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.

All the important bitcoin bottoms are inside the 95% confidence bands (dotted lines)

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).

TradingView Indicator

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

Bitcoin Model 1.3

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.

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.

Volume Ratio Oscillator Kraken/Bitstamp Comparison (click to enlarge)

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!

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.

KrakenUSD-P4 31 Day #7 2019-06-24 14_58_33.739

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

btcusd
XBTUSD Monthly Chart since 2013

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: 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!

Long Term Update: new support area

kama
Weekly Chart BTCUSD – KAMA V1.2 Average + Price Deviation Bands

This is the last version of my KAMA indicator (public at tradingview) with default settings tuned for BTCUSD weekly chart.

In the previous two occasions the first deviation line held well the drop, notice how in the recent November bottom the price was at an intermediate level between the Kama and the first negative line, an high bottom interpretable as a strong signal followed by a strong top (~19600$)

At the moment the bottom is around 11k usd, done again at an intermediate level while the 7500$-9000$ support area is defined by the 1st and 2nd deviation line.
This support area should work especially if the market goes sideways, it could be a nice zone where to buy with reduced risk of high initial drawdown.

There are no guarantees that the market will stay sideways enough to test that support area but strategically isn’t a bad move to buy inside it with a stoploss below 7000$.

Quantitative Analysis of Altcoins, part III

In part I and II I did a quantitative analysis on altcoins and possible strategies on how to capitalize on their weakness compared to bitcoin.

In Part III we will see how to allocate a portfolio starting with Fiat currencies.

In the table below you will find cryptos with relative gains (G) and volatility (RMS) against the dollar using all the historical data available, as data source was mainly used  poloniex and bittrex exchanges, for bitcoin has been used Bitstamp.

In red the cryptocurrencies with negative Gain against the USD.

Cryptocurrency  Gain (G) Volatility (RMS)
Bitcoin 1.0045 0.0796
Ethereum 1.0033 0.0863
Ethereum Classic 1.0031 0.076
BCash 1.0029 0.1535
Eos 1.0024 0.1355
Dash 1.0013 0.0894
Monero 1.0007 0.0781
Stellar Lumens 1.0005 0.1207
Ripple 1.0004 0.1115
Iota 0.9984 0.127
Qtum 0.9979 0.1224
Litecoin 0.9976 0.095
Bitcoin Gold 0.9966 0.0983
Next 0.9954 0.11
Zetacash 0.9902 0.1104

It’s pretty obvious that i’ll not consider any crypto with negative Gain and Bitcoin is clearly the winner with the best Gain and low volatility compared to the rest. I exclude also all the crypto with positive Gain but with high volatility because the main objective is to allocate a portfolio with the lowest possible volatility.

The remaining crypto are:

  1. Bitcoin
  2. Ethereum
  3. Ethereum Classic
  4. Dash
  5. Monero

Ideally it should be allocated the same amount of money on each asset but to compute the fraction of your capital to put on each asset i use the same formula seen in Part I & II.

F = 2P - 1

Where F is the optimal fraction of your capital to wage in a single trade and P the persistence or Shannon Probability, concepts already explained in Part I & II.

Cryptocurrency  Persistence (P) Fraction of your capital to wage (F)
Bitcoin 0.55 10%
Ethereum 0.5441 9%
Ethereum Classic 0.5317 6%
Dash 0.535 7%
Monero 0.5286 6%

Thanks to the formula F=2P-1 I know how much to wage on each crypto for a total of around 40% of your capital to invest in crypto. The remaining 60% could be invested in traditional stuff of your choice (equities, bonds, real estate). But let see in detail a simple portfolio management strategy.

Simple Portfolio Management Strategy

  1. Maintain about ten, or more, equities in the portfolio.
  2. Maintain about equal asset allocation between the ten equities.
  3. Consider the investment horizon from one to four calendar years.
  4. Be skeptical of investing in assets with less than a two and a half year history with a minimum of four and a half years.

Four simple policies listed in order of importance, and the second policy is the one that makes the money or the “engine” of the strategy. A short investment horizon is mandatory because “risk management” is an important part of financial engineerin g and given enough time, no matter how small the risk, it will bite.

