One of the coolest aspects of blockchain today (who knows what might change with updates like Taproot) is how we can track when a coin was moved or, more accurately, when a certain UTXO was created. By connecting this info with the current market price, we can figure out how an on-chain trade is doing.
For instance, Glassnode has some metrics for this. One that stands out is the Net Unrealized Profit/Loss.
Thinking about it intuitively, it’s described best as the difference between realized market cap and the realized market. This represents the average P&L for each UTXO.
If the value is positive or negative, it indicates whether the average UTXO is in profit or loss by that amount. So if NUPL is at 20%, the average UTXO is in profit by 20%.
Keep in mind that the base metric here is the current market cap, not the original one. This leads to two key points:
First, it prevents any skew from those UTXOs that are way deep in profit since they were moved at a low price. Second, the measure can even drop below -100% because we’re using the current market cap. For example, if a UTXO moved at $60,000 and the market is now at $20,000, it would show a UNPL of -200%.
So, while NUPL is accurate and not distorted, looking at median values instead of averages gives a better view of the market's nature.
as all coins are "born" from miners, and most UTXO have a very long history and changed hands a lot, this is a problematic metric to track a "random" bitcoin investor.
A much simpler approach would be to just look at addresses balances. Total value when received x total value for current balance.
You can see that in blockchair.com, just like this. Check this example:
https://blockchair.com/bitcoin/address/bc1q8jjqfldhtu247hewmwy9a9gy7rx0g79y3hxnm0
He received 12k and has now about 20.9k
Ofc you would need some filters, removing addresses which have a lot of reuses and some other problems which can make this metric problematic. But it is a start i guess.
Bringing back this thread as the user @gbianchi implemented the idea:
There is his graph:
The idea is the following:
NUMPL<NUPL bullish NUMPL>NUPL: bearish
We are just starting to notice a bearish divergence.
Today Bitcoin price $100450 at the time of writing and when I invested market was bullish at $116000
Net Unrealized Profit and loss (NUPL)= 100450- 116000 = -$15550
I invested 300 dollar at Bitcoin then NUPL = 15550×300 ÷116000 = ~40 dollar
Thank you for reminding me that averages can be deceiving and that the NUPL (Number of Points) can mislead market perception. Initially, I felt this discussion would be too technical, but it's quite interesting to discuss the intersection of statistics and digital economics by discussing NUPL and NUMPL. On-chain analysis focuses more on aggregates (capitalization, average, and total profit) without considering the distribution of wealth and profit on-chain. It would be very interesting if the discussion could be linked to wealth distribution, market behavior and sentiment, and the well-being of the digital economy, with the initial premise that GDP per capita figures do not always reflect the true well-being of society. GDP per capita may be high because there are a handful of super-rich individuals, but that doesn't necessarily mean the population is prosperous. So, with NUMPL, it's hoped to answer the curiosity: is this bull market felt by everyone, or only elite whales?
NUPL only calculates the average unrealized profit, without considering the distribution of coin holders. When the UTXO distribution is highly skewed, NUPL can mislead market perception. So, NUMPL attempts to address distributional issues, similar to those faced by ordinary investors. However, in the blockchain context, the NUMPL concept has several weaknesses, including the vague definition of the median (UTXO, wallet, address, value-weighted?). The median only takes a single midpoint, not the entire distribution, and ignores skewness and kurtosis.
If I'm not mistaken, your ultimate goal is to understand how balanced profits are among Bitcoin investors. Starting from the premise that the economy and markets are distributed phenomena, not single points, and that NUMPL only answers the typical investor's profit, I believe another macroeconomic approach is needed that can be paralleled on-chain to be more informative.
The logic of NUMPL can be expressed as:
GDP per capita = mean output per person
Median income = middle income of the population
In fact, we don't stop at the median; we continue to measure the overall wealth distribution to understand the structure of inequality. Perhaps you could consider some more accurate options for analyzing market stability, sentiment, and wealth inequality.
Gini Coefficient-Based PDI
In macroeconomics, we measure income inequality with the Gini coefficient (Gini), and on-chain profit inequality with the PDI. If the Gini profit increases, it means the market is becoming more concentrated in the hands of whales.
Learning from banking stress testing, I believe we need an approach that combines risk statistics and market behavior. By using an on-chain VAR (Value at Risk) model, we can try to determine the number of investors who will lose if prices fall.
Is it possible to gain a deeper understanding of market structure by applying the on-chain version of the Lorenz curve, not just a single number (mean/median)?
What if we use a richer statistical variation (Weighted Percentile Profit Index) and combine it with the median (NUMPL) to determine the extent of inequality and the evenness of profits?
Everything is still in the conceptual stage for the technical calculations; let me try to figure it out.
https://en.wikipedia.org/wiki/Skewness
https://en.wikipedia.org/wiki/Kurtosis
https://en.wikipedia.org/wiki/Quantile
https://www.investopedia.com/terms/w/weightedaverage.asp
In my country, there's a saying, "mendung tidak selalu hujan" (clouds don't always rain). Bearish divergence is only an early warning sign of weakening momentum, not a guarantee of an immediate market decline. This is similar to the difference between a still-high GDP per capita and a stagnant median income, a signal of inequality and a potential economic slowdown. In the real economy, GDP can still grow, but median income is declining, indicating that the welfare of the majority has deteriorated, even though the headline data still looks good. From a digital economic perspective, in the late stages of a bull market, microeconomic indicators (such as NUPL and realized profit) usually begin to decline before prices fall. So, bearish divergence is a signal of asymmetric welfare distribution in the crypto market. Some whales are still strong, but the majority of retail investors are starting to weaken as profit distribution becomes increasingly unequal and selling pressure increases. We may not be in a crash yet, but the market is starting to run out of steam.
CMIIW, perhaps I misread and misinterpreted your chart.
Congrats on the good explanation of the graph.
Yes, the point is the one you pointed out, trying to "smooth" out the effect of the very in the money first initial holders, that could displace the mean of the P&Ls, also knowing that those holders probably won't move their coins anytime soon!
The only thing I can say about the specific point highlighted above: mean and median both don't account for higher moments of the distribution, like the one you mentioned.