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A General Strategy on How to Select a Crypto Fund, Part 2



With about 800 crypto funds relying on a new asset class, which has its own properties, it is essential to assess them through an appropriate framework. We provide a basic framework of useful metrics to assess the true risk of a crypto fund as a quantitative screening tool. Short-listed funds can then be assessed in more detail through a classic due diligence process.

Assessing the return/risk profile of a directional trading crypto fund

Assessing the expected return of a directional fund

Investors in a directional fund should first have a clear understanding of the dynamic of the fund’s overall strategy in order to realize where the performance will come from and over what period before assessing whether the risk taken to achieve such results is worth it. This is achieved through discussions with the fund manager.

Warning: If a fund manager refuses to explain any of the fund’s strategies, beware!

When asking about a fund’s strategies, a truthful and experienced manager should be able to explain it in plain English. If a fund manager doesn’t want to disclose anything stating that it’s a trade secret, you could still try to understand what the fund tries to achieve by analyzing its past track record. However, in such a case, it’s unlikely that the manager will provide daily returns of the strategy for a more granular analysis, which may thus be worthless.

A transparent fund manager inspires trust, a secretive one inspires defiance, but even if a manager is transparent about strategy, investors should verify that these pitches from fund managers are credible and not take their word for granted. The Bernie Madoff Ponzi scheme was just that. Madoff explained that he was trading S&P 100 options as the basis of his strategy. Why not? But given the size of this specific market (~$100 million daily on average), there was no way he could have been trading the size of his fund ($6 billion), but he still lured many naïve investors.

Understanding the fundamentals of the strategy

Directional funds try to achieve their goals in different ways, and investors have to understand in which market environments they are going to perform well or not; some funds may perform very well during smooth trending markets but can be crushed during times of high volatility, whereas funds performing well during hectic markets can dramatically underperform in strong trending markets.

No single strategy can perform well in every market environment, as each strategy is designed to only fully capture specific moves and avoid being crushed otherwise. Directional funds tend to embed different strategies, each designed to capture specific market moves; but since these strategies are usually blended together, the resulting blend should perform well during most market environments, but will always underperform the best single strategy in a given market environment.

Understanding the strategy timeframe

Understanding the timeframe through which a fund strategy works — i.e., intraday and/or on a several-day basis — and the broad expectations of the strategy in terms of capturing market movements — e.g., captures 80% of an upward move, 30% of a downward move on average — are necessary to make a meaningful comparison against a potential benchmark.

In the example just quoted, such a fund would underperform a passive index representative of the traded underlying asset during strong upward movements but should prove its value when the passive index reverses course by limiting the losses, leading to a better performance against the passive index but over a full up/down market cycle. 

Assessing the risk profile of a directional fund

In order to assess the risk profile of a directional fund, an advanced — i.e., nonlinear — hedge fund analysis framework is useful, but metrics of a crypto fund cannot be compared with the metrics of a traditional hedge fund — e.g., volatility, Sharpe ratio, etc.

We will assume that the past behavior of a fund is expected to continue more or less in the near future if the manager’s strategy is robust and well designed.

A nonlinear analysis framework

If an instrument behaves the same during different market conditions, it is said to have a linear behavior, but if it behaves differently during different market conditions, it is said to have a nonlinear behavior.

For example, when a fund gains 1% every time the broad market gains 1% and loses 1% every time the broad market losses 1%, it is linear; but when a fund gains 1% every time the broad market gains 1% and loses 2% every time the broad market losses 1%, it is nonlinear, as its behavior during negative markets doesn’t have the same amplitude as during positive markets.

Assessing the nonlinearity of a fund

The question is: “Is a given fund linear or nonlinear?” The quick answer is that most active funds will be nonlinear, but there’s a statistical test to answer the question more precisely, the Jarque–Bera test for normality.

However, metrics from a nonlinear framework can also be used to assess linear instruments, but not the other way around. 

Nonlinear risk metrics

The four main metrics of a linear framework adapted to assess nonlinear asset behaviors are volatility, correlation, beta and value at risk. 

Simple time series are used in the section below to illustrate the purpose.

