CM Perspectives

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An on-chain analysis of Ripple’s escrow system

Coin Metrics recently released version 3.0 of our CM Network Data Pro product. This release includes many new assets, including XRP. When Coin Metrics adds support for any new asset, we do a deep dive into its digital ledger to gain a full comprehension of the ledger and identify any unique behaviors that may affect the creation of our data. For XRP, that meant evaluating its supply schedule and escrow system.


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Coin Metrics CM Reference Rates now live!

The Coin Metrics team is happy to announce that it has successfully begun publishing its CM Reference Rates for midnight UTC.


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Ripple, Tezos and stablecoins coming to Pro plus launch of CM Reference Rates

Highlights

  • Launch of CM Network Data Pro v3.0
  • Launch of CM Reference Rates v1.0
  • Announcing our CM Pro Analytics Suite
  • Community upgrades


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A new face, same vision

When we set up Coin Metrics in early 2017, the vision was to create and share methods to extract data from blockchains and other public crypto networks, and to use the resultant data to demystify the economics of these networks.


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Evaluating Bitcoin forks with network data

Determining the success of a UTXO fork is a fraught exercise. There is no agreed-upon standard for measuring adoption. Establishing the existence of genuine activity is therefore challenging to the say the least. The list of potential proxies is long: full blocks, a robust fee market, an elevated transaction volume, a high transaction count, to name a few. And, unhelpfully, given that forks are often competitive with the parent chain, and compete for scarce attention and resources, quantitative metrics which offer insight into the usage of a given chain are often targeted for spoofing or manipulation.

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Granular mining pool mapping with Bitcoin’s coinbase outputs

How concentrated is block formation in Bitcoin? Which mining pools are dominant, and how long have they held that position? Are major pools in a position to collude and censor transactions? Which miners should developers and key stakeholders engage with? How exposed is Bitcoin to potentially malicious miners? Are major pools reinforcing their dominant positions or is the industry becoming more competitive? (This was an issue covered by Ark Invest in mid 2018.)

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Don’t trust, verify: A Bitcoin Private case study

Bitcoin Private (BTCP) is a fork-merge of Bitcoin and ZClassic (ZCL, a fork of ZCash that removed the founders’ reward). BTCP defined its initial supply according to the sum of the outstanding supply of Bitcoin at the time (16.8m), ZClassic (3.4m) and a small 62,500-unit miner program. This was intended to give it an initial supply of ~20.4m BTCP, with a decaying miner reward, capping the total supply at 21m units as with Bitcoin.

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Introducing Realized Capitalization

The motivation for the creation of Realized Cap was the realization that "Market Capitalization" is often an empty metric when applied to cryptocurrencies. Market Capitalization, borrowed from the world of equities, is calculated for cryptocurrencies as

circulating supply * latest market price

However, unlike with equities, large fractions of cryptocurrencies tend to get lost, go unclaimed, or become otherwise inert through bugs.

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What should we expect from Bitcoin’s block size in the coming years?

tl;dr: under full SegWit adoption you should expect blocks in the 1.6-2 mb range, with larger outliers. 

How big will Bitcoin blocks get under reasonable assumptions? This investigation began with the simple realization that Bitcoin had recently hit an all time high in its average block size over a 24 hour period, at 1.20 mb.

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Introducing our adjusted transaction volume estimates

As we have exhaustively covered, getting accurate estimates of the actual economic throughput of public blockchains is not a trivial task. Due to the existence of mixers, self-churn, privacy enhancements, spam, and change outputs (in UTXO chains), raw estimates of transactional value are often misstated by a factor of 10 or more.

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An update to our ERC-20 methodology

Recently, it was brought to our attention that our figures for transaction count and transaction volume for ERC-20 tokens were quite significantly different from those of Etherscan.io. We looked in to this and realized that we were undercounting these figures – dramatically in some cases.

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An analysis of batching in Bitcoin

On May 6th, 2017, Bitcoin hit an all-time high in transactions processed on the network in a single day: it moved 375,000 transactions which accounted for a nominal output of about $2.5b. Average fees on the Bitcoin network had climbed over a dollar for the first time a couple days prior.

