My Bitcoin Price Model Part II

For those who follow me on twitter know that my bitcoin price model v1.1 that I presented on this blog last September 2019 has been invalidated by the recent low of March 13 at \$3850.  I use 95% confidence level bands around my model forecast and that day the lower confidence level has been violated thus invalidating my model.
Since that day I have at various times pondered how to improve my old model and I recycled an idea that came to my mind last year when I presented the first model.
This idea is not to use the time factor to calculate the price of bitcoin but instead use the number of existing bitcoins that as you know grows over time and halves about every 4 years (until now it happened in 2012,2016 and 2020).
In doing so I discovered that there is a fairly strong linear relationship between the logarithm of the bitcoin price and the number of existing bitcoins at that particular moment.

With the software i use isn’t complicated to find a formula that approximate all the selected bitcoin bottoms.
This is the dataset used to compute the model:

 Date Low Bitcoin Supply 2010/07/17 \$0.05 3436900 2010/10/08 \$0.06 4205200 2010/12/07 \$0.17 4812650 2011/04/04 \$0.56 5835300 2011/11/23 \$1.99 7686200 2012/06/02 \$5.21 9135150 2013/01/08 \$13.20 10643750 2015/08/26 \$198.19 14536950 2015/09/22 \$224.08 14637300 2016/04/17 \$414.61 15439525 2016/05/25 \$444.63 15582350 2016/10/23 \$650.32 15943563 2017/03/25 \$889.08 16235100 2019/02/08 \$3,350.49 17525700 2018/12/15 \$3,124.00 17423175 2019/03/25 \$3,855.21 17608213 2020/03/13 \$3,850.00 18270000

The Formula is a very simple one, a first order price regression  between log(Low) and Bitcoin supply:

Where:
FPL = expected line where bitcoin is fairly priced
intercept = a costant
c1 = another coefficient that defines the slope of the Bitcoin supply input.

Here’s the resulting model after computing the parameters of the above formula.

This is the new bitcoin price model “FPL Line” v1.3 applied to a monthly bitcoin/usd chart:

Next Step: Computing the formula for the TopLine

The formula for computing the Top is:

Where:
TopLine= is the forecasted price where the next long term top might be.
intercept = a costant
c1 = another coefficient that defines at which pow the bitcoin supply is elevated

This formula is different from the one used to compute the FPL or bottom line. I’ve seen that there is not a strong linear relationship betweel the logarithm of important Bitcoin Tops and the Bitcoin supply, so i decided to switch to the formula used for the old model and it works better.

This is the dataset used to compute the model:

 Date Price Bitcoin Supply 2010/07/17 \$      0.05 3436900 2011/06/08 \$      31.91 6471200 2013/11/30 \$      1,163.00 12058375 2013/12/04 \$      1,153.27 12076500 2017/12/19 \$    19,245.59 16750613

Here’s the resulting model after computing the parameters of the above formula.

This is the new bitcoin price model “Top Line” v1.3 applied to a monthly bitcoin/usd chart:

95% Confidence Error Bands

With the indicator that i give you for TradingView i included also the error bands.
This are the error bands for the TopLine:

And for the bottom line or FPL (FairPriceLine)

It is quite obvious that with fewer points available the error bands for the TopLine are wider and less accurate compared to the FPL error bands where I have more points (17 instead of 5).

I have also included an indicator for TradingView to give you the opportunity to experience the concepts and model illustrated in this update. You can also check the code and/or modify it as you like.

On April 10th, 2020 tradingview staff decided to censor my indicator and threatened to close my account, because of this i publish here the code so you can create your own indicator by yourself.

Bitcoin Model v1.3 Sourcecode:

Code is also available at pastebin

Remember to add a “TAB” key once before stock (line 10 and 13), in the process of copying and pasting data back and forth from tradingview the tab key is gone probably because there is not a tab code in HTML.

//@version=2

study(“Bitcoin Price Model v1.3”, overlay=true)

//stock = security(stock, period, close)
stock = security(“QUANDL:BCHAIN/TOTBC”,’M’, close)

if(isweekly)
//insert “TAB” key before stock
stock = security(“QUANDL:BCHAIN/TOTBC”,’W’, close)
if(isdaily)
//insert “TAB” key before stock
stock = security(“QUANDL:BCHAIN/TOTBC”,’D’, close)

FairPriceLine = exp(-5.48389898381523+stock*0.000000759937156985051)

FairPriceLineLoConfLimit = exp(-5.86270418884089+stock*0.000000759937156985051)
FairPriceLineUpConfLimit = exp(-5.10509377878956+stock*0.000000759937156985051)

