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.

Tech Addendum

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).

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.

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.

 

 

Offtopic: Quantitative Analysis of Altcoins, part II

In part I, I did a quick analysis of altcoins compared to XBT, this time i’m going to check their performance using all available data of each altcoin since inception date using daily data instead of weekly to improve the granularity of the analysis because, the finer the granularity of the analysis, the better the insights for understanding the characteristic of the asset.

ALTCOIN Gain (G) Volatility (RMS)
Ethereum      0.998               0.072
Monero      0.996               0.073
Next      0.995               0.074
Dash      0.993               0.111
Litecoin      0.991               0.115
Ethereum Classic      0.990               0.084
Stellar Lumens      0.987               0.135
Eos      0.981               0.126
Iota      0.979               0.117
Bitcoin Cash      0.977               0.161
Ripple      0.975               0.187
Zetacash      0.947               0.201
Qtum      0.912               0.265
Bitcoin Gold      0.740*               0.570*

*Note that because of the very short size of the Bitcoin Gold dataset, its Gain (G) and volatility might change a lot in the long run.

If I were forced to assemble a portfolio of altcoins, i’ll probably opt for low volatility alts, like Ethereum, Monero, Next and Ethereum Classic. Eventually I would add Dash and Litecoin because by increasing the number of assets as a result I will reduce the final volatility of the portfolio.
At the end of this post you will find what i’d actually do if asked to diversify an initial capital of bitcoins.

To give you an idea of the Gain (G), you have to power this number to the number of days interested, for example (G)^365 will give you the average value of your asset in 1 calendar year.

For Ethereum is:

0.998^365 = 0.4815

or a 52% expected decrease in value towards XBT in 365 days.

How Much to allocate individually on each altcoin?

This is a simple question with a simple answer, the formula to obtain the fraction of your capital to wage on a particular asset is:

F = 2P - 1

Where P is the Shannon Probability and F the optimal fraction of your capital to wage. The Shannon probability of a time series is the likelihood that the value of the time series will increase in the next time interval. The Shannon probability is measured using the average, avg, and root mean square (volatility), rms, of the normalized increments of the time series as i explained in previous udpates.

For Monero is:

F = 2 * 0.4953 - 1 = -0.0096 or ~1% as an optimal fraction to wage

For Ethereum is:

F = 2 * 0.5079 – 1 = 0.0158 or 1.6% as an optimal fraction of your capital

Monero has both the Persistence and Gain negative but what about Ethereum? How is it possible to have a positive persistence and negative Gain (G = 0.998)?

Well the point is that an asset’s gain in value can be negative, even though the likelihood of an up movement is greater than 50% or 0.50 (in this case 0.5079). How can the time average of something be positive, and result in negative values?

It may seem counter intuitive, but just because the average daily gain in value of an asset is positive, is not sufficient evidence that the asset’s value will increase or be a decent investment.

Do we really see asset class with these kinds of price characteristics?

The answer is that we do. During the dotcom equities bubble of the 2000, about half of the equities had these characteristics; many were to fall the hardest, too. I think the same about many altcoins/ICO, they will end badly in comparison to Bitcoin.

This is why a possible asset allocation might be to go short against Altcoins with a fraction of your Bitcoins (says 10% shorting 5 crypto); it is a strategy that might suffer some losses in the short term if you are unlucky with volatility going against you, but it will surely win in the long run.

In the upcoming PART III – We will better understand how to assemble a portfolio of cryptocurrencies starting with EURO or USD instead of Bitcoin and it’s intended for who hasn’t yet invested in any Crypto.

 

Offtopic: Quantitative Analysis of Altcoins, part I

In this update we will compare major altcoins towards bitcoin with the quantitative systems I use in the annual forecast that I publish in the first post of each year. Concisely we will list the volatility and gain values of these alternative assets to bitcoin. I would remind you that it is always preferable to invest in assets with low volatility and consequently high returns, volatility always deteriorates the gain of an asset.
I used weekly data, the last 52 weeks where possible. Only Bitcoin Gold hasn’t enough data for a proper computation of its quantitative values.

ALTCOIN Gain (G) Volatility (RMS)
Ethereum 0.9865 0.176
Dash 0.9862 0.188
Monero 0.979 0.1509
Litecoin 0.977 0.1511
Ethereum Classic 0.9765 0.169
Next 0.961 0.182
Ripple 0.96 0.236
Zetacash 0.951 0.1631
Stellar Lumens 0.9313 0.29
Bitcoin Cash 0.93 0.207
Qtum 0.909 0.206
Iota 0.88 0.179
Eos 0.8421 0.322
Bitcoin Gold n/a n/a

This table explains why every time you ask me an advice about altcoins, I tell you that it is better to ignore them because NO ALTCOIN shows positive gains above the unit due to the fact that they are dominated by volatility that highly reduce the final gain of the asset.
Said this, the less bad altcoins are Dash and Ethereum followed by Monero and Litecoin with a preference to these last two because of lower volatility compared to Ethereum and Dash.

