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Can cryptocurrencies be used as proxies for the strength of the underlying trend?

Can cryptocurrencies be used as proxies for the strength of the underlying trend?


Take a look at a heat map of the cryptocurrency markets, and it’s easy to get bullish. The performance returns between the 15th December 2018 to the 23rd April 2019 look impressive:

Bitcoin is up 70%,

Ethereum 102%,

EOS 171%,

ADA 161%,

Litecoin 214%,

Bitcoin Cash 275%, and

Binance coin up 400%.

Google “crypto news” and recently, especially after the up moves of April 2nd, the format is the same, with the headlines focusing on price gains, the opinions of crypto celebrities, or the launch or emergence of new blockchain technology.

Take a step back, and you’ll see a different picture.

This is the performance of Bitcoin since its all-time high.

Bitcoin since its all-time high

Bitcoin since its all-time high

This is the same chart except this time using a log scale.


Here’s Ethereum since the all-time high.

Ethereum since the all-time high

Ethereum since the all-time high

And again using a log scale.

It’s easy to manipulate data using a chart to make it look more impressive than it is.

This is Bitcoin since 2012 using a regular scale.

Bitcoin since 2012 using a regular scale

Bitcoin since 2012 using a regular scale

Here’s Bitcoin again, from 2012, but using a log scale.

2012, but using a log scale

2012, but using a log scale

Using a log scale gives the appearance the 2018 84% drop is a typical down move in an uptrend, because, in percentage terms, the 2018 price drop matched the 2013 to 2015 move down. It’s easy to persuade the unwary, using technical analysis,

If you try this on Ethereum or any cryptocurrency that had significant moves down from their all-time highs, you’ll see the same thing. It’s possible to make an 80-95% price drop seem like it’s easy to recover from. It’s not.



Take EOS. It’s moved up around 171% since December 2018, but on the 7th December 2018, EOS had lost 92% of its value and using the percentage recovery formula of Y = X/(1- X), where X is the percentage lost, it will take a 1,150% move for EOS to get back to the all-time high.

Yes, technical analysis can and is used successfully as a short term trigger mechanism, giving clear entry and exit zones, but when attempting to use technical analysis to position yourself into long term trends, trends that will last not for weeks or months but for years, even decades, exclusively using TA will have you at a disadvantage to those who understand the drivers of tomorrow’s societal trends.

Horizon and Place

Pure technical analysts will be shaking their heads, arguing you can use TA successfully to position into long term trends. And that, as they say, is what makes a market.

The purist TA approach to entering long term trends is to deploy the moving average. The most common average used for long term trend entry is the 200 day, with some technicians using even longer periods of 300 or even 400 days.

One technique is to wait for the price to close above the moving average. Another is to use a shorter period, typically around half the length of the longer term, as a trigger point. For example, using a 200-day average with a 50 day as the trigger.


Using two averages, instead of waiting for prices to cross the longer term average, technicians take a signal from the shorter term crossing the long term average. Technicians have names for this phenomena, the 50 day crossing above the 200 day is called a Golden Cross, the 50 day dropping below is called the Dead Cross.

Moving average crossovers are the basis for shorter-term moving average based indicators too. The MACD (moving average convergence divergence) indicator is the net difference of two averages expressed as either a line or a histogram.

But using moving averages to enter into trends comes with two big problems. The first is that the average lags the price by half its length. This means today’s 200-day reading should be moved back in time 100 days.


Moving averages smooth sequences of data, and an average for any given look back period reduces the magnitude of the data fluctuations for that period to zero, so a 200-day average reduces to zero all 200-day cycle fluctuations, but it also dramatically eliminates all cycles of less than 200 days too.

Only timespans of greater than 200 days come through affecting the moving average line. While cycles slightly higher than 200 days will also be significantly reduced, periods of 300 or 400 days will not be affected.

Technicians attempt to reduce the lag factor by using exponential averages that give more weight to more recent data points. But the problem remains. Moving averages lag the price leaving technicians who use them late to the party.

Place a long term moving average on a chart and notice the number of days it takes for prices to cross the average.