  1. Volatility

Volatility measures the degree of dispersion of returns around their mean. The higher the volatility, the higher the dispersion of the returns. If an asset has a linear behavior, a high dispersion of returns around their mean indicates that returns can be far above but also far below their mean, and this is generally considered as an easily understandable measure of risk. However, if the asset has a nonlinear behavior, overall volatility can be highly misleading, either over or underestimating the risk of loss.

In order to assess the behavior of a nonlinear asset from a volatility point of view, we will split the metric into two sub-metrics: positive volatility and negative volatility. Positive volatility is a classic volatility measure but is only applied to the positive returns of the asset. Likewise, negative volatility is a classic volatility measure but is only applied to the negative returns of the asset. Thus, we assess the dispersion of the returns on the positive side and on the negative side. If the asset is linear, these two metrics are close to each other.

Example: Let’s consider three funds, A, B and C as having had the following returns over the same period:

Fund A: { -3%; -8%; 5%; 58%; -1%; 2; 48%; -2%; 1%; 38% }

Fund B: { -3%; -8%; 5%; 12%; -1%; 2; 6%; -2%; 1%; 4% }

Fund C: { -45%; -8%; 5%; 12%; -1%; 2; 6%; -2%; 1%; 4% }

High volatility does not equate high risk

The volatility of Fund B is 5.3%, whereas the volatility of Fund A is 23.1%. Thus, if considering the overall volatility as a risk measure, then Fund B is much less risky than Fund A, whereas Fund C lies between.

When assessing the positive and negative volatility of funds A, B and C, we have:

Volatility of Funds A, B and C

Looking at the positive and negative volatility of each fund leads to a very different conclusion from just looking at their overall volatility: Fund C having the highest negative volatility and the lowest positive volatility is actually the riskiest of the three funds, whereas fund A having the highest positive volatility and the lowest negative volatility is the least risky, and fund B lies in between.

In fact, by taking a closer look at the returns of the three funds, Fund A contained its losses as much as Fund B but was able to capitalize on three strong returns that Fund B couldn’t capture. On the other hand, Fund C is similar to Fund B but has only been heavily hit once, whereas Fund B hasn’t.

Therefore, would one rather invest in a fund that delivers good returns, controlling the downside, but without any upswing either (Fund B), or invest in a fund that controls the downside as well, but which can deliver a winning lottery ticket from time to time (Fund A)?

Assessing the volatility of a crypto fund with a nonlinear framework is the only way to assess its true risk from a volatility point of view — i.e., understanding what contributes to high volatility.

Debunked myth #1: A crypto fund with overall high volatility doesn’t necessarily equate a highly risky one.

  1. Correlation

Correlation measures how an asset is moving in relation to another one. The closer an asset is to 1, the more the assets will move in sync; the closer an asset is to -1, the more the assets will move in the opposite direction one from each other.

Again, measuring the overall correlation of a nonlinear asset can lead to misleading conclusions about how one asset moves in comparison with another.


Fund A: { -9%; 13%; -1%; 15%; -9%; 1; 28%; -6%; -2%; 0% } 

Fund B: { 5%; 13%; 1%; 28%; 6%; 1; 25%; -5%; 2%; -1% }

Benchmark: { -28%; 2%; -33%; 34%; -19%; -15; 21%; -10%; -6%; -5% }

High correlation does’t mean move in tandem

The correlation of Fund A to the benchmark is 0.81, which is similar to the correlation of Fund B to the benchmark. By looking at how these two funds correlate with their common benchmark, they are identical when assessing their overall correlation.

Now assessing the positive and negative correlations of Funds A and B with their benchmark, we have: a more subtle manner to assess the correlation of a fund with a benchmark. It consists of breaking the global correlation measure described above into two sub-correlation analyses: The positive correlation is the measured correlation of the fund with a benchmark only during positive returns of the benchmark, whereas the negative correlation is the measured correlation of the fund with a benchmark only during negative returns of the benchmark. The positive and negative correlation measures range like the standard correlation measure between -1 and +1 with the same meaning.

Therefore, an investor should look for a fund that has a high positive (i.e., the closest to +1) positive-correlation, meaning the fund moves up when the benchmark moves up, and a low negative (i.e., the closest to -1) negative-correlation, meaning that the fund moves up when the benchmark moves down.