FairPriceLineLoConfLimit1 = exp(-5.66669176679684+stock*0.000000759937156985051)
FairPriceLineUpConfLimit1 = exp(-5.30110620083361+stock*0.000000759937156985051)

plot(FairPriceLine, color=gray, title=”FairPriceLine”, linewidth=4)

show_FPLErrorBands = input(true, type=bool, title = “Show Fair Price Line Error Bands 95% Confidence 2St.Dev.”)
plot(show_FPLErrorBands ? FairPriceLineLoConfLimit : na, color=gray, title=”FairPriceLine Lower Limit”, linewidth=2)
plot(show_FPLErrorBands ? FairPriceLineUpConfLimit : na, color=gray, title=”FairPriceLine Upper Limit”, linewidth=2)

show_FPLErrorBands1 = input(false, type=bool, title = “Show Fair Price Line Error Bands 68% Confidence 1St.Dev.”)
plot(show_FPLErrorBands1 ? FairPriceLineLoConfLimit1 : na, color=gray, title=”FairPriceLine Lower Limit”, linewidth=1)
plot(show_FPLErrorBands1 ? FairPriceLineUpConfLimit1 : na, color=gray, title=”FairPriceLine Upper Limit”, linewidth=1)

TopPriceLine = exp(-30.1874869318185+pow(stock,0.221847047326554))
TopPriceLineLoConfLimit = exp(-30.780909776998+pow(stock,0.220955789986605))
TopPriceLineUpConfLimit = exp(-29.5940640866389+pow(stock,0.222738304666504))

TopPriceLineLoConfLimit1 = exp(-30.3683801339907+pow(stock,0.221575365176983))
TopPriceLineUpConfLimit1 = exp(-30.0065937296462+pow(stock,0.222118729476125))

plot(TopPriceLine, color=white, title=”TopPriceLine”, linewidth=2)

show_TOPErrorBands = input(false, type=bool, title = “Show Top Price Line Error Bands 95% Confidence 1St.Dev.”)
plot(show_TOPErrorBands ? TopPriceLineLoConfLimit : na, color=white, title=”TopPriceLine Lower Limit”, linewidth=1)
plot(show_TOPErrorBands ? TopPriceLineUpConfLimit : na, color=white, title=”TopPriceLine Upper Limit”, linewidth=1)

show_TOPErrorBands1 = input(false, type=bool, title = “Show Top Price Line Error Bands 68% Confidence 1St.Dev.”)
plot(show_TOPErrorBands1 ? TopPriceLineLoConfLimit1 : na, color=white, title=”TopPriceLine Lower Limit”, linewidth=1)
plot(show_TOPErrorBands1 ? TopPriceLineUpConfLimit1 : na, color=white, title=”TopPriceLine Upper Limit”, linewidth=1)

Forecast up to 2032

This is a forecast up to 2032 halving, price will saturate between 27,000\$ and 130,000\$ with a maximum possible peak at 450,000\$ in case of a strong bubble.

Conclusions

This model is clearly experimental, we will see in the future how it will behave. It is probably questionable my choice to use the existing bitcoin supply instead of using time as a main input for the model, I’m curious to know your opinion about it. Thank you.

Quantitative Update: Bitcoin vs. The Rest of the World

This post is meant to be an addition to what I said earlier this year. Here we compare, in the same historical period of existence of bitcoin, Bitcoin vs other assets: us stock market indexes, US stocks of different sectors and Gold.

Let’s start with this summary table, who follow me regularly should already know the meaning of Shannon’s probability, RMS, G yield and compounded annual G yield; for all the others I refer you to the end of the article.
The data have been sorted in descending order according to Compounded Yearly Gain  G.

Comparison Bitcoin vs. The rest of the world July 17, 2010 – Dec 31, 2019

 Asset RMS or Volatility Shannon Probability P Daily Gain G Compounded Yearly Gain  G Optimal Fraction of your capital to wage Bitcoin 0.0567 0.5219 1.00087 38% 4.4% MasterCard 0.0155 0.5265 1.0007 19% 5.3% Visa 0.0144 0.5241 1.0006 16% 4.8% Amazon 0.0193 0.5196 1.00057 15% 3.9% Apple 0.0161 0.5172 1.00042 11% 3.4% Google 0.0148 0.5167 1.00038 10% 3.3% Microsoft 0.0143 0.5169 1.00038 10% 3.4% Nasdaq Composite Index 0.0106 0.5179 1.00032 8% 3.6% Standard & Poor’s 500 Index 0.0091 0.5160 1.00025 6% 3.2% McDonald 0.0098 0.5119 1.00019 5% 2.4% Berkshire Hathaway Inc. (W.Buffett) 0.0105 0.5112 1.00018 5% 2.2% Pfizer 0.0115 0.5078 1.00011 3% 1.6% Facebook 0.0226 0.5080 1.00010 3% 1.6% Tesla 0.0318 0.5096 1.00010 3% 1.9% JPMorgan 0.0155 0.5070 1.00010 2% 1.4% Intel 0.0153 0.5040 1.00001 0% 0.8% **Gold (XAUUSD) 0.0094 0.4951 0.99986 -3% 0% *Ethereum 0.0634 0.5138 0.99974 -6% 0% General Motors 0.0178 0.4903 0.99950 -12% 0% General Electric 0.0164 0.4868 0.99943 -13% 0%

*Ethereum Data since Aug 7, 2015, source coinmarketcap.
**Gold since 1970 has been a bit better with +3% yearly compounded gain.