A special mention to Bitcoin Cash that, considering its high market cap, both Gain and Volatility are bad.

To conclude, the big mistake is to compare altcoins to USD, in my opinion it’s wise to compare them against Bitcoin; against fiat currencies is easy to perform well.

Technical Appendix

The procedure to compute volatility and gain is always the same explained in the past:

  1. Compute log of Today bar divided by yesterday bar
  2. Average values (avg) of last 52 periods (n) (1 year using weekly data)
  3. Compute Volatility (rms)
  4. Compute price momentum probability with the formula P = (((avg / rms) – (1 / sqrt (n))) + 1) / 2
  5. Compute Gain using the formula G = ((1+rms)^P*((1-rms)^(1-P))

ITA Version here

Technical Update: KAMA, an underrated indicator

Kama moving average is a very interesting filtering technique developed by P.Kaufman.
This moving average has been designed to account for market noise or volatility, KAMA will closely follow prices when swings are small and the noise is low, instead when the price swings widen KAMA will adjust trying to follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements when the market is flat avoiding annoying whipsaw false signals trades.

At the moment i’m working on a modified version of this indicator with the help of the tradingview online platform, i added deviation lines from the KAMA average and settings have been optimized to better track XBTUSD price movements on daily and weekly chart.

Here’s an example of this indicator applied to a weekly chart of XBTUSD with some comments.
xbtusd_w

It’s evident that when the market is flat the KAMA average is relatively stable without giving false signals, when this happens you can try to trade deviation lines to catch bottoms or tops.

About this week the secondary positive deviation line is around 3200$, should you go short at that level? Well is at your own risk to trade against the main tendency that now is bullish, instead you might follow the trend buying at the midline or below it, i’m enough sure that today sell-off is healthy and the price was just moving back to the midline point (KAMA average) from the first positive deviation line.

Beware that because of very large price variation during the same bar the KAMA and its deviation lines can change a bit. The indicator is available for free at tradingview.com, just look for “KAMA – Enky v1.0”.

Your feedback is highly appreciated and will help me improving this trading tool.

Offtopic: Cacoethes scribendi

“Cacoethes scribendi” or translated from Latin to english “a burning desire to write”, writing always of bitcoin sometimes is boring, today i show you the template i use with metatrader 4 applied to other assets like stocks, indexes and altcoin.

I already explained that an idea that i like is to do a price regression of our asset using a filter that eliminates all cycles below 30-40 periods with the intent to extract the underlying long term trend, then you can try to earn some money trading the secondary cycles that move the price up and down inside the price channel.

SP500/N100 weekly chart

sp500

Mid channel line color is white thus this market is neutral the ideal situation to trade the price levels, at the moment there could be a short opportunity, stoploss above the dotted positive deviation line.

n100

Similar situation for the nasdaq 100 index.

GBPUSD

gbpusd

The big drop of the Brexit is clearly visible, the pound should stay above 1.29, it’s the moment to buy with a stoploss below 1.28

Nikkei 225

nikkei225

Nikkei is short for Japan’s Nikkei 225 Stock Average, it is a price-weighted index comprised of Japan’s top 225 blue-chip companies traded on the Tokyo Stock Exchange. The Nikkei is equivalent to the Dow Jones Industrial Average Index in the United States.
In this mothky chart is visibile the big rise fueled by the quantitative easing of the Japan Central Bank and the subsequent drop after flirting with the resistance at 21000.
At the moment it is holding above the first negative deviation line, i don’t see any trading opportunity.

Tesla

tesla.png

On the weekly chart the tendency is neutral, midline color is white, ideal to trade secondary cycles like the one that pushed down Tesla below 150 usd and below our support, a very good trading opportunity, it is possible also to trade the dotted levels but they are less safe.

Ferrari

ferrari

Daily chart of ferrari (RACE ticker), after an interesting double bottom on the support this stock trended higher above the resistance after the earnings.
I think we are seeing a buying climax and in this situations is smart to sell the good news, i see a short trade opportunity here but it’s wise to wait some weak signals from this stock before going on.

Nintendo and the Pokemon Go Bubble

nintendo

No comment here, the bubble is evident but this stock is still hovering above midline and the long term trend is bullish. Again a nice double bottom at the support.

