2/17 Last year, @Glassnode learned that when an Unspent Transaction Output (UTXO) is >155 days old, its has a relatively low probability of being spent
Based on this, they created Short-Term Holder (STH) and Long-Term Holder (LTH) supply metrics
3/17 If you divide @glassnode's LTH supply by the circulating #bitcoin supply, you get a LTH Supply Ratio that quantifies the portion of the supply that is estimated to belong to LTHs
LTHs tend to sell against market strength (🟥) and accumulate during market weakness (🟩)
4/17 The LTH Supply Ratio also allows us to more accurately compare recent and historical relative LTH supply ownership
During the recent $65k top the LTH Supply Ratio decreased, but hasn't reached the lows of the prior market cycle tops (yet)
5/17 @glassnode also applied algorithmic clustering of addresses, recognizing entities that were labelled illiquid (🟩) or liquid (🟥) based on spending history
6/17 To get a better look at changes in the illiquid supply, @glassnode also created a (30-day) net illiquid supply change metric
This metric clearly shows how previously illiquid coins suddenly became liquid during the early May FUD, whereas the market is now accumulating again
7/17 Since supply increases with each block, we need to adjust for circulating supply to allow historical comparison
We then see that: 1) Recent illiquid supply decreases were reached 3x during the previous cycle 2) Current values were last seen mid-2017, before the blow-off top
8/17 Similar to the Liquid Supply Ratio that @WClementeIII & @woonomic recently introduced, you can also visualize the (il)liquid supply as a percentage of the circulating supply (h/t @typerbole), which shows us how much of the #bitcoin supply is (il)liquid
9/17 If we look at the illiquid supply percentage, we see that after flooding the market with liquid coins after #bitcoin got a market price, the illiquid supply percentage's contraction rate decreased after each halving
10/17 If we then zoom in on the last year, we see that since the start of July, the illiquid supply increased relatively drastically and is now at levels not seen since #bitcoin was trading around $55k, which has not been fully reflected into price yet
11/17 Since the May 19th capitulation event, sentiment went bearish and the futures markets went increasingly short
After bouncing off the recent ~$30k lows, a supply squeeze occurred, as the liquidation of (naked) shorts lead to a cascading price increase by >$10k within a week
12/17 Upon this price increase, coins that moved on-chain remained relatively young but increasingly moved at a profit
This may mean that inexperienced market participants jumped on the opportunity to sell at or above break-even & thus squeezing out more relatively weak hands
13/17 Entities holding 1-100 #bitcoin were selling all the way up to capitulation - but have resumed accumulation since
Entities holding up to 1 #bitcoin are on average always stacking 💪
Entities holding 100-1k #bitcoin bought close to the top & just sat on their positions
14/17 During this last bounce off the ~$30k lows, almost 112k #bitcoin have been withdrawn from exchanges, adding fuel to the fire that a new supply squeeze might be ongoing
15/17 Since this recent price turnaround, #bitcoin market sentiment appears to have turned quite a bit
On a Twitter poll that ended on July 31st, respondents were predominantly bullish on all timeframes 🐂
16/17 Those poll results align with the Fear & Greed Index (FGI), that scrapes multiple social media platforms and algorithmically assesses the market sentiment on #Bitcoin related posts
FGI was full of fear & anxiety throughout the price dip, but has now flipped to greed again
17/17 On-chain metrics themselves are not predictive, but some models try to
Each have their own limitations, but together they form an overview of where price may go if history repeats or rhymes
Will this cycle break some models, or will another supply squeeze drive up price?
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2/11 Money can be defined as "the most salable good to transfer value across space and time"
#Bitcoin can be seamlessly transferred across both space and time thanks to its digital nature and 21-million maximum supply
3/11 When valuing #bitcoin, those aspects need to be taken into account
Some models focus on scarcity (e.g., @100trillionUSD's S2F models), whereas others may look at its transactional capacity (e.g., @woonomic's Network-Value-to-Transactions (NVT) Price model)
2/5 The first concept to grasp is that of Realized Value (RV), introduced by @nic__carter & @khannib in 2018
RV is the total value of all circulating coins at the last time they moved on-chain, therefore representing the estimated cost-base of all existing #bitcoin
3/5 Briefly after, @kenoshaking & @MustStopMurad divided the total #bitcoin Market Value (MV) by the RV, creating a groundbreaking metric called the MVRV Ratio
A pseudonym called Awe and Wonder then iterated upon it by standardizing it ((MV-RV)/MVsd), creating the MVRV Z-Score
2/22 Since mid-April, China came down hard on #Bitcoin, banning its institutions to offer #bitcoin services, censoring related search results and shutting down mining operations in recent weeks
Hash rate dropped ~50%, to levels not seen since briefly after last year's halving 🤕
3/22 A result of the hash rate drop is that #Bitcoin blocks are coming in much slower than the usual 10 minute block intervals
In fact; block creation slowed down to more than twice the intended interval & levels not seen in >11 years, illustrating the magnitude of this drop 🤯
1/25 @BitcoinMagazine just posted the first edition of a new monthly series titled 'Cycling On-Chain', in which on-chain and price-related data are used to estimate where in #Bitcoin's market cycle we are
2/25 Just like the periods after the 2012 and 2016 halvings, the 2020 #Bitcoin halving created a supply shock that triggered an exponential price increase
However, compared to the previous one, this cycle got heated much faster 🥵
3/25 When the #bitcoin price ran towards and beyond its previous (2017) all-time high at $20k, market participants increasingly started to secure profits
After the January local top, this profit-taking has been decreasing - despite price still grinding up until recently
1/7 Just published an article at @BitcoinMagazine that uses on-chain data visualizations to explain how #Bitcoin's difficulty adjustment mechanism works & how it relates to hash rate, block intervals, fees & the mempool
2/7 #Bitcoin reaches its 21 million hard cap by starting with a 50 BTC block subsidy and halving that each 210k blocks, until the block subsidy falls away after 33 halvings
#Bitcoin needs block intervals of ~10 min to ensure these halvings are spread out over ~4 years. But why?
3/7 If #Bitcoin had a fixed difficulty, it would have had an adoption threshold if it started high, or quickly run through its supply issuance schedule if it started low
Relatively stable block interval times are needed to spread out miner incentives & ensure stable throughput