The first comparison to make is with the main competitors of bitcoin, credit cards. I’m surprised to see how good are quantitative parameters of Mastercard and Visa, on the other hand they are monopolies, perhaps that’s why the CEO of mastercard hates so much Bitcoin, he sees it as a strong threat. Even Amazon has worse parameters compared to Visa and MC.

I included only Ethereum  in the comparison because in terms of market cap is second to Bitcoin, its yearly yield G is negative and i’m not surprised because I remind you that volatility reduces by far the yield G and in the case of all altcoins, not only Ethereum, the volatility reaches very high levels and therefore as an investment vehicle altcoins in general are absolutely not recommended, can eventually be considered as purely speculative assets for short-term trading.

Unfortunately for Mr.P.Schiff, in the last ten years Gold performed badly, for your curiosity i computed Gold parameters using available daily data since January 1970 and its yearly gain G or yield has been +3%, nothing exceptional, basically Gold protected you against inflation in the last fifty years but nothing more then this.

As i said 20 days ago Bitcoin volatility is dropping but it remains very high compared to other assets, despite this Bitcoin yearly compounded gain G is an astonishing +38% and it’s the best investment vehicle of the world.
Compared to other bitcoin price models this value is not much, ten years from now compounding 38% yearly bitcoin should be at around 200k usd while, for example, the stock to flow model has a forecast of 10 millions usd after 2028 halving, this is the equivalent of 144% yearly compounded gain instead of 38%.
Let me know what you think, does the stock to flow model price return appear realistic to you or not? Personally i prefer to rely on numbers and they say a clear “no” to me. This is why i’m a bit skeptic about also the bitcoin price model i developed on tradingview but i’m curious to see how it’ll end in a couple of years.

The concept of entropic analysis of equity prices is old and it was first proposed by Louis Bachelier in his “theory of speculation”, this thesis anticipated many of the mathematical discoveries made later by Wiener and Markov underlying the importance of these ideas in today’s financial markets. Then in the mid 1940’s we have had the information theory developed by Claude Shannon , theory that is applicable to the analysis and optimization of speculative endeavors and it is exactly what i’ve done just applied to bitcoin and the other assets considered in the above table, especially using the Shannon Probability or entropy that in terms of information theory, entropy is considered to be a measure of the uncertainty in a message.
To put it intuitively, suppose $p=0$, at this probability, the event is certain never to occur, and so there is no uncertainty at all, leading to an entropy of 0; at the same time if $p=1$ the result is again certain, so the entropy is 0 here as well. When $p=1/2$ or 0.50 the uncertainty is at a maximum or basically there is no information and only noise.

Applying this entropy concept to an equity like a stock or a commodity or even bitcoin itself common values for P are 0.52 that can be interpreted as a slightly persistence or tendency to go up, this means that for example stock markets aren’t totally random and up to some extend they are exploitable, same for btc.
Knowing the entropy level of bitcoin/usd is crucial if we want to compute its main quantitative characteristics, as i explained in the technical background of my blog this process is quickly doable once you have all the formulas, the process is as follows:

To compute the Shannon Probability P you should follow these steps:

1. compute natural logarithm of data increments (today price / yesterday price)
2. compute the mean for all data increment computed in step 1
3. compute RMS (root mean square) of all data increments, squaring each data increment and sum all togheter
4. Compute price momentum probability with the formula P = (((avg / rms) – (1 / sqrt (n))) + 1) / 2
where avg = data computed in step 2, rms = data computed in step 3, n = total samples of your dataset. If the resulting probability is above 0.5 then there is positive momentum, otherwise under 0.5 negative momentum

To compute the Gain Factor use the following formula:

 G = ((1+RMS)^P*((1-RMS)^(1-P))

To compute the yearly gain G or growth just raise daily gain G to the 365th power for Bitcon or 252 for stocks (252 trading days in a year).

Long Term Update: Volume Analisys at Kraken/Bitstamp Part II

Old post is here

The trading platform used here is unchanged: Sierrachart 64 bit.
The big amount of tick data processed to compute this interesting volume oscillator wouldn’t be possible to do at TradingView or similar online platforms.
The “up/down Volume Ratio” oscillator is computed and smoothed using a 18 periods (18 months or 1 year and a half) linear regression moving average.
Volume made on an uptick is considered positive while if made on a downtick is negative, then the aforementioned oscillator is applied.
I added also in the chart the widely know ALMA moving average (9 periods, standard settings).

