JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1059 10.6339/22-JDS1059 Computing in Data Science The Python Package open-crypto: A Cryptocurrency Data Collector GüntherSteffen1 FiebergChristiancfieberg@uni-bremen.de2∗ PoddigThorsten1 Chair of Finance, University of Bremen, Germany Empirical Capital Market Research and Derivatives, University of Bremen, Germany Corresponding author. Email: cfieberg@uni-bremen.de. 20231082022211127144 Supplementary Material

We provide several additional information in the supplementary materials, regarding (1) further information on the installation process, (2) troubleshooting list, (3) requesting real-time data, (4) exchanges and endpoints, (5) cross-software usability from R and Matlab and (6) connectivity to server-based database management systems.

1442022372022 2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2023 Open access article under the CC BY license.

This paper introduces the package open-crypto for free-of-charge and systematic cryptocurrency data collecting. The package supports several methods to request (1) static data, (2) real-time data and (3) historical data. It allows to retrieve data from over 100 of the most popular and liquid exchanges world-wide. New exchanges can easily be added with the help of provided templates or updated with build-in functions from the project repository. The package is available on GitHub and the Python package index (PyPi). The data is stored in a relational SQL database and therefore accessible from many different programming languages. We provide a hands-on and illustrations for each data type, explanations on the received data and also demonstrate the usability from R and Matlab. Academic research heavily relies on costly or confidential data, however, open data projects are becoming increasingly important. This project is mainly motivated to contribute to openly accessible software and free data in the cryptocurrency markets to improve transparency and reproducibility in research and any other disciplines.

 cryptocurrency open data Python References Aloosh A, Li J (2019). Direct evidence of bitcoin wash trading. Working paper. Bayer M (2012). Sqlalchemy. In: The Architecture of Open Source Applications Volume II: Structure, Scale, and a Few More Fearless Hacks (A Brown, G Wilson, eds.). aosabook.org. Cong L, Li X, Tang K, Yang Y (2019). Crypto wash trading. Working paper. Gewin V (2016). Data sharing: An open mind on open data. Nature, 529(7584): 117–119. Hougan M, Kim H, Lerner M (2019). Economic and non-economic trading in bitcoin: Exploring the real spot market for the world’s first digital commodity. Working Paper. Iacus SM (2015). Automated data collection with R – A practical guide to web scraping and text mining. Journal of Statistical Software, Book Reviews, 68(3): 1–3. Kim N, Svetlov A (2020). Aiohttp. Release 4.0.0a1. Makarov I, Schoar A (2020). Trading and arbitrage in cryptocurrency markets. Journal of Financial Economics, 135(2): 293–319. Nakamoto S (2008). Bitcoin: A peer-to-peer electronic cash system. Pennec GL, Fiedler I, Ante L (2021). Wash trading at cryptocurrency exchanges. Finance Research Letters, 43: 101982. R Core Team (2020). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Reichman OJ, Jones MB, Schildhauer MP (2011). Challenges and opportunities of open data in ecology. Science, 331(6018): 703–705. Szöcs E, Stirling T, Scott ER, Scharmüller A, Schäfer RB (2020). webchem: An R package to retrieve chemical information from the web. Journal of Statistical Software, 93(13): 1–17. The pandas development team (2020). pandas-dev/pandas: Pandas. van Rossum G (2012). Asyncio: Asynchronous IO support rebooted: The “asyncio” module. van Rossum G, Drake FL (2009). Python 3 Reference Manual. CreateSpace, Scotts Valley, CA.