The altcoin season represents a distinct phase in the cryptocurrency market, offering seasoned traders the opportunity for significant returns. Typically transitioning from the Bitcoin Season, this market cycle [1] is known for its potential lucrative outcomes. Nevertheless, novice traders often grapple with the complexities of discerning optimal entry and exit points for altcoins.

In response to this challenge, a predictive model named Altcoin Waves [2] has been developed, employing a linear combination approach to forecast trends within the altcoin market from 2024 to 2025. The model specifically centers around the market capitalization of the three most influential altcoins (Ethereum, Binance coin, and Ripple), utilizing advanced data treatments facilitated by the R statistics platform. This initiative is designed to empower traders with a more insightful perspective, facilitating strategic decision-making throughout the upcoming altcoin season.

Altcoin Waves data treatment

Alternative coins, often referred to as “altcoins,” represent a relatively nascent category of digital currencies. Two distinct altcoin waves have transpired, and the anticipation for the forthcoming wave is palpable, characterized by an expected surge in market activity. While predicting the future remains inherently challenging, models can serve as valuable benchmarks to navigate prospective market movements. As we delve into the intricate landscape of cryptocurrency dynamics, the upcoming altcoin wave holds the promise of being particularly impactful, laying the groundwork for innovative and dynamic developments in the digital asset sphere.

Price data were sourced from Coingecko [3], spanning the period from 2016 to 2023. Analyzing the initial wave, the peak day for altcoin market capitalization was observed at 546 ± 12 days post the Bitcoin halving event on July 9, 2016. Subsequently, during the second altcoin wave (2020–2024), a similar pattern emerged with the peak occurring 547 ± 13 days following the Bitcoin halving event on May 11, 2020.

Data import, pre-processing, and multivariate procedures were executed using the R software [4], incorporating Signal Processing (signal) [5]. The entirety of data processing activities was undertaken utilizing the open-source R software provided by the R Foundation for Statistical Computing. Embrace the power of R in your analytical endeavors!

Altcoin Waves model

The Altcoin Waves model has afforded an avenue for delving into the altcoin market. A notable aspect of this exploration lies in the common behavior where individuals invest in altcoins with the anticipation of substantial gains, often referred to colloquially as “waiting for the moon.” Contrary to this popular sentiment, market dynamics unfold in discernible waves. Drawing inspiration from the Bitcoin Waves hypothesis, which posits the predictability of subsequent waves through the approximation of future waves as linear combinations of their predecessors, the Altcoin Waves model was crafted. This framework seeks to enhance our understanding of the nuanced patterns inherent in the altcoin market, offering information into the cyclical nature of its movements.

The 2016–2020 wave is depicted in Figure 1, illustrating the market capitalization of leading altcoins. It is discernible from the figure that Ethereum and Ripple shared the market spotlight during this period.

Figure 2 illustrates the altcoin wave from 2020 to 2023. This wave was prominently characterized by the dominance of Ethereum, which emerged as one of the leading altcoins showcasing significant programming innovations within the community.

Finally, the Altcoin Waves model has forecasted the altcoin market for the upcoming altseason, revealing a pronounced surge expected in 2025, as depicted in Figure 3.

It is important to note that not all altcoins will yield similar outcomes. The Altcoin Waves model predicts the following results:

These projections underscore the varying potential trajectories for different altcoins as envisaged by the Altcoin Waves model.

Conclusion

The presented model, although delicate, offers a depiction of the impending altcoin season for Ethereum, Binance Coin, Ripple, and Polkadot. Delving into this subject matter enables both novices and seasoned professionals to acquire a broad understanding of the altcoin market.

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Acknowledgments

The author is grateful to Satoshi Nakamoto, Hal Finney, Nick Szabo, Changpeng Zhao, Sabrina Moraes, Fernando Ulrich, André Fauth, Criptomaníacos, and Vela Trader.

References

1. Altcoin Season — Exploring Alt Seasons and Crypto Market Cycles (ivanontech.com)

2. Altcoin Waves: A linear combination for altcoin season modeling | by Edgar Moraes | Coinmonks | Medium

3. Cryptocurrency Prices, Charts, and Crypto Market Cap | CoinGecko

4. https://www.R-project.org/

http://127.0.0.1:26406/library/signal/html/signal.package.html

Altcoin Waves: Modeling and Predicting Price Trends in the Next Altcoin Wave was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

The Bitcoin halving is approaching, and it will mark the beginning of the 5th wave of Bitcoin, which is expected to be an explosive wave. Halving takes place every 210,000 blocks, and with the Bitcoin network having surpassed 815,500 blocks mined, the next halving is anticipated to occur at block 840,000. Specialized websites are currently forecasting its date for April 2024 [1,2]. This impending event is generating significant excitement and anticipation in the cryptocurrency community, as it has historically had a profound impact on the Bitcoin ecosystem, including supply dynamics and market sentiment.

