Bitcoin blockchain metrics: usage, price, statistical analysis informed trend predictions

How many people are currently using Bitcoin? How fast is the Bitcoin network growing relative to other revolutionary technologies and top tech companies? Is Bitcoin a bubble? Is BTC overvalued or undervalued? Is Bitcoin dead? Or will it someday take over entire industries? Does Bitcoin have the potential to someday become integral to global finance, commerce and society? If so, when?

What follows is a detailed statistical analysis of many relevant global technology trends, blockchain metrics, market penetration estimates, top tech companies’ and BTC trading prices—all aimed at answering the above questions, which I believe are foremost on the minds of many people interested in the modern crypto-phenomenon that is Bitcoin.

Historical technology trends

The S-curve

Figure 1: Historical adoption rates of new technologies in the U.S. during the 20th century.

Nearly all globally-impactful technologies, particularly communications networks, follow a similar adoption curve referred to as a logistical function. Logistical curves are used to model systems that grow exponentially towards some upper bound: living populations, chemical reactions, market penetrations, etc. A good example is the global adoption rate of the most revolutionary telecommunication technologies (Fig. 1). The World Bank’s global development indicators database provides information on telecommunications adoption rates (% of world population), in the form of Internet users and mobile cellular subscriptions (Fig. 2).

Global telecoms

Figure 2: Global Internet users, mobile cellular subscriptions, and fixed broadband subscriptions per 100 people.

The mobile cellular subscriptions data, from the Figure 2 inset, is clearly following a logistical curve, though there is not yet enough data to clearly show the entire S-curve in the other plots. Notice how all three curves (Fig. 2) begin growing at the same rate [TODO: quote and link to growth rate coefficients sheet] before tapering off into the middle-upper S-curve sections of their respective logistical functions. Also, note how the mobile cellular subscriptions is approaching a market saturation greater than 100% of the global population. As there are many people without access to cellular service, there is obviously a significant proportion of people with more than one associated cellular subscription. Although mobile cellular subscriptions may provide a relatively good benchmark for potential Bitcoin adoption rates (for many of the same reasons listed below), perhaps an even better benchmark is Internet users (Fig. 1-3). The growth of global Internet users should serve as a reasonable benchmark for the growth of Bitcoin for the following reasons:

  1. both are open source peer-to-peer (p2p) protocols
  2. both essentially require everyone to use the same network and protocol for it to be maximally useful
  3. both require rather significant infrastructures, but can be deployed atop existing communications networks (though not ideally)
    • e.g. Internet was first rolled out over phone-lines, before eventual infrastructure inversion (phone calls now routed over Internet lines)

But despite their similarities, there are also important differences between telecommunications technologies and Bitcoin that are likely to have important impacts on the growth of Bitcoin. For example, the Bitcoin protocol is designed to transfer trust and value rather than just information. The consequences of loss of trust/value are much more severe than mere loss of information, which may slow adoption rates due to the need to overcome larger trust barriers to entry. On the other hand, the ability to securely transact value and trust over a p2p network has the potential to be tremendously more valuable and innovative than the mere transfer of information afforded by the Internet. Because Bitcoin is the first network in human history capable of transferring value/trust securely, p2p, unsensorably, and potentially anonymously across the globe, we simple cannot predict many of its future properties. That said, as long as it remains a viable protocol, it will surely continue to grow along a similar logistical curve as other impactful technologies have done (Fig. 1-3).

Figure 3: Increasing rates of market penetration of new technologies in the U.S. during the 20th century


By analyzing the growth curves of other technologies, we can hopefully glean some insight into the potential growth of Bitcoin. During their respective linear growth phases (middle of S-curves), U.S. market penetration of the personal computer (PC) sustained growth of ~2.5%/year (Fig. 1, 3), while the more recent Internet and cellular services grew at ~5%/year (Fig. 1-3). Other technologies to have sustained a U.S. market penetration rate of ~5%/year, during the middle of their logistical S-curves, were the radio, television, VCR, and microwave oven (Fig. 3). In general, the speed of adoption of new technologies has continued to increase over recorded history (Fig. 1, 3). For this reason alone, it is certainly reasonable to expect Bitcoin adoption to proceed faster than Internet adoption, if only because there was no Internet when the Internet was being created, i.e. the spread of information is exponentially faster now than during the pre-Internet age.

In addition, the use cases of Bitcoin are much less limited to world population caps than even mobile cellular subscriptions, because machines can transact with other machines with much more ease than humans. There are a lot more machines on Earth than humans, all of which can be upgraded to participate in the global economy with little to no human involvement or oversight, e.g. self-owning vehicles, hotels, corporations, etc. Even if, and when, we reach near ubiquitous market penetration of Earth’s ~7.6 billion people, this will only be a fraction of the potential users of Bitcoin. I, for one, welcome our machine overlords, as they surely cannot be as evil as our human overlords!?

