Author’s: Chris Burniske and Jack Tatar
Bitcoin, with a capital B, is a platform that carries upon it programmable money, known as bitcoin with a lowercase b.
Chapter 3 – Blockchain, Not bitcoin?
5 stages of Gartner’s Hype Cycle for Emerging Technologies
• Innovation Trigger
• Peak of Inflated Expectations
• Trough of Disillusionment
• Slope of Enlightenment
• Plateau of Productivity
First is the Innovation Trigger that brings the technology into the world. While not very visible, just as Bitcoin wasn’t visible in the early years of its life, word spreads and expectations grow. Over time the murmurs gain momentum, building into a crescendo that is Gartner’s second stage, the Peak of Inflated Expectations.
The peak represents the height of confusion around the definition of the original technology, because people often apply it optimistically to everything they see. No technology is a panacea. As companies sprout to life and attempt to transition ideas into reality, shifting from proof-of-concepts to at-scale implementations, it frequently turns out that implementing a new disruptive technology in the wild is much harder than anticipated. The new technology must integrate with many other systems, often requiring a wide-reaching redesign. It also requires retraining of employees and consumers.
These difficulties slowly push the technology into the Trough of Disillusionment, as people lament that this technology will never work or is too difficult to deal with. When enough people have given up, but the loyal keep working in dedication, the technology begins to rise again, this time not with the irrational exuberance of its early years, but instead with a sustained release of improvements and productivity. Over time the technology matures, ultimately becoming a steady platform in the Plateau of Productivity that provides a base on which to build other technologies.
Chapter 4 – The Taxonomy of Cryptoassets
Historically, cryptoassets have most commonly been referred to as cryptocurrencies, which we think confuses new users and constrains the conversation on the future of these assets. We would not classify the majority of cryptoassets as currencies, but rather most are either digital commodities (cryptocommodities), provisioning raw digital resources, or digital tokens (cryptotokens), provisioning finished digital goods and services.
Currency fulfills three well-defined purposes: to serve as a means of exchange, store of value, and unit of account. However, the form of currency itself often has little inherent value. For example, the paper bills in people’s wallets have about as little value as the paper in their printer. Instead, they have the illusion of value, which if shared widely enough by society and endorsed by the government, allows these monetary bills to be used to buy goods and services, to store value for later purchases, and to serve as a metric to price the value of other things.
Meanwhile, commodities are wide-ranging and most commonly thought of as raw material building blocks that serve as inputs into finished products. For example, oil, wheat, and copper are all common commodities. However, to assume that a commodity must be physical ignores the overarching “offline to online” transition occurring in every sector of the economy. In an increasingly digital world, it only makes sense that we have digital commodities, such as compute power, storage capacity, and network bandwidth. While compute, storage, and bandwidth are not yet widely referred to as commodities, they are building blocks that are arguably just as important as our physical commodities, and when provisioned via a blockchain network, they are most clearly defined as cryptocommodities.
Beyond cryptocurrencies and cryptocommodities—and also provisioned via blockchain networks—are “finished-product” digital goods and services like media, social networks, games, and more, which are orchestrated by cryptotokens. Just as in the physical world, where currencies and commodities fuel an economy to create finished goods and services, so too in the digital world the infrastructures provided by cryptocurrencies and cryptocommodities are coming together to support the aforementioned finished-product digital goods and services. Cryptotokens are in the earliest stage of development, and will likely be the last to gain traction as they require a robust cryptocurrency and cryptocommodity infrastructure to be built before they can reliably function. In summation, we believe that a clearer view of this brave new world of blockchain architecture includes cryptocurrencies, cryptocommodities, and cryptotokens, just as we have had currencies, commodities, and finished goods and services in the preceding centuries. Be it a currency, commodity, or service, blockchain architectures help provision these digital resources in a distributed and market-based manner.
The combination of current use cases and investors buying bitcoin based on the expectation for even greater future use cases creates market demand for bitcoin.