We are currently approximately 160 days away from the upcoming halving [1,2], where the inflation rate will decrease to 0.8% in 2024. At that point, Bitcoin will boast one of the most meager inflation rates in the global economy [3]. This presents our generation with an exceptional opportunity that our predecessors were deprived of.

In the current study, we have applied and fine-tuned the Bitcoin Waves model to forecast Bitcoin prices for the 5th wave. This model anticipates the forthcoming wave by estimating future waves through linear combinations of previous ones [4–6].

All Bitcoin Waves

The Bitcoin Waves model aims to predict the next wave by approximating the future wave as linear combinations of prior waves, much like Molecular Orbital Theory or Quantum Mechanics. When using the 1st wave to forecast the 2nd wave, the cosine similarity stood at an impressive 83.98%. Following this pattern, the 1st and 2nd waves were combined and compared to the 3rd wave, resulting in a cosine similarity of 96.18%. As for the 4th wave, the model exhibited favorable performance characteristics, with a cosine similarity of 88.8% (Figure 1).

Impacted by the pandemic, the 4th wave did not reach the value it could have achieved in the first half of 2021, when a minimum of $99,700 was anticipated. However, the second peak at the end of 2021 closely aligned with projections.

The 5th Wave

After 2024 halving, the Bitcoin annual inflation rate will drop to 0.8% and the Bitcoin Waves model designs sunny days for bitcoiners. The price could reach 176.7k in Apr 2025 (Figure 2).

According to the model, the bull run is expected to kick off in February 2024, reaching its zenith on April 19, 2025, with a slight margin of error of 36 days and a value of $176,722.90. In the event of another black swan event, such as the COVID-19 pandemic affecting the market or significant geopolitical conflicts and economic downturns, the model predicts a peak of $106,033.8. However, under the circumstances of a robust global economy, Bitcoin could potentially surge to $247,412.10. Will you be on the sidelines for this?

Conclusion

The approach employed in this study is versatile, swift, and straightforward. The Bitcoin Waves model has introduced intriguing adjustments, enabling us to envision a potential future for Bitcoin through linear combinations. What if the model proves to be accurate?

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Acknowledgments

The author is grateful to Satoshi Nakamoto, Hal Finney, Nick Szabo, Changpeng Zhao, Sabrina Moraes, Fernando Ulrich, André Fauth, Criptomaníacos, and Vela Trader.

References

1. When Is The Next Bitcoin Halving? [Updated] | CoinMarketCap | CoinMarketCap

2. Bitcoin Halving Countdown | CoinGecko

3. https://tradingeconomics.com/country-list/inflation-rate

4. Bitcoin Waves: A linear combination for price modeling | by Edgar Moraes | Coinmonks | Mar, 2021 | Medium

5. https://medium.com/coinmonks/bitcoin-what-was-what-is-and-what-probably-will-be-8aba4d01eb7b

6. Predicting 2022–2032 price action with Bitcoin Waves model | by Edgar Moraes | Coinmonks | Medium

The 5th Wave of Bitcoin was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

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)

2. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. | Analytical Chemistry (acs.org)

3. https://www.R-project.org/

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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Polynomial LTH/STH Support Bands and Moraes Multiple on the Bitcoin trading was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Many analysts and investors are always looking for information on the stock market indexes to understand the Bitcoin price movement. In this context, Plan B created a model correlating the Standard and Poor´s 500 index (S&P 500) and Bitcoin price [1]. But, can Nasdaq 100, the index made up of the largest non-financial companies listed on the Nasdaq stock exchange, be more correlated with Bitcoin price than S&P 500?

In this study, the S&P 500 and Nasdaq 100 were selected to evaluate the correlation between these indexes and Bitcoin price. Linear and polynomial models were applied for finding the answer to this question.

Weak correlation

Prices and stock market indexes from Investing were used [2].

The first general idea extracted from Figure 1 is that there is a weak correlation between these indexes and Bitcoin price, instead, Bitcoin proves to have a life of its own and goes its way.