Bitcoin protocol and network trends

Besides fulfilling my own curiosity, I wanted to conduct a statistical analysis of the Bitcoin protocol, network, and trading price because there seems to be surprisingly few published, thorough analysis of such. In the interest of full transparency, and so that other nerds may hopefully expand on this work, I’ve provided all my scraping, processing, analyzing, fitting, and organizing python scripts and Matlab functions, along with resulting data and figures, in this Github repository.

Statistical and analytical techniques

The data sources used in my figures (Fig. 2, 4-15) are as follows:

I chose to use the blockchain data from because, short running a full node and compiling the various metrics manually, it provides one of the most comprehensive, organized, and accessible sources publicly available, for a wide range of blockchain metrics—shout out to Lawrent with Samouri Wallet for providing me beta access, fixing relevant data formatting bugs, and answering my questions regarding the database. I’ve chosen to use Bitstamp trading data mainly because it’s one of the longest running sources of reliable trading data. There is earlier trading data than the start of Bitstamp trading, from Mt. Gox, starting July 2010, during which time the price basically moved from USD/BTC$0.10 to $10. But I’ve not included this data in my analysis here because the relatively small price move during that time is largely extraneous for our purposes, and Mt. Gox trading has been shown to have been heavily manipulated by trading bots.

Short-term fit

Equation 1, ‘exp1’: y = a * exp( b * t )

Because Bitcoin is a network, in the early stage of its initial expansion, most blockchain metrics, along with its trading price, are clearly following exponential growth curves. I’ve used the exponential Equation 1 to model the various growth curves because its capable of fitting all the relevant exponential plots and is convenient for transposing data to logarithmic scales. In addition, Equation 1 has the following useful properties, for our purposes:

  1. time t units are normally measured in seconds and recorded as Unix timestamps
    • i.e seconds since Unix epoch, Jan 1, 1973
    • t units can easily be converted into years by multiplying by 1/(60s/m * 60m/h * 24h/d * 365d/y) = 1/3.1536e+07s/y = 3.1710-08y/s
  2. coefficient a = y(0) = y-intercept at t0
  3. coefficient b = rate of growth
    • when time t units are measured in seconds, growth rate a units are 1/second
    • growth rate at any instant equals a times value of y at that instant
      • i.e. a is percent growth per time period (y grows a % per time period t)
    • function is squeezed horizontally by the factor a
    • b rate units: [1/second]
  4. slope of curve is everywhere equal to y and goes through the point (t = 0, y = y0)
    • e.g. on May 22, 2010 (t = 1.274e+09s), Laszlo Hanyecz traded 2 pizza’s worth ~$41 for 10,000BTC
      • thus, y(1.274e+09) = 0.0041USD/BTC = slope of curve at t = 1.274e+09

Long-term fit

Equation 2, ‘poly1’: ln(y) = p1 * t + p2

When the data is converted to the logarithmic scale by taking the natural log ln() of each data point, the exponential Equation 1 curve plots as a straight line on the transposed ln(y) axis and is modeled by the simple linear Equation 2. For our purposes, Equation 2 has the following properties:

  1. coefficient p1 = slope of ln(y) at time t
    • p1 rate units: [1/second]
  2. coefficient p2 = ln(y)-intercept at t0 (t = 0)
    • p2 price units: [USD/BTC]

In the associated Matlab functions, Equations 1 and 2 are notated as ‘exp1’ and ‘poly1’, respectively, which are also the names of the respective Matlab best-fit functions. For our purposes, the fitting functions have the following properties:

  1. ‘exp1’ is biased to larger values, and thus to the more recent end of the curves
    • i.e. most representative of most recent history, because more proximal values are exponentially larger than early values
    • as such, ‘exp1’ is a good indicator for analysis of shorter term future trends
  2. ‘poly1’ is an unbiased fit across all time periods, because its calculated on the natural logarithm of values ln(y)
    • as such, ‘poly1’ provides a good indicator for analysis of longer term future trends
  3. the coefficient pairs for the above fits are related:
    • y = a*exp(b * x) –> ln(y) = ln(a*exp(b * x)) = b * x + ln(a)
      • thus: p1 = b, p2 = ln(a), and a = exp(p2)
  4. all fits are fitted to complete data sets, except for the partial price fit to July 2007 (Fig. 13)
    • the July 2007 subset fit is present because it altogether ignores the most recent price spike of the past ~6 months
      • as such, it may provide a more impartial fit of price data

So much for the boring (yet important) maths; let’s get into crunching the datas!

