Bruzgė Rasa, Šapkauskienė Alfreda
Faculty of Economics and Business Administration, Vilnius University, Saulėtekio al. 9, Vilnius LT- 10222, Lithuania.
Data Brief. 2021 Dec 20;40:107731. doi: 10.1016/j.dib.2021.107731. eCollection 2022 Feb.
Bitcoin market's efficiency and liquidity questions are being comprehensively analyzed in scientific literature. This dataset serves academics for deeper analysis of these topics as well as it gives relevant information for spotting and evaluating risks in the market. Moreover, practitioners can benefit from the dataset and use it to identify patterns in the market, discover potential earning capabilities, and create effective arbitrage trading strategies. This is the first publicly available dataset that provides unique arbitrage data about pairs of cryptocurrency exchanges. The raw dataset was received by the Bitlocus LT, UAB. Using packages in R we transformed dataset to show the amount of arbitrage which could be earned in 13 different cryptocurrency exchanges from 2019-01-01 to 2020-04-01. We used this dataset to create matrices for each day from 2019-01-01 to 2020-04-01 in order to perform network analysis on Bitcoin arbitrage opportunities (Bruzgė and Šapkauskienė [1]). However, this dataset is beneficial for other purposes such as the evaluation of market's seasonality and day of week effects. The dataset provides values in high-frequency intervals but it is possible to convert data to a suitable data format depending on the research question.
科学文献正在全面分析比特币市场的效率和流动性问题。该数据集可供学者深入分析这些主题,同时也为发现和评估市场风险提供相关信息。此外,从业者可以从该数据集中受益,并用它来识别市场模式、发现潜在盈利机会并制定有效的套利交易策略。这是首个公开可用的数据集,提供了有关加密货币交易对的独特套利数据。原始数据集由UAB的Bitlocus LT接收。我们使用R语言中的包对数据集进行转换,以展示2019年1月1日至2020年4月1日期间在13个不同加密货币交易所可获得的套利金额。我们使用该数据集创建了2019年1月1日至2020年4月1日每天的矩阵,以便对比特币套利机会进行网络分析(布鲁热和沙普考斯基涅[1])。然而,该数据集还有其他用途,比如评估市场季节性和一周中的日期效应。该数据集提供高频区间的值,但也可以根据研究问题将数据转换为合适的数据格式。