Second, 8 years ago Bitcoin price was lower than both indexes, but in 2022 the price is higher, demonstrating the great value of the Bitcoin network and how it has been profitable since its establishment.

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Models

As can be seen in Figure 2, there is a weak positive correlation between Bitcoin Price and the S&P 500 index, once a strong correlation is indicated for a coefficient of determination (R2) closest to 1. The linear model found an R squared of 0.8316 and 0.9006 for the polynomial model.

The important information from Figure 3 is that Nasdaq 100 index is stronger correlated with Bitcoin Price than the S&P 500 correlation. The linear model for Nasdaq 100 reached an R2 of 0.8355 and 0.9148 for the polynomial model. In other words, if you are looking for information from the stock market, probably using Nasdaq 100 index will be more effective.

Analyzing just the last year, the coefficients of determination presented no acceptable linearity, R² = 0.6483 (Linear) and R² = 0.6991 (Polynomial) for S&P 500, R² = 0.7094 (Linear), and R² = 0.7308 (Polynomial) for Nasdaq 100, highlighting the unique path that Bitcoin follows, influenced by the global economy, but on its way exclusive.

Conclusion

The simple proposal presented here was an excellent opportunity to show traders and investors a new way to look for pieces of information from the stock market. In addition, the models showed Nasdaq 100 index could be more useful than S&P 500 in the way to understand the Bitcoin price movement.

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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. (2) PlanB no Twitter: “S&P500 and Bitcoin have been correlated and co-integrated last 10 years. S&P implied BTC price: $25K .. interesting times ahead! https://t.co/mi8gLmKcmf" / Twitter

2. https://www.investing.com/

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Investigating the correlation of S&P 500 and Nasdaq 100 with Bitcoin price was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Introduction

Support and resistance levels are essentials for big traders and refer to price levels at which an exchange rate trend is likely to be interrupted or reversed [1,2].

Normally, support and resistance are found by tracing trend lines, pivot point, round numbers, moving averages, Fibonacci sequence, and other indicators.

Hence, the present study was designed to investigate the potential of Differentiation (Fundamental Theorem of Calculus) of a price probability density function (PDF) for finding support and resistance levels, in other words, when you can buy or sell using Probability Theory.

Data treatment

Prices from Coingecko were used [3]. Data import, pre-processing and mathematics procedures were carried out using R software [4] with Signal Processing (signal) package [5]. All data processing was performed using the free R software from the R Foundation for Statistical Computing. Use R!

How to find Bitcoin Supports and Resistances

Download R (www.r-project.org). Install and open. Install geckor package [6] and extract Bitcoin prices from Coingecko:

> install.packages(“geckor”)
> library(“geckor”)
> btc.i = coin_history( coin_id = “bitcoin”, vs_currency = “usd”, days = “1825”)

If we want to visualize the Bitcoin (BTC) Price Chart (Figure 1), we can provide the price graph as the at argument:

> plot(btc.i$timestamp, btc.i$price, type=”l”, col=”orange2", main = “BTC-USD”, xlab=”time”, ylab=”BTC price (usd)”)
> abline(h=c(10000,20000,30000,40000,50000,60000,70000), col=”gray”, lwd=2, lty=3)

The goal of this study was to determine the probability density function (PDF) of Bitcoin price to find the support and resistance levels. A common way of plotting the PDF (Figure 2) in R is to apply the functions hist and density to plot histograms:

> hist(btc.i$price,breaks=480,col = “orange”, main=”Histogram of Prices (USD)”, xlab=”Price (USD)”, ylab=”Freq”, prob = TRUE, las=1)
> lines(density(btc.i$price), lwd = 4,col=”yellow”)

The important information from Figure 2 is that impossible to find the end of each price movement. In this way, a derivative test was used to locate the critical points of the bitcoin PDF, in other words, these critical points represent an interruption or reversion of the price movement. We can derivative the probability distribution function by using the function sgolayfilt from the signal package [7].