Statistical analysis of Bitcoin blockchain metrics

For more information, or to audit the preparation of these metrics, please go to the associated bitcoin-analysis github repo. All plots, except where otherwise noted, are based on weekly averages of daily data, i.e. each data point represents the average of seven days of data.

Figure 4: Cumulative block size, i.e. cumulative total size of all blocks mined per day. Source:

The growth of the daily cumulative block size is by design slowing overtime towards an upper limit (Fig. 4), despite an exponential increase in hashing power, because mining difficulty is regularly adjusted (and also growing exponentially) to hold block discovery times at approximately 10 minutes. Of course the growing adoption of SegWit over the past six months or so has somewhat raised the block size limit and should help eased this bottleneck as more of the network implements the protocol update.

Figure 5: Total addresses included in Bitcoin blockchain. Source:

Because its one of the lease constrained metrics (details below), is cumulative, and a few orders of magnitude larger than other metrics, the plot of total addresses (Fig. 5) is much smoother and very closely follows its exponential curve relative to other more volatile metrics. The same also applies to the total UTXOs plot (Fig. 8).

Figure 6: New addresses included in Bitcoin blockchain. Source:

The new addresses metric (Fig. 6) provides a better representation of active usage of the network, as most of the dust-only and abandoned addresses, present in total addresses, are removed. Of course, even this metric cannot be used as a direct indication of users, because many addresses (ideally a new one for each transaction) are controlled by individual wallets, of which users may have many.

Figure 7: Transactions included in Bitcoin blockchain. Source:

A better proxy to current Bitcoin activity is perhaps accepted transactions (Fig. 7); however, this metric also isn’t entirely representative of network activity as each transaction may contain thousands of UTXOs (Fig. 8). In addition, accepted transactions give no indication regarding current Bitcoin holdings (hodlers) and investment, store of value use cases. Certainly such use cases currently comprise much of the utility of Bitcoin, as can be seen in the dramatic decline in transactions (Fig. 7) directly inline with the recent price decline (Fig. 13-14). At this time, many users are choosing not spending their Bitcoin, as they believe it is currently undervalued, due to its long-term deflationary trait. Another interesting event is the 5x increase in transactions, summer 2012, associated with the rise in popularity of Satoshi Dice.

Figure 8: Unconfirmed Transaction Outputs (UTXOs). Source:

Unlike accepted transactions, the UTXO set includes all unspent balances, a.k.a. otherwise inactive hodlers’ ‘savings accounts’; however, unfortunately all dust outputs, unprofitable outputs, and other such unspendable outputs (e.g. outputs with permanently lost keys) are also included. Unprofitable outputs have been estimated to comprise a significant proportion of total UTXOs: anywhere from 20-70%, based on current mining fees. Hence, the UTXO set can provide us with some further insight into certain use cases, such as store-of-value, but is not without it’s own inherent noise that complicates data interpretation. Also, note the sudden order of magnitude increase in UTXOs, summer 2015, owing to a so called ‘stress test’, when millions of UTXOs were spammed to the network within a few days.

Figure 9: Bitcoin days destroyed, BDD. Source:

In an attempt to eliminate such spam from the network and provide a better estimate of actual economic volume, the BDD metric was proposed (Fig. 9), in which transactions are weighted by the age of their input UTXOs. The BDD metric is supposed to remove artificial noise transactions sent back and forth from single entities (e.g. spam) from legitimate transactions. Though, some would argue that all transactions are legitimate and there is no such thing as spam on the blockchain, at least for our purposes, this debate is largely semantic.

Figure 10: Estimated Bitcoin payments. Source:

Another metric proposed to provide a better estimate of actual economic activity is payments (i.e. #UTXOsCreated – #Tx – #OpReturn). Essentially, the estimated payments metric (Fig. 10) accounts for all newly created UTXOs that are not change addresses. Unfortunately, the estimate does include so called spam payments, as can be seen in the dramatic fluctuations surrounding the Satoshi Dice and ‘stress-test’ events noted above (Fig. 7, 8, 10). At this point, no single ideal metric exists as a direct proxy for network usage or number of users; although, there have been attempts made to estimate Bitcoin network usage and market penetration. Currently, such estimations are not very accurate, but may at least provide us with a very rough idea of the percent of the global population that are active Bitcoin users. Examples of such estimates range from ~320 million active users.

