Original Article Title: "On-Chain Data Academy (Part 7): A Brand New, Ark Participatory Research $BTC Magical Pricing Methodology (II)"

Original Article Author: Mr. Berg, On-Chain Data Analyst

This article is the 7th in the On-Chain Data Academy series, with a total of 10 articles. Take you step by step to understand on-chain data analysis. Readers interested in tracking this series of articles are welcome.

Related Reading: "On-Chain Data Academy (Part 6): A Brand New, Ark Participatory Research BTC Magical Pricing Methodology (I)"

TLDR

- The Cointime Price series consists of three articles, this being the second article

- This article will introduce the application method of Cointime Price in Sell-Off Time  

- This article will introduce a personally designed deviation model

1. Brief Review of Cointime Price

The concept of Cointime Price comes from Cointime Economics, which evaluates BTC's fair price using a "time-weighted" approach.

Compared to pure LTH (Long-Term Holders) and STH (Short-Term Holders), Cointime Price is more elastic and sensitive, and the model can effectively exclude the impact of ancient lost BTC.

The first article detailed the Cointime Price and its Buy-the-Dip application. If you already understand the concept, then let's officially move on to today's topic: the Sell-Off Time application

2. Sell-Off Time Application Methodology: Cointime Price Deviation Model Design

Cointime Price Deviation is one of the models I designed myself while researching on-chain data, and it has been applied in the weekly Sell-Off Time analysis report.

Related Tweet: Sell-Off Model Introduction  

The following text will explain the model design principle and how to use this model to determine the BTC top. All content in this article is original research, and the research process is not easy, so your support is greatly appreciated.

1. Quantifying the Delta Between Current Price and Cointime Price

Why measure the delta?

- The Cointime Price strongly represents the true holding cost of BTC chips, especially for Long-Term Holders (LTH).

- Since Long-Term Holders have a greater impact on the Cointime Price, when the BTC current price is significantly higher than the Cointime Price, the profit-taking motivation of Long-Term Holders increases, potentially triggering distribution behavior.

· Calculation Formula: Delta Rate = (Current Price - Cointime Price) / Current Price

· Observe Delta Rate (Distribution Rate)

As shown in the chart, we can obtain the Distribution Rate curve (purple line). We can see that whenever the Distribution Rate is at a high level, it often corresponds to the BTC top.

So, how do we define a "high level"? Next, we will use a statistical method to address this question

2. Defining Cointime Price Deviation Extreme Values

If we observe historical data, we will find that the peak of Deviation is not fixed. In each bull market cycle, the peak value of Deviation has slightly decreased. Therefore, it is not rigorous to rely solely on a fixed value to define a "high level."

In addressing this, I adopt the concept of statistical "standard deviation":

· Calculate the mean and standard deviation of historical Deviation data.

· Define "mean + n standard deviations" as the "high level (top signal)," referred to as the Threshold.

· Smooth the Deviation data with a moving average to reduce noise.

· When the moving average value of Deviation> Threshold, trigger a top signal.

· Why use standard deviation?

· The historical trend of Deviation exhibits mean reversion characteristics (as shown in the chart).

· Standard deviation measures volatility, so when the BTC price volatility decreases, the Threshold will also dynamically adjust, making it more elastic.

As shown in the above figure, after the above processing, we can get such a chart.

· Additional Explanation

- In Point 2, the "Mean + n Standard Deviations," where n is a tunable parameter: The larger the n, the lower the probability of vertex signal occurrence, making the model more strict.

- In Point 3, the moving average smoothing process mainly filters out short-term market fluctuations to improve signal reliability.

3. Top Signal Example

As shown in the figure, when the purple line (Distribution Ratio) exceeds the orange line (Threshold), the corresponding BTC price is often at a phase top.

III. Conclusion

This article is the second piece of the Cointime Price series, continuing the concepts from the previous article, sharing how individuals design a top signal model using Cointime Price.

· Summarize Key Points:  

- Cointime Price Deviation quantifies the deviation of the current BTC price from the Cointime Price, speculating on the distribution motivation of long-term holders to determine the BTC top.

- By adopting the "Standard Deviation" method to dynamically define the top signal, the model ensures greater adaptability.

- The model has been practically applied in the weekly report and can effectively capture BTC's high-level signals.

Future Plans:  

- The third article in this series will continue to explore the application of Cointime Price in top signal identification, so stay tuned.

Original Article Link

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