Português English

5 crypto-native indicators to enrich your market cycle analysis

5 crypto-native indicators to enrich your market cycle analysis
7 de novembro de 2018 admin

5 crypto-native indicators to enrich your market cycle analysis

Por Felipe Gaúcho Pereira

Markets that are in balance cannot be exploited.

Bubbles only burst once. Bitcoin has “bursted” (and recovered) a handful of times already. For long-term believers, accumulating bitcoin is a game of timing.

After a succession of bear and bull cycles, the infamous cybercoin attracted not only a reputation for defying forecasts, but also a horde of scientists hungry to decipher its peculiar behaviour. At their disposal, tools that take advantage of the Bitcoin ledger’s unparalleled transparency.

Over the past years, a range of crypto-native indicators emerged to track imbalances, emotions and cycles in the market.

The indicators summarised below can form a pretty clear picture of how Bitcoin is behaving at a fundamental level, in any given moment. They can all be extracted from public, on-chain data.

This text is just one exercise upon many others. The aim is to piece together parts of a puzzle that can improve our understanding of Bitcoin fundamentals — and their impact on price.

1. 👮‍ Aggregate security spend
Market capitalisation is an indicator for assessing the value of equity in companies. Crypto has inherited the metric from stocks and is having a hard time trying to get rid of it. Problems here are:

1st: Market Cap takes into account all coins distributed so far, not distinguishing lost coins within the circulating supply (or coins intended to be hodled for long periods).
2nd: Some coins have aggressive inflation schedules. Others have very conservative ones, or no inflation at all. To compare them on equal standards, one must consider future dilution. The Y2050 market cap is a useful proposal by OnChainFX. But then, why 2050 and not 2025? Where to draw the line?

The point is: if a cryptocurrency has a market cap of $1 billion, it doesn’t mean that $1 billion has flown into that asset. One can create a billion coins; sell 2 of them for $2; and thus pump into CoinMarketCap an excess of $999.999.998 in relation to the actual amount that asset has been traded for.

A more appropriate measure of network value was recently put forth by Nic Carter. Remember capital flows in crypto generally do not come in via exchanges (miners notably like to sell OTC). Every buy in an exchange is matched by a sell. Money that comes in = money that goes out.

True inflows (in Bitcoin, at least) are the aggregate of resources spent by miners¹. And a good proxy for that is the amount these folks are earning back from networks they support in return for their investments. That’s aggregate security spend (or Thermocap): what was actually paid out to miners (coinbase transactions * their price in USD at the time they were mined).

🔍 What does it portray?
A more effective measure of wealth in illiquid markets. How much the network has been worth to its maintainers, in cash flows.

2. 🔃 MVRV (Market-Value-to-Realised-Value) and MVRV-z
Apart from aggregate security spend, market cap can also be relativised by a metric named “Realised value”. Realised value (RV) is found not by counting all mined coins equally at current price, but rather assigning them a U$ price based on the time when they were last moved.

RV aims to find the actual value that’s been realised by holders and miners. As David Puell puts it, RV seems to suggest “the final layer of people’s cumulative cost basis and, in recent history, the ultimate line of ‘center of mass’”— stripping out local emotions and manias on the charts.

The MVRV ratio, with two thresholds marked: 3.7 to denote overvaluation; 1 to denote undervaluation. By David Puell and Murad Mahmudov.
In turn, the MVRV is a ratio calculated by dividing market value by realised value on a daily basis (on left)².

A useful extension is the MRVR-Z, which tracks the z-score distance between market value and realised value³. Z-score (in simple terms) is the number of standard deviations above or below the mean.

In other words, MVRV-z tells you how strongly detached from realised value the market value is, at any given time.
Charting the evolution of MVRV-z shows us parabolic spikes right before prolonged downturns (one, two and three weeks in advance; respectively in early ’13, late ’13 and late ‘17).

Market value (white dotted) and realised value (red dotted). In blue, the MVRV-z. Chart by Awe & Wonder
Establishing thresholds here can be tricky. A slightly lower baseline could’ve triggered false alarms during the bull run of ’17, for example.

