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On-Chain Data School (Part 9): Market Health Monitor RUPL(I) Data Overview & Buy-the-Dip Strategies

On-Chain Data School (Part 9): Market Health Monitor RUPL(I) Data Overview & Buy-the-Dip Strategies

BlockBeats2025/04/22 07:00
By: BlockBeats
BTC-0.11%
This article mainly introduces the definition and calculation method of the RUPL indicator, as well as the logic of the dip-buying model designed based on this indicator.
原文标题:《链上数据学堂(九):市场晴雨表 RUPL (I) - 数据介绍 & 抄底应用》
原文作者:貝格先生,链上数据分析师


TLDR  


- RUPL 系列文章将分为 2 篇,这是第 1 篇  

- RUPL 可以呈现市场当前的未实现盈利状况  

- 通过观察 RUPL 可以发现市场顶部和底部的运行规律  

- 本文将分享一个基于 RUPL 设计的抄底模型  


一、什么是 RUPL?  


RUPL,全称 Relative Unrealized Profit & Loss,中文为「相对未实现损益」。该指标可以拆解为两部分:RUP 和 RUL。


以 RUP 的计算方式为例:  


1、比较当前价格与每一枚 $BTC 最后转移时的价格,将「现价 > 最后转移价格」的筹码归类为盈利筹码。  

2、将每个盈利筹码的盈利额乘上对应数量,得到 Unrealized Profit(未实现利润)。  

3、最后将该数据基于当时市值进行标准化处理。


换句话说,Unrealized Profit 就是当前市场上的总未实现利润;而 RUP 是将其根据市值标准化,以便横向比较各时期市场的盈利情况。RUL 的算法与 RUP 相同,本文不再赘述。


On-Chain Data School (Part 9): Market Health Monitor RUPL(I) Data Overview & Buy-the-Dip Strategies image 0


如上图一,绿色线为 RUP,红色线为 RUL。可以看出:价格与 RUP 高度正相关,与 RUL 高度负相关。这很直观,因为随着币价上涨,盈利筹码及未实现利润自然增加。


但如果进一步观察上图,会发现 RUL 在某些时间段会高于 RUP(即红线高于绿线,如图中黄色框处),这表示市场整体处于未实现亏损状态,那么这些时间段是否具有特殊意义?请继续往下阅读


二、RUPL 的抄底应用  


承上,有句话说得好:「别人恐惧时我贪婪」,当市场大多数筹码处于亏损状态时,或许就是我们进场收集筹码的好时机。


On-Chain Data School (Part 9): Market Health Monitor RUPL(I) Data Overview & Buy-the-Dip Strategies image 1


如上图二,我将图一中 RUL > RUP 的时间段标记出来后绘制了该图表。可以清晰看到:当 RUL > RUP 时,几乎都处于历史性的周期底部。


这并非刻舟求剑,其逻辑在于:

「当市场整体处于亏损状态时,意味着持有大量低价筹码的投资者已基本完成派发;而被套牢的投资者往往因为价格过低不愿意割肉,这两种情绪交织,导致卖压大幅下降,因此只要稍有买盘介入,就可能推动趋势反转并开始上涨。」


这个逻辑与先前分享的 LTH-RP 抄底策略非常相似,有兴趣的读者可参考这篇贴文:《链上数据学堂(二):老是赚钱的 Hodlers 们,他们买 BTC 的成本是多少?


三、抄底模型设计逻辑分享  


接下来,我们暂时不看 RUL,单独观察 RUP 图表,可以发现 RUP 在历史底部时有一个较为接近的数值区间:


On-Chain Data School (Part 9): Market Health Monitor RUPL(I) Data Overview & Buy-the-Dip Strategies image 2


举个例子,我在图表中加入一条 0.4 的水平线,可以明显看到 RUP < 0.4 的区域。(这里的 0.4 是模型参数,可调,后续还会提到)


既然发现 RUP 存在明显的底部区间,我们可以将 RUP < 0.4 这个条件叠加到前面的 RUP < RUL 条件上,进行二次信号过滤,得到以下结果:


On-Chain Data School (Part 9): Market Health Monitor RUPL(I) Data Overview & Buy-the-Dip Strategies image 3


这是模型设计中常用的方式,目的是通过信号筛选达到更精准的效果,让模型输出的信号更具参考价值。


上图显示的是(RUP < 0.4)+(RUP < RUL)两个条件的组合,虽然过滤效果不算非常显著,但仍能看出比单独使用 RUP < RUL 更严谨。如果将 0.4 调小(比如设为 0.38),模型会更严格,但参数调优过程中要注意过度拟合(overfitting)的问题,因为一味根据历史数据微调模型,可能导致模型未来失效。


补充:Overfitting 即「过度拟合」,类似于我们常说的刻舟求剑。


四、结语  


本文是 RUPL 系列的第 1 篇,主要介绍了 RUPL 指标的定义及计算方式,并分享了基于该指标设计的抄底模型逻辑。


下一篇我将介绍一个基于 RUPL 的逃顶实战应用,并对历史周期顶进行回顾分析,保证干货满满,敬请期待。


原文链接

Disclaimer: The content of this article solely reflects the author's opinion and does not represent the platform in any capacity. This article is not intended to serve as a reference for making investment decisions.
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