A word to the wise for the innovative investor: with a new cryptocurrency, it’s always important to understand how it’s being distributed and to whom (we’ll discuss this further in Chapter 12). If the core community feels the distribution is unfair, that may forever plague the growth of the cryptocurrency.
Chapter 6 – The Importance of Portfolio Management and Alternative Assets
Standard deviation of returns, or the range that an asset’s price will vary from its mean value, is one of the most common measures of risk. While Markowitz’s approach makes clear the need for risk in a portfolio, most investors are risk-averse to one degree or another, and so they must be compelled by the potential for increased reward if they are to increase their risk. To help with the anxiety of risk, MPT defines it quantitatively, removing much of the uncertainty. Typically, simply being well informed lets investors sleep better at night. The standard deviation of returns draws from the statistics of normal bell curves. If the average value, or mean, of a bell curve is 10 and its standard deviation is 5, then 68 percent of the time a randomly chosen entity from the sample will fall between 5 and 15.
Five is one standard deviation to the left of 10, and 15 is one standard deviation to the right of 10. Due to the way normal curves work, 95 percent of the time a random sample will fall within 2 standard deviations of the mean, so between 0 and 20 for our example. This is illustrated in Figure 6.1.
The standard deviation of expected returns informs investors of the amount of risk they’re taking if they were to hold only that asset. For a more holistic view, compare a portfolio with a standard deviation of returns of 4 percent to one that has a standard deviation of 8 percent. If both portfolios have the same expected return of 7 percent, it wouldn’t be a prudent decision to invest in the portfolio with more volatility, as they both have the same expected return.…
Similar to the concepts behind MPT, the Sharpe ratio was also created by a Nobel Prize winner, William F. Sharpe. The Sharpe ratio differs from the standard deviation of returns in that it calibrates returns per the unit of risk taken. The ratio divides the average expected return of an asset (minus the risk-free rate) by its standard deviation of returns. For example, if the expected return is 8 percent, and the standard deviation of returns is 5 percent, then its Sharpe ratio is 1.6. The higher the Sharpe ratio, the better an asset is compensating an investor for the associated risk. An asset with a negative Sharpe ratio is punishing the investor with negative returns and volatility.
Importantly, absolute returns are only half the story for the Sharpe ratio. An asset with lower absolute returns can have a higher Sharpe ratio than a high-flying asset that experiences extreme volatility. For example, consider an equity asset that has an expected return of 12 percent with a volatility of 10 percent, versus a bond with an expected return of 5 percent but volatility of 3 percent. The former has a Sharpe ratio of 1.2 while the latter of 1.67
Correlation of Returns and the Efficient Frontier
One of the key breakthroughs of modern portfolio theory was to show that a riskier asset can be added to a portfolio, and if its behavior differs significantly from the preexisting assets in that portfolio, it can actually decrease the overall risk of the portfolio. How can a risky asset make a portfolio less risky? The key is correlation of returns.
Correlation simply measures how assets move in relation to one another. The measurement ranges from a value of +1 to −1. If assets are perfectly positively correlated, then they move in tandem: if one is up 10 percent, the other is up 10 percent as well, for a score of +1. Similarly, if they are perfectly negatively correlated at −1, then when one is up 10 percent the other will be down 10 percent. If there is zero correlation, then the assets are completely independent.
Historically, stocks and bonds have moved differently from each other. When the economy is strong and stocks are generally rising, money flows out of bonds as investors fear they’re missing out, causing bond prices to slump and stocks to go higher. Investors are alive and well, with risk-on attitudes. When stock prices falter, investors become concerned by the potential losses, and money flows from stocks into the relative safety of bonds, known as a flight to safety. Such risk-off markets depress the price of stocks and float the price of bonds.
The two assets move in different directions based on the same news. They act almost like two people on a seesaw. This historical balancing of risk between stocks and bonds should be done as precisely as possible, otherwise wild market swings one way or the other will have a painful impact on the innovative investor’s portfolio.
