BitMEX Research Launches Ethereum Node Metrics Website –

Abstract: BitMEX Research is delighted to announce the launch of a new website to monitor the Ethereum network, The website connects to five different Ethereum nodes and collects data every five seconds. The main focus of the website is providing metrics related to the computational resources each Ethereum node requires. While analysing some of the metrics, we may have identified issues with respect to the integrity of the data reported by the nodes, which may be of concern to some Ethereum users. was produced in collaboration with TokenAnalyst, who are BitMEX Research’s Ethereum network data and analysis partner.

(Screenshot of website as at 12 March 2019)

Overview compares the statistics of the two largest Ethereum node client implementations by overall adoption – Geth and Parity. Within these client implementations, compares the performance of different node configurations – fast, full, and archive nodes.

The main purpose of is as follows:

  1. To provide metrics comparing the computational efficiency of the different Ethereum implementations. For instance by comparing requirements related to:
    • CPU usage
    • Memory (RAM)
    • Bandwidth
    • Storage space
  2. To compare the resource requirements between running Ethereum node software and that of other coins, such as Bitcoin
  3. To evaluate the strength of the Ethereum P2P network and transaction processing speed, by looking at metrics related to whether the nodes have processed blocks fast enough to be at the chain tip or whether poor block propagation results in nodes being out of sync for a significant proportion of the time began collecting data at the start of March 2019 and it is too early to draw any firm conclusions. However, we are saving the data and hope to analyse the long term trends at a later point. The data is produced by querying our five Ethereum nodes or machines running the nodes, every five seconds (720 times per hour) and then storing the results in a database. Various rolling averages and other metrics produced from this data, are displayed on the website.

Description of the Nodestats metrics

Name Description Initial findings
% of time in sync This represents the percentage of time the node has verified and downloaded all the block data, up what the P2P network is informing the node is the chain tip.

The hourly metric is calculated by determining if the node is at the tip every 5 seconds, which should be 720 queries per hour. The proportion of these queries where the node says it is at the tip is the reported metric.

This field is based on the web3 “isSyncing” field, which we believe uses the highest block the node has seen, the “highestBlock” field, to determine if the node is behind what its peers regard as the highest block ever seen.
Nodes typically report they are at the tip around 99.8% of the time, which means that in only around 1 of the 720 hourly queries are the nodes not at the chain tip.

The only exception here is the Ethereum Parity full node, which we talk about later in this report.

We believe the data integrity of this metric is poor, for instance in the case of the Parity full node the integrity of the information provided is weak, as we explain later in this report. Going forwards we aim to establish a more effective way of calculating this metric.
% of time on conflicting chainThis represents the percentage of time the node is following a different or conflicting chain to the node opposite it on the website.

This is determined by storing all the block hashes in our database, if the nodes have a different block hash at the same height, they are considered to be on different chains.
Typically is not able to identify times when the clients are following different chains. As such this metric is normally 0%. (i.e. 0 times out of 720 in a one hour period)
CPU UsageThis represents the average percentage utilization of the machine’s CPU resources.

All the machines are using have the “Xeon(R) CPU E5-2686 @ 2.30GHz” processing unit with two cores. The exception to this is the archive node, which has 16 cores.

All the nodes are using the AWS “i3.large” machines, with the exception of the archive node, which is running “i3.4xlarge”.
Generally speaking, CPU usage tends to be between 0.01% and 1.0%. Parity tends to be towards the 1% level, while Geth appears to use less CPU power.

Geth’s CPU usage appears less stable than Parity’s, with Geth’s CPU demand occasionally spiking to around the 1% level.
Memory takes a reading from the machines every 5 seconds, related to how much memory is being utilized by the Ethereum client.

All the machines are using have 14GB of Ram, with the exception of the archive node, which is a 120GB of Ram machine.
Generally speaking, however much RAM is available, the nodes use up the overwhelming majority of it (e.g. over 95%).

The memory demands of the clients appear to be reasonably stable.
Peer countThe node provides with the number of network peers, every 5 seconds.Parity tends to have around 450 peers, while Geth only has around 8.

