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

Bitcoin Market Cap (Log Scale)

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
Bottom Points considered and converted to market cap.

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

Bitcoin Bottom Line

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

Bitcoin Top Line

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:

Midline and 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:

Time Price Forecast
  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.

My First Model and Gompertz

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:

TopLine saturated using Gompertz Formula

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.

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

BTCUSD Monthly Chart KAMA Average 5 periods and deviation lines.

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.

monthly chart btcusd
BTCUSD Monthly Chart – Kama and deviation lines

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.

kama
Kama Average and deviation lines – XBTUSD weekly chart

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!

Weekly Range Update: Bottom Done

eng
XBTUSD Daily Chart

XBT/USD weekly price range 14300$-17500$ | This week we return to a lower volatility level for calculating VWAP volume deviation lines, so we have a third and fourth deviation line not as far apart as they were in previous updates. The trend remains steadily UP also because the minimum done at 11000 dollars is clearly higher than that of 12 November at around 5600 dollars; I therefore expect a new maximum in the weeks to follow above 20000 dollars.

For the resistance zone the most probable range is from 17k to 21k dollars while for the support zone the range is 11000$-14300$.

The RSI oscillator has dropped almost to the oversold area with its average at about 47 that still has to turn upward to confirm a reversal in the trend.

With the other template i follow with KAMA average and deviation lines i’ve some doubts that XBTUSD will go towards the interesting long term support zone of 7500-9000 USD.

In the event of an unexpected catastrophic news, the support area on the weekly chart rises to USD 4300-5800.

Italian version here at bitchanger.com

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.

 

 

Weekly Range Update: CBOE/CME is coming!

bitstampUSD 1 Day #1 2017-12-10 23_03_45.582
XBTUSD Daily Chart

XBT/USD weekly price range 11500$-19100$ | The resistance zone for this week is very wide but less than the previous week and ranges from 17500 to 19100 dollars, the support area around the 2nd deviation line, from 11500 to 11300 dollars.

Clearly, intraday volatility from tomorrow will increase due to the impact of CME and CBOE futures, so there may be very good buying opportunities if the price were to fall below $11500 for short periods of time. Given that the arrival of derivatives is not an unexpected news and I don’t expect any kind of impact on the current bullish trend, what we will see will only be an increase in intraday volatility as already proven in the past by academic studies.

Also this week to compensate for record volatility levels I refined again the coefficients to calculate the various VWAP deviation levels so as to have greater consistency with other indicators I use.

The RSI oscillator is always clearly overbought even if it has downloaded a little while keeping always above the threshold of 70; as long as there is this great force this oscillator will remain constantly above 70 and therefore it must always be contextualized the reading of RSI according to the condition of the market, today still in extreme force.

In the event of an unexpected catastrophic news, the support area on the weekly chart rises to 4100-5300 USD.

ITA Version here