Correlations of funds A and B

Fund A exhibits a moderate positive positive-correlation with its benchmark (0.23) and a moderate positive negative-correlation with its benchmark (0.30), whereas Fund B shows a very high positive positive-correlation with the benchmark (0.97) and a medium negative negative-correlation with its benchmark (-0.45).

This means that Fund A moved more or less in sync with its benchmark either on the upside or the downside, whereas Fund B moved upward when the benchmark was up most of the time but moved also upward from time to time when the benchmark was moving down. This is exactly the characteristic of a fund investors should look for, but this is only visible in a nonlinear framework.

Debunked myth #2: A high global correlation of a crypto fund to a benchmark doesn’t necessarily mean that the fund will move in sync with the benchmark most of the time.

  1. Beta

The beta measures the amplitude of how an asset is moving compared to another. Its value is a rough estimate of how much an asset will move vs. another one considered. A value above 1 means that an asset moves more than 1x than another one in the same direction; a value between 0 and 1 means that an asset moves less than 1x than another one in the same direction. Negative values can be interpreted as positive values in terms of multiplying effect, but with moves on the opposite directions.

Note: The beta of an asset vs. another should only be calculated if there’s a statistically significant correlation between the two assets.

Example: Let’s consider the two funds used previously with the correlation analysis, which were both highly correlated with the benchmark (0.81).

Fund A: {-9%; 13%; -1%; 15%; -9%; 1; 28%; -6%; -2%; 0%}

Fund B: {5%; 13%; 1%; 28%; 6%; 1; 25%; -5%; 2%; -1%}

Benchmark: {-28%; 2%; -33%; 34%; -19%; -15; 21%; -10%; -6%; -5%}

Beta doesn’t always mean "move as much as"

The beta of Fund A to the benchmark is 0.46, and the beta of Fund B to the benchmark 0.43 — i.e., both funds have a similar beta to their benchmark. But are they really equal?

Assessing the positive and negative beta of Funds A and B with their benchmark, we have: 

Beta of funds A and B

Unsurprisingly, when looking at the beta of these two funds through a nonlinear prism, we have a different story. Fund A tends to capture on average about 11% of an up or down move of its benchmark, whereas Fund B tends to capture on average 48% of an up move of its benchmark while capturing -15% of a negative move of its benchmark — i.e., capturing 15% of the amplitude of the down move of its benchmark, but delivering it in positive terms instead.

Just like with the correlation, investors should seek to invest with funds showing an as-high-as-possible positive positive-beta and an as-high-as-possible negative negative-beta vs. the funds’ benchmarks. 

Debunked myth #3: The overall beta of a crypto fund has no value unless it is assessed in a nonlinear manner.

  1. Value at Risk

The value at risk, or VaR, is an estimate of how much an investment might lose, with a given probability, given normal market conditions, and in a set time period.

Example: VaR (Fund, 95%) = -7.5% means that over the considered period, the fund can lose more than -7.5% with 5% (= 100%–95%) probability. In other words, there’s a 95% chance that the fund will lose less than -7.5% over the considered period.

There are many ways to compute the VaR of an asset that go beyond the scope of this paper, but again, if the nonlinear behavior of the asset is not taken into account in estimating the VaR, the results lead to false conclusions.

However, given the often-hectic behavior of digital assets, it is difficult to assess their VaR, no matter the model used, and the obtained results may not be of great help to calibrate risk. This is why VaR is not really used to assess crypto funds. 

Comparing the risk metrics of traditional hedge funds and crypto funds

Now that the main die-hard myths about fund metric analysis have been debunked, another misleading analysis aspect of crypto funds is to compare the metrics side by side with the well-known metrics of traditional assets.

Essentially, digital assets are way more volatile than their traditional cousins, and some of their metrics can be of several orders of magnitude different: from annualized return and volatility to the Sharpe and Sortino ratios.

Sharpe ratio

For example, a Sharpe ratio above 1 is more of an exception rather than the norm for funds dealing with traditional assets, as their annualized return is usually in the 5%–15% range and an annualized volatility of 10%–15% that doesn’t imply insignificant returns from their means. 