Geth’s peer count is more volatile than Parity, as it appears to occasionally fall to around 6.
Upstream bandwidth takes a reading from the machine every 5 seconds, related to the total network upstream bandwidth of the server. Parity, which has more peers, tends to use over 100KB/s of bandwidth (in each direction). In contrast Geth tends to only use around 4KB/s of bandwidth.

Geth’s bandwidth demand tends to be more volatile than Parity, with occasional spikes to around 60KB/s.
Downstream bandwidth takes a reading from the machine every 5 seconds, related to the total network downstream bandwidth of the server.
Chain data sizeThis metric represents the total data utilized by all the directories dedicated to the client.

Unlike the other metrics, the disclosed figure is the absolute value, not a rolling 1 hour average.
Currently, Parity requires around 180GB, Geth uses just under 200GB, and the full archive node uses up 2.36TB of data.

The Parity full node is still syncing

The Parity full node was started on 1 March 2019, at the time of writing (12 March 2019) it has still not fully synced with the Ethereum chain. The client is around 450,000 blocks behind, and based on its current trajectory, it should catch up with the main chain tip in a few days. Due to the slow initial sync, the “% of time in sync” metric is shown as near 0%, as the client is never in sync.

The Ethereum Parity Full node machine has the following specifications:

  • Dual Core 2.3GHz
  • 14GB of RAM
  • SSD storage
  • 10 Gb/s internet connection

The fact that a machine with the above specification takes over 12 days to sync may indicate that it is the initial sync issues could be a greater concern for the Ethereum network than post sync issues, such as block propagation. While the slow initial sync is a potential problem, at least for this system setup, Ethereum has not yet reached a point where the node cannot catch up, as the sync is faster than the rate of blockchain growth.

Data integrity issues

The Parity full node also sometimes reports that it is in sync, despite being several hundred thousand blocks behind the chain tip. For instance in the screenshot at the start of this piece, the website reports that the node is fully synced 0.02% of the time, indicating the node falsely thought it was at the tip for some periods of time.

As the chart generated from the Parity full node logs below illustrates, the highest block seen on the network figure, in blue, appears potentially incorrect. The highest block number seen on the network figure, sometimes falls in value as time progresses and has remained consistently well behind the actual chain tip (shown in green). On occasion this potentially buggy figure fell towards the height of the verified chain (orange) and our website incorrectly reports the node as in sync. This may be of concern to some Ethereum users, since the Parity full node has many connections to the network, therefore this may be a bug.

Ethereum Parity Full Node Block Height Data – 11 and 12 March 2019 (UTC)

(Source: Ethereum Parity full node logs)

This potential bug could undermine this whole metric for our website, even for the other nodes, as the highest tip seen field may not function appropriately and our figures may be inaccurate. However, we continue to include this metric, since the website displays the data reported by the nodes, regardless of our view on the integrity of the data. We may look to implement our own improved metric in the future.

One could argue the impact of this potential bug could be severe in some limited circumstances, if exploited by an attacker in the right way. For example a user could accept an incoming payment or smart contract execution as verified, while their node claims to be at the network chain tip. However, the client may not really be at the chain tip and an attacker could exploit this to trick the recipient into delivering a good or service. The attacker would need to double spend at a height the vulnerable node wrongly thought was the chain tip, which could have a lower proof of work requirement than the main chain tip. Although successful execution of this attack is highly unlikely and users are not likely to be using the highest seen block feature anyway.


Like its sister website,, is very much a work in progress. Along with TokenAnalyst, over the coming months and years, we plan to add more features, such as:

  • Improving the integrity of the data, by being less reliant on what the nodes report and developing our own calculation methodologies
  • Charts & tools for analysing longer term trends
  • Improved granularity of the data
  • Fork detection systems
  • Data related to other peers

For now, provides a useful tool to assess the approximate system requirements for running Ethereum nodes. At at a very basic level, it also provides mechanisms to assess the reliability of the Ethereum network and its various software implementations. However, we accept that the “% of time in sync” metric may not be reliable, but it does highlight a potential issue.

Notice of Minor System Outage 19 February 2019

On 19 February at 05:31 UTC, BitMEX experienced a minor outage for approximately 1 minute whereby all trading engine operations were suspended.