> HIST.a <- density(btc.i$price)
> install.packages(“signal”)
> library(“signal”)
> HIST.a.sg = sgolayfilt(HIST.a$y, n=7, m=1)
> plot(HIST.a$x[140:512],HIST.a.sg[140:512], type=”l”, col=”green”, main = “Support and Resistance Zones”, ylab=”PDF derivative”, xlab=”Price”, xlim=c(27000,70000))
> axis(1,c(20000,30000,40000,50000,60000,70000))
> text(HIST.a$x[213], HIST.a.sg[213], labels = round(HIST.a$x[213],2))
> text(HIST.a$x[238], HIST.a.sg[238], labels = round(HIST.a$x[238],2))
> text(HIST.a$x[287], HIST.a.sg[287], labels = round(HIST.a$x[287],2))
> text(HIST.a$x[315], HIST.a.sg[315], labels = round(HIST.a$x[315],2))
> text(HIST.a$x[367], HIST.a.sg[367], labels = round(HIST.a$x[367],2))
> text(HIST.a$x[401], HIST.a.sg[401], labels = round(HIST.a$x[401],2))
> text(HIST.a$x[428], HIST.a.sg[428], labels = round(HIST.a$x[428],2))
> text(HIST.a$x[336], HIST.a.sg[336], labels = round(HIST.a$x[336],2))

Note that max and min functions select the largest and smallest values in their arguments.

Conclusion

This simple proposal was an excellent opportunity to show traders a new way to find support and resistance levels based on statistics. In addition, the method can be applied for other digital assets just by changing the coin in coin_history function. In other words, there are no different ways for different coins. In the present case Bitcoin supports and resistances were found together for the completion of the study.

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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. Osler, Carol L., Support for Resistance: Technical Analysis and Intraday Exchange Rates. Economic Policy Review, Vol. 6, №2, July 2000, Available at SSRN: https://ssrn.com/abstract=888805

2. Support and Resistance Lines: What to Know (investopedia.com)

3. www.coingecko.com

4. https://www.R-project.org/

5. https://cran.r-project.org/web/packages/signal/index.html

6. Sergey Mastitsky (2021). geckor: R Client for the ‘CoinGecko’ API. R package version 0.2.0. https://CRAN.R-project.org/package=geckor

7. signal developers (2013). signal: Signal processing. URL: http://r-forge.r-project.org/projects/signal/

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How to find Bitcoin support and resistance levels using the derivative of a price probability… was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

It’s hard and surreal to imagine that all the work of our ancestors, our parents, our grandparents, was confiscated through inflation and loss of property rights. Bitcoin emerges to fix this issue by decentralizing decisions and controlling inflation.

Figure 1 illustrates the halving effect in the Bitcoin annual inflation rate [1]. As can be observed, Bitcoin inflation dropped from 3.9% in 2020 to 1.8% in 2021.

There are approximately 815 days to the next halving[2] when inflation will drop to 0.8% in 2024 and Bitcoin will have one of the lowest inflation rates of any economy in the world[3]. Our generation has a great opportunity that was stolen from our ascendents.

In the present study, therefore, the Bitcoin Waves model was applied and optimized for the prediction of Bitcoin prices between 2022 and 2032. This model predicts the next waves by approximating the future waves as linear combinations of prior waves[4,5].

The worst scenario

The linear combination of 1st, 2nd, and 3rd waves produced the model presented in Figure 2 for the present scenario (2022). On the other hand, this wave has faced the covid-19 pandemic that has affected the entire world economy, therefore affecting all prediction models. Even though, the model showed good performance characteristics (cosine similarity of 87.6%).

In the worst scenario, in case of pandemic persists, the predicted min price date for bitcoin after Jan 2022 will be in June 2022, more or less 778 days after the halving. However, the price will rebound in Dec 2022, reaching 62k.

The best scenario

After 2024 halving, the Bitcoin annual inflation rate will drop to 0.8% and the Bitcoin Waves model designs sunny days for bitcoiners. The price could reach 328k in Jan 2025 (Figure 3).

Finally, long-term holders will find a price of 847k in 2028 (Figure 4) and 1.4M in 2032 (Figure 5).

Conclusion

The method applied in this report is versatile, fast, and simple. Bitcoin Waves model provided an interesting adjust, as well as allowed us to visualize a possible future for bitcoin using linear combinations. What if the model is right?

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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. Network Chart | Coin Metrics

2. https://www.buybitcoinworldwide.com/pt/bitcoin-clock/

3. https://tradingeconomics.com/country-list/inflation-rate

4. Bitcoin Waves: A linear combination for price modeling | by Edgar Moraes | Coinmonks | Mar, 2021 | Medium

5. https://medium.com/coinmonks/bitcoin-what-was-what-is-and-what-probably-will-be-8aba4d01eb7b

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Predicting 2022–2032 price action with Bitcoin Waves model was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Sentiment lies at the heart of many aspects of cryptocurrency investors and it is therefore desirable to understand its price effect as well as possible. One attempt to improve our understanding of sentiment is the computational study of investors’ opinions called in the great area Sentiment Analysis (SA) [1].

