Cryptocurrency summary dataset

Today, we're releasing an additional six cryptocurrency blockchains. We are also including a set of queries and views that map all blockchain datasets to a double-entry book data structure that enables multi-chain meta-analyses, as well as integration with conventional financial record processing systems.

Five of these datasets, along with the previously published Bitcoin dataset now follow a common schema that enables comparative analyses. We are releasing this group of Bitcoin-like datasets Bitcoin, Bitcoin Cash, Dash, Dogecoin, Litecoin and Zcash together because they all have similar implementations, i. A unified data ingest architecture All datasets update every 24 hours via a common codebase, the Blockchain ETL ingestion framework built with Cloud Composer , previously described here , to accommodate a variety of Bitcoin-like cryptocurrencies.

While this means higher latency for loading Bitcoin blocks into BigQuery, it also means that: We are able to ingest additional BigQuery datasets with less effort, meaning additional datasets can be onboarded more quickly in the future. We can implement a low-latency loading solution once that can be used to enable real-time streaming transactions for all blockchains. Some of these changes address performance and convenience concerns, yielding faster and lower cost queries commonly accessed nested data are denormalized; each table is partitioned by time.

Having these scripts available for Bitcoin-like datasets enables more advanced analyses similar to this smart contract analyzer that Tomasz Kolinko recently built on top of the BigQuery Ethereum dataset. For example, we can now identify and report on patterns of activity involving multi-signature wallets.

This is particularly important for analyzing privacy-oriented cryptocurrencies like Zcash. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries.

To further interoperate with Ethereum and ERC token transactions, we also created some views that abstract the blockchain ledger to be presented as a double-entry accounting ledger. This comparison is the simplest way to verify that a cryptocurrency is operating as intended, and at least operationally, is a mathematically correct store of value. Balance queries demonstrating preservation of value Heres are some equivalent balance queries for the Bitcoin and Dogecoin datasets: Loading Understanding miner economics on Bitcoin The BigQuery dataset makes it possible to analyze how miners are allocating space in the blocks they mine.

This query shows that transaction fees on the bitcoin network follows a Poisson distribution, confirming that there are zero-fee transactions being included in mined blocks. Given that miners are incentivized to profit from transaction fees, it begs the question: why are they including zero-fee transactions? Possible reasons include: Miners are including their own transactions for zero fees. Miners run transaction accelerators , i.

Creating a new Bitcoin address for each inbound payment is a suggested best practice for users seeking to protect their privacy. This query can be plotted to show the relationship between addresses and the number of transacting partners: Multi-chain crypto-econometrics Beyond quality control and auditing applications, presenting cryptocurrency in a traditional format enables integration with other financial data management systems.

In the field of macroeconomics, the Gini Coefficient is a member of a family of econometric measures of wealth inequality. Values range between 0. For crypto-economies, we have complete transparency of the data at the highest possible resolution.

In addition to data transparency, one of the purported benefits of cryptocurrencies is that they allow the implementation of money to more closely resemble the implementation of digital information. It follows that a fully digitized money network will come to resemble the internet, with reduced transactional friction and fewer barriers that impede capital flow. Frequently, implicit in this narrative is that capital will distribute more equally.

You can read more about using the Gini coefficient to reason about crypto-economic network performance in Quantifying Decentralization. To set a baseline to interpret our findings, consider how resources are distributed in traditional, non-crypto economies.

According to a World Bank analysis in , recent Gini coefficients for world economies have a mean value of We plot a histogram of the reported data below. Some recent Gini measures include: South Africa : 67 United States : 48 Venezuela : 39 We use the double-entry book pattern to compare the equality of cryptocurrency distribution of the Bitcoin-like datasets being released today along with Ethereum and a few Ethereum-based ERC tokens. In the figure below, the Gini coefficient is rendered for the top 10, address balances within each dataset, tabulated daily and across the entire history.

The Bitcoin-like cryptocurrencies are rendered in ochre tones while the Ethereum chains and ERC Maker token are rendered in blue tones. Note that Bitcoin Cash is rendered as a dotted line, diverging from Bitcoin in mid Similarly, Ethereum classic diverges as a dotted line away from Ethereum. This biases the Gini coefficient toward accumulation. Gini is known to be sensitive to including small balances in the analysis and is usually done on large addresses only.

Removing small balances, as we did here, biases the Gini coefficient toward distribution. In our analysis all addresses are treated as individual holders. Low-latency data feeds for all cryptocurrency data types. Resilient infrastructure with guaranteed uptime. Extensive documentation and support. Bring Your Ideas To Life Cryptocurrency market data optimized for your use case Strategy Backtesting Leverage granular cryptocurrency trade and order book data to run simulations and backtest trading strategies.

Charting and Analytics Integrate live and historical cryptocurrency market data into your third-party platform, app, or website. Research and Analysis Study cryptocurrency markets with historical datasets optimized for advanced research. Indices Build your own indices, indicators, or visualizations using normalized data feeds from dozens of exchanges.

And most efficient bitcoin mining gpu any more

Theta Cryptocurrency Dataset Theta is unique because it pioneered next-generation, blockchain-powered esports entertainment platforms. In addition, Theta rewards anyone who restreams video and enables existing video platforms to deepen viewer engagement, drive incremental revenues, and reduce CDN costs.

The Theta network also allows users to earn rewards by relaying video on a peer-to-peer basis. The BigQuery explorer lets you interact with the Theta Cryptocurrency dataset. Zcash Cryptocurrency Dataset Finally, Zcash is a cryptocurrency and distributed ledger system that provides enhanced privacy by including additional cryptographic features relative to Bitcoin.

This dataset contains the blockchain data in its entirety, pre-processed to be human-friendly and to support everyday use cases such as auditing, investigating, and researching the economic and financial properties of the system. Here is the link to the BigQuery explorer that you can use to interact with the Zcash Cryptocurrency dataset.

Regardless, when consumers were googling for information on cryptocurrencies in, for instance, Poland , Bitcoin searches far outweighed those of other digital coins. This page investigates these cryptocurrencies, other than Bitcoin. Statista also offers a dedicated page for Bitcoin BTC alone.

Cryptocurrencies, NFTs, and the metaverse all featured in a January webinar created and hosted by Statista, called "The cryptoverse - cryptocurrencies becoming mainstream". The original, unedited minute webinar can be seen here for free, although registration is required. The number of cryptocurrencies soars Whilst this page predominantly looks at a handful cryptocurrencies, it is good to keep in mind that many more are available: Estimates state there could be over 4, in circulation in The majority of these are relatively small, and do not play a big role within the crypto market.

Other coins saw significant gains, however. Despite the price of a Dogecoin being much lower than that of many other cryptocurrencies , DOGE grew much faster. Acquiring cryptocurrencies: mining, exchanges, and wallets When planning to invest or trade in cryptocurrencies, people can either mine the currency themselves or buy from an exchange.

Whether it is profitable or not to mine a digital coin varies greatly: Some currencies are more difficult to mine than others — due to the complexity of the logical puzzles a computer must solve, which in turn needs more computing power — and too much electricity consumption can potentially lose any form of profitability from mining.