At the moment there aren’t ten cryptocurrencies that satisfy my needs in terms of Gain (G) and Volatility (RMS) so I have to find a compromise, using only five crypto and, personally, i prefer to don’t maintain an equally asset allocation among all cryptos because there is a huge difference in terms of size between Bitcoin and the others. Another issue is that many altcoins have less then 2 years of history because this new sector is relatively new so it is difficult to respect rule number 4.

Another important concept is how frequent to balance the portfolio. Doing it every day is really not necessary for the casual long term investor. An interesting choice is to rebalance asset allocation if there is an asset that exceed all the others by 5-10%. Basically when one asset increased in value more than the others, money should be removed from the investment, and re-invested in all the others thus defending the gains through investment diversification.

Aggressive Portfolio

Aggressive Asset Allocation with ~40% in crypto (click to enlarge)

The suggested asset allocation is intended as very aggressive having almost 40% allocated in cryptocurrencies, I would advise not to follow this if you are over 65 or if you have a family with kids. In this case I would suggest a maximum of 10% invested in cryptocurrencies (e.g. 6% Bitcoin, 4% Ethereum or 6% Bitcoin, 2% Dash, 2% Monero).

In the case of a very conservative asset allocation, for who has a very low risk tolerance, I would not go beyond 5% allocated in cryptocurrencies.

Personally i’ve a very high aggressive asset allocation but I’ve all the time and experience to follow carefully my Portfolio and to act accordingly to new information on a daily basis.

In the future i might publish other updates about the subject with updated quantitative data on altcoins/bitcoin.

 

 

Long Term Update: approaching resistance area

download
Weekly Chart XBTUSD composite index of 7 exchanges

I include in this long-term update the template I mainly use with the tradingview platform that includes my KAMA average with deviation lines tuned for XBTUSD currency pair.
You can see that this currency cross is entering an interesting resistance zone ranging from 3200 to 3900 dollars; I included in the chart also the 2 months VWAP average and the 1 year too.
The average VWAP at 2 months is slightly above the 5 periods KAMA average while the 1-year VWAP is slowly rising and has come close to the negative deviation lines, around 1260 dollars.

It will be interesting to see the behavior of the next few weeks to see if this rise is just a spike or a solid trend, in the latter case it could mean that this year we could also see prices over 4000$, to understand this we will have to wait at least four weeks or this month.

Price range for this week is 2950$-3450$ with ALMA average firmly up and the RSI oscillator not yet overbought.

Long Term Update: Weekly Price Channel

weekly_chart_xbtusd
Weekly Chart – Price Channel

It seems that a correction has started on the weekly chart although the ALMA average is still bullish and not yet 100% confirming the move; sometimes a return to the mean or average is healthy for an uptrend, here the level is around 825$.

First deviation line is 650$ and the second one 530$. I fail to see an event strong enough to increase the volatility level so much to push this pair down to 530$, i remain confident that it’s not going so low and that the 650-825 usd price zone is a good support for long term buyers.

It is needed a close of this weekly bar above 1030$ to avert the risk of a correction of some weeks. The weekly RSI is above its mid-line or 50; in a strong uptrend the RSI usually stays above it as XBTUSD is doing since october 2015.

Weekly Top Estimate

weekly
In the previous update of 18 November I wrote that I would have expected a weekly Top in the price area between 770 and 910 US dollars. This week the cross xbtusd did a strong acceleration with an up movement of more than $ 80; these large movements concentrated in a single week can move the price regression channel that i use with MetaTrader 4, especially if they happen in a single price bar. The indicator adapts to new information that comes in projecting new supports and resistances levels with the effort to obtain a better fit of what is happening.

The new resistance zone ranges from $820 to $970 but you have to consider that if this week bitcoin price will continue to rise with this strength even this new resistance area will change again.

For those who are anxious to take profit or go short I strongly suggest to wait and see where this currency pair will form the next congestion zone on the daily chart.

For my italian followers here’s the italian version at bitchanger.com