I added for comparison the same template applied to BTCUSD at Bitstamp exchange.

Very curious to see a perfectly balanced volume activity at Kraken exchange for 3 months in a row while at the Bitstamp the volume activity is unbalanced upwards.
As a positive note i can say that i don’t see any negative volume activity in either of the two exchanges considered. Said this my best guess is that the price retracement from about \$13800 to \$6400 was a normal correction of a bullish market and that the bear market ended on March ’19.

Long Term Update: 2020 Outlook with entropic methods

Every year i post an outlook using entropic methods explained in the technical section of this blog. Here you can find the 2015, 2016, 2017  2018 and 2019, forecast update, where you can find more information about this approach.

Updated values for bitcoin (in brackets values of last year) using daily data since August 2010 (average data of 4 important exchanges when possible).

 BTC/USD Growth Factor G 1.00087 (1.00088) Shannon Probability P 0.5219 (0.5222) Root mean square RMS (see this as volatility) 0.056 (0.058 )

Bitcoin’s entropic values versus the Usd stayed stable during 2019 although volatility has fallen a bit like in 2018,  the Growth Factor (G) decreased a bit to 1.00087% compounded daily or 137.7% yearly, close to the value of 1y ago. The optimal fraction of your total wealth to invest in bitcoin is unchanged to 4.4%  (~0.522*2=1.044 – 1 = 0.044 or 4.4% roundable to 5%)
These values are still much better then conventional markets except the Shannon Probability that still match the US Stock Markets (around 0.522); it means that out of 100 days an asset goes up 52 days and down for 48 days, on average.

 2020 Price forecast Full Historical Volatility Half Historical Volatility Forecast using only G* ~9951\$ ~9951\$ Upper bound adding volatility ~29380\$ ~17097\$ Lower bound subtracting volatility ~3370\$ ~5790\$

*9949 is obtained with 1st January as a starting price (around 7227\$) times (1.00087^365)=~1.377   |   7227*1.377=~9951, just change 365 with the number of days you prefer for a different forecast.

What went wrong in 2019? Nothing:)

A year ago, I forecasted a maximum top of \$16150 never reached during the year.
This market stayed above the 3000\$ support forecasted 1 year ago but it didn’t go to the 1700\$ support level using full historical volatility. On the other side it tried to reach the 16150\$ resistance level with a top at 13880\$ on June ’19.
During 2020 I recommend to buy inside the half volatility support area between 5790\$ and 9950\$ (target price using only the growth factor G) having already an open position from ~9000\$ I will not buy more bitcoins during 2020.

Conclusions

For this year i think that there is a good probability to stay inside the 5790\$-17100\$ price zone with an equilibrium point at 9950\$.
Like one year ago,  i think that at the end of a strong buying climax period, if any, it will be wise to reduce your bitcoin investment if the price goes above 50k USD (price calculated using the equivalent of 1.5 times the historical volatility of bitcoin while the other 17k usd target is calculated using 0.5 times historical volatility)

For all of you that are probably asking why i haven’t mentioned my fresh new bitcoin price model in this update i answer saying that i prefer to don’t mix different approaches. Aniway actual value of the Bitcoin FairPriceLine is roughly 5800\$ and it’ll be at 10600\$ at the end of 2020, same support price area of my quantitative approach (5790\$-9950\$)

I’m at your disposal for any questions; see you at the next update and Happy New Year!

My Bitcoin Price Model

Network economics

By definition Network economics is business economics that benefit from the network effect (Metcalfe Law), also known as Netromix and basically is when the value of a good or service increases when others buy the same good or service. Examples are website such as EBay where the community comes together and shares thoughts to help the website become a better business organization.

Since 2010 bitcoin has been depicted as silly, a permanent bubble, denigrated by major economists, financial institutions etc…, everyone thinking that the true value of bitcoin is unknown and not knowable or zero as modern currencies have no intrinsic value because they lack scarcity or durability advantage like commodities.

The value of a currency is the use and acceptance of that currency and so they still have some value despite the fact that there are zero intrinsic value. Looking Bitcoin i can say that its value is determined by the great functions it has that are considered valuable from the user base.

MetCalfe’S Law

The problem of extrapolating future values of Bitcoin is difficult because the number of users does not grow forever, at some point you reach a saturation level as the internet, if you look historical data you can see that since 2013-2014 the internet reached maximum capacity in terms of  number of worlwide hosts.

Metcalfe’s formula is:
V=n(n-1)/2
and determines the value of a network for a given n. Without entering too much in details this law says that a 5% increase in number of users should correspond to a 10% increase in the overall value of the system.

This law has already been successfully used to model the value of Facebook stock because it is strongly linked to its users, the same for bitcoin although is unclear how to estimate bitcoin number of users. A good estimation might be the number of bitcoin active addresses but all the traders providing liquidity to the system don’t do much bitcoin transactions and so they are not included in the count.

