On-chain Data Academy (9): Market Barometer RUPL (I) - Data Introduction & Buy the Dip Application

This article is the 9th article in the On-Chain Data Academy series, with a total of 10 articles. Take you step by step to understand on-chain data analysis, welcome interested readers to follow this series of articles. (Synopsis: On-Chain Data Academy (6): A new BTC magic pricing methodology with ARK participation (I) ) (Background supplement: On-chain data academy (7): A new set of BTC magic pricing methodology with ARK participation (II) TLDR RUPL series of articles will be divided into 2, this is the first RUPL can present the current "unrealized profit and loss" situation of the market By observing RUPL, you can find the operation law of the market top and bottom One according to RUPL The designed bottom-reading model shares RUPL Introduction RUPL, full name Relative Unrealized Profit & Loss, Chinese translation "relative unrealized profit and loss". The indicator itself can be split into two pieces, RUP and RUL. Taking RUP as an example, the calculation is as follows: Compare the "current price" with the "price at last transfer of each BTC" and classify the chips with the "current price > last transfer price" as profit chips. Multiply the profit of each chip by the corresponding number of chips to get Unrealized Profit. Finally, the obtained data will be standardized according to the market value at that time. In other words, Unrealized Profit is the "sum of unrealized profits" in the current market; RUP, on the other hand, normalizes this data based on market capitalization in order to compare market earnings over different periods. RUL's algorithm is exactly the same as RUP's logic, so I won't go into detail here. As shown above, the green line is RUP and the red line is RUL. We can find that price is highly positively correlated with RUP and highly negatively correlated with RUL. This is intuitive, because as the price of the coin rises, the sum of the profits of the unrealized profit chips naturally increases. But if we look further at the chart above, we will find that RUL exceeds RUP (the red line is above the green line) on a few periods, which means that the unrealized P&L position of the market as a whole is negative, is this situation of particular significance? Read on ... RUPL's bottom-reading application There is an old saying: "I am greedy when others are afraid", when the chip holders of the market, the overall average is in a loss state, it may be a time worthy of us to enter the market to collect chips. As shown in the figure above, I marked the time period of RUL > RUP to get this signal graph. We can clearly find that when RUL > RUP, it basically corresponds to a periodic big bottom! This is by no means a simple sword, the logic is: "when the market as a whole is in a losing state, it means that the trapper is likely to be unwilling to sell his chips because the price is too low", in the case of a sharp reduction in selling pressure, as long as there is a slight increase in buying, the trend may reverse and start to rise. This logic is very similar to the LTH-RP bottom-hunting strategy introduced in the previous article, and interested readers can flip through the previous posts. Sharing the design logic of the RUPL bottom reading model Then, let's ignore the RUL for a moment and focus on the RUP chart itself, and we will find that the bottom values of RUP in history are actually very close. For example, I added a horizontal line of 0,4 to the RUP chart so that we can clearly see where the RUP < 0.4. (0.4 here is an adjustable parameter, which will be mentioned again later) When we find that RUP has a relatively obvious bottom area, we can superimpose the condition of RUP < 0.4 on the condition of the previous "RUP < RUL" to perform secondary filtering on the signal, and the result is as follows: This is a very common method when designing models, in order to achieve the filtering effect through the signal screen, so that our final design model can be more accurate. The two conditions in the above figure (RUP < 0.4 > RUP < RUL), the filtering effect is not very obvious, but if you look closely, you can still find that there are indeed stricter than simple RUP < RUL. Here, if you adjust 0.4 down (for example, to 0.38), you can make the overall signal tighter; But in the process of adjusting parameters, you still have to pay attention to the problem of overfitting, after all, simply fitting the model based on historical data is likely to fail in the future! Conclusion The above is all about the on-chain data academy (nine), the next article will give a more in-depth introduction to RUP, and share a classic top signal with you. Readers who are interested in learning more about on-chain data analysis, be sure to follow this series of articles! If you want to see more on-chain data analysis and teaching content, please follow my Twitter (X) account! Hope this article helps you, thanks for reading. Related stories on-chain data academy (8): A new BTC magic pricing methodology with ARK research! (III) On-Chain Data Academy (1): Do you know what the average cost of BTC in the whole market is? On-Chain Data Academy (II): How much does it cost for the Hodlers who are always making money? "On-Chain Data Academy (9): Market Barometer RUPL(I) - Data Introduction & Bottom Reading Application" This article was first published in BlockTempo's "Dynamic Trend - The Most Influential Blockchain News Media".