In general, though, just as the upper levels of MVRV suggest the climax of euphoria (overshooting it’s “fair” value at the peaks), price discovery at exchanges tends to undershoot beyond BTC’s “real” value at the bottoms (a dip below 0 has historically signalled great entry points).

🔍 What does it portray?
Major divergences between price discovery at exchanges and the steadier rise of unmoved coins. A ratio between high time preference vs. low time preference (or exuberance vs. acclimation) in the market.

3. ✊ UTXO age distribution (HODL waves)
Assessing the Realised Value of Bitcoin is only possible because of the ledger’s transparency and traceability — a property not common to other financial assets.

Since all bitcoin out there is contained in some UTXO, this means that all bitcoins have an age: not the age/time when that coin was first mined, but when it was last used in a transaction (h/t Dhruv Bansal).

Charting the age distribution of all UTXOs in the circulating supply gives one a “trader vs. hodler” profile of current coin owners (including those who’ve lost their private keys and won’t transact any time soon).

One’s relationship with bitcoin changes over time, doesn’t it? How did you perceive the currency when you first took possession of it? How do you see it today? If the market was divided in cohorts, which co-hort would your coins belong to? And which cohort represents most of the market, at any given time? These are questions that the UTXO Age Distribution, first analysed by Unchained Capital⁴, helps to answer.

The relative fraction of bitcoin in existence that last transacted within the time window indicated in the legend. Bottom, warmer colors represent bitcoin very recently transacted. Top, cooler colors represent bitcoin that hasn’t transacted in a long time. Source: https://plot.ly/~unchained/37.embed
The graphical pattern has also been dubbed HODL waves, since it highlights accumulation periods by isolating the amount of idle bitcoin between rallies. “A HODL wave is created when a large amount of bitcoin transacts on the way up to and through a local price high, becoming recent (1 day — 1 week old); and then slowly ages into each later band as its new owners HODL”.

Tamar Blummer’s “hodler candy”.
Note that, in the chart above, the Y axis is normalised at each date by the total supply at that date. An alternative visualisation, with non-normalised BTC values on the Y axis, can be seen on the left.

Yet another alternative visualisation can be obtained by ignoring the historical dimension and slicing a vertical piece off the chart at any given point in time.

That gives you a clear view of the % of the network (in coins, not addresses) that last transacted at different periods (co-horts), and allows for easy comparison between two moments in time. Below, we see how ownership of coins has changed between (approximately) today vs. a year ago. Worth noting, the >5y co-hort has been growing steadily, without any “downward dent”, since 2015.

A sliced view of two different moments in the charts above.
🔍 What does it portray?
Large-scale shifts in Bitcoin’s ownership through history.
Of the coins out there today, how many have moved recently, and what share has been staying still?

4. 📢 NVT / NVT Signal
If UTXO Age Distribution gives us a measure of holders’ confidence in the store of value case for bitcoin, the NVT / NVT Signal can be interpreted as the strength of market confidence in the means of payment / settlement layer narrative.

NVT (Network Value to Transaction) is found by dividing network value by the USD amount of daily transactions flowing through the chain⁵. It is comparable to the P/E ratios of stocks, where earnings are used as a denominator and denote utility the company has created for shareholders. The idea here, since crypto networks have no earnings, is to use transaction volume as a proxy for the utility derived from the chain.

It is worth highlighting that Daily Transaction Volume in the NVT takes into account only on-chain transactions. Trading activity on exchanges’ order books is ignored.

“Generally speaking, a “low” market to transaction value denotes an asset which is more cheaply valued per unit of on-chain transaction volume” (Coinmetrics).

The NVT ratio is believed to mimic the behaviour of P/E ratios in that either metric spikes up when the asset is valued higher than its actual market usage implies.

The NVT Signal, in turn, is a derivative of NVT ratio proposed by Dimitry Kalichkin. It emphasises predictive signalling ahead of price peaks (it interpolates Daily Transaction Volume using forward/backward moving averages to create a smooth line)⁶.

NVT Signal = Network Value / 90d MA of Daily Transaction Value⁷.