Stock To Flow Model

Recently this model is gaining popularity, i consider this model a bit optimistic in forecasting future values of bitcoin, regardless of my opinion here is the original article of the creator of this model.

My Approach

My Idea is to don’t use the Metcalfe’s Law because i can’t estimate the number of worldwide bitcoin users fairly well, i prefer to model the size of the system just looking the Market Cap instead of Price, Why? Because in the first years the Bitcoin Supply greatly changed from few btcs to some millions and this is a big distorsion not included in the price chart.
Thanks to the software i already use for my conventional trading activity i perfectly know how to derive a formula to approximate the expansion of the bitcoin system using Market Cap as metric.
Looking the above chart it appears clear that there is a line holding bitcoin min values over time. Let’s find it!
I can’t use all the data values to find this line but i’ve to select ideal points, specifically thse are the points used:

 Date Price 17-Jul-10 \$        0.05 8-Oct-10 \$        0.06 7-Dec-10 \$        0.17 4-Apr-11 \$        0.56 23-Nov-11 \$        1.99 2-Jun-12 \$        5.21 8-Jan-13 \$      13.20 26-Aug-15 \$     198.19 22-Sep-15 \$     224.08 17-Apr-16 \$     414.61 25-May-16 \$     444.63 23-Oct-16 \$     650.32 25-Mar-17 \$     889.08 8-Feb-19 \$  3,350.49 25-Mar-19 \$  3,855.21

The Formula i’m going to use is this:

Log(Mcap)=constant#1+time^constant#2+constant#3*exp(time/constant#4)

The lats part of the formula is to give more credit to recent values because i’m interested to perfectly fit last data over old data that has more noise.
The best result i’ve obtained to compute the different constants is:
Log(Market Cap)~=7.775+time^0.352+(0.1*exp(-time/0.1))

From this to obtain price you have to exponentiate everything and divide by the bitcoin supply for that particulare date, time is intended in number of days since July 17, 2010

Now that i’ve a formula i can easily forecast it to see the corresponding Price for a particular date.
Next step is to do the same job to derive a formula for the Tops.
Points considered (only 4):

 Date Price 8-Jun-11 \$      31.91 30-Nov-13 \$  1,163.00 4-Dec-13 \$  1,153.27 19-Dec-17 \$19,245.59

Skipping now to the final result:
Bitcoin Top Line = Exp(12.1929+time^(0.337559)-1.74202*Exp(-time/2.35151))/Bitcoin Supply

A careful observer has probably noticed that the coefficient for time is lower when calculating Tops instead of Bottoms (0.337 instead of 0.352) and there is an easy explanation for this: as long as bitcoin market cap grows in size there is more inertia and it is more difficult to manipulate the price far away from the bottom line, therefore I expect with the passage of time to have the next important Tops closer and closer to the reference line or bottom line or if you prefer the “Fair Price Line”, call it as you want.

4 Years in to the Future

Once you have a model to define the boundaries where bitcoin price moves is easy to do a forecast and have a look where bitcoin might go in the next years. Here i propose a four years look in to the future.
Before let’see very quickly a third line very important: the MIDLINE. I computed it using all available data points since July 2010. Here the result plus the 4 years forecast:

I really like the result achieved with this model, apart the perfect fitting of all bottom and top points even the Mid Line is very important to understand where is the boundary between Fair Price and Overprice.
Furthermore bitcoin spends not much time above the Mid Line and very few days near the Top Line. Moreover the time spent below the Mid Line is equal to 62% and 38% of the time bitcoin price is above the Mid Line, I don’t know if Fibonacci is involved or not but the coincidence is odd.
Looking the above chart 4 years from now the forecasted price is impressive, i highly doubt the Top Line will work but i’d be already satisfied if Bitcoin will stay above the Bottom Line or Fair Price line, for your curiosity next September 2023 bottom line is at 47000\$.

Timing the next All Time High, is it possible or not?

Well, looking the chart out careful observer probably noticed that there is a progression in time between all the Tops. Have a look:

1. First top happened after 327 days since July 17, 2010.
2. Second top after around 1235 days.
3. Third top after 2713 days.

Using the same approach used to derive previous formulas the formula for this numerical progression is:

TopDate=323.5*TopNumber^1.9354

Topnumber is the # of top considered, 4 for the next one and we obtain 4733 days since July 17, 2010 that is due on July 2, 2023. Knowing the time, just look for the price in the TopLine using my formula and the corresponding price is around 367000\$.

I recognize that it is very ambitious to predict in advance of 4 years the next Top but I have extrapolated to the future what i observe today on the historical data.

I know that many of you are already asking “why is required so much time for the next top?” I think the answer is that due to the big growth of the bitcoin ecosystem this growth process is slowing down with the pass of time and therefore more and more time is needed for each new bubble to develop.