However, with Bitcoin (BTC), for example, its annualized return from 2016 to date has been slightly above 100%, while its annualized volatility is close to 85%, leading to a ratio above 1 despite its frequent booms and busts.

Thus, the Sharpe ratio of a good crypto fund — one that is able to provide to capture most of the upside of its underlying asset while protecting on the downside — can be in a high single to a low double-digit range, which can appear highly suspicious if compared to the Sharpe ratio of a typical hedge fund.

Sortino ratio

The same is even more true for the Sortino ratio. For example, Bitcoin has a 30% annualized downside volatility, which is roughly three times that of the S&P 500, meaning negative returns reaching three times further than the ones of the S&P 500, which leads to a three times lower value of the denominator of the Sortino ratio of Bitcoin. However, if Bitcoin has an annualized return 10 times bigger than that of the S&P 500, the numerator of the Sortino ratio of Bitcoin will be 10 times higher than the numerator of the Sortino ratio of the S&P 500. Thus, when calculating the Sortino ratio of Bitcoin, dividing a numerator that is 10 times bigger (than the one of the S&P 500) by a denominator that is 3 times bigger (than the one of the S&P 500), we obtain roughly a ratio for Bitcoin that is about 3.3 (=10/3) times higher than that of the S&P 500. More precisely, the Sortino ratio of Bitcoin is above three, whereas the Sortino ratio of the S&P 500 is about 0.8.

Therefore, for a good crypto fund, posting a high annualized return over limited downside volatility can easily lead to a high double-digit Sortino ratio.


Drawdowns are bounded metrics between 0% and -100%, contrary to the unbounded metrics that are the Sharpe and Sortino ratios described above. Thus, an investor can compare side by side the drawdowns of a crypto fund to the ones of a traditional fund without having to take into account the scaling of the metrics.

However, investors have to understand that the magnitude of drawdowns of crypto funds can be more substantial than the ones of a fund trading only traditional assets, as the digital assets can swing more wildly. For example, a 40% drawdown for a crypto fund can be “equivalent” to a 15% drawdown for a traditional fund, but the crypto fund lost is nevertheless more than the traditional fund. The idea is just to put things into perspective here.

A loss due to a drawdown is never pleasant to experience, especially when it is a big loss; therefore, investors have to pay more attention to the shapes of the fund drawdowns. The shape of a drawdown refers to the shape described by the drawdown curve of a fund. These shapes are triangles more or less tilted, which tell how the fund manager dealt with losses and are highly instructive, as we will detail below.

Let’s consider these three funds:

Fund A: { 1%; 3%; -1%; 5%; 2%; -23.5; 2%; 6%; -2%; 3%; 1%; 5%; 2%; -3%; 6%; 3% }

Fund B: { 1%; -2%; -1%; -0.5%; -2%; -1.5%; -2%; 0.5%; -2%; -3%; -1%; -2%; -1%; 23%; -1%; 2% }

Fund C: { 2%; -1%; 3%; 1%; -0.5%; 1%; -0.5%; -19%; 21%; -3%; 2%; 1%; -0.5%; 2%; 0%; 1% }

They all have the same performance (around +5%) and maximum drawdown (around -20%) over the same period, but the shapes of their drawdowns depict a very different story for each fund.

Drawdown shapes matter

Generally, there are three cases:

  1. A sudden loss followed by a steady recovery over several weeks. This is the shape of the drawdowns one could expect. At some point, the fund manager’s strategy is caught wrong-footed and a sudden, steep loss occurs. As discussed earlier, as the old Wall Street adage says “markets take the elevator down, but the stairs up” — i.e., a sudden panic move downward happens quickly, but it takes time for the markets to calm down and realize that what caused the panic move in the first place is over, which explains the slow recovery. These drawdowns are normal and inherent to the strategy. Investors have to simply make sure that all of the past major drawdowns were about the same magnitude, showing the robustness of the underlying strategy; bad trades occur, but they are always controlled and will eventually recover.

