Original Title: "On-chain Data School (Part 9): Market Barometer RUPL (I) - Data Introduction & Bottom Fishing Application"

Original Author: Mr. Berger, On-chain Data Analyst

This article is the 9th part of the On-chain Data School series, with a total of 10 parts. It will take you step by step to understand on-chain data analysis. Interested readers are welcome to follow this series of articles.

Related Reading: "On-chain Data School (Part 8): A brand-new, Ark-involved BTC magical pricing methodology (III)"

TLDR  

- The RUPL series of articles will be divided into 2 parts, this is part 1  

- RUPL can present the current unrealized profit/loss situation of the market  

- By observing RUPL, one can discover the operating rules of market tops and bottoms  

- This article will share a bottom fishing model designed based on RUPL  

1. What is RUPL?  

RUPL, short for Relative Unrealized Profit & Loss, can be translated into "Relative Unrealized Profit & Loss" in Chinese. This indicator can be broken down into two parts: RUP and RUL.

Take the calculation method of RUP as an example:  

1. Compare the current price with the price at which each $BTC was last transferred, classifying the chips where "current price> last transfer price" as profit chips.  

2. Multiply the profit amount of each profit chip by the corresponding quantity to obtain Unrealized Profit.  

3. Finally, standardize this data based on the market value at that time.

In other words, Unrealized Profit is the total unrealized profit in the current market; and RUP standardizes it based on market value for horizontal comparison of the profitability situation in different market periods. The algorithm for RUL is the same as that for RUP, and this article will not repeat it.

As shown in the above Figure 1, the green line represents RUP, and the red line represents RUL. It can be seen that the price is highly positively correlated with RUP and highly negatively correlated with RUL. This is intuitive because as the coin price rises, unrealized profits and profit-chasing naturally increase.

However, upon further observation of the above chart, it can be noticed that RUL is higher than RUP during certain time periods (i.e., the red line is above the green line, as highlighted in the yellow box in the chart), indicating that the market as a whole is in an unrealized loss state. Do these time periods hold any special significance? Please continue reading below.

二、RUPL Bottom Fishing Application  

Building upon the previous discussion, there is a saying: "Be greedy when others are fearful." When the majority of the market chips are in a losing position, perhaps it is a good opportunity for us to enter the market and collect chips.

As shown in the above Figure 2, I have marked the time periods from Figure 1 where RUL> RUP and plotted this chart. It can be clearly seen that when RUL> RUP, it is almost always at a historic cycle bottom.

This is not a case of using a sledgehammer to crack a nut; the logic behind this is:

“When the market as a whole is in a loss state, it means that investors holding a large amount of low-priced chips have essentially completed distribution. Investors who are stuck often do not want to sell at a low price, so these two emotions intertwine, resulting in a significant decrease in selling pressure. Therefore, as long as there is some buying interest, it could potentially trigger a trend reversal and start an uptrend.”

This logic is very similar to the previously shared LTH-RP bottom fishing strategy. Interested readers can refer to this post: "On-chain Data Academy (2): The Hodlers Who Keep Making Money, What is the Cost of Their BTC Purchase?"

三、Bottom Fishing Model Design Logic Share  

Next, let's temporarily ignore RUL and separately observe the RUP chart. It can be seen that RUP has a relatively close numerical range at historical bottoms:

For example, I have added a horizontal line at 0.4 on the chart, and it is clearly visible that there is an area where RUP <0.4. (Here, 0.4 is a model parameter that can be adjusted, and will be mentioned later)

Since we have identified a clear bottom range for RUP, we can add the condition RUP < 0.4 to the previous condition RUP < RUL for a secondary signal filtering, resulting in the following:

This is a common approach in model design, aiming to achieve a more precise effect through signal filtering, making the model's output signal more referenceable.

The above figure shows the combination of the two conditions (RUP < 0.4) + (RUP < RUL). Although the filtering effect is not very significant, it can still be seen to be more rigorous than using RUP < RUL alone. If 0.4 is lowered (e.g., set to 0.38), the model will be more stringent. However, during the parameter optimization process, attention should be paid to the issue of overfitting because blindly fine-tuning the model based on historical data may lead to the model's future failure.

Supplement: Overfitting, also known as "overfitting," is similar to what we often refer to as "looking for the sword at the spot where it was lost."

IV. Conclusion  

This article is the first in the RUPL series, mainly introducing the definition and calculation method of the RUPL indicator and sharing the dip-buying model logic based on this indicator.

In the next article, I will introduce a practical application of top-selling based on RUPL and conduct a retrospective analysis of historical cycle tops to ensure full of practical content. Stay tuned.

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