This will not mean that bitcoin price will stay all the time below my MidLine, some mini bubble might occurs in the next years and I’ll work on this subject to identify Intermediate Levels to accurately predict where these mini-bubble will end.
For example the recent June 26 Top at 13880\$ happened outside the MidLine indicating that Bitcoin price was a bit overpriced. At that time the Midline was at 9050\$ and Bitcoin at 13880\$ was overpriced by 53%.
This month of September the Midline will move from 10150\$ to 10650\$ and bitcoin these days is rising to recover that line after a quick drop to 9320\$.

Next Thing To Do

As i said i’m satisfied with the result obtained also in terms of R2 of my regression of Tops/Bottoms points, R2 is around 0.99 or basically a perfect FIT of the data points. Of course academically speaking my model doesn’t pass the infamous Durbin-Watson test because the residual of my model has some autocorrelation (as it has also the stock to flow model proposed by PlanB). By the way an academic invalidated model doesn’t mean it will not work but we must play by the rules.
Said this i’ve to found a way to model the distance between my Fair Price Line or Bottom Line and Bitcoin Price trying to have residual without autocorrelation and i’ll probably fail. Why? Because it is difficult to model price behaviour if there is fraudolent activity going on as it could have happened during 2013 with the famous bot “Willy” pumping prices at MtGox or recent manipulation by hedge funds that pushed the price from the fair value of 800\$ (January 2017) up to 19800\$.

Final Considerations and Gompertz

Benjamin Gompertz is the author of a sigmoid function which describes growth as being slowest at the start and end of a given time period and the future value asymptote of the function is approached more gradually by the curve than the left-hand or lower valued asymptote. I started this article talking about the importance of the user base as a way to simulate saturation because there is a limited number of users that limit the growth of a system, and now i conclude this article trying to model bitcoin price using the formula provided by Mr.Gompertz.
The formula is:

Price=Exp(27.3225*Exp(-0.682014*Exp(-0.00061457*time)))/BitcoinSupply

Time as usual is counted in days since July 17 , 2010.
In the below chart there is a comparison between the above formula and the formula for the Bottom Line or Fair Price Line.

As you can see simulating saturation lower the Fair Price of Btc in a significative way. The saturation point is around 26000\$, instead working with tops the saturation point is 72000\$, here is the chart:

There is a big consideration to do about this attempt to simulate the saturation of the bitcoin system, the quality of the user base.
I can’t know if in the upcoming years the user base wealth distribution will remain the same or not, if there will be a new influx of user with more money because attracted by the “digital gold” aspect of bitcoin this will surely push bitcoin prices above the forecasted range of 26-72 k\$ of this model.

Another consideration looking the last chart is that the starting point is well in the past, 4000 days before July 2010 or December 1999, it is a date close where everything started, the publication of “BitGold” by Nick Szabo, a direct precursor to the Bitcoin architecture.

For now i stay stick with my first model that doesn’t include saturation but i’ll keep an eye on my second model.

Thank you for your attention and I hope to have been quite clear and detailed, as always if you have any questions do not hesitate to leave a comment or write to me on Twitter.

Long Term Update: Volume Analisys at Kraken

For those who follow me on Twitter already knows that I opened a long term position after many months out of the market, to support this decision today i like to share with you this analysis made on volumes thanks to the data of the Kraken exchange using the BTCUSD cross. With other exchanges the analysis is however interesting but less accurate, I got the best results with Kraken.
The analysis starts from an accurate measurement of volume activity using tick data, each trade is considered in the calculation to have a net result about the flow of volume.

For who follow me on this blog should know that the trading platform used here is Sierrachart 64 bit, the big amount of tick data processed to compute this interesting volume oscillator wouldn’t be possible to do at TradingView or similar online platforms.
The “up/down Volume Ratio” oscillator is computed and smoothed using a 18 periods linear regression moving average. I added also in the chart the widely know ALMA moving average (9 periods, standard settings).

I think i’ll probably use this template to help me to understand when the current uptrend will end and at the same time to maximize the trade exit efficiency of my current long term trade.

I conclude this short post spending two words about what i said in my previous blog update (Jan 2019). Well, this market broke the first resistance at 9k usd (it was a conservative target already met) and the next one is around 16k usd. At that time i wrote also that increasing the volatility factor from 1x to 1.5x the next important resistance is near 30k usd. I really think that if a big trend develops this year, it will probably end near this resistance level.

The current reason beyond this uptrend might be that the market is discounting the next year block halving that in the past always pushed the price very high; i’ve seen a model out there on the web forecasting bitcoin at 55k usd because of this incoming halving, basically an attempt to model bitcoin price starting from its scarcity.

Don’t forget to follow me at Twitter, it’s quicker to post update there for me.

Long Term Update: 2019 Outlook with entropic methods

Every year i post an outlook using entropic methods explained in the technical section of this blog. Here you can find the 2015, 2016, 2017 and 2018 forecast update, where you can find more information about this approach.