The majority of traders, speculators, and investors look for certainty before they act, wanting to know if the trend is up and the bull market is back. This behaviour is the driver behind articles using sensationalistic headlines and bold claims to attract new subscribers to their site and services.

Technicians who exclusively use technical analysis are at a disadvantage because tools like moving averages, MACDs, RSIs, et al. can’t read changes in social mood. One technique that proposes it can is called Elliott Wave Analysis. The leading Elliott Wave newsletter has been calling for a major top in the stock market since the mid-1990s. Not just any top, like the ones seen in 2000 and 2007, but a multi-cycle top using cycles that span centuries, predicting that the Dow Jones Industrials will eventually trade under 1,000.

One day they might be right, but if you were subscribing in real time, in twenty-five years, the cataclysmic topping event they have been predicting has not happened. Elliott Wave analysis has one other well-known issue.

Elliott Wave

Elliott Wave

Wave analysis involves labelling the highs and lows of a market move, and the problem is, using the rules of Elliott, if you put twenty Elliotticians into a room, you’ll have twenty different interpretations of the same market.

Technical analysis can give a place or a zone to enter, but the destination remains unknown. TA can’t tell you if the current rally in Bitcoin is going to $20,000, but it can give you a near term horizon for the most probable direction of price.

Instead of spending time predicting the next multi-generational market top or bottom, the 5%, the group of investors and speculators who are consistently profitable look for zones where they can enter the market with a point of reference. If the entry does not work, they receive information or market feedback because of the failure at the point of reference, and get out of the position for a small loss relative to the size of their account.

The 5% understand that consistency and success comes from keeping losses small relative to the profit gained.

Technical analysis’ strength is that it can provide a place to enter with a point of reference to indicate your idea is not working. But it comes with no vision of the future. At best, TA will give you a horizon, but not the time and place of the destination.

There is a technique, to be complete, with plenty of followers that does state it’s possible to know the time and destination of a move. It’s called Gann Analysis, named after William Gann, the trader who brought his market secrets to the general public in the teeth of the 1930s depression.

Gann is surrounded by myth and mystery. His work uses a technique called the squaring of price and time. The theory being that time and price must be treated as equal and plotted on a properly scaled chart, using, for example, one centimetre of every price movement of ten cents and time movement of one day.

This method is found on many charting platforms today in the form of Gann angles and Gann fans. The prevailing myth behind Gann’s method of using scaled charts and angles has at its root, a deeper more esoteric cause.

And, for a considerable sum, you too can learn these secrets, known only to the few. Ready, here it is. Astrology.


Charts exist that show one of the astrological techniques Gann supposedly used. On a scaled chart, Gann transposed the geocentric coordinates of the relatively slow-moving planet Jupiter, and a faster-moving planet like Mercury, Venus, or Mars. It’s possible to scale planet co-ordinates into a range of prices using MOD 360 math and plot them on to a chart. If you think of Jupiter as a long term moving average and Venus as a slow moving average then just like moving average crossovers when the fast planet crosses over the slow planet this signals a buy signal, vice versa for a sell signal.

Gann’s planets have one advantage over moving averages. They don’t lag. And the reason for having no lag is simple. They aren’t averages they are transposed planet geocentric coordinates.

For the sharp reader, you might have figured out another feature of using planets transposed onto charts. You know with 100% accuracy exactly where that planet is going to be in the future. How? Use an ephemeris.

This technique sounds fantastic, having all the benefits of moving averages with zero lag and the ability to know what will happen in the future.

If you’re ready to buy, there’s a place where you can purchase ownership of Brooklyn bridge.

It’s called Sucker Street.

Technical analysis’ strength lies in the shorter term as a trigger point for entry and exit. Yes, moving averages can be used, but they lag the data. The longer you expect to be in a trade, the less accurate TA is in timing your entry, and your entry comes with a horizon, unable to see too far into the future.

Speculators have, over the years, tried many approaches to solve this problem, using ideas and techniques ranging from TA to esoterica. They’re all opaque. Traditional financial markets are a discounting mechanism operating in a window between six and eighteen months into the future.

So if TA has limited forward vision, is there any other method you could use?