Owing to insufficient public KYC data (FTW!), such estimates must at present rely on proxies for users such as active wallets, unique addresses, or number of transactions. At least, such indirect proxies can certainly provide us with upper and lower bound estimates. Let’s say there’s 20 million active Bitcoin users; this would mean that less than ~0.3% (0.02/7.6B) of people on Earth currently use Bitcoin. For our own cross-reference estimation, let’s say the average global consumer conducts ~10 (1 is too low, 100 too high) economic transactions per day, that’s ~76e+09non-btc-txs/day. The largest non-spam payments volume there’s ever been on the blockchain is ~6e+05payments/day (Fig. 10 inset), which is ~8e-06payments/non-btc-tx ~= 0.0008%. Let’s also assume that an average user still conducts 100x more non-btc-txs than Bitcoin payments: 0.0008% * 100 = 0.08%. Thus, a reasonable order-of-magnitude estimation of the global percentage of Bitcoin users is likely ~0.2% (average of 0.3% and 0.08%). Though one recent study of 2001 Americans found that perhaps the percentage of holders is as high as 5% in the most affluent nations.

I think it’s safe to say we haven’t even come close to hitting the mainstream yet, which at least by one estimate of the current exponential growth, may be reached by approximately 2024, at 200 million users, ~3% of the global population. Using the average (50%/year) of the ‘exp1’ recent blockchain growth rates (Tab. 1), we find this time line to agree well with our estimated short-term growth rates: t3% = ln(0.03 / 0.002) / ln(1.5) ~= 7yrs.

Figure 11: Transacted BTC volumes. Source:

There are yet other metrics more focused on directly measuring the value associated with the blockchain, such as BTC volume (Fig. 11), mining fees (Fig. 12), and of course USD/BTC trading price (Fig. 13-14), which we’ll cover in the following section.

Figure 12: Bitcoin mining fees. Source:

Such metrics surely provide a better estimation of actual value present in the network, than other popular metrics, such as coin market cap, often falsely assumed to do the same. Using coin market caps as an estimate of their real-world value is problematic for a number of reasons. Firstly, market caps are not reflective of the actual economic usage of a particular coin. For example, BCash’s market cap is a significant portion of Bitcoin’s, but its actual usage (e.g. transactions per block) is far less significant relative to Bitcoin, i.e. all those big blocks are mostly empty. It’s also likely that, considering the most recent, obvious Bitcoin mempool spam attacks (Nov.2017-Jan.2018)—resulting in drastically increased mining fees (Fig. 12) and suspiciously correlated with artificial BCash pumps—a high percentage of those present on BCash are also spam: meant to provide a facade of non-existent value and utility.

Secondly, anyone can fork their shitcoin off their coin of choice, tweak a couple parameters with minimal effort, and instantly create a shitcoin with a marketcap rivaling Bitcoin’s own. For a good example of such, see Morgan Rockwell’s purported $400 Billion Counterparty Asset, BANKCOIN. If the shitcoin is a Bitcoin clone, this can be accomplished with even less effort because by their nature all Bitcoin-forks inherit Bitcoin’s associated market cap by default. Keeping this in mind, such metrics may be able to provide a very rough benchmark to compare Bitcoin against, as we’ll discuss in more detail later.

Metric trends

Decelerating growth

Very recently, there has been a pronounced decline across most metrics—particularly new addresses, transactions, payments, BTC volumes, and mining fees (Fig. 6, 7, 10-12)—most likely caused by the recent fall in trading price, which we’ll cover in the next section. Such declines in transaction volumes are typically correlated closely with large declines in BTC price, apparently hodling really is a thing ;).

Table 1: Combined and averaged ‘exp1’, ‘poly1’ fit coefficients data spreadsheet. Source: Eq. 1, 2

Note from Table 1 that the average BTC valuation is growing an order-of-magnitude faster than that of the top 5 tech companies, and faster than their combined totals! Approximate R2 goodness of fit values are provided along with block0-intercepts (Tab. 1); a live version of the spreadsheet is available at the associated bitcoin-analytics GitHub repo.

Over the longer term, there appear to be a few overall Bitcoin usage trends reflected in the above blockchain metric plots that are more easily recognized when directly comparing their growth coefficients (Tab. 1). Firstly, the ‘poly1’ fits is greater than that of ‘exp1’ fits, i.e. in general, blockchain growth has been slowing overtime. Most metrics are still growing exponentially, just at a slower rate than very early growth rates. There has been a similar leveling out trend across nearly all blockchain metrics, at least relative to their clear early exponential growth. This recent slowing could be interpreted as blockchain metrics stretching out at the top of the logistical S-curve, and although this is surely the case for certain constrained metrics (discussed below), it’s unlikely to be the case for relatively unconstrained metrics, due to the relatively low market penetration percentage (~0.2%) to date.