In this study, Uptrend Sentiment Indicator (USI) will, therefore, be introduced and discussed its relation with price action conditions.

Uptrend Sentiment Indicator data treatment

The current investigation involved tracking and analyzing Google Trends information using the R statistics platform [2] with the gtrendsR package [3]. The uptrend words frequencies (“Bitcoin pump”, “Bitcoin bull”) were selected and subtracted from the downtrend words frequencies (“Bitcoin dump”, “Bitcoin bear”). Data prices were recruited from Quandl [4], 2021. The script repository is stored on Github [5].

Uptrend Sentiment Indicator (USI) equation of the sentiment model is shown in Eqn (1):

Where the USI signal is the dependent variable and f is the frequency of the words.

Next, the standard deviation of USI was used to normalize the USI as can be observed in Eqn (2):

Finally, a Savitzky–Golay filter was applied to smooth the USI data [6]. Data smoothing is achieved by a local polynomial regression, being one of the most popular smoothing filters and far superior to the moving averages commonly used in the crypto world.

Uptrend Sentiment Indicator

The goal of the USI is to express the investor´s sentiment by approximating this feeling as Google Trends queries. Using Eqn (2), the USI was plotted for the past 12 months (Figure 1).

Figure 2 shows the USI for the last seven days. Even though price action in the basis is affected by the global market, on-chain analysis, and others, it is possible to notice that USI presents a related behavior with the price and, this opens the opportunity to model this behavior.

As can be observed, the representative graphic in Figure 1 gave an uptrend sentiment in the long term, but Figure 2 shows a downtrend sentiment in the short term.

Conclusion

The indicator developed in this work is versatile, fast, simple, and low-cost. The sentiment information made it possible to obtain an excellent price action tool. Applied to Bitcoin prices this year, an interesting relation was confirmed.

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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. Walaa Medhat, Ahmed Hassan, Hoda Korashy, Sentiment analysis algorithms and applications: A survey, Ain Shams Engineering Journal, 5 (4), 2014, https://doi.org/10.1016/j.asej.2014.04.011.

2. R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/

3. Philippe Massicotte and Dirk Eddelbuettel (2021). gtrendsR: Perform and Display Google Trends Queries. R package version 1.5.0. https://CRAN.R-project.org/package=gtrendsR

4. Raymond McTaggart, Gergely Daroczi and Clement Leung (2021). Quandl: API Wrapper for Quandl.com. R package version 2.11.0. https://CRAN.R-project.org/package=Quandl

5. https://github.com/edgarmoraesufrn

6. signal developers (2013). signal: Signal processing. http://r-forge.r-project.org/projects/signal/

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Uptrend Sentiment Indicator: A new signal for price tendency in the cryptocurrency was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

A new cryptocurrency is released every day in the crypto industry, promising to kill Bitcoin by implementing some dubious benefits. They promise to make you rich by holding, the biggest lie used, or lower fees, privacy, smart contracts, security, etc. Up to date, there are over 10,000 different types of digital money [1].

However, is there a Bitcoin killer?

No, there is not. Bitcoin is the first disruptive innovation in the cryptocurrency market when Satoshi Nakamoto introduced a peer-to-peer electronic cash system [2]. Bitcoin has the most secure system, the biggest community [3], the best programmers, and is now going to implement great innovations.

This report presents Bitcoin Taproot softfork, a Bitcoin update that will impact the investing market overall. Once this implementation is approved, forget about the death cross, the symmetrical triangle, downtrend lines, or Wyckoff distribution and buy the dip.

What is Taproot?

In a few words, Taproot is a proposed Bitcoin protocol upgrade that promises to expand Bitcoin’s smart contract flexibility, more privacy, Lightning Network boost, lower transaction fees, and increases Bitcoin’s fungibility. Taproot was proposed by the genius programmer of Bitcoin Core Gregory Maxwell in January 2018 [4].

Bitcoin’s smart contract flexibility — Taproot softfork will implement more flexibility in the Bitcoin Network by introducing a new type of signature that enables users to set more complicated conditions for a transaction;

More privacy — Taproot and Schnorr signatures will make complex transactions no longer be distinguishable from simple single signatures;

Lightning Network boost — Taproot also improves efficiency and the privacy of routing in the Lightning Network, flexibility, and cheaper transactions.

