Polynomial LTH/STH Support Bands and Moraes Multiple on the Bitcoin trading
Introduction
Mayer Multiple is one of the most important metrics for Bitcoin traders to understand. This powerful tool can help you identify market trends, manage risk, and make informed decisions about when to buy or sell Bitcoin. At its core, the Mayer Multiple is a simple calculation that divides the current price of Bitcoin by its 200-day moving average [1].
But the Mayer Multiple isn’t just useful for identifying market trends — it can also help you manage risk. By using the Mayer Multiple to identify potential overvalued or undervalued conditions, you can make informed decisions about when to enter or exit the market. This can help you minimize losses and maximize profits, which is essential for long-term success in the trading world.
However, it has some limitations that should be considered. Most important, it is based on a simple metric, the 200-day moving average, which may not be sufficient to capture the complexity of the market. It is important to note that the Mayer Multiple is just one tool among many and should not be relied upon solely for investment decisions.
The goal of this study was to apply polynomial adjustment data by using a Savitzky-Golay smoothing filter instead the 200-day moving average. A polynomial fit is a better choice than a fit using simple averages because it can capture more complex relationships between variables. While a linear fit assumes a constant rate of change between variables, a polynomial fit can account for non-linear relationships, such as curves or bends in the data. The Moraes Multiple proposed herein allows for a more accurate representation of the Bitcoin data and can lead to better predictions and insights.
Data treatment
Data import, pre-processing, and multivariate procedures were carried out using R software [3] with Signal Processing (signal) package [4]. All data processing was performed using the free R software from the R Foundation for Statistical Computing. Use R!
Polynomial LTH/STH Support Bands backtesting
Polynomial LTH/STH support bands were obtained from Savitzky–Golay smoothing of Bitcoin price data using the filter length of 155 days. This period classifies investors into two groups, Long-Term Holders (LTH), that keep possession of their coins for periods spanning several months to years, and Short-Term Holders (STH), newer market entrants, active traders, and “weak hands” more likely to exit positions in response to market volatility.
In this way, the support bands were 0.8 and 1.2 around the smoothing curve. For the backtesting, the model was applied between 2013 and 2021 (Figures 1–3), as can be observed, the model achieved an interesting sensitivity and effectiveness.
Moraes Multiple
The Polynomial LTH/STH Support Bands and Moraes Multiple Script are fully described at the end of this article, including information about how to plot the graphic and the script in R using the freely accessible R Foundation for Statistical Computing (for downloading access http://www.r-project.org/). This smoothing process gives rise to the plot shown in Figure 4.
Finally, the Moraes Multiple is a calculation that divides the price of Bitcoin by the polynomial LTH/STH support bands. But don’t let its simplicity fool you — this metric is incredibly powerful. By analyzing the Moraes Multiple, you can determine whether Bitcoin is overvalued or undervalued, and use that information to make smart trading decisions.
If the Moraes Multiple is consistently high over a while, it may indicate that the market is bullish and that Bitcoin prices are likely to continue to rise. Conversely, if the Moraes Multiple is consistently low over some time, it may indicate that the market is bearish and that Bitcoin prices are likely to continue to fall. 0.8 and 1.2 are often market reversal zones.
Conclusion
In short, the Moraes Multiple is a powerful tool that every Bitcoin trader should understand. By using this metric to analyze market trends, manage risk, and make informed trading decisions, they can take their trading game to the next level and achieve long-term success in the exciting world of Bitcoin trading.
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Acknowledgments
The author is grateful to Satoshi Nakamoto, Hal Finney, Nick Szabo, Changpeng Zhao, Sabrina Moraes, and André Fauth.
References
1. The Bitcoin Mayer Multiple — We Study Billionaires (theinvestorspodcast.com)
4. https://www.rdocumentation.org/packages/signal/versions/0.7-6
5. Supply Held by Long and Short-Term Holders — Glassnode Academy
# Polynomial LTH STH Support Bands and Moraes Multiple Script
# Installing and loading the geckor package
install.packages(“geckor”)
library(“geckor”)
# Data extracting from Coingecko
btc.i = coin_history(
coin_id = “bitcoin”,
vs_currency = “usd”,
days = “max”
)
# Installing and loading the signal package
install.packages(“signal”)
library(“signal”)
# Model
price155.sg = sgolayfilt(btc.i$price, n=155)
Moraes.Multiple = btc.i$price/price155.sg
plot(btc.i$timestamp[3315:3615],Moraes.Multiple[3315:3615], type=”l”, col=”darkturquoise”,
main = “Moraes Multiple (BTC Price/POL155)”,
xlab=”time”, ylab=”Moraes Multiple”, ylim=c(0.7,1.2))
abline(h=c(0.7,0.75,0.8,0.85,0.9,0.95,1,1.05,1.1,1.15,1.2), col=”gray”,
lwd=2,lty=3)
par(new = T)
plot(btc.i$timestamp[3315:3615],btc.i$price[3315:3615], type = “l”, col = “blue”, axes = FALSE, ann = FALSE)
legend(“bottomleft”,c(“Moraes Multiple”, “Bitcoin Price (USD)”),
col=c(“darkturquoise”,”blue”)
,bty=”n”, pch=16)
axis(4, ylim=c(10000,70000), col=”blue”,col.axis=”blue”,las=0.9)
lines(btc.i$timestamp[3315:3615],price155.sg[3315:3615]*1.20, type=”l”, col=”green”, lwd=1)
lines(btc.i$timestamp[3315:3615],price155.sg[3315:3615], type=”l”, col=”brown”, lwd=1, lty=2)
lines(btc.i$timestamp[3315:3615],price155.sg[3315:3615]*0.80, type=”l”, col=”red”, lwd=1)
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