Indeed, when we compare the recent ‘exp1’ Bitcoin growth rates to those of the historically fastest growing technologies (~5%/year, Fig. 1-3), even the relatively slow Bitcoin growth of ~50%/year (Tab. 1) remains an order of magnitude greater than the Internet, mobile cellular, radio, etc. Also keep in mind, as previously mentioned, that Bitcoin is potentially far less limited by the global population than these previous technologies, because of the massive machine infrastructures we now have in place along with our rapidly advancing ability to automate these infrastructures: machines paying machines paying machines…

Inherent constraints

Of course some blockchain metrics have inherent limits and are surely reaching their capacity saturation points. The cumulative blockchain size and accepted transactions are such metrics that are directly constrained by the Bitcoin protocol. Most directly constrained metrics are already approaching the upper bounds of their associated s-curves, i.e. their growth rates are leveling off according to the current blockchain specifications.

Other metrics, though not directly constrained at the protocol level, can nonetheless be indirectly constrained for a couple or reasons:

  1. they are dependant on directly constrained metrics
    • UTXOs:
      • technically little constrained by transaction processing rate, as batching allows thousands of UTXOs per transaction
      • however, owing to bad practices (non-batchers), UTXOs are likely somewhat constrained by blocksize limit
    • payments, BDD:
      • derived in part from UTXOs, so share similar indirect constraints with the latter
  2. they are indirectly constrained by the current scaling pressures on the network (discussed in detail later)
    • mining fees:
      • as the transaction capacity bottleneck increases fees are driven upwards
    • new, total addresses:
      • usability difficulties, such as high fees and obtuse UX/UI, slow the adoption/usage rates
      • though addresses can by no means be directly correlated with users, they can be somewhat reflective of usage rates
    • BTC volume:
      • as transactions can be for any legitimate amount, BTC volumes should be fairly unrelated to transaction volumes or speeds
      • although certainly usage rates will impact the amount of BTC being transacted

Overcoming these limitations is the current challenge to scaling the Bitcoin—but have no fear, Lightning mainnet is here! Already we’re seeing the impacts of increased efficiencies as SegWit and batching become more widely used: Coinbase and Bitfinex implement SegWit, has immediate easing effects on fees, and mempool.

Statistical analysis of Bitcoin price

There is one metric that appears, at least for the time being, more or less decoupled from most blockchain metrics: BTC trading price. Although at least in theory, issues such as scaling should affect Bitcoin valuation, due to potential limitations of usability and other practicalities, such considerations seem to be at most secondary driving factors of BTC price. Perhaps such mundane considerations will come to bear more directly on the trading price, but at least for the foreseeable future, this seems unlikely to be the case as trading continues to be driven primarily by hype and speculation cycles.

Conversely, the trading price does often have significant impacts on other blockchain metrics, particularly those involving valuation measurements (Fig. 11-12). A good example of this is the most recent fall off of mining hashing power, as represented by cumulative block size (Fig. 4), very likely a direct result of the recent price decline (Fig. 13-14). A significant portion of the most inefficient miners have likely been taken offline, owing not only the the lower valuation of mined coinbases, but also the massive decline in transactions, payments, BTC volumes, and fees (Fig. 7, 10-12).

Figure 13: Unconstrained USD/BTC price fits. Source:

Figure 13 is a combined plot of full and partial (to July 2017) price fits, all unconstrained regarding their y-intercept values. The partial fits are meant to provide more conservative price fits, by removing any bias introduced by the most proximal bull run. Interestingly, these partial fits, despite their indifference to the recent price spike, plot very similar trends to the long-term ‘poly1’ full fit.

Figure 14: USD/BTC price fits, constrained through trading origin. Source:

Figure 14 plots full price fits constrained through BTC/USD1 at the start of Bitstamp trading, in an effort to mitigate unreasonably high or low block0-intercepts (i.e. y-intercepts at t = 1.316+09Unix-time, Tab. 1) that may be present in other price fits. This forces an emphasis on the beginning of Bitstamp trading and hopefully produces more reasonable block0-intercept values than the unconstrained fits (Tab. 1), i.e. prevents unreasonably high or low valuations at inception of Bitcoin.

It appears that trading price is generally the first mover and is much more a driver of other metrics (as mentioned above) than vice versa: network usage seems to decline during large-scale bear market price declines and increases during bull market price rises. Of course correlation doesn’t equal causation, but it’s improbable that blockchain metrics hold sway over trading prices (at least at present). For example, it’s exceedingly unlikely that a large swath of miners decided to shutdown proximal to the ATH, or that huge swaths of users stopped transacting all at once, leading to a collapse in price. Instead it’s much more sensible to assume the price decline drove the most unprofitable mining operations to shut down and users are hodling during bear markets and spending at ATHs.