This issue occurred due to a sustained period of data transfers between the internal market data distribution components. This was part of a regularly scheduled update to improve the overall resiliency of the platform. The root cause has been identified and a fix via internal processes has been put in place to prevent a recurrence. Additionally, we are continuing to re-work our market data distribution architecture to eliminate any potential impact to the trading engine in such a scenario.

We apologise for the inconvenience. Should you have any questions, please contact customer support.

Anatomy Of The Next Global Financial Crisis

Abstract: We examine a question which many in the crypto-currency community frequently ask: “When is the next global financial crisis going to happen?” We attempt to answer this by first explaining how that since 2008, the epicentre of financial risk seems to have shifted from the banks to the asset management industry. We therefore argue that a repeat of 2008, where retail banking deposits and payment systems are under threat, is unlikely. In particular, we assert that corporate debt investment funds and unconventional debt investment vehicles, encouraged by the deceptively low volatility and low return environment, could be the area where the fragility in the financial system is most significant.

(Ten years since the global financial crisis, as the newspapers from the time exposed to the sunlight turn yellow and pink, at some point credit conditions could tighten significantly again, but will the asset management industry, rather than the banking sector, be the epicentre of risk?)


Due in part to the timing of its launch, Bitcoin is said to have been born out of the financial chaos and scepticism resulting from the 2008 global financial crisis. Therefore, many Bitcoin investors and members of the crypto-currency community often seem to ask:

When is the next global financial crisis going to happen?

Due to the demand, we will attempt to address the issue.

First, we will look into the question itself. In our view, there appears to be three main assumptions behind the question:

  1. The next global financial crisis will arrive in the next few years and it’s inevitable one will occur every decade or so;
  2. Such a crisis will have a positive impact on the price of Bitcoin;
  3. The next global financial crisis will look similar to the last one, resulting in many questioning the integrity of the banking system and electronic payment systems.

Of these three assumptions, we only really agree with the first one. Although we think the latter two assumptions could possibly hold true, there is significant uncertainty about them.

As for the second assumption, we touched on this issue in March 2018, when we noted that Bitcoin was trading more like a risk-on asset than a safe-haven asset. Of course the Bitcoin price has fallen a lot since then and this could change going forwards. If Bitcoin does respond well in the next crisis (when liquidity is constrained), that will be a huge positive for Bitcoin and the store of value investment thesis. Although, there is no significant evidence for this yet. A decoupling of the Bitcoin price from many of the alternative coins, which more clearly have a risk-on type investment thesis (e.g. world computer or high capacity payment network), is necessary for this to occur, in our view.

As for the third assumption, the mechanics of the next global financial crisis, that is the focus of this report.

Bank Balance Sheets In Developed Markets Are Relatively Healthy

As the famous saying goes, “History doesn’t repeat itself but it often rhymes.” Over the last decade, bank management teams and banking regulators have operated in the shadows of 2008. As a result, bank balance sheets and capital ratios have significantly strengthened. Bank Tier 1 capital ratios in developed markets have improved from around 5% pre-crisis to around 12% today (Figure 1). The more basic ratio of equity to total assets, which is more difficult to manipulate, also illustrates a similar story: improving from c5% to c9% in the period (Figure 2).

Figure 1 – US & UK Bank’s Aggregate CET1 Capital Ratios

(Source: UK aggregate data from the Bank of England, US data from the Federal Reserve)

Figure 2 – US Bank’s Aggregate Tangible Equity to Total Assets Ratio (Banks with over $50 billion in assets)

(Source: Federal Reserve)

Perhaps even more revealing and compelling than the above ratios, is the following more simple chart (Figure 3). It illustrates that the main western banks have not expanded their balance sheets at all since the global financial crisis. Actually, the sample of the nine major banks we have reviewed experienced a significant decline in total assets in aggregate, from US$19.3 trillion in 2008 to US$15.6 trillion in 2018. One could argue that M&A activity is a driver of the below chart, but our point still stands.

Figure 3 – Total Assets Of Selected Banks In Developed Markets – US$ Trillion

(Source: BitMEX Research, Bank Earnings, Bloomberg)

(Note: The chart represents the total reported assets for JP Morgan, Bank of America, Citigroup, Wells Fargo, HSBC, RBS, Deutsche Bank, Credit Suisse and UBS.)