Lower transaction fees — The softfork reduces dramatically the transaction data size and transaction fee costs will decrease.

Bitcoin’s fungibility — Once outputs will be indistinguishable, it will increase privacy.

Taproot activation

You can watch Taproot activation by accessing: https://taproot.watch/ [5]:

As can be observed in Figure 1, the current signaling is 97.74%. After 2016 blocks, if the signaling ratio is more than 90%, the Taproot softfork is approved. We are taking big steps towards approval.

Finally, important mining pools are changing their positions about Taproot, like Marathon and Binance pools, and now 99.15% of mining pools are supporting Taproot activation (Figure 2).

Conclusion

Dr. Ron Paul is quoted as saying “Monetary freedom is not about free money”. This points to the fact of the importance of understanding and investigating the networks before investing. Monetary freedom is the way to guarantee individual freedoms. You have to look besides technical analysis.

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Acknowledgments

The author is grateful to Satoshi Nakamoto, Hal Finney, Nick Szabo, Changpeng Zhao, Sabrina Moraes, Fernando Ulrich, André Fauth, Criptomaníacos, and Vela Trader.

References

1. https://finance.yahoo.com/news/number-cryptocurrencies-exploding-learn-different-124923879.html

2. https://bitcoin.org/bitcoin.pdf

3. https://medium.com/coinmonks/bitcoin-what-was-what-is-and-what-probably-will-be-8aba4d01eb7b

4. https://hackernoon.com/taproot-and-schnorr-the-biggest-btc-upgrade-for-2021-2t1w332g

5. https://github.com/taprootactivation/Taproot-Activation

Taproot: The next Bitcoin disruptive innovation was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

In these turbulent days, many people look for me and ask if Bitcoin is dead. Is it possible a single man destroys Bitcoin? No, it is impossible, because Bitcoin has not a leader and will never have.

Up to date, Pi Cycle is the only indicator to match the end of this bull run [1]. On the other hand, famous indicators as MVRV, Reserve Risk, 2-year Moving Average Multiplier, Puell Multiple, Logarithmic Growth Curves, Stock to Flow and S2Fx of the great Plan B, Golden Ratio, rHODL, and Net Unrealized Profit/Loss did not reach the top and, humbly, my indicators Bitcoin LineUp and Bitcoin Waves point to market manipulation, just it.

With this, the present study proposes to investigate the potentiality and the possible bull run continuity implicit and explicitly disclosed by three downtrend lines observation and finally the projection of Bitcoin LineUp and Bitcoin Waves.

The three downtrend lines

Bitcoin has three big challenges to overcome and to confirm the continuity of this bull run. The first one is the Market Cap BTC Dominance (Figure 1).

The first important information from Figure 1 is that Bitcoin Dominance overcame the purple downtrend line. If the dominance follows the red line, probably Bitcoin dives on the bear market. But, if the dominance follows the blue line or similar, Bitcoin will still alive.

Now, Bitcoin has two big downtrend lines to overcome: on the Bitcoin price (Figure 2) and on the Relative Strength Index, RSI, (Figure 3).

In my opinion, this is a small dispute for this great technology that challenges the state and the elite. HODL!

Bitcoin LineUp

Based on the Bitcoin LineUp indicator [2], the derivative test was used to locate the critical points of the bitcoin price function. The Second-derivative test can help to find the best time to sell, as can be observed in Figure 4.

If the Bitcoin LineUp indicator is less than -0.2, it is the best moment to sell, 2nd derivative is very negative. In fact, the indicator is far from the end.

Bitcoin Waves

The Bitcoin Waves model projects the possibility of a macrocycle after all (Figure 5). The model is adjusted using the growth rate of Bitcoin price, the number of bitcoin transactions per day, and the average time to mine a block in minutes.

Finally, according to the model, if the macrocycle is confirmed by overcoming the three downline trends, the predicted price points to a max price between $81,600 and $190,500. This is not financial advice.

Conclusion

Bitcoin is still alive.

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Acknowledgments

The author is grateful to Satoshi Nakamoto, Hal Finney, Nick Szabo, Changpeng Zhao, Sabrina Moraes, Fernando Ulrich, André Fauth, Criptomaníacos, and Vela Trader.