Obviously Bitcoin valuation is still VERY volatile and not currently very helpful in evaluating actual utility of Bitcoin because it’s generally decoupled from most underlying blockchain metrics, e.g. short-term ‘exp1’ price fits greatly exceed long-term ‘poly1’ price fits, where the relationship is opposite for almost all other blockchain metrics (Tab. 1).

Top tech valuations

To provide some context for the unprecedented increase in valuation of Bitcoin since it’s inception, I’ve compiled a combined plot of the major five tech companies historical trading prices (Fig. 15), in the same format as my other figures in order to directly compare their valuation growth rates.

Figure 15: Historic growth of top 5 publicly traded tech companies. Source:

The stock prices of IBM, Apple, Microsoft, Amazon, and Google have all increased exponentially over time (Fig. 15), despite individual rises and falls in price owing to competitive advantages, shortcomings etc.; however, their growth rates are nearly an order of magnitude less than that of Bitcoin (Tab. 1). Yet, if we derive a more directly analogous trading price for BTC (according to respective marketcaps and trading prices) to those of the top four tech companies (excluding IBM), we arrive at an equivalent price of ~USD/BTC45,000. Using this comparison, the Bitcoin network is currently valued in the same vicinity as IBM, NVidia, or Netflix, after the latest price decline.

Global wealth

To provide another example valuation metric, others have noted that, if Bitcoin succeeds in largely supplanting gold’s marketcap (~USD$8.5 trillion) as a superior store of value, BTC would have to increase in value to over USD/BTC500,000 ($380,000 if all 21 million coins were in circulation). Yet, as large as these valuations may appear now, they remain a mere drop in the bucket next to global exchanges, banking, federal reserves, equities, assets, and wealth valuations. Of course due to many unaccounted for complexities, such direct comparisons are at best a rough benchmark to put BTC valuation in context and are largely beyond the scope of this article.

But, despite the certain imprecision of such contextual benchmarks, they can provide important guides to help us put Bitcoin’s potential in better perspective, in the absence of any other good comparison standards. Considering the technological and societal potential of Bitcoin next to such valuations of existing competitors, we can hope to gain some insight into the valuation of Bitcoin, now and in the future.

Is Bitcoin currently overvalued or undervalued? Could Bitcoin compete with and eventually supplant gold and federal reserves as a global store of value? What portion of existing global commerce is Bitcoin capable of supplanting? How about the banking industry [LINK: banking article]? Portions of governance structures? Obviously, no one can reliably answer such question yet; we must simply wait and let the free market decide.

Hand-waving arguments

The above questions naturally lead into the next section: wild speculation and waving of hands! Now that the above statistical analysis is done, I’ll do my best to summarize some likely patterns and interpretations of the above analysis, a.k.a. hand-waving.

Scalability issues

There certainly seems to be a common trend of recently decelerating growth across most of the Bitcoin network metrics. It has been argued this is because Bitcoin is a FOMO bubble, it’s unusable, it’s pure speculation, etc. Perhaps there is some merit to these arguments, though they are often themselves based on speculation and FUD rather than logical or statistical analysis. Surely such criticisms of Bitcoin do impact its growth and valuation; however, it seems probable to me, at least, that much of the recent slowing trend is primarily due to more predictable scalability issues and market cycles that Bitcoin must go through as it continues to grow. As the full utility of the network has hardly been imagined, let alone realized, it’s improbable that Bitcoin is already reaching it’s long-term market saturation point typical of the leveling off period at the top of its S-curve.

Emerging markets commonly follow a relatively predictable growth pattern referred to as hype cycles (discussed in the next section). For the past few months the BTC price has been moving downwards from its latest astounding bull run to ~USD/BTC20,000. As such, we’re currently in the declining lull position of the market cycle. This pattern has already repeated many times, during the short trading history of BTC. No, Bitcoin is not dead; no, it’s not an exit scam, like other crypto-scams.

The primary use case of Bitcoin is currently speculation on its future growth in valuation; secondary, but rapidly growing, use cases are investment and store of value, which are related to above speculation, but are still limited by early stage volatility. Bitcoin has barely scratched the surface of traditional investment and financial markets, and it’s fair to say that its uses in traditional trade and commerce marketplaces have hardly been considered, let alone implemented in any real way. Why? Because, it is very new. Technically, it’s still in beta as Core has yet to release 1.0 standards.

