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49 results for “Bitcoin”
ORBITAAL: cOmpRehensive BItcoin daTaset for temporAl grAph anaLysis
<h3>Dataset Construction</h3> <p>This dataset captures the temporal network of Bitcoin (BTC) flow exchanged between entities at the finest time resolution in UNIX timestamp. Its construction is based on the blockchain covering the period from January, 3rd of 2009 to January the 25th of 2021. The blockchain extraction has been made using bitcoin-etl (<a href="https://github.com/blockchain-etl/bitcoin-etl">https://github.com/blockchain-etl/bitcoin-etl</a>) Python package. The entity-entity network is built by aggregating Bitcoin addresses using the common-input heuristic [1] as well as popular Bitcoin users' addresses provided by <a href="https://www.walletexplorer.com/">https://www.walletexplorer.com/</a></p> <p>[1] M. Harrigan and C. Fretter, "The Unreasonable Effectiveness of Address Clustering," <em>2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld)</em>, Toulouse, France, 2016, pp. 368-373, doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0071.<br>keywords: {Online banking;Merging;Protocols;Upper bound;Bipartite graph;Electronic mail;Size measurement;bitcoin;cryptocurrency;blockchain},</p> <p> </p> <h3>Dataset Description</h3> <p><strong>Bitcoin Activity Temporal Coverage</strong>: From 03 January 2009 to 25 January 2021</p> <h4>Overview:</h4> <p>This <strong>dataset </strong>provides a <strong>comprehensive</strong> representation of <strong>Bitcoin exchanges</strong> between entities over a s<strong>ignificant temporal span</strong>, spanning from the inception of Bitcoin to recent years. It encompasses <strong>various temporal resolutions</strong> and <strong>representations</strong> to <strong>facilitate Bitcoin transaction network analysis </strong>in the context of <strong>temporal graphs</strong>.</p> <p>Every dates have been retrieved from bloc UNIX timestamp and GMT timezone.</p> <h4>Contents:</h4> <p>The dataset is distributed across three compressed archives:</p> <p>All data are stored in the <strong>Apache Parquet file format</strong>, a columnar storage format optimized for analytical queries. It can be used with pyspark Python package.</p> <ol> <li> <p><strong>orbitaal-stream_graph.tar.gz</strong>:</p> <ul> <li>The root directory is <em>STREAM_GRAPH/</em></li> <li>Contains a <strong>stream graph</strong> representation of Bitcoin exchanges at the <strong>finest temporal scale</strong>, corresponding to the validation time of <strong>each block</strong> (averaging approximately 10 minutes).</li> <li>The stream graph is divided into 13 files, one for each year</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-stream_graph-date-[YYYY]-file-id-[ID].snappy.parquet,</strong> where <em>[YYYY]</em> stands for the corresponding <em>year</em> and <em>[ID]</em> is <em>an integer</em> from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering</li> <li>These files are in the subdirectory <em>STREAM_GRAPH/EDGES/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-all.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>snapshot</strong> network representing <strong>all transactions aggregated </strong>over the whole dataset period (from Jan. 2009 to Jan. 2021).</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-all.snappy.parquet</strong>.</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/ALL/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-year.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>yearly</strong><em> </em>resolution of <strong>snapshot</strong> networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-file-id-[ID].snappy.parquet</strong>, where <em>[YYYY]</em> stands for the corresponding <em>year </em>and <em>[ID]</em> is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/year/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-month.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>monthly </strong>resoluted <strong>snapshot </strong>networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-[MM]-file-id-[ID].snappy.parquet</strong>, where</li> <li><em>[YYYY] </em>and <em>[MM] </em>stands for the corresponding <em>year </em>and <em>month, </em>and <em>[ID] </em>is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year and month ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/month/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-day.