-face survey and relying on blockchain data to obtain all Chivo transactions, we document how usage of digital payments and Bitcoin is low, concentrated, and has been decreasing over time. We find that privacy concerns are key barriers to adoption, which speaks to a policy debate on crypto and CBDCs that has had anonymity at its core. We also estimate the technology's adoption cost and its network externalities.,

We provide the data from the survey implemented in El Salvador by Cid Gallup, and the codes required for the analysis of these data. The blockchain data, which is used in the last section of the paper, was obtained from Crystal Blockchain B.V (crystalblockchain.com), and we share the Stata code and data required to generate all the figures in the paper.

, Only Stata is required to run all programs. ,  Are Cryptocurrencies Currencies? Bitcoin as Legal Tender in El Salvador (README)  This README file was generated on 2023-08-01 by Fernando Alvarez, David Argente, and Diana Van Patten

University of Chicago Yale University Yale University

GENERAL INFORMATION

1. Title of Dataset: Are Cryptocurrencies Currencies? Bitcoin as Legal Tender in El Salvador

We conduct a nationally representative face-to-face survey spanning 1,800 households during February 2022. Respondents are all adults, which is a prerequisite to be eligible to use Chivo Wallet. The national survey was conducted in partnership with CID-Gallup. This replication package contains data, Stata codes, and output files all figures and tables in the paper. We provide the data from the survey implemented in El Salvador by Cid Gallup, and the codes required for the analysis of these data. The blockchain data, which is used in the last section of the paper, was obtained from Crystal Blockchain B.V (crystalblockchain.com), and ...

Part 1

1 second timeframe dataset from 2017-08-17 and 2024-08-28. Dataset contain 221343465 rows. Due to kaggle doesn't allow upload 20GB+, I decided to slice into half. This part contain only 110671733 rows.

Slice method: ```python midpoint = 110671733 # Includes the header in the first half !head -n {midpoint} combined_BTCUSDT_1s_2017-08-17_to_2024-08-27.csv > half1_BTCUSDT_1s.csv !tail -n +{midpoint + 1} combined_BTCUSDT_1s_2017-08-17_to_2024-08-27.csv > half2_BTCUSDT_1s.csv

**Here is [Part 2](https://www.kaggle.com/datasets/tzelal/binance-bitcoin-dataset-1s-timeframe-p2) !**

**Note: This is public data of binance everyone can access it. I only merge large files that you don't need to do it.**

### **Beside of OHLCV there is information about other columns:**


**1. Quote Asset Volume:** The total volume of the quote currency traded during the interval. Format: A floating-point number. The quote asset is the second currency in the trading pair (e.g., USDT in BTC/USDT).

**2. Number of Trades:** The total number of trades that took place during the interval. Format: An integer representing the number of trades.

**3. Taker Buy Base Asset Volume:** The amount of the base asset bought by takers during the interval. Format: A floating-point number. The base asset is the first currency in the trading pair (e.g., BTC in BTC/USDT).

**4. Taker Buy Quote Asset Volume:**The amount of the quote asset spent by takers to buy the base asset during the interval. Format: A floating-point number.

**5. Ignore:** This column is often present but usually contains no relevant data. It may be a placeholder for future data or included for backward compatibility. Format: This column typically contains zeros or null values.

###### ==================================================================================
This dataset collected from https://data.binance.vision/ and merged. 

##### My Social Media:
- https://www.youtube.com/@dataworm_official
- https://github.com/tzelalouzeir
- https://www.linkedin.com/in/tzelalouzeir/
- https://tafou.io/
- https://algo.tafou.io/

Context

This dataset is obtain using the API of cryptocompare and it's streamed to BigQuery every 15 minutes.

Content

This dataset contains information per minute and per hour of the following cryptocurrencies in BigQuery [personal-lalo.cryptos]:

hour_ADA_USD hour_BCH_USD hour_BTC_USD hour_BTG_USD hour_DASH_USD hour_EOS_USD hour_ETH_USD hour_LTC_USD hour_NEO_USD hour_TRX_USD hour_XEM_USD hour_XLM_USD hour_XMR_USD hour_XRP_USD minute_ADA_USD minute_BCH_USD minute_BTC_USD minute_BTG_USD minute_DASH_USD minute_EOS_USD minute_ETH_USD minute_LTC_USD minute_MIOTA_USD minute_NEO_USD minute_TRX_USD minute_XEM_USD minute_XLM_USD minute_XMR_USD minute_XRP_USD

Per hour tables contains historical data from the beginning of the records of cryptocompare.