The network has many scalability difficulties yet to overcome:

  • prohibitively high computational demands:
    • full nodes require large amounts of memory and bandwidth
      • can be cost prohibitive for low income individuals
      • can be practically prohibitive over low-bandwidth Internet
    • regular proof of work difficulty adjustments:
      • increased mining power does not translate into increased block discovery times
        • thus, protocol does not allow for improvements to mining efficiency, hashing power, or block/transaction throughput
    • hard block size limit (excepting SegWit update):
      • limited capacity for transaction volumes and throughput
  • non-user-friendly UI/UX
    • clunky, often command line, tools designed by, and for, software developers
    • exclusively beta software availability
  • market fractionation and dilution by scams

Many of the above scaling bottlenecks can be all but eliminated by continued improvement and development; however, Bitcoin mining has been specifically designed not to scale. Though some, e.g. big-blockers, see this as a bug, it’s really a feature that secures the network. Don’t like it? Feel free to go over to the big-block party at BTrash. But be warned that such short sighted, quick fixes, such as merely increasing blocksize, will not solve the scaling problem in the long run and simply put off finding real solutions into the future when scaling problems will be even harder to deal with. Core devs have instead decided to focus working on protocol optimizations and Bitcoin enthusiasts are building second layer solutions like Lightning, to promote long-term stability and security of the network.

BTC price drivers

Current events

Often the most impactful current events—Silk Road shutdown and Ulbricht arrest, Oct. 2, 2013 (Cht. 1; start of CME futures trading, Dec. 17, 2017 (Fig. 13-14); SEC/CFTC senate hearing, Feb. 6, 2018 (Fig. 13-14)—act as major market inflection points. Certainly such major events affect movements in the trading price of Bitcoin (Cht. 1); however, such movements are generally on a small scale, relative to a much larger, overarching driver of price. Michael B. Casey has made a very convincing argument, in his Speculation Adoption Price Theory, for the primary driver of Bitcoin price being the fractal, Gartner Hype cycle. He does a great job of describing the details of the hypothesis in the above links; so please do follow up on those.

Chart 1: Local impacts of media events on BTC trading price. Source: Tone Vays,

Fractal hype cycles

A Gartner hype cycle is a well known market pattern driven largely by speculations and emotions, e.g. hype, FOMO, FUD, dissolution, despair. When such a cycle is present in the adoption curve of a new and revolutionary technology, it is simply stretched exponentially in the vertical axis. The result is that each repeat of the hype cycle is roughly an order of magnitude larger than the one that came before. For example, if we look at the last major, 2013 boom and bust cycle, when the price rose from ~USD/BTC100-1000 and subsequently fell back to ~USD/BTC250 (Fig. 13, 14), we’re effectively looking at a single mega-hype cycle: price quickly increases by an order of magnitude (~10x) and then gradually subsides back to a quarter (~25%) of its latest all time high (ATH). We’ve seen this fractal pattern iterate perhaps seven times already, at different scales during the trading history of Bitcoin.

Michael B. Casey’s fractal hype cycle hypothesis is very clearly supported by Google Trends data for worldwide searches for ‘Bitcoin’ over time. An intuitive interpretation of the fractal hype cycle, in the context of technology adoption curves, is summarized well in Amara’s Law:

“We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”

Marketshare FUD

But hasn’t Bitcoin lost significant marketshare to other cryptocurrencies? And isn’t it in danger of losing even more, owing to its 1000s of perceived competitors? Marketshare is simply a comparison between the marketcaps of various coins. Marketcap is a metric used to assess the size and valuation of traditional publicly traded companies, which can be exceedingly deceptive (for reasons previously discussed) when applied to cryptocurrencies: coin-marketcap = trading-price * total-coins. The problem is that, being centrally controlled, both the price and number of coins are trivially manipulated in the vast majority of shitcoins. For example, see the more-than-suspiciously-timed BCash pump during the exact moment of Roger Ver’s appearance on Alex Jones’ Infowars, amidst an otherwise entirely bearish crypto-market.

For these reasons, among others, it seems exceedingly unlikely that Bitcoin has any real competition for supremacy or real-world marketshare. Of course there have been, and there will continue to be, such attacks aimed at dethroning Bitcoin; however, the network effect that Bitcoin has already achieved is an ever-rising, already nearly-insurmountable defense against any would-be competitors. By definition, there can be only one global reserve currency, of which Bitcoin is already clearly defined as in the crypto-space. The first mover advantage is tremendous with Bitcoin, which is indisputably recognized and accepted globally as the de facto cryptocurrency. Potential usurpers are already unable to compete directly against the Bitcoin protocol but must find some niche not currently supported by Bitcoin. Such niches, if proven valuable, are sure to be incorporated into the Bitcoin protocol over the long-term—indeed, the majority of such niches are first discovered by Bitcoin developers themselves. And of course, Bitcoin continues to attract the best and most developers in the space.