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>daily </strong>resoluted <strong>snapshot </strong>networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-file-id-[ID].snappy.parquet</strong>, where</li> <li><em>[YYYY]</em>, <em>[MM]</em>, and <em>[DD] </em>stand for the corresponding <em>year</em>, <em>month</em>, and <em>day</em>, and <em>[ID] </em>is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, and day ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/day/</em></li> </ul> </li> <li> <p><strong>orbitaal-snapshot-hour.tar.gz</strong>:</p> <ul> <li>The root directory is <em>SNAPSHOT/</em></li> <li>Contains the <strong>hourly </strong>resoluted <strong>snapshot </strong>networks</li> <li>Files format is parquet</li> <li>Name format is <strong>orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-[hh]-file-id-[ID].snappy.parquet</strong>, where</li> <li><em>[YYYY]</em>, <em>[MM]</em>, <em>[DD]</em>, and <em>[hh]</em> stand for the corresponding <em>year, month, day, </em>and <em>hour</em>, and <em>[ID] </em>is an <em>integer </em>from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, day and hour ordering</li> <li>These files are in the subdirectory <em>SNAPSHOT/EDGES/hour/</em></li> </ul> </li> <li> <p><strong>orbitaal-nodetable.tar.gz</strong>:</p> <ul> <li>The root directory is <em>NODE_TABLE/</em></li> <li>Contains two files in parquet format, the first one gives <strong>information </strong>related to <strong>nodes </strong>present in stream graphs and snapshots such as <strong>period of activity</strong> and associated global <strong>Bitcoin balance</strong>, and the other one contains the list of <strong>all associated Bitcoin addresses.</strong></li> </ul> </li> </ol> <p> </p> <p>Small samples in CSV format</p> <ol> <li> <p><strong>orbitaal-stream_graph-2016_07_08.csv</strong> and <strong>orbitaal-stream_graph-2016_07_09.csv</strong></p> <ul> <li>These two CSV files are related to stream graph representations of an halvening happening in 2016.</li> </ul> </li> <li> <p><strong>orbitaal-snapshot-2016_07_08.csv </strong>and<strong> orbitaal-snapshot-2016_07_09.csv</strong></p> <ul> <li>These two CSV files are related to daily snapshot representations of an halvening happening in 2016.</li> <li><strong> </strong></li> </ul> </li> </ol> <p> </p> <p> </p> <p> </p>
Bitcoin Data
<p>The data set is collected for a quantile regression analysis testing the predictive ability of investor sentiment on bitcoin return and volatility. The data is obtained from several online sources including Google trends, Wikipedia, Twitter, News headlines, Bitcointalk.org, and market indexes available at yahoo finance. the dataset includes daily values from mid-2015 to the end of 2020.</p>
Are cryptocurrencies currencies? Bitcoin as legal tender in El Salvador
<p>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.</p>
Investor Sentiment and Information Efficiency: Evidence from the Bitcoin Market.
<p>This dataset contains information about the volume of Google searches for the term "bitcoin," the values of the Twitter happiness index and the Fear Index, the number of page views for the term "Bitcoin" on Wikipedia, and the online participation of the Bitcointalk.org online forum. Additionally, bitcoin prices and historical returns for the period July 2015 to June 2021 are included.</p>
A Deep Dive into Bitcoin Mining Pools
<p>Dataset retrieved and used with the code hosted <a href="https://github.com/MatteoRomiti/Deep_Dive_BTC_Mining_Pools">here</a> for this <a href="https://arxiv.org/abs/1905.05999">paper</a> published at <a href="https://weis2019.econinfosec.org/">WEIS 2019</a></p> <p>Abstract</p> <p>Miners play a key role in cryptocurrencies such as Bitcoin: they invest substantial computational resources in processing transactions and minting new currency units. It is well known that an attacker controlling more than half of the network’s mining power could manipulate the state of the system at will. While the influence of large mining pools appears evenly split, the actual distribution of mining power within these pools and their economic relationships with other actors remain undisclosed. To this end, we conduct the first in-depth analysis of mining reward distribution within three of the four largest Bitcoin mining pools and examine their cross-pool economic relationships. Our results suggest that individual miners are simultaneously operating across all three pools and that in each analyzed pool a small number of actors (≤ 20) receives over 50% of all BTC payouts. While the extent of an operator’s control over the resources of a mining pool remains an open debate, our findings are in line with previous research, pointing out centralization tendencies in large mining pools and cryptocurrencies in general</p> <p> </p>
Are cryptocurrencies currencies? Bitcoin as legal tender in El Salvador
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Bitcoin Transaction Network
<p>This Bitcoin transaction network data as extracted with the bitcoind client. Due to the nature of Bitcoin, all transactions are published to a P2P network. These can be downloaded and extracted with any of the available client programs. This dataset includes the data used in our papers:</p> <ol> <li>Kondor, D., Pósfai, M., Csabai, I., & Vattay, G. (2014). Do the rich get richer? An empirical analysis of the BitCoin transaction network. PLoS ONE, 9(2), e86197. https://doi.org/10.1371/journal.pone.0086197</li> <li>Kondor, D., Csabai, I., Szüle, J., Pósfai, M., & Vattay, G. (2014). Inferring the interplay between network structure and market effects in Bitcoin. New Journal of Physics, 16(12), 125003. https://doi.org/10.1088/1367-2630/16/12/125003</li> </ol>
The Future of Bitcoin: A Quadrillion-Dollar Industry?