Zeitgeist: Noun the defining mood or spirit of a period of history as shown by the ideas and beliefs at the time.

In the Pincer Movement series of articles, podcasts, and videos, we talked about Faith Popcorn and her ability to guesstimate what the future would look like. Not the next year or two, but decades into the future. She foresaw cocooning, and sitting in your connected home, with Skype, Netflix, Facebook, and Twitter, is easy to think so what, but Popcorn was talking about this nearly thirty years ago.

Could you have predicted an acceleration in the rise of the feminism in the 1990s?

You could use the trilemma, discussed in Games without Frontiers, as a thought experiment, where only two out of three scenarios can be true at any one time.

But there are other ways of estimating likelihoods.

History shows a connection between social injustice, inequality, and art. By tracking and rebasing the art prices of leading feminist artists, against a basket of non-feminist art you have one visual representation of the strength of the movement.

It’s one way to guesstimate the zeitgeist mood or spirit.

Another way is in tracking companies that are likely to profit from changing market moods.

In 2008 the British company, Avon Rubber, was trading for around 30 pence.

It made this.


Rubberised milking machine components for the dairy industry.

The management at Avon, recognising a change in the social mood, realised their secret sauce rubberisation process could be used to make something else.


Today Avon Rubber is a world leader in providing protective equipment to global military and police forces. And the share price? Today it’s 1,477 pence. That’s an increase of 4,700% in ten years.

Moving averages can’t see over the horizon, but figuring out the Zeitgeist can.

Using two frameworks to assist in building up the big picture, we’ve talked about the price cycle, a tool that can help place the current price action in one of four states: Downtrend, Sideways Accumulation, Uptrend and Sideways Distribution.

And, we’ve discussed the Gartner curve, aka the hype cycle, which uses behaviour patterns and price action to help make a guesstimation of the location of the product or service within the curve. The locations being: 1. Trigger, 2. Peak of inflated expectations, 3. Trough of disillusionment, 4, Slope of enlightenment, and 5. Plateau of productivity.

In the Chain Reaction article, podcast, and video series we postulated the likelihood of cryptocurrencies breaking out of the sideways accumulation phase of the price cycle, and moving into an uptrend, and if that’s true, using the Gartner curve, we asked how likely this was given the evidence.

What is the likelihood December 2018 was the bottom of the cryptocurrency bear market?

The question being, if the bottom is in, what is most likely to be the driver or drivers of the cryptocurrency market in stage four of the Gartner curve, the slope of enlightenment?

The Chain Reaction series discussed the phasing out of physical cash, and if this could be a potential driver.

Like the board of a company using existing expertise to develop products into new market demand driven by social change, ask, what social change will most likely create new demand for the mass adoption of blockchains?

You could start with motivating factors. Is it more likely the driver of new demand will be individuals wanting to protect their assets from debasement by central banks or is it more likely that central banks themselves will be motivated to ban physical cash and force the general population into using 100% digital money?



Telltales in yachting allow sailors to optimise their sheet trim and fine tune the course they are steering.

Likewise, traders and speculators use technical indicators like MACD and RSI to fine tune their entries.

In sailing, telltales are only of use when the sail has wind across both sides, and the sail is acting as an aerofoil. One major problem the majority of traders and speculators have when they deploy indicators as telltales for the market, is they don’t realise their technical indicators are useful only some of the time too.

Most indicators provided by chart services are attempting to do the same thing by visually tracking the momentum of the underlying price move. And, most indicators like MACD use lagging moving averages to do so. When markets trade sideways, the integrity of the trend dissipates, and so does the effectiveness of momentum based technical indicators.

Because markets spend most of their time not trending, the most successful speculators and investors study not the MACD, but the underlying conditions, macroeconomic events, and social mood.

Most traders get caught up in the minutia and ignore signals generated outside of the market they are trading, thinking that the process of gaining an edge is complex, or that it takes too much work.

It’s true, gaining insight from inter-market analysis and macroeconomic events requires some effort, but this is where fortunes are made.


In Great Britain, September 16th, 1992 is called Black Wednesday. George Soros was short the pound. Do you think Soros timed his entry with a MACD or moving average crossover?