Combining assets that have a variety of correlations makes it possible to create a portfolio that can perform in both bull and bear markets. Just because a few players are feeling sick doesn’t mean the whole team has to fail. One of the crown jewels of Markowitz’s MPT was his concept of the efficient frontier, which indicates where a portfolio can provide the best expectation of return for its level of risk
This traditional approach to asset allocation ran aground in 2008, when the financial markets collapsed and investors found that even if they had both stocks and bonds in their portfolio, they all fell together.5 The average investor felt betrayed by the tried and trusted model of stocks and bonds moving in a noncorrelated fashion. The crash of 2008 shook these investors from their “economic lullaby.”6 In an increasingly globalized world where capital market assets are more closely intertwined, it was becoming clear that twentieth- century diversification models wouldn’t cut it for twenty-first-century investing.
While the crash of 2008 was felt by most everyone, it soon surfaced that some people had not only weathered the storm but made significant money by leveraging the strong winds of fortune.7 Hedge fund managers who had been operating in relative secrecy were now being named as the new “masters of the universe” for their ability to avoid much of the damage of the crash and, for some, to profit greatly from it.
THE RISE OF ALTERNATIVE INVESTMENTS
The financial crisis of 2008 caused many financial advisors and wealth managers to evaluate different approaches to portfolio construction other than solely stocks and bonds. The returns seen by hedge funds during the crisis were identified as examples where nontraditional and alternative investment vehicles had provided positive (in some cases, drastically so) performance returns.
Investors wanted to know what they were doing differently and whether it was something they could do as well. First, let’s understand what we mean by a hedge fund and how they differ among themselves. It’s difficult to lump hedge funds together in one group, as they often have different investment objectives and approaches. Historically, one of the easiest ways to spot hedge funds has been their high fee structure. For example, many hedge funds operate under a 2 and 20 model, or sometimes 3 and 30, where they charge a 2 percent annual management fee and take 20 percent of the profits from a year. Other common characteristics include their exclusivity and general secrecy. Prior to the 2008 financial crisis, investors who took advantage of hedge fund performance and the alternative investments they utilized were typically of ultra-high net worth.
While mutual funds provide a prospectus that outlines exactly the approach and asset classes to be used, hedge funds are often veiled in secrecy. They might publicly advertise a broad investment strategy, but specifics are often withheld to preserve the secret sauce of the hedge fund. Hedge fund managers demand a high amount of flexibility and tolerance from their clients. For example, hedge fund managers could buy real estate or take ownership in what they believe to be an undervalued company (either publicly or privately held). If they believe upcoming political changes may favor oil, they could lease oil tankers or make a sizeable investment in a foreign oil partnership. They can also utilize assets such as timber, short positions in stocks (meaning they’re betting on the price falling), commodity derivatives, and yes, germane to this book, bitcoin and other cryptoassets.
For the typical investor, the high asset commitments, illiquidity, and lack of transparency kept hedge funds beyond their reach.
A concise way to describe an alternative investment is that it’s an asset with its own unique economic and value-based characteristics that are separate from those of the primary investments of stocks and bonds. For an investor, the main concern is to have assets that perform in a noncorrelated fashion to stocks and bonds—which have historically made up most investors’ portfolio models—and many alternative assets fit that bill.
If done properly, when the overall market has a severe meltdown as happened in 2008, specific alternative investments within portfolios may not decrease. Equally, in market upturns those same assets may or may not also increase in value; they may lose value, but such is the cost of overall risk reduction.
Chapter 7 – The Most Compelling Alternative Asset of the 21st Century
Bitcoin is the most exciting alternative asset in the twenty-first century, and it has paved the way for its digital siblings to enjoy similar success. In this chapter, we dive into how bitcoin evolved as an asset in the context of absolute returns, volatility, and correlations, concluding with how a small allocation of bitcoin would have affected a portfolio over different holding periods. Because bitcoin can claim the title of being the oldest cryptoasset—giving us the most data to investigate its maturation—understanding its longitudinal market behavior will give us a window into how other cryptoassets may evolve over time.