Updated values for bitcoin (in brackets values of last year) using daily data since August 2010 (average data of 4 main exchanges when possible).

 BTC/USD Growth Factor G 1.00088 (1.00280) Shannon Probability P 0.5222 (0.5384) Root mean square RMS (see this as volatility) 0.058 (0.059 )

Bitcoin’s entropic values versus the Usd deteriorated in 2018 although volatility has fallen a little bit,  the Growth Factor (G) decreased down to 1.00088% compounded daily or 138% yearly down from 280% of 1y ago. Also the optimal fraction of your total wealth to invest in bitcoin dropped a bit in 2018 with a 4.4% instead of 7.7% of 1y ago (0.522*2=1.044 – 1 = 0.044 or 4.4% roundable to 5%)
Generally these values are still much better then conventional markets except the Shannon Probability that now match the US Stock Markets (around 0.522); it means that out of 100 days an asset goes up 52 days and down for 48 days, on average.

 2019 Price forecast Full Historical Volatility Half Historical Volatility Forecast using only G* ~5269\$ ~5269\$ Upper bound adding volatility ~16150\$ ~9230\$ Lower bound subtracting volatility ~1720\$ ~3000\$

*5269 is obtained with 1st January as a starting price (around 3820\$) times (1.00088^365)=~1.37   |   3823*1.37=~5269, just change 365 with the number of days you prefer for a different forecast.

Using different approaches the support area for 2019 is around 1700\$-3200\$ while the resistance price area is above 9000\$.

What went wrong in 2018?

A year ago, I forecasted a maximum top of \$121000 never reached during the year. I halved the volatility factor (rms) to find a more realistic price level and i obtained 68000\$, a value missed again by BTC/USD.

This market has been very weak all the year but the definitive sign of weakness has been the breaking of the support around six thousand dollars followed by an important minimum at about 3100\$, a price level that I showed you a few months ago.
In that tweet i identified an additional support area from 2100\$ to 3200\$ that so far has not yet been visited.
If possible I recommend to buy inside this price area otherwise another trading opportunity will be to buy on strength when BTCUSD will break above the monthly 5 periods Kama average (i’ll tell you when with a tweet), this average is now around 5000\$ but next month will probably drop to 4800\$ .

Conclusions

For this year i think that i’ll consider the support/resistance levels obtained with a full volatility value with the result to have for the whole 2019 a good probability to stay inside the 1700\$-16000\$ price zone.
At the same time i think that at the end of a strong buying climax period, if any, it will be wise to reduce your bitcoin investment if the price goes above 30k USD (price calculated using the equivalent of 1.5 times the historical volatility of bitcoin while the initial 16k usd target is calculated using the historical volatility)

I’m at your disposal for any questions; see you at the next update and Happy New Year!

Long Term Update: nothing new to report

After several months since the last update there are no particular news, at the time I wrote that “…..My opinion is that the bitcoin will continue to remain for most of the year within the levels calculated with the KAMA (yellow) and therefore remains a good opportunity to buy the price area from 4000 to 5500 dollars…..” and my opinion has not changed since then.

I have read everywhere that the descending triangle pattern will soon tell where bitcoin will go, whether to break upwards or downwards. I can’t say which way the price will take but usually when too many investors/traders expect one thing the market has the habit of doing the exact opposite.

The bitcoin usd cross might test the 4k level for a short period of time followed by a strong upmove; a last shakeout move tends to shake out the weak hands before the next big move, many investors will be very scared in seeing the bitcoin go down to 4k usd, I do not and possibly I could decide to buy again in that price range (4000\$-5200\$).

To conclude it’s very important to see if the level of 5200\$ will be tested and broken before year’s end, if so an interesting buying opportunity might arise. If not, then another buying opportunity might be to enter the market if bitcoin moves above the Kama monthly average now at 8650\$.

Long Term Update

Since the last update on February 13 there are not many new developments. The monthly KAMA average is flat, which allows us to calculate fairly reliable levels of support and resistance. As you can see nothing interesting happened with the BTCUSD cross that remains inside the supports and resistance levels (yellow lines).

I have added a new indicator that calculates supports and resistances using as a starting point the close of the previous month (with the idea to forecast next month support/resistance levels), in this case the close of February at about \$ 10300. I have used the last 50 months, just over 4 years from the bear market’s lowest point in 2015, to calculate volatility.

The drop we have seen in recent days has reached an intermediate level, -1.5 standard deviations, so I can say that there has not been a level of extreme volatility but not even normal.

My opinion is that the bitcoin will continue to remain for most of the year within the levels calculated with the KAMA (yellow) and therefore remains a good opportunity to buy the price area from 4000 to 5500 dollars, while it is to be evaluated a reduction of any bullish position should the BTCUSD go above 25 thousand dollars.