Soros knew macro-economic conditions at the time favoured a drop in the pound. In December 1991 the Maastricht Treaty was agreed by the twelve leading countries in Europe as a pathway for the introduction of the Euro.

Maastricht covered two issues. One was European citizenship, where citizens of the member countries would become members of the new European state, the European Union, and the other issue was economic policy, concerning central banking and the creation of a common currency, the Euro.

In 1979, twenty years before the Euro became a currency in circulation, the leading economic members of the European Economic Community introduced currency pegs, called the European Exchange Rate Mechanism, intending to reduce currency volatility in preparing for a single common currency. Britain stayed out of the ERM during the 1980s, opting instead to have the pound closely maintain its value against the German mark. In the early 1990s Britain’s Prime Minister, Margaret Thatcher, took the pound into the ERM at a rate of 2.95 to the pound.

Britain’s rate of inflation was three times that of Germany, partly because of the effect of German reunification in 1989. By the early 1990s, Great Britain had high inflation and low interest rates because of its efforts to maintain the value of the pound against the German mark. With Britain now in the ERM, any move of 6% down against the entry of 2.95 would force the government to intervene to strengthen the pound.

As the pound dropped against the German Mark the bank of England was forced as per the conditions of the ERM to step in and buy. Soros knew these conditions couldn’t be maintained for long periods, and he started heavily shorting the pound. When the move began, Soros pushed harder, realising the British central bank did not have the liquidity to abate the fall. (Of course, when trading in this size, the actual trade was complex, involving many currencies and the UK stock market.)

In less than a month Soros and his partners had cleared over £1 billion in profit — a world record, and Great Britain was forced out of the ERM.

The point is that Soros used telltales, conditions outside of the market he was targeting, to position for his move.

Market Feedback

In the Pincer Movement series of articles, podcasts, and videos, we discussed a report carried out by Bitwise, that claims to prove that 95% of all cryptocurrency trading is fake.

The gist of the Bitwise report is this: Bitcoin is a globally fungible, low transaction cost commodity, with near zero transportation costs, and low to near zero storage costs. You would expect a commodity like this to trade in an efficient and orderly way with tight bid-offer spreads and near perfect arbitrage. Bitwise asked if this is true, why is Bitcoin trading with differences of hundreds of dollars between exchanges?

In the Misbehaviour of Markets, Benoit Mandelbrot talks about how bad humans are at generating randomness, and starting with that premise Bitwise showed trades from many exchanges that have a minuscule chance of being real.

Out of the hundreds of exchanges listed on, only ten had real volume you can trust, and they are Binance, Bitfinex, Kraken, Bitstamp, Coinbase, bitFlyer, Gemini, itBit, Bittrex, and Poloniex, with Binance having over three times the reported volume of the other nine exchanges.

When the Binance exchange was launched, Binance coin, symbol, BNB, was also launched as an ICO, an initial coin offering.

Originally built as an Ethereum ERC20 token, and recently moved onto its own blockchain, the Binance Chain, the function of Binance coin is to pay for fees generated on the Binance exchange. As an incentive to use Binance coins, Binance gives a discount of 50% on its fees in the first year, reducing to zero over the next five years.

With a total limited supply at 200 million BNB, Binance state they won’t create any more supply. Since the BNB rebate reduces over time, Binance also periodically burn coins, reducing the total supply. As the percentage in fee discount goes down, the supply will be cut to stabilise the coin.

BNB can also be traded, although most investors use the coin to reduce their trading fees.


Since the December lows, Binance coin is up 400%.

Because Binance has the highest amount of real cryptocurrency volume, and because of an active coin burn stabilisation policy, BNB could be used as an investment vehicle, but it could also be useful as something else.

As a proxy for traders enthusiasm or sentiment in the cryptocurrency markets.

The 5% use several external market indicators, like sentiment proxies, along with frameworks like the price cycle and the hype cycle, to help build their understanding of the trend.

Using Binance coin on its own as a sentiment indicator is not enough, but when used in combination with tools that are proxies for volatility they are powerful gauges, monitoring the strength of the underlying trend, searching out disruptive forces with the ability to change our lives.

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