It is possible for an asset to be added to a portfolio that both decreases the risk of the portfolio and increases the returns. Finding assets that can do this is rare and almost feels like cheating the laws of risk-reward. After all, we’ve already learned that the more rewarding an asset is, the riskier it likely is. But with a portfolio we are not talking about a single asset but rather a group of them. It is the way in which a new asset behaves with the preexisting group of assets in a portfolio that is the key to both reducing risk and increasing returns.
Most people would reasonably expect that if they added bitcoin to their portfolio it would increase the absolute returns but it would also make the portfolio significantly riskier (more volatile). However, it’s important to remember that bitcoin’s propensity toward volatility proved true early in its life when volume was low (thin). In contrast, the past few years have been more nuanced: bitcoin’s volatility has calmed, yet it retains a low correlation with other assets. In some years, bitcoin even provided the magical and elusive combination mentioned above of increasing the returns while also decreasing risk within a portfolio.
Operating in the wild, innovative investors would have experienced the joy of a golden asset that both decreased volatility and increased returns when added to their portfolio, providing a double boost to the Sharpe ratio.
Greer defines three superclasses of assets:
• Capital assets
• Consumable/transformable assets
• Store of value assets
Greer has the following to say about how to identify each superclass from the others (boldface ours):
Capital Assets – One thing all capital assets have in common. A capital asset might reasonably be valued on the basis of the net present value of its expected returns. Therefore, everything else being equal (which it never really is), a financial capital asset (such as a stock or a bond) will decline in value as the investor’s discount rate increases, or rise as that rate decreases. This economic characteristic unifies the superclass of capital assets.
Consumable/Transformable (C/T) Assets – You can consume it. You can transform it into another asset. It has economic value. But it does not yield an ongoing stream of value. The profound implication of this distinction is that C/T assets, not being capital in nature, cannot be valued using net present value analysis. This makes them truly economically distinct from the superclass of capital assets. C/T assets must be valued more often on the basis of the particular supply and demand characteristics of their specific market.
Store of Value Assets – The third superclass of asset cannot be consumed; nor can it generate income. Nevertheless, it has value; it is a store of value asset. One example is fine art. A broader and more relevant example is the category of currency, either foreign or domestic. Store of value assets, can serve as a refuge during uncertainty (U.S. Cash), or offer currency diversification to the portfolio. [Author note: He does not define how to price it.]
Greer’s superclasses are not clear-cut, as some assets can fall into two camps. For example, precious metals are both C/T assets and store of value assets. They are used in the circuitry of electronics or transformed into ornate forms of decoration (C/T asset), and they are also held solely as bars of value, not meant for consumption or transformation of any kind (store of value asset).
Cryptoassets most obviously fall into the C/T realm because they have utility and are consumed digitally. For example, developers use ether to gain access to Ethereum’s world computer, which then can perform operations on smart contracts stored in Ethereum’s blockchain. Hence, ether is consumed in the operation of a world computer. Then there is “attention,” the fuel of advertising, which is leading to the creation of blockchain-based attention markets. Steemit is a social media platform with the native cryptoasset steem that rewards content creators and curators. Steem creates an economic system that rewards creators for new, quality content because that content enhances the platform, thereby increasing the value of steem. While many cryptoassets are priced by the dynamics of supply and demand in markets, similar to more traditional C/T assets, for some holders of bitcoin—like holders of gold bars—it is solely a store of value. Other investors use cryptoassets beyond bitcoin in a similar way, holding the asset in the hope that it appreciates over time.
Chapter 9 – The Evolution of CryptoAsset Behavior
The rapidity with which cryptoassets can be moved sets them apart from other asset classes—especially alternative assets like art, real estate, and fine wines—and should enable more liquid markets much earlier in their developmental history.
Chapter 10 – The Speculation of Crowds and “This Time is Different Thinking”
Cryptoassets are not going through bizarre growing pains unique to them. Instead, they are experiencing the same evolutionary process that new asset classes over hundreds of years have had to go through as they matured.
While the way in which markets become dangerous to investors changes over time, and often becomes less insidious the more the asset and its associated markets mature, the potential for markets to destabilize never disappears. Much of the world learned that lesson during the financial crisis of 2008. Broadly, we categorize five main patterns that lead to markets destabilizing:
• The speculation of crowds
• “This time is different”
• Ponzi schemes
• Misleading information from asset issuers
Chapter 11 – It’s Just a Ponzi Scheme, Isn’t It?
Dash, a coin that rose to fame in late 2016 and early 2017 due to its stratospheric price increase, had what many would call a misleading issuance. In the first 24 hours that the coin went live, over 1.9 million dash were mined, which was not part of the original plan. While Dash’s founder supplied explanations—mainly that this was caused by an inadvertent software bug—a concern many still hold is that the Dash team misled new investors.20 As of March 2017, those first 24 hours still account for nearly 30 percent of the coins outstanding. This is a situation in which the innovative investor must discern the difference between a misleading issuer and an honest mistake. We believe that Dash’s initial distribution could have been corrected, just as its competing anonymity cryptoasset, Monero, did, when it was forked off from Bytecoin to solve for an unfair distribution of coins. The Dash team could have relaunched to ensure a fair initial distribution.
Dash has worked to overcome its rocky beginning and at the start of April 2017 was one of the top four cryptoassets in network value. The asset is backed by a few interesting innovations, and its team has successfully navigated to a position of increasing mainstream acceptance.
In the cryptoasset markets, characters toying with asset prices can often obfuscate their identity through the veil of the Internet, which unfortunately makes it even easier for them to escape. Often, they will target small and relatively unknown assets, which makes it important for the innovative investor who ventures into these smaller markets to pay particular attention to the details of those assets and the characters associated with them.
Cryptoassets that have small network values are particularly susceptible to the cornering of their markets. For example, at the start of April 2017, the 200 smallest cryptoassets had markets of less than $20,000. Therefore, a bad actor could come in with $10,000 and buy up half the assets outstanding. This increased buying pressure will drive up the price of the asset, which tends to draw curiosity from others. If several speculators are in collusion, then they will work together to drive up the price of these small cryptoassets, while spreading hype on different social media platforms (a tweet or two from an “influencer” is enough).
The intent is to lure unknowing speculators to take the bait and buy the asset based on what they think is genuine market interest. The innovative investor who does due diligence would never buy solely based on market interest, and for good reason. The colluders will slowly work to exit their positions, while the inertia of enthusiasm leads more unknowing speculators to continue buying, as we saw with Gould. These pump-and-dumps, or P&Ds, are unfortunately becoming common in the smaller cryptoassets.
Cornering is also important to consider in crowdsales, especially if the founding team has given itself a significant chunk of the assets. While crowdsales will be further detailed in Chapter 16, the key takeaway for now is that if the founding team gives themselves too much of the assets outstanding, then they have immense power over the market price of the cryptoasset and this is potentially concerning.
Control over the asset supply goes beyond crowdsales and founders, as it can spread to the miners or other entities required to support a cryptoasset. This is where it becomes important to consider the monetary policy of a cryptoasset. For example, one of the concerns with Dash is that it created a supply structure prone to cornering. In addition to miners, in Dash there are entities called masternodes, which are also controlled by people or groups of people. Masternodes play an integral role in performing near instant and anonymous transactions with Dash. However, as a security mechanism, the entity has to bond at least 1,000 dash to be a masternode.45 Bond is a fancy word for hold, but it’s a term commonly used in the cryptoasset space to imply that those assets can’t move. If the masternode moves those bonded dash, and subsequently holds less than 1,000 dash, then that person or group can no longer be a masternode.
Given that there were over 4,000 masternodes in March 2017, that means 4 million dash were bonded, or illiquid. With just over 7 million dash available on the market, that 4 million means that roughly 60 percent of the supply is unavailable. Add to that the nearly 2 million dash that were instamined in the first 24 hours, and it implies that 6 million of the 7 million dash available are likely under the control of power players in the space, leaving only 15 percent of the remaining dash in free-floating.