I also give you some short term indications for the next days, the first resistance is \$9500, you might see a Top not exceeding \$9500 before the BTCUSD resumes its descent. A break above \$10000 would mean that at least in the short term the bearish trend is over.

ITA Version here

Long Term Update: Bottom Done Yes or No?

On 18 January 2018, I wrote that the bottom was probably done but I hinted that at the break of the same I would have closed my long term position, unfortunately there was a subsequent very strong selling activity after a weak reaction from the support of the weekly chart (at about 9500\$). In these cases it is useful to scale the time frame to the next one (from weekly to monthly), so i applied the KAMA average and its deviation lines to the monthly graph instead of applying them to the weekly graph.
The resulting graph is this at the moment and the market has reacted strongly from this support area.

You can see that the first deviation  line has hold the price from further lows at the end of the 2014-2015 bearish market, the same negative deviation line reported to date is at about \$ 5300 and the market, for now, has done a bottom at \$ 5900. I’ts difficult for me to say if the bearish market started in December 2017 is over, I remain convinced that we will hardly see stable prices under \$3900 and that the support area from \$3900 to \$5300 will be very strong for this year.

If during ther year the trend of the Kama average becomes bearish from flat we will have a confirmation that a down trend, even on the monthly chart, has been established and this would undermine a little the validity of the support area indicated in the chart.
For now I think that the market is still stronger than the 2014-2015 period and that any medium-term correction should be above the indicated support area.

Italian version here.

Long Term Update: Bottom Done.

This is the main template I use with the tradingview platform, it is a weekly XBTUSD chart with the Kaufmann moving average I modified by adding the deviation lines.
These deviation lines have been appropriately calibrated according to the volatility of the underlying asset.
As you can clearly see, the market has never tested the second negative deviation line and has always reacted from the first line.
So it was also yesterday after a minimum at 9200\$ where a strong reaction took place up to 11600\$.

I think that this market is headed well above 20000\$ in the upcoming weeks/months, for completeness a possible bearish scenario would imply first a drop down to 7500\$, a subsequent reaction to 9500\$-10000\$ before resuming the fall to new lows. This possible bearish scenario would convince me to liquidate all the bitcoins i bought in 2014-2015. As long XBTUSD stays above 9500\$ i’m not worried for my long term position.

Long Term Update: 2018 Outlook with entropic methods

Every beginning of a new year i post an outlook using entropic methods explained in the technical section of this blog. Here you can find the 2015, 2016 and 2017 forecast update, where you can find more information about this approach.

Updated values for bitcoin (in brackets values of last year) using daily data since August 2010 (average of 4 exchanges when possible).

 BTC/USD Growth Factor G 1.0028 (1.0007) Shannon Probability P 0.5384 (0.519) Root mean square RMS (see this as volatility) 0.059 (0.045 )

Bitcoin’s entropic values versus the Usd strongly improved in 2017 but volatility increased a bit, despite this the Growth Factor (G) increased up to 1.0028% (remember that volatility is detrimental to the Growth Factor) compounded daily or 280% yearly up from 30% of 1y ago. Also the optimal fraction of your capital to invest in bitcoin improved in 2017 with a 7.7% instead of 6.4% of 1y ago.

 2018 Price forecast Full volatility Half volatility Forecast using only G* ~38700\$ ~38700\$ Upper bound adding volatility ~121000\$ ~68000\$ Lower bound subtracting volatility ~12300\$ ~21800\$

*38700 is obtained with today price (around 13800\$) times (1.0028^365)=~2.77
13800*2.77=38740, just change 365 with the number of days you prefer for a different forecast.

It’s interesting to notice that with reduced volatility the support level is above the actual quote of XBTUSD (13800\$ at the moment i’m writing) because the growth factor (G) is very high and is skewing everything to the upside. If volatility stays low the uptrend should push bitcoin above 22k USD during the year without too much effort, it’s a scenario i prefer instead of wild price swings.

What went wrong in 2017?

A year ago, I forecasted a top of \$2900, reached in July 2017 with an intermediate Top well ahead of the end of the year. I tried to double the volatility factor (rms) to see the next level after a reader asked me about the possibility that bitcoin was in a bubble above 2900\$. The next level was around 6000\$, again this new level has been broken at the end of October.
The last 2 months have been crazy and the explanation is a huge change in shift in this market happened in March 2017 (with altcoins literally exploding) that basically erased the reliability of the January 2017 forecast. I think that this year forecast should be more accurate compared to last year.

Conclusions

For this year i think that i’ll consider the support/resistance levels obtained with a full volatility value with the result to have for the whole 2018 a good probability to stay inside the 12300\$-121000\$ price zone.
At the same time i think that at the end of a strong buying climax period, if any, it will be wise to reduce your bitcoin investment if the price goes above 60k-70k USD.

I’m at your disposal for any questions; see you at the next update and Happy New Year!