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Thoughts on ratios

Coinmetrics was created to publish hard-to-acquire data about major public blockchains, and to promote some ratios we thought were instructive. Since the founding of this website, the field of cryptoasset valuation has matured and grown significantly. The cryptoassets in question also continue to grow and change, meriting thoughtfulness about various analytical tools. While users are more empowered than ever, uncertainty remains about a) whether ratio analysis is appropriate, b) how to interpret major ratios, and c) the shortcomings of such analyses. In this piece, we'll discuss ratio analysis and discuss its shortcomings and some common mistakes. As always, we urge skepticism and restraint in the interpretation of our data.

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On data and certainty

Being certain is a lovely thing. Despite what many would allege about the poor finality of proof of work, the relative certainty it provides is part of the appeal. Once that inbound transaction is buried six confirmations deep, it's almost certainly yours. Of course, even more certainty is achievable with an in-person cash transaction. But you can't send those over the internet.

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On the difficulty of estimating on-chain transaction volume

We are happy to report that this site is getting quite a bit of attention these days. We never anticipated this when we first decided to put together a public repository of cryptocurrency data. With lots of attention comes lots of trouble. One thing we've always worried about is how to carefully present data which is very noisy by its very nature.

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How to use our correlation tool

If you head over to coinmetrics.io/correlations, you'll see a new addition: time-series correlation charts. There are other places to find cryptoasset correlations: see cointrading.ninja and sifrdata, as well as the individual charts on onchainfx, but we wanted to build a tool to visualize multiple correlations of major cryptoassets on the same graph.

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New additions explained

If you're firing up Coinmetrics for the first time in a while, you'll notice a trove of new content. Here I'll give a little bit of detail into how you can use these indicators and incorporate them into your investment strategy.

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Our guide to the cryptoasset research universe: Indices

As new asset classes emerge, parallel information markets spring up to accomodate them. After all, financial markets are simply mechanisms to compensate the informed. Ultimately, markets are information-discovery systems, and it's no surprise that a huge set of cryptoasset information services have appeared in the last few months to cater to investor demand. Coinmetrics.io is one such entity.

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An important network value to transaction ratio caveat

If you've been following coinmetrics closely, you might be convinced of the usefulness of our network value to transactions metric (NVT) in determining relative value. However there is an important caveat that must be mentioned.

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Mean-reversion and reflexivity: a Litecoin case study

Litecoin is a remarkable cryptocurrency. Of the seven profiled on coinmetrics.io/mtv, its MTV ratio is among the steadiest (in our sample period of the last two years). Only bitcoin boasts a stabler market to transaction value. (Read our intro to MTV and a handy explainer.) As mentioned in our last article, useful fundamental ratios ought to generally be stable, so that price movements can be compared to the fundamental.

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Why should I pay attention to the NVT ratio, anyway?

Please note: By consensus, we have renamed the "MTV" (market to transaction value) to "NVT" (network value to transactions ratio). We're leaving this post as is.  

Our very first contribution to the body of research on cryptoassets is one we think will become mainstream as this discipline matures. It has intuitive strength – the market to transaction value ratio makes diverse cryptoassets easily comparable. This ratio wasn't summoned out of thin air; we put careful thought into its legitimacy and usefulness. Read on for a discussion of why we chose this ratio above all.

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An introduction to MTV

Please note: By consensus, we have renamed the “MTV” (market to transaction value) to “NVT” (network value to transactions ratio). We’re leaving this post as is, aside from changing some links. 

Currency serves as a medium of exchange, store of value, and unit of account. The Market to Transaction Value metric captures its efficacy at enshrining that first property. For a cryptocurrency to intermediate effectively, it must have sufficient on-chain volumes. This reduces spread size and enhances convenience. Of course gross numbers aren’t particularly comparable, so we construct a ratio between transaction volume and market cap. We extract actual transaction volumes from blockchain explorers and construct a time-series metric so you can see how the market cap to volume ratio changes over time.

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Introducing Coinmetrics

Cryptoasset investors often speak of investing based on “fundamentals” rather than hype, sentiment, or technical analysis. However the analytic infrastructure for a rigorous understanding of a cryptoasset’s value relative to another is virtually nonexistent. Equity investors have had 80 years to mull over and refine Graham and Dodd’s principles of value investing, but digital currency investors have had no such privilege.

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