<h3>Research on Bitcoin's Potential Future Valuation</h3> <p><strong>Title: The Future of Bitcoin: A Quadrillion-Dollar Industry?</strong></p> <p>This research paper investigates the potential for Bitcoin (BTC) to reach a quadrillion-dollar market cap, with individual BTC prices surpassing $48,000,000. The study examines historical data, market trends, technological advancements, macroeconomic influences, and expert opinions to understand the factors driving such projections.</p> <p>Key highlights include:</p> <ol> <li> <p><strong>Historical Data and Market Trends</strong>:</p> <ul> <li>Bitcoin's price evolution from $0.0041 in 2009 to $75,830 in 2024, marked by significant events such as the Genesis block, Silk Road shutdown, and Tesla's investment.</li> <li>The dataset provides annual high and low prices, alongside major milestones.</li> </ul> </li> <li> <p><strong>Technological Advancements</strong>:</p> <ul> <li>Developments like the Lightning Network and Taproot upgrade enhance transaction speed and efficiency, boosting adoption.</li> </ul> </li> <li> <p><strong>Macroeconomic Influences</strong>:</p> <ul> <li>Bitcoin's role as a hedge against inflation and its fixed supply contrast with fiat currencies.</li> <li>Growth predictions based on M0, M1, and M2 money supply comparisons.</li> </ul> </li> <li> <p><strong>Expert Opinions and Future Projections</strong>:</p> <ul> <li>Insights from Marion Laboure of Deutsche Bank and predictions from CoinCheckup indicate a bullish trend, with potential prices reaching $173,818.51 in a year.</li> </ul> </li> </ol> <p><strong>Conclusion</strong>: The combination of technological progress, economic factors, and growing adoption points to significant growth potential for Bitcoin. This research offers valuable insights for investors and policymakers.</p> <p><strong>References</strong>:</p> <ul> <li>Investopedia, TradingView, CoinCheckup, Deutsche Bank Research, 99bitcoins, Bitcoin Magazine, Forbes India.</li> </ul>
Cotitzacions en viu i paraules claus de notícies relaciones per Bitcoin, Ethereum, S&P500 i Nasdaq
<p>Realtime dataset that contains crypto metrics, market metrics and news articles word analysis</p>
Does Investor Sentiment Predict Bitcoin Return and Volatility? - A Quantile Regression Approach
<p>This dataset was used in generating findings for the paper titled "<strong>Does Investor Sentiment Predict Bitcoin Return and Volatility? - A Quantile Regression Approach".</strong></p>
Dataset used in "What drives the Bitcoin price? A Factor Augmented Error Correction Mechanism investigation"
<p><strong>What drives the </strong><strong>Bitcoin price</strong><strong>? A Factor Augmented Error Correction Mechanism investigation</strong></p> <p>This article aims to determine what drives the price of Bitcoin. To achieve this aim, a large set of data is analysed using VEC models augmented by factors representing unobservable economic forces. They have been obtained by means of principal component analysis. This method enables us to contribute to the existing literature on Bitcoin in two ways. First, we employ the dimension reduction technique to combine variables from several papers. Second, we estimate several unobservable economic concepts instead of utilizing proxy variables as is usually done. We find that the main factor driving the Bitcoin price is its popularity. Hence, our result not only confirms some previous findings but reinforces them by providing a better definition of popularity. Finally, we conclude that the Bitcoin price is not affected by supply and demand factors in the way that is natural for conventional currencies. </p>
Spams meet Cryptocurrencies: Sextortion in the Bitcoin Ecosystem
<p>In the past year, a new spamming scheme has emerged: sexual extortion messages requiring payments in the cryptocurrency Bitcoin, also known as sextortion. This scheme represents a first integration of the use of cryptocurrencies by members of the spamming industry. Using a dataset of 4,340,736 sextortion spams, this research aims at understanding such new amalgamation by uncovering spammers’ operations. To do so, a simple, yet effective method for projecting Bitcoin addresses mentioned in sextortion spams onto transaction graph abstractions is computed over the entire Bitcoin blockchain. This allows us to track and investigate monetary flows between involved actors and gain insights into the financial structure of sextortion campaigns. We find that sextortion spammers are somewhat sophisticated, following pricing strategies and benefiting from cost reductions as their operations cut the upper-tail of the spamming supply chain. We discover that one single entity is likely controlling the financial backbone of the majority of the sextortion campaigns and that the 11-month operation studied yielded a lower-bound revenue between $1,300,620 and $1,352,266. We conclude that sextortion spamming is a lucrative business and spammers will likely continue to send bulk emails that try to extort money through cryptocurrencies.</p>
Web of Science Dataset on Bitcoin
<p>Web of Science Dataset on Bitcoin</p>
DO UNITED STATES STOCK INDICES AND GOLD PRICES DETERMINE BITCOIN PRICES? EVIDENCE FROM NONLINEAR SHORT- AND LONG-RUN ASYMMETRIC APPROACHES