References

1. https://www.inbitcoinwetrust.net/pi-cycle-says-bitcoins-bull-market-just-ended-and-nobody-cares-d5e00b5c8858

2. https://medium.com/coinmonks/bitcoin-lineup-a-linear-transformation-for-price-support-modeling-e513715b8288

3. https://medium.com/coinmonks/bitcoin-waves-a-linear-combination-for-price-modeling-2b89f72e552d

Is Bitcoin dead? A Market Analysis was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Disruptive technologies have shown a significant increase in the cryptocurrency market since the implementation of Bitcoin digital currency, becoming of great interest due to their potential effects on the market and value network. A better comprehension of some major disrupts science and technology-related parameters, such as strategic proposals, performance trajectories, and sustaining technologies [1], is relevant for providing market management strategies and business.

Although disruptive technologies tend to be excellent opportunities, the established customers tend to ignore them.

Therefore, this research investigated the Polkadot ecosystem, focusing on a better understanding of the programmer’s team, the Polkadot community, and the probable future of the project.

The Polkadot ecosystem

Polkadot is a very younger coin (DOT). On November 14, 2016, programmer genius Gavin Wood introduced the first interoperability smart contracts system by publishing the article entitled “Polkadot: vision for a heterogeneous multi-chain framework” [2].

In my opinion, it is the third disruptive innovation in the cryptocurrency market. The first one was introduced by Satoshi Nakamoto, a peer-to-peer electronic cash system [3], the second was the invention of smart contracts from Vitalik Buterin [4]. And finally, the connection between all chains, a multi-chain framework created by Gavin Wood [5]. Paraphrasing Neil Armstrong, true interoperability is one small step for Gavin, one giant leap for the crypto community.

The Polkadot community on Github is composed of an interesting committed leader and a critical mass of members. There are active online discussions, 3,200 ‘stargazers’, people who observe the program, including me, 656 forks, copies of the repository, 360 watchers, persons monitoring all activity, 216 issues, tasks and bugs to solve, 48 pull requests, the heart of collaboration on GitHub, 125 contributors, programmers from the outside of the project that wants to contribute, and 51 members. Again, serious projects have great teams. Protect yourself from scams by examining the coin community before investing.

Polkadot went public in 2020, reaching a 4 billion Market Cap at $4.80 per DOT token [6]. At the time of this writing, tokens are trading for more than $36 per DOT (Profit +750%).

Polkadot Waves model

Based on the Altcoin Waves hypothesis [7], the Polkadot Waves model was built and provided an opportunity to investigate possible scenarios in the altcoins market, because many people buy altcoins and then wait for the moon, but in fact, the market occurs in waves.

Using the Altcoin Waves model to predict the wave of Polkadot, the cosine similarity was 84.87%. In this way, the 1st Polkadot wave was created by combining the waves from the top 3 altcoins market cap (Figure 2).

Figure 2 shows the Polkadot Waves model processed and projected in the present scenario (2021). The model did not present a good similarity and a statistical difference was observed at a confidence level of 95%. This simple model may be envisaged as an alternative tool for the cryptocurrency cycles study.

Finally, according to the model, if the supercycle is confirmed, the predicted price points to possible profits between +105% and +330%. This is not financial advice.

Conclusion

The Polkadot ecosystem has been investigated and a prevalence of a strong team in Polkadot was observed. But the DOT token presents the same problem to solve of other tokens, the inflation. For example, DOT in September 2020 had a Circulating Supply of 852,647,705 DOT, while in April 2021 has 933,842,633 DOT, an annual inflation rate of ~16% (insane).

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Acknowledgments

The author is grateful to Satoshi Nakamoto, Hal Finney, Nick Szabo, Changpeng Zhao, Sabrina Moraes, Fernando Ulrich, André Fauth, Criptomaníacos, and Vela Trader.

References

1. https://hbr.org/1995/01/disruptive-technologies-catching-the-wave

2. https://polkadot.network/PolkaDotPaper.pdf

3. https://bitcoin.org/bitcoin.pdf

4. https://ethereum.org/en/whitepaper/

5. https://medium.com/on-the-origin-of-smart-contract-platforms/on-the-origin-of-polkadot-c7750e2fc5ff

6. https://coinmarketcap.com/historical/20200906/

7. https://medium.com/coinmonks/altcoin-waves-a-linear-combination-for-altcoin-season-modeling-7c0b13bcabed

Polkadot: The next disruptive innovation was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.