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Date News 13 minutes ago. Meta has made it easier to run crypto-related ads on its platform The Block. Vite Biweekly Report Medium. Immunefi Bounty Announcement Medium. Date Event Sub-Category 5 hours ago. Centralized Exchange Listing. Liquidity Mining Program. Private Token Sale. Core Client Release. Mainnet Launch. Messari Lists. Top Screeners. Explore a library of hundreds of curated screeners and charts. Sign-up for our daily newsletter.
We believe crypto is the technology of free people, free thinking and free markets. USD Coin. Shiba Inu. Wrapped Bitcoin. Ocean Protocol. Kylin Network. The Financialization of NFTs. Slip Sliding Away? Coinbase Listing. Avalanche Rush. Series B Funding Round. Erigon v Arbitrum v1. These requirements lead us to four tables. This is the Bitcoin table, but the Ethereum table is very similar:.
A hypertable is an abstraction of a single continuous table across time intervals, so that you can query it using standard SQL. For more on hypertables, see the Timescale docs and this blog post. For this analysis, the free key is sufficient.
Before you start, you need a working installation of TimescaleDB. Now all your hard work at the beginning comes in handy, and you can use the SQL script you created to set up the TimescaleDB unstance. Log in to the TimescaleDB instance. Locate your host , port , and password and then connect to the database:. From the psql command line, create a database. Make sure you are logged into TimescaleDB using psql so that you can run each of these commands in turn:.
At the beginning of the tutorial, we defined some questions to answer. Naturally, each of those questions has an answer in the form of a SQL query. Now that you database is set up properly, and the data is captured and ingested, you can find some answers:. How did BTC daily returns vary over time? Which days had the worst and best returns? How did the trading volume of Bitcoin vary over time in different fiat currencies? Follow the companion tutorial to this piece and learn how to use TimescaleDB and Tableau together to visualize your time-series data.
Getting started. How-to Guides. Introduction to IoT. Introduction to time-series forecasting. Analyze cryptocurrency data. Analyze NFT sales data. Analyze intraday stock data. Analyze data using hyperfunctions. Using Promscale and Prometheus. Monitor MST with Prometheus.
Monitor a Django application with Prometheus. Collect metrics with Telegraf. Visualize data in Tableau.
Yup, looks good. Most altcoins cannot be bought directly with USD; to acquire these coins individuals often buy Bitcoins and then trade the Bitcoins for altcoins on cryptocurrency exchanges. Now we have a dictionary with 9 dataframes, each containing the historical daily average exchange prices between the altcoin and Bitcoin. Now we can combine this BTC-altcoin exchange rate data with our Bitcoin pricing index to directly calculate the historical USD values for each altcoin.
This graph provides a pretty solid "big picture" view of how the exchange rates for each currency have varied over the past few years. You might notice is that the cryptocurrency exchange rates, despite their wildly different values and volatility, look slightly correlated. Especially since the spike in April , even many of the smaller fluctuations appear to be occurring in sync across the entire market.
We can test our correlation hypothesis using the Pandas corr method, which computes a Pearson correlation coefficient for each column in the dataframe against each other column. Computing correlations directly on a non-stationary time series such as raw pricing data can give biased correlation values. These correlation coefficients are all over the place. Here, the dark red values represent strong correlations note that each currency is, obviously, strongly correlated with itself , and the dark blue values represent strong inverse correlations.
What does this chart tell us? Essentially, it shows that there was little statistically significant linkage between how the prices of different cryptocurrencies fluctuated during These are somewhat more significant correlation coefficients. Strong enough to use as the sole basis for an investment?
Certainly not. It is notable, however, that almost all of the cryptocurrencies have become more correlated with each other across the board. The most immediate explanation that comes to mind is that hedge funds have recently begun publicly trading in crypto-currency markets  . These funds have vastly more capital to play with than the average trader, so if a fund is hedging their bets across multiple cryptocurrencies, and using similar trading strategies for each based on independent variables say, the stock market , it could make sense that this trend of increasing correlations would emerge.
For instance, one noticeable trait of the above chart is that XRP the token for Ripple , is the least correlated cryptocurrency. The notable exception here is with STR the token for Stellar , officially known as "Lumens" , which has a stronger 0. What is interesting here is that Stellar and Ripple are both fairly similar fintech platforms aimed at reducing the friction of international money transfers between banks.
It is conceivable that some big-money players and hedge funds might be using similar trading strategies for their investments in Stellar and Ripple, due to the similarity of the blockchain services that use each token. This explanation is, however, largely speculative.
Maybe you can do better. Hopefully, now you have the skills to do your own analysis and to think critically about any speculative cryptocurrency articles you might read in the future, especially those written without any data to back up the provided predictions. Thanks for reading, and please comment below if you have any ideas, suggestions, or criticisms regarding this tutorial. If you find problems with the code, you can also feel free to open an issue in the Github repository here.
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