Drawdown curve type A

  1. Continuous and increasing losses over several months recovered in just a few weeks. Such drawdowns are more problematic, as they may show that the manager’s strategy hasn’t worked for a long time, but facing investors’ redemptions, the fund manager went “all in” in order to stop the bleeding: It’s make or break. However, such drawdown shapes can sometimes also be explained by the way the strategy works and may not be a sign of a gambling fund manager. This is why it is always important to understand what the fund strategy tends to capture in order to assess its behavior.

Drawdown curve type B

  1. A sudden loss, followed by a quick recovery. These drawdowns can take place from time to time and are usually linked to a market dislocation, leading to a fast and deep loss followed by an equally strong recovery.

Drawdown curve type C

Finally, when looking at fund drawdowns, having data-sampling as precise as possible is key: Looking at drawdowns on a daily basis or on a monthly basis can lead to very different conclusions.

If managers just report their performance on a monthly basis, as is generally the case, only the change of the fund’s net asset value, or NAV, between the last day of the current month and the last day of the previous month are disclosed. There’s no information about what occurred during the month. For performance-reporting purposes, that’s fine, but for risk assessment, this can be highly misleading.

Indeed, if the fund witnessed a 30% drawdown during the month that fully recovered by the end of the month, then looking only at monthly NAVs won’t show it, and investors will have a false sense of confidence by assuming that the fund never had any 30% drawdown in this example. Reporting performance on a daily basis shows what happened from day to day, which is far more informative than just from month to month.

For passive index, drawdowns measured on a daily or monthly basis are very close because there’s no active management involved. However, with actively traded strategies, short but steep drawdowns can occur from time to time, and if investors are not aware of that possibility, they may be in for a rude awakening, possibly panicking and selling their holdings. 


Crypto funds come in different shapes and sizes, as we have briefly described in this article.

No matter their nature, since they are all dealing with highly volatile underlying assets, they tend to exhibit nonlinear behavior, which requires a proper framework to analyze them. Through a nonlinear analysis of such funds, we have highlighted that:

  1. A crypto fund with overall high volatility doesn’t necessarily equate to a highly risky one. 
  2. A high global correlation of a crypto fund to a benchmark doesn’t necessarily mean that the fund will move in sync with the benchmark most of the time.
  3. The global beta of a crypto fund has no value unless it is assessed in a nonlinear manner.

Another point we touched upon is that comparing metrics of traditional funds vs. crypto funds is like comparing apples to oranges, given the very different nature of the underlying instruments traded.

We concluded on the drawdowns of crypto funds, which, to us, are a very powerful risk metric when properly analyzed. If an investor had to look at just one risk metric to assess the risk taken vs. the delivered performance, it would be the fund drawdowns, not just their depth, but also their shapes.

We gave some directions on which metrics to look at and analyze, but metrics without their context are meaningless. This is why such an analysis should always be conducted under the supervision of the professional fund manager’s explanations about his strategy.

This is part two of a two-part series on how to sort crypto funds — read part one with an overview of the main types of crypto funds here.

This article does not contain investment advice or recommendations. Every investment and trading move involves risk, you should conduct your own research when making a decision.

The views, thoughts and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

David Lifchitz is the chief investment officer and managing partner at ExoAlpha — an expert in quantitative trading, portfolio construction and risk management. With over 20 years of experience in these fields and 8+ years in information technology with financial firms, he has notably been the former head of risk management at the U.S. subsidiary of Ashmore Group, which had $74 billion in assets under management in 2018. ExoAlpha has developed proprietary, institutional-grade trading strategies and infrastructure to operate seamlessly in the digital asset markets applying strong risk management principles.



Is Bitcoin’s growth conservative or real?



132 days post the third halving and Bitcoin is trading at $10450. The price has recovered nearly 25% from the post halving dip, it hit a high of 43.7% ROI last month. The ROI growth is in line with post halving prediction with YTD of 54%.

Is Bitcoin's growth Conservative or Real?

Source: Ecoinometrics

Though there is scope for real growth in price, above December 2017 level, the growth cannot be entirely attributed to the pandemic or Bitcoin’s correlation with Gold, Silver or the USD. There are several triggers along the way that led to the boost in price. 