Informed predictions

Let me now pull out my informed crystal-ball. Barring some unforeseen technical failure of the protocol—which becomes evermore unlikely every day that Bitcoin survives and thrives—I believe the following predictions follow directly from the above analysis:


Presently, look forward to the Lightning Network alleviating many of Bitcoin’s scaling pressures—after all, Lightning is specifically built for this very purpose. The widespread implementation of Lightning should bring on renewed accelerating growth of most blockchain metrics not specifically constrained by the Bitcoin protocol (not to mention introduce entirely new metrics!). Very likely, a soon-to-be-released, killer Lightning app will bring unprecedented growth to the Bitcoin network.

In the short-term, unconstrained metrics—addresses, fees, volumes, and price—are very likely to continue increasing exponentially at ~50%/year (Tab. 1), more or less along their respective ‘exp1’ curves. Collectively, metric growth may continue slowing slightly in near term, but should again accelerate far beyond long-term logistical growth curve ‘poly1’ rates greater than 90%/year (Tab. 1). Other more constrained metrics, should continue to level out along their respective upper bounds.

Meanwhile, BTC price is likely to continue sliding downwards towards ~USD/BTC5000 (~25% of last ATH). Although, there remains some hope that, similar to the two-phase, 2013 mega-bull-run (Fig. 13-14), there is still an immanent second bull-run left in this particular mega-hype cycle. But if Google Trends data remains a good indication of bull runs, it seems the slide will continue for the time being.


In the medium-term, ‘poly1’ growth rates of unconstrained metrics (~90%/year, Tab. 1) should generally prevail over short-term ‘exp1’ rates and become more accurate and predictive, as additional data accumulates. Dramatic fluctuations above and below the ‘poly1’ growth curves will predictably continue according to fluctuating ‘exp1’ rates. Volatility will continue, and likely increase, as volumes increase by orders-of-magnitude), as we continue to proceed along the hockey-stick bend of the tremendous exponential growth yet to come.

Over the course of the next mega-hype cycle’s order-of-magnitude increase, the trading price of BTC should rocket to the vicinity of ~USD/BTC200,000 by 2021 (t200k = ln(200000 / 20000) / ln(2.7) ~= 2.3yrs), if the recent ‘exp1’ price growth rate of ~170%/year holds. And yes, I feel like a crazy person writing this right now, but as long as current statistical trends hold, this is inevitable. Something that makes me feel somewhat less crazy, is that someone else correctly predicted the recent valuation of $10,000 back in 2014, using a very similar logarithmic plot of BTC price, which also corroborates this estimate.

If the medium-term price growth proceeds along the slower long-term ‘poly1’ rate of ~100%/year, it will only take an additional year to reach the vicinity of USD/BTC200,000 (t200k = ln(10) / ln(2) ~= 3.3yrs). Subsequent to reaching this order-of-magnitude-higher ATH, the price will again likely slide downwards toward the vicinity of ~USD/BTC50,000 (25% of ATH), in preparation for the next mega-hype cycle.

The previous mega-hype cycle, peaking late-2013 (Fig. 13-14), took ~4 years to complete (assuming is ended in the new ATH peak in late-2017). This time period is in better agreement with the faster ‘poly1’ growth rate; however, keep in mind that, owing to the nature of exponentials, the period it takes each mega-hype wave to complete is also rapidly decreasing, i.e. each successive mega-hype cycle will happen faster than the last.


In the long-term, the local impacts of outside manipulations, would-be regulators, and growth hype-cycles will cease to be the driving forces behind the trading price of Bitcoin. At such time, BTC trading price should become much more stable and reflective of the actual utility and value of Bitcoin, as it becomes ever more integral to the collective global economy and society!

Many years from now, we’ll exit the exponential growth phase and gradually settle into the final saturation equilibrium top portion of the logistical S-curve. As we approach this top saturation limit, volatility should largely cease to be an issue, as the volumes across most metrics will be many orders of magnitude greater than at present, and any potential market movers will themselves have to be orders of magnitude more influential. For now, these absolute equilibrium levels remain difficult to predict, but we can gain important insight as to their magnitudes by continuously comparing Bitcoin metrics against existing semi-analogous financial benchmarks.

Until that time comes, just keep in mind just how early in Bitcoin’s adoption curve we remain at present; how far down those first technology adoption curve figures (Fig. 1-3) is 0.2%? Go ahead and have a second look. Remember how far we have yet to grow from here; we’re still at least 3 orders-of-magnitude below the level of broad and narrow money supplies!

“The greatest shortcoming of the human race is our inability to understand the exponential function.” Albert Allen Bartlett

Despite spending far too much time on this article, statistical errors may remain (math was never my strong suit), and almost certainly, logical errors are present in my hand-waving arguments, though I’ve tried to base as much of my logic on statistical analysis as possible. For corrections, questions, or comments, please contact me directly via twitter, email, or GitHub.

Thank you for bearing with me; I hope to have provided some insight into our beloved Bitcoin.