For BTC, any level above 150 is in the overbought zone, indicative of a market top… and levels below 45 tend to be oversold. From http://charts.woobull.com/bitcoin-nvt-signal/
🔍 What does it portray?
A “measure of the chain’s strength as a payment network compared to its market value — a low NVT may suggest that a network is undervalued compared to the service it is providing as a settlement layer” (Matteo Leibowitz).

5. 🏄‍ Network Momentum
Bitcoin Network Momentum is a complement to the NVT / NVT Signal in the evaluation of a network’s usage as a settlement system, relative to its market value. The only difference is that it measures on-chain activity in BTC value (rather than USD).

From http://charts.woobull.com/bitcoin-network-momentum.
Zooming out, in hindsight, the indicator has sustained a semi-steady baseline during the 2013 and 2016–17 bull runs. Also, note that the network’s “healthy level” kicking off a new bull run was higher in the 2015–2017 market than it was in the 2012–14 market. The reason for this is most likely due to the increased number of coins in circulation — meaning baselines for kicking off further bull cycles can be expected at increasingly higher levels, too⁸.

Both daily transaction values and price exhibit cyclical patterns, but not in sync with each other. A hypothesis to explain the mismatch is that short-term mindset traders (using exchanges) heavily influence price; but long-term mindset investments (more likely to be directly recorded on-chain) have a greater contribution to the daily transaction value recorded in the ledger.

🔍 What does it portray?
An alternative to the NVT / NVT Signal – tracks the relationship between Bitcoin’s price and BTC volume flowing through the network.

⚠️ This can all go wrong
There’s two main reasons why one should take all indicators above with a grain of salt.

There’s under a decade of data to back historical analysis. Bitcoin has defied forecasting based on past evidence multiple times already. We also have no idea of how the asset behaves during a macro financial crisis.
Sidechains have the potential to reshape transaction volume as its currently measured on-chain, skewing indicators that rely on on-chain throughput and that may have worked for years. Bitcoin Liquid (by Blockstream) is a sidechain that allows large exchanges to transfer funds to each other off the main chain, faster, securely and, optionally, confidentially. It’s not possible to track Liquid transactions using “traditional” blockchain analysis. Liquid is relevant because exchanges that account for ~50–60% of all trading volume have signed up for it.
🃏 On the positive side…
The beginning of trading on sidechains still leaves a sizeable section of the market to be analysed. One could argue sidechains will actually make such analysis cleaner as the transactions it will look at will capture less short-term driven transactions and have a higher proportion of transactions from long-term investors — maybe even propelling the discovery of new indicators.

🔚 Conclusion
A range of tools have been made available to measure the health of a blockchain and its native asset. That’s specially the case when it comes to the longest, hardest-worked chain of all — although NVT analysis, HODL waves and MVRV indexes have been applied to other coins too.

Worth noting, some recognised metrics were left out of this compendium (e.g. the Mayer Multiple). As always, the indicators one decides to rely upon reflect one’s own perspective regarding the asset in question. For a simple example, notice how the NVT and HOLD waves indicators emphasise different roles of the network (settlement layer vs. long-term store of value).

For a deeper dive into competing narratives that have driven Bitcoin’s evolution, seek no further than Nic Carter’s excellent “Visions of Bitcoin”¹⁰. Beyond the overarching themes brought to light by this piece, there are countless other mental models through which one can dissect Bitcoin:

As protected by a combination of Stock & Flow, like Hugo Nguyen has elegantly put⁹;
As a thermodynamical phenomena that makes for the most counterfeit-proof representation of the universe’s primary asset: energy (as tweeted by Bob McElrath);
As predictable (somehow) by a topological function (as argued by John McAfee);
and many more. Your chosen prism(s) directly influence the indicators you’ll pick to interpret market momentum and inform investment decisions.

As for the current market situation, I’ll refrain from commenting and, instead, point to two people who’ve made compelling bear and bull cases – based on the indicators discussed above and more. Make sure to read down the threads.

🎁 Next piece in this series will cover indicators applicable to the analysis of Altcoins. Watch out for “5 crypto-native indicators (AltCoin edition)” – or subscribe here to receive it in your inbox as soon as published.

Publicado originalmente em Medium no dia 06 de novembro de 2018, conforme link: www.medium.com