Apple

aapl

Last five years of Apple in this monthly chart, here the support levels worked almost perfectly. Apple is losing some steam as the midline color is white, neutral long term tendency despite you can see a sequence of higher highs and lows. The stock reacted from the dotted line at around 90 usd but i’m not sure is going up yet, the trading opportunity here is a test at 75-80 usd this year or the next one.

ETCUSD – Hourly chart

etcusd

Hourly chart of ethereum classic, again bubbles are clearly visible. Some congestion outside the upper solid deviation line it’s the warning signal, be prepared to open a short. Now volatility is a bit lower and this altcoin is moving inside the dotted deviation lines. I see a buying opportunity once 1.85 usd is tested.

Conclusions

This price channel indicator is an improvement of the classic bollinger bands indicator, what i don’t like of the bollinger bands is the wrong way to compute the upper and lower bands that might lead to very misleading values sometimes as i explained at the end of this old article. To keep things simple i omitted to include some timing indicators, for example adding the Walter Bressert DSS oscillator with ethereum classic we have:

etcusd+dss

Clean cycles togheter with a correct approach to spot support/resistance levels and you have a decent guide to follow. This oscillator is configured using 9 periods and 5 periods for a second pass smoothing.

 

 

Thank You

Since May 2016 I’ve seen a strong Bitcoin price rally after the breakout of the $500 resistance.

“Move fast for profit regardless security and customer satisfaction” may be applied to companies but not for a true decentralized cryptocurrency like Bitcoin.

I want to thank Bitcoin Core for the very conservative approach to its development. Core has been doing a supreme job maintaining the reference client, making sure that the network is running smoothly and insuring that the 11 billions $ market cap doesn’t decline due to bugs in the software because of developers negligency like happened today to another altcoin.

Thanks again for doing a great job.

Project maintainers

Contributors

  • Dr. Pieter Wuille

  • Cory Fields

  • Gregory Maxwell

  • Luke-Jr

  • Jorge Timón

  • Peter Todd

  • Patrick Strateman

  • Dr. Johnson Lau

  • Suhas Daftuar

  • ฿tcDrak

  • Michael Ford

  • paveljanik

OFFTOPIC: a quick view of an altcoin

This is an attempt to forecast where the next big movement will end of this altcoin, an altcoin that recently is getting lot of unjustified attention from the bitcoin community. I don’t know if people is bored of bitcoin and is trying to pursuit a “get rich quick scheme” pumping their bitcoins in this altcoin, aniway it is not the purpose of this article to prove it or not.

Here there is the weekly chart with logscale for the price axis.I highlighted the first big up movement and the subsequent price drop, a perfect 62%  fibonacci retracement down from the top to 0.015 btc/unit. Assuming that the current bullish movement will be of the same size of the previous one using a logarithmic scale the target should be at 0.135 Btc/Unit. There would be price retracements for sure before reaching such an optimistic target therefore a more likely long term top could be the point half way between our initial target and the bottom used as a starting point for our forecast. As indicated in the chart this potential resistance level is around 0.047 Btc/unit. IMO it would be a decent point where to open a short position because it is above the previous high of 0.035 Btc/unit and in a strong uptrend i’d prefer to avoid to open a short position from a lower high. In any case it is better to confirm the trade with a bearish signal with an oscillator of your choice.
altcoin

Technical update: Consolidation Breakout Trading System

Today after many years i’ve reinstalled Metastock Professional, i was curious to see if there was some old Expert Advisors giving good result with bitcoin and i’ve found this one: “Consolidation Breakout” from Trading Systems Analysis Group.

Basically it works with volatility breakouts to identify entry/exit points and while this system uses John Bollinger’s Bollinger Bands and Welles Wilder’s Average True Range indicator, it is not linked to the methods of those two authors.
It’s based upon a strict observation of the Bollinger Band width compressing/decompressing (a method used by many traders) around the prices until the distance between the upper and lower bands is less than 1 ¾ times the 1 period average true range; it then looks for a breakout in either direction of the Bollinger Bands to capture the movement of the breakout. Once a position is entered, it looks to cross the 20-period simple moving average to exit the position but any other money management approach can be used for the exit.

I attach below the above expert advisor applied to a daily bitcoin chart. It works fairly well when volatility is high enough, even with less volatility performances aren’t so bad without substantial losses.
chart
At the moment the system is flat and exited a short position on 4 Sept. at $231.

I’d like to add that because bitcoin recently has been very boring from now on there will be updates about Currencies, Equities and Gold, all instruments that i trade regularly with my btc broker since September 2012.