<p>Prices of Bitcoin, US stock indices, Gold and crude oil</p>
Bitcoin volatility in bull vs. bear market - insights from analyzing on-chain metrics and Twitter posts
<p>On-Chain Metrics.xlsx contains a description of the on-chain metrics.<br> Merged_df.xlsx is the main data source containing the BTC prices, the on-chain metrics and the sentiment scores.<br> btc_twets_new.csv and training.1600000.processed.noemoticon.csv are the data sources for calculating the sentiment scores.<br> Sentiment_Analysis.py contains the code to calculate the sentiment scores. The scores are in Merged_df.xlsx<br> BTC_Prediction.py contains the implementation of the main approach described in the paper, especially in Fig. 11.</p>
Data from: Social media and Bitcoin Metrics: which words matter
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Bitcoin Transaction Network
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Bitcoin Network Transaction Inv Data with Java Timestamp and Originator Id
<p>The data is a gz compression of a csv file with the following layout:</p> <p><timestamp in ms>,<originator id as sha-256 hash in hexadecimal string representation>, <hash of BTC Transaction in hexadecimal string representation></p> <p>Example lines:</p> <p>1547779473468,6cf1100aaccec75da23995512fc7c7a5b6e25224f5903af011e78691c03d0455,a73578820a41aa6180621bcd90af1997c88794b33d8db2f004ee37c3e09b10ec<br> 1547779473468,6cf1100aaccec75da23995512fc7c7a5b6e25224f5903af011e78691c03d0455,0beb35a4338b026fb4ceb0d215d372f67dfb0c73f1e21157e03dde2fbc6b94ee</p> <p>The data was gathered with a modified version of bitcoinj 0.14.7. The modification consisted of:</p> <ul> <li>Blinding Originator IP+Port by hashing with a key generated at application startup</li> <li>Logging every Transaction Inv message to a file</li> </ul> <p>The application running the modified library just consisted of creating a Peer Pool of at most 6000 connections and limiting broadcasting to require 100000 connections, set via bitcoinj parameters.</p> <p>The data of crawler-82GB.gz was gathered between 17.01.2019, 15:30 and 18.01.2019, 03:00 at Ulm University in Ulm, Germany. The data of crawler-01-02-2019-09-54-59-ULM.csv.gz was gathered on 01.02.2019 at Ulm Iniversity in Ulm, Germany as well.</p> <p>The data of crawler-24.01.2019-*.gz was gathered between 24.01.2019, 10:00 and 25.01.2019 10:00 on Microsoft Azure instances. The selected region of Azure is indicated in the filename: UK, South; US, East; Southeast Asia.</p> <p>The datasets of crawler-06.02.2019-*-SIMUL.csv.gz were gathered with the same runtime key so ID's can be linked within the datasets. Locations again are Ulm University, Germany, and Azure UK, South (GBS); US, East (USA); Southeast Asia (SEA).</p> <p>ETH-crawler-20+21.02.2019.csv contains a similar collection of the Ethereum network for comparisson.</p>
Data from: Are Bitcoin bubbles predictable? Combining a generalized Metcalfe's law and the LPPLS model
We develop a strong diagnostic for bubbles and crashes in Bitcoin, by analyzing the coincidence (and its absence) of fundamental and technical indicators. Using a generalized Metcalfe's law based on network properties, a fundamental value is quantified and shown to be heavily exceeded, on at least four occasions, by bubbles that grow and burst. In these bubbles, we detect a universal super-exponential unsustainable growth. We model this universal pattern with the Log-Periodic Power Law Singularity (LPPLS) model, which parsimoniously captures diverse positive feedback phenomena, such as herding and imitation. The LPPLS model is shown to provide an ex-ante warning of market instabilities, quantifying a high crash hazard and probabilistic bracket of the crash time consistent with the actual corrections; although, as always, the precise time and trigger (which straw breaks the camel's back) being exogenous and unpredictable. Looking forward, our analysis identifies a substantial but not unprecedented overvaluation in the price of Bitcoin, suggesting many months of volatile sideways Bitcoin prices ahead (from the time of writing, March 2018).
Data from: Committing to quantum resistance: a slow defence for Bitcoin against a fast quantum computing attack
Quantum computers are expected to have a dramatic impact on numerous fields, due to their anticipated ability to solve classes of mathematical problems much more efficiently than their classical counterparts. This particularly applies to domains involving integer factorisation and discrete logarithms, such as public key cryptography. In this paper we consider the threats a quantum-capable adversary could impose on Bitcoin, which currently uses the Elliptic Curve Digital Signature Algorithm (ECDSA) to sign transactions. We then propose a simple but slow commit--delay--reveal protocol, which allows users to securely move their funds from old (non-quantum-resistant) outputs to those adhering to a quantum-resistant digital signature scheme. The transition protocol functions even if ECDSA has already been compromised. While our scheme requires modifications to the Bitcoin protocol, these can be implemented as a soft fork.
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