Per minute tables contains information since mid January 2018.

Acknowledgements

Thanks to cryptocompare for the very useful API

Inspiration

It's difficult to find a complete dataset of the many cryptos that are available today, so I decide to create an updated version.

Overview:

This dataset contains detailed information on cryptocurrency transactions, specifically focusing on Bitcoin (BTC) and Ethereum (ETH). The dataset includes transaction details such as sender and receiver addresses, transaction amounts, fees, timestamps, and mining pool information. It serves as a valuable resource for analyzing market trends, identifying patterns in trading behavior, and studying blockchain transaction dynamics across different mining pools.

Data Fields (Columns):

1. Transaction_ID:

• Description: A unique identifier for each cryptocurrency transaction.
• Type: String
• Example: TX2QW62Q5XM17K

2. Sender_Address:

• Description: The blockchain address of the sender initiating the transaction.
• Type: String
• Example: 0xd377b9203ad74038664c08f658c0245632651f55

3. Receiver_Address:

• Description: The blockchain address of the recipient receiving the transaction.
• Type: String
• Example: 0x4a3370c0f0b83d519ddf50892d006f64d7425880

4. Amount:

• Description: The total amount of cryptocurrency transferred in the transaction (in either BTC or ETH).
• Type: Float
• Example: 11.39618058 (BTC or ETH depending on the currency type)

5. Transaction_Fee:

• Description: The transaction fee paid to process the transaction.
• Type: Float
• Example: 6.226e-05

6. Timestamp:

• Description: The date and time when the transaction was processed, in ISO 8601 format.
• Type: Datetime (ISO 8601)
• Example: 2025-03-30T23:32:40.589676Z

7. Block_ID:

• Description: The unique identifier for the block that the transaction was included in.
• Type: String
• Example: f4A4D894b9Ee166B3F75F4Fb

8. Mining_Pool:

• Description: The name of the mining pool that confirmed the transaction.
• Type: String
• Example: Ethermine

9. Currency:

• Description: The cryptocurrency type involved in the transaction (either BTC or ETH).
• Type: String
• Example: ETH

10. Transaction_Type:

• Description: The type of the transaction. Typically, it is "Transfer" for regular cryptocurrency transfers.
• Type: String
• Example: Transfer

11. Transaction_Status:

• Description: The current status of the transaction, such as "Confirmed".
• Type: String
• Example: Confirmed

12. Gas_Price_Gwei:

• Description: The gas price for Ethereum transactions, measured in Gwei (used for ETH transactions only).
• Type: Integer
• Example: 50 (Only applicable to Ethereum transactions)

Use Cases:

1. Market Trend Analysis

Analyze transaction patterns to identify market trends and behaviors.
- Use the data to track spikes or drops in transaction volumes and correlate them with market events or price movements.

2. Blockchain Dynamics

Study how mining pools and transaction fees interact with blockchain dynamics.
- Investigate how different mining pools impact transaction confirmation times and fees across Bitcoin and Ethereum networks.

3. Trading Behavior

Investigate the behavior of users sending or receiving cryptocurrency.
- Identify patterns such as frequent senders/receivers, average transaction amounts, and transaction frequency.

4. Fee and Gas Price Optimization

Explore transaction fees and gas price fluctuations across different mining pools and blockchains.
- Examine how Ethereum’s gas prices and Bitcoin’s transaction fees fluctuate over time and how this affects user behavior.

Dataset

This dataset was created by WanqiWang

Released under CC0: Public Domain

Contents

Content

These are historical datasets of the current top 10 most popular cryptocurrencies. As of now: 1. Bitcoin 2. Ethereum 3. Binance Coin 4. Tether 5. Solana 6. Cardano 7. USD Coin 8. XRP 9. Polkadot 10. Terra

Date : Date of observation Open : Opening price on the given day High : Highest price on the given day Low : Lowest price on the given day Close : Closing price on the given day Volume : Volume of transactions on the given day Market Cap : Market capitalization

Acknowledgements

Found all the historical data from website: https://coinmarketcap.com/