DeFi’s explosive growth did for Bitcoin what ICOs did back in 2017. Before the ICO bubble burst, when top ICO projects like Filecoin, Namecoin, and Tezos raised funds from investors, they were held in Bitcoin and this significantly increased the demand for Bitcoin on spot exchanges. The investment raised by these ICO projects was held in Bitcoin wallets on exchanges or offline and this added to the scarcity in supply, by driving demand across exchanges, globally. 

With $9.77 billion locked in DeFi and projects like Yield Farming that have surpassed Bitcoin’s price, DeFi’s TVL is giving a boost for the demand of top cryptocurrencies like Bitcoin and Ethereum. The increased demand along with scarce supply may drive the price to 2017 levels by the end of 2020. Bitcoin Influencer A Pompliano is quoted commenting on the scarcity of supply in an interview with

“Any time that you have got an asset that has scarce supply, people are going to be interested because as we know if the supply is capped and demand increases, of course, the price goes up”

This scarcity is visible on exchanges, where Bitcoin inflow is the lowest in 180 days.

Is Bitcoin's growth Conservative or Real?

Source: Chainalysis

When the inflow goes up on exchanges, based on trigger events like increased open interest by institutional investors on CME or movement of BTC by HODLers/ Whales, the price may fluctuate based on our position in the market cycle. 

Based on the Ecoinometrics chart above, there is scope for growth beyond the $19k price level and this depends on the next price rally. Institutional investors like MicroStrategy can drive the price higher by creating demand for the asset and its options/ futures. Growth attained post triggers will continue to be conservative, however, it is not as conservative as Gold or Stocks, hence the rewards are higher in the current phase of the market cycle. When the price hits the $19k level, then growth may get real.


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The great unbanking: How DeFi is completing the job Bitcoin started



In a broad sense, 2020 has been the year of the COVID-19 pandemic. As it charges toward 1 million deaths and over 30 million infections, governments have been found wanting. Our institutions have crumbled, leaders reacted too slowly, and all of the systems both in place and newly created to protect us — healthcare, aged care, testing, protective equipment supply chains, contact tracing, etc. — have collapsed. But 2020 has also very much been the year of decentralized finance, which has come to be known as DeFi.

DeFi is crypto

To understand why DeFi has captured the imagination of the entire crypto landscape is to understand that it is less about the outrageous returns offered to yield farmers and more about the future possibilities it presents.

Cryptocurrency, and the technology behind it, has always been about future possibilities.

When Bitcoin (BTC) was born to little fanfare in 2009, it was quickly recognized by those familiar with it as having the potential to be the future of money. 11 years on, Bitcoin, with its decentralized global system of nodes and miners keeping the network operational and secure, has met its promise and more.

Not only is it a reliable and fast way for people to permissionlessly send money to each other, it has also become a genuine enterprise-grade investment vehicle, and its investment worthiness appears to be growing. Large and enterprise owners are holding onto it in anticipation of capital growth.

“Bitcoin as an investment vehicle” aside, it remains, in essence, money — a new currency for a new, hyper-connected world.

Bitcoin and/or DeFi

“Bitcoin as money” still works like money insofar as it still relies on a financial ecosystem around it to keep it alive. But that ecosystem is somewhat limited; it consists of those that secure the network on which transactions are transmitted (miners and node operators), wallets, and exchanges where it can be exchanged for other digital and, increasingly, fiat assets.

But a financial services architecture as we know it incorporates a whole lot more in terms of functionality: lending, borrowing, earning interest, paying interest, investing, etc. Bitcoin was never intended to cater to all those mechanisms — but DeFi is.

The next logical step in the evolution of crypto’s gradual assumption of the roles played by traditional finance is being taken by the growing Ethereum-based decentralized finance ecosystem.

DeFi, in many ways, is Bitcoin 2.0. And for that reason, DeFi — although based on Ethereum’s composability and smart contract functionality — furthers the Bitcoin narrative into the future that Bitcoin first allowed us to believe in. With each new DeFi protocol, that future is closing in on us: a world without banks as we have come to know them.

DeFi demonstrates the complementary nature of Ethereum to Bitcoin. By recreating the financial system not from within but from the outside, Ethereum is hosting a movement that completes the circle Bitcoin started.