In our view, financial leverage is one of the the primary drivers of financial risk. The epicentre of risk in the financial system appears to have shifted since 2008. In 2008, the risk was caused by leverage in the banking system and the interrelationships between this and the securitisation of the mortgage market. Today, the equivalent risk is leverage in the asset management industry and in particular the corporate debt sector, driven by the deceptively low volatility environment.

Growth In Leverage In The Asset Management Industry

The asset management industry is far more opaque than banking and determining the degree of leverage is far more challenging. Therefore, it is difficult to conclude on either the extent of leverage in the asset management industry or the timing of any financial crisis related to this leverage.

A 2015 Bank of International Settlement (BIS) report entitled “Leverage on the buy side,”  focused on the shift in risk from the banking system to the asset management industry. The report notes that while investment fund leverage has been reasonably stable in equities, in the fixed income area, it has grown considerably since 2008, especially in emerging markets. The BIS report concludes with the following:

Leverage in the banking system was an important ingredient in the 2008 global financial crisis. Since then, asset managers (the “buy side”) have quickly increased their footprint in global financing, helped by the sharp retrenchment of banks nursing their balance sheets back to health. Balance sheet information for investment funds is much less readily available than for highly regulated banks. Using information provided by a market data vendor, we found that leverage on the buy side is not negligible, although it seems to vary considerably depending on the type of fund. Equity fund portfolios seem to be minimally leveraged, while fixed income funds tend to resort abundantly to borrowed money.

(Source: BIS)

The BIS report used data from investment fund flow specialist EPFR, and although we agree with the report’s conclusions, it is difficult to formulate a strong view of the data’s reliability. Although we have not found good sources of global data ourselves, US-domiciled investment funds over a certain size are required to submit data to the SEC about the extent of leverage used. The SEC has complied this data since Q2 2013 and we have summarised the main trends in the below charts (Figures 4, 5, and 6).

The data shows that, unlike the banking sector, the asset management industry has expanded considerably since 2008 (Figure 4). At the same time, leverage also appears to have increased, although producing a clear chart since 2008 illustrating this is difficult.

Figure 4 – US Fund Industry Gross Asset Value (US$ billion)

(Source: BitMEX Research, SEC)

Although there are competing methodologies, the most basic method for establishing the degree of leverage for an investment fund is calculating the gross asset value over the net asset value, sometimes referred to as the gearing ratio. Unfortunately the time span in the below chart (Figure 5) is limited, but it appears to indicate a moderate expansion of leverage, at least in the hedge fund sector.

Figure 5 – US Private Fund Industry Gearing Ratio – Gross Asset Value/Net Asset Value

(Source: BitMEX Research, SEC)

The gearing ratio underestimates true leverage, by ignoring the impact of derivatives. Disclosure of the notional value of derivative exposure is also required by the SEC. The below chart illustrates the growth in usage of derivatives by US-based hedge funds.

Figure 6 – US Private Fund Industry – Hedge Funds – Notional Value Of Derivatives/Net Asset Value

(Source: BitMEX Research, SEC)
(Note: Adjustments were made to reflect changes in how the SEC reports the data.)

New Corporate Debt Market Vehicles

In addition to the increased use of leverage in the fixed income market by investment funds, the mechanics of the debt markets are becoming increasingly complicated and opaque. The replacement of the role of the banks in the corporate debt markets, has resulted in the rapid growth of a whole range of interrelated, non-mutually exclusive investment structures. Some of these structures are summarized in the table below.

Type of Debt Description/Comments Reference
Collateralized Loan Obligations (CLOs) CLOs are when a group of loans from multiple companies, are pooled together to form a security. The product is typically split into different tranches, lower risk tranches with lower returns and higher risk tranches with higher returns. Investors in the highest risk tranche are the last to be paid in the event of any insolvencies.

Typical buyers of these products are pension funds, insurance companies and hedge funds. They are particularly popular with yield hungry Asian investors.

Market growth – Figure 7
Leveraged Loans These are typically variable rate loans provided to companies who are already highly indebted. In the majority of cases the loan is fully unsecured. The typical holders of such instruments are pension funds and other private investors.

The Bank of England recently estimated the size of the leveraged loan market globally at US$2.2 trillion and compared it to the size of the US subprime mortgage market in 2006 (US$1.3 trillion).

Market growth – Figure 8

Credit quