This article is the 9th in the On-chain Data Academy series, with a total of 10 articles. It guides you step by step in understanding on-chain data analysis. Interested readers are welcome to follow this series.
(Previous recap: On-chain Data Academy (VI): A New, ARK-Participated Research Methodology for BTC Pricing (I))
(Background supplement: On-chain Data Academy (VII): A New, ARK-Participated Research Methodology for BTC Pricing (II))
TLDR
- The RUPL series will be divided into 2 articles, this is the first one.
- RUPL can present the current market’s “unrealized profit and loss” situation.
- By observing RUPL, one can discover the operational patterns at market peaks and troughs.
- A bottom-fishing model designed based on RUPL will be shared.
RUPL Introduction
RUPL, short for Relative Unrealized Profit & Loss, refers to “relative unrealized profit and loss.” The indicator itself can be broken down into two components, RUP and RUL. Taking RUP as an example, the calculation method is as follows:
Comparing the “current price” with “the price at which each BTC was last transferred,” chips categorized as profitable chips are those where “current price > last transfer price.” The profit of each chip is multiplied by the corresponding chip quantity to obtain Unrealized Profit. Finally, the resulting data is standardized based on the market capitalization at that time.
In other words, Unrealized Profit is the “total of unrealized profits currently in the market”; RUP standardizes this data according to market capitalization to facilitate comparisons of market profit situations over different periods. The algorithm for RUL is identical to that of RUP, so it will not be elaborated further.
As shown in the figure above, the green line represents RUP, while the red line represents RUL. We can observe that:
The price is highly positively correlated with RUP and highly negatively correlated with RUL. This is intuitive, as the total profit of unrealized profitable chips naturally increases with the rise in coin price. However, if we further observe the figure, we will notice that RUL exceeds RUP during a few periods (the red line is above the green line), which indicates that the overall unrealized profit and loss situation in the market is negative. Does this situation hold any special significance? Please continue reading…
RUPL’s Bottom-Fishing Application
An ancient saying goes: “I am greedy when others are fearful.” When the overall average of market chip holders is in a state of loss, it may be a time worth considering for us to enter and accumulate chips.
As shown in the above figure, I have marked the time periods when RUL > RUP, creating this signal chart. We can clearly see that:
When RUL > RUP, it generally corresponds to significant cyclical bottoms! This is not merely a simplistic approach; the logic is based on:
“When the overall market is in a state of loss, it means that those who are trapped are likely unwilling to sell their chips due to the low price,” and under the condition of greatly reduced selling pressure, as long as there is a slight increase in buying, it may lead to a trend reversal and start rising. This logic is very similar to the bottom-fishing strategy using LTH-RP introduced in earlier articles; interested readers can refer to previous posts.
Sharing the Design Logic of the RUPL Bottom-Fishing Model
Next, let’s temporarily ignore RUL and focus on the RUP chart itself. We will find that the historical bottom values of RUP are actually quite close.
For example, I have added a horizontal line at 0.4 on the RUP chart, allowing us to clearly see the position where RUP < 0.4. (The 0.4 here is an adjustable parameter and will be mentioned again later.) Upon discovering that RUP has a relatively obvious bottom area, we can overlay the condition RUP < 0.4 onto the previous condition "RUP < RUL" for secondary filtering of the signals, resulting in the following:
This is a commonly used method in model design, aiming to achieve filtering effects through signal screening, allowing our final designed model to be more accurate. The two conditions in the above figure (RUP < 0.4 & RUP < RUL) do not exhibit a very obvious filtering effect, but upon careful observation, it is still clear that it is indeed stricter than simply RUP < RUL. Here, if the 0.4 is adjusted down (for example, to 0.38), it will make the overall signal stricter; however, during the parameter adjustment process, one must still pay attention to the issue of overfitting. After all, simply fitting the model based on historical data may lead to failures in the future!
Conclusion
This concludes the entire content of On-chain Data Academy (IX). The next article will provide a deeper introduction to RUP and share a classic top signal with everyone. Interested readers who wish to learn more about on-chain data analysis should remember to follow this series! If you want to see more analysis and instructional content about on-chain data, you are also welcome to follow my Twitter (X) account! I hope this article has been helpful to you. Thank you for reading.