The vampires aren’t even that bad

Our banking system is as broken as our COVID-19 response was, but can DeFi actually replace it? The DeFi subsector’s most vocal critics would point to the emergence of food-meme protocols SushiSwap, Cream and Yam, along with many others, to suggest the movement resembles more of a circus than a legitimate threat to a giant financial services sector.

Those protocols are considered vampire forks, which are forks of existing protocols, designed to suck liquidity from them. If vampire forks are destructive — and there is no certainty they are — a seminal Rolling Stone article helps put them into perspective. When running through the central role Goldman Sachs played in virtually every financial collapse of the last century, Matt Taibbi called the behemoth:

“The great vampire squid wrapped around the face of humanity, relentlessly jamming its blood funnel into anything that smells like money.”

DeFi’s vampires probably serve to further the ecosystem by stress-testing it. Legacy finance’s vampires have had only one function: to take money from everyone else to strengthen themselves.

From the Great Depression, to the dot-com bubble and burst, to the housing crisis, the “great vampire squid” had self-serving financial destruction in mind and its tentacles on virtually every lever that produced those catastrophic episodes in our recent economic histories.

The sector as a whole has long since stopped serving most of our needs. Checking accounts no longer pay interest, accessing money costs money, and large enterprises find financing easy, while small and medium enterprises are left floundering. Try getting a mortgage as an independent contractor without benefits or job security.

Bitcoin democratized money by freeing us from it in its legacy form. Now, DeFi has captured the imagination of the crypto world as its natural extension — not just the democratization of money but the democratization of finance, promising a seismic shift in the way people bank in the future.

That seismic shift will confer benefits on society we could only have dreamed of a decade ago.

Enter the great unbanking.

The views, thoughts and opinions expressed here are the author’s alone and do not necessarily reflect or represent the views and opinions of Cointelegraph.

Paul de Havilland is a fan of disruptive technology and an active investor in startups. He has experience covering both traditional and emerging asset classes and also pens columns on politics and the development sector. His passions include the violin and opera.


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There Are Now Over 10,000 Bitcoin ATMs Throughout the World

The number of bitcoin ATMs installed throughout the world has exceeded 10,000, and the United States continue to lead the rest of the world in crypto ATMs with 76% of machines installed. According to CoinATMRadar, the first Bitcoin ATM was installed back in 2013 and in seven years the industry grew to over 10,000 machines […]



The number of bitcoin ATMs installed throughout the world has exceeded 10,000, and the United States continue to lead the rest of the world in crypto ATMs with 76% of machines installed.

According to CoinATMRadar, the first Bitcoin ATM was installed back in 2013 and in seven years the industry grew to over 10,000 machines installed. In comparison, bank ATMs needed nine years to get to 10,000 machines back in the 1970s.

CoinATMRadar writes that after only 3.5 years the number of crypto ATMs grew to 1,000, and that over the next 3.5 years the number surged by 9,000. The first bitocin ATM was installed in Vancouver at the Waves Coffee House, and there is still a machine there.

Source: CoinATMRadar

The idea of a Bitcoin ATM came ot be, according to CoinATMRadar, as entrepreneurs were looking for ways to make bitocin easily available to people, and the general populace was already familiar with ATMs. Companies started working on the project and in 2013 the first one was installed in Canada.

In 2014, the first Bitcoin ATM was installed in the U.S., in Albuquerque, New Mexico, and since then most crypto ATMs have been installed in the country. By 2015, there were only 300 Bitcoin ATMs throughout the world, being installed by small organizations trying to bolster crypto adoption.

CoinATMRadar adds that bitcoin initially dominated crypto ATMs, with only 4% of machines supported an altcoin: litecoin. In 2017, however, the BTC network was congested and transaction fees and confirmation times surged. The firm added:

This resulted in worse UX when transactions started getting stuck and users of ATMs needed to reach support to get it resolved.

It’s worth noting that in 2017 the price of bitcoin hit its near $20,000 all-time high, and as the network was clogged users started using other cryptoassets. ATM operators followed users and started adding support for cryptocurrencies like ETH, LTC, DASH, and others. Nevertheless, around 30% of all crypto ATMs only support Bitcoin transactions.

Featured image via Unsplash.

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