Chapter 12 – Fundamental Analysis and a Valuation Framework for CryptoAssets
As it pertains to evaluating cryptoassets, the process of conducting fundamental analysis is different from stocks because cryptoassets are not companies. The assets may have been created by a company or group of individuals, and an understanding of that company or those individuals is vital, but the cryptoassets themselves should be valued more as commodities, with markets priced by the balance of supply and demand.
One of the most common questions is: What gives a cryptoasset value? After all, these assets have no physical manifestation. Since they are born of software, the value is derived from the community and the marketplace that naturally develops around the asset. Broadly, there are two kinds of value that the community places on any kind of cryptoasset: utility value and speculative value.
Utility Value and Speculative Value
Utility value refers to the use of the cryptoasset to gain access to the digital resource its architecture provisions and is dictated by supply and demand characteristics. For bitcoin, its utility is that it can safely, quickly, and efficiently transfer value to anyone, anywhere in the world. All it takes is typing in the person’s bitcoin address and clicking send, a functionality that all exchanges and wallets provide (which we cover in Chapter 14). Bitcoin’s utility in sending value using the Internet is similar to that of Skype, which can safely, quickly, and efficiently transmit anyone’s voice and image to anyone, anywhere in the world.
The innovative investor might say: “OK, I understand that bitcoin can have utility as MoIP, just as Skype has utility as VoIP, but how does that translate to bitcoin being worth $1,000 a coin?” Bitcoin’s utility value can be determined by assessing how much bitcoin is necessary for it to serve the Internet
Chapter 13 – Operating Health of Cryptoasset Networks and Technical Analysis
One way to determine the relative safety of a cryptoasset is through its hash rate. A cryptoasset’s hash rate is representative of the combined power of the mining computers connected to the network.
Using $660 million for Bitcoin and $294 million for Ethereum, while the network values for the two cryptocurrencies are respectively US$17.1 billion and $4.7 billion, we get a range of 3.9 cents to 6.3 cents of capital expenditure per dollar secured by the network. This range is a good baseline for the innovative investor to use for other cryptoassets to ensure they are secured with a similar level of capital spend as Bitcoin and Ethereum, which are the two best secured assets in the blockchain ecosystem.
BE CAREFUL WHEN DIRECTLY COMPARING HASH RATES BETWEEN CRYPTOASSETS
While it may initially seem logical to do, it’s often not appropriate to directly compare the hash rate of different cryptoassets to judge relative security, because the type of machines providing the hash rate can vary among different blockchains, as can their cost. As we covered in Chapters 4 and 5, different blockchain architectures use different hash functions in the consensus process. Different hash functions are suitable for different kinds of chips, be they CPUs, GPUs, or ASICs, and these chips come in computers that vary in cost. For example, Bitcoin is mined with ASICs, which yield the greatest hash rate per dollar spent, while Ethereum is mined mostly with GPUs. Therefore, $1,000 will purchase more hash rate for a Bitcoin computer than an Ethereum computer, and it is this dollar value that’s most important in deterring attackers from attempting to recreate the network. Hence, while as of March 2017 Bitcoin’s hash rate of 4,000,000 TH/s was technically 250,000-fold higher than Ethereum’s 16,000 GH/s, this does not mean Bitcoin was 250,000 times more secure than Ethereum.
A way to quantify the decentralization is the Herfindahl-Hirschman Index (HHI), which is a metric to measure competition and market concentration. For example, the U.S. Department of Justice uses the HHI when examining potential mergers and acquisitions, to assess how they may influence the centralization of the industry.The metric is calculated by taking the percent market share of each entity, squaring each market share, and summing these squares before multiplying by 10,000.
For example, a system that has two players with 50 percent market share apiece would have an HHI of 5,000, because (0.25) + (.25) = 0.5, and 0.5 × 10,000 = 5,000. For the HHI, anything less than 1,500 qualifies as a competitive marketplace, anything between 1,500 to 2,500 is a moderately concentrated marketplace, and anything greater than 2,500 is a highly concentrated marketplace.
Blockchain networks should never classify as a highly concentrated marketplace, and ideally, should always fall into the competitive marketplace category. The more concentrated a marketplace is, the closer a single entity can be to gaining majority share of the compute power and performing a 51 percent attack.
Developers have their own network effect: the more smart developers there are working on a project, the more useful and intriguing that project becomes to other developers. These developers are then drawn to the project, and a positively reinforcing flywheel is created.
Chapter 14 – Investing Directly in Cryptoassets: Mining, Exchanges, and Wallets
The greater the value of the asset, the more money miners make, which draws new miners into the ecosystem, thereby increasing the security of the network. It’s a virtuous cycle that ensures the bigger the network value of a cryptoasset, the more security there is to support it.
Some exchanges “socialize losses” for leverage gone wrong because there is no other way the products can be offered.
Chapter 16 – The World of ICOs
Self-reinforcing economic ecosystems. The more people use the protocol, the more valuable the native assets within it become, drawing more people to use the protocol, creating a self-reinforcing positive feedback loop.
The online site BnktotheFuture.com provides angel investing opportunities in cryptoassets and related companies to accredited investors.
Chris and Jack’s Go-to Crypto Resources
Bitcoin Magazine: https://bitcoinmagazine.com/ This is our go-to resource for long-form articles that dive deep into critical developments in the cryptoasset space. While there is day-to-day coverage, we rely on it mostly for deep dives into complex topics.
BitInfoCharts: https://bitinfocharts.com/ While the user interface has historically been an eyesore, don’t judge a book by its cover. The site is a data trove for information that’s hard to find elsewhere, such as transaction characteristics, hash rate, rich lists, and so on for most all of the notable cryptoassets.
Blockchain.info: https://blockchain.info/charts The best place for charts and easily downloadable CSV files of Bitcoin network statistics.
BraveNewCoin: https://bravenewcoin.com/ A bevy of resources from analysis, to APIs, to carefully crafted indices, BraveNewCoin is focused on providing professional-grade resources.
CoinCap: https://coincap.io/ One of the best mobile apps for getting a quick view of the latest market action on all the cryptoassets. It also has a website, but in our opinion the mobile app is the gem, and even includes a feature for tracking your customized cryptoasset portfolio.
CoinDance: https://coin.dance/ Touting itself as “community-driven Bitcoin statistics and services,” CoinDance is loaded with unique Bitcoin charts, including statistics on LocalBitcoins trading volumes, node activity, sentiment polls, user demographics, and more.
CoinDesk: http://www.coindesk.com/ The ledger of record for the latest bitcoin, blockchain, and cryptoasset news. If you want to know what’s happened over the last 24 hours, a skim of CoinDesk is your best bet.
CoinMarketCap: https://coinmarketcap.com/ Provides pricing and trading volumes for all cryptoasset markets, as well as charts for aggregate cryptoasset action. One of the sites we visit most frequently during the day when the markets are hot.
CryptoCompare: https://www.cryptocompare.com/ The site where we consistently download the most data on the widest array of cryptoassets, CryptoCompare not only gives great (free) data on trading and volume patterns, but also technical indicators, social media stats, developer activity, and more.
Education: https://www.coursera.org/learn/cryptocurrency There are a growing number of quality courses available online that provide a deep understanding of bitcoin and cryptoassets. One of our favorites is the “Bitcoin and Cryptocurrency Technologies” course provided by Princeton University via Coursera.
Etherscan: https://etherscan.io/charts The best place for charts and easily downloadable CSV files of Ethereum network statistics, as well as insight into the cryptotokens operating on top of Ethereum.
Exchange War: https://exchangewar.info/ An all-encompassing website to track the activity of different cryptoasset exchanges globally and their respective share in different trading pairs.
I’d recommend ready the book in its entirety as it is a worthwhile investment for those interested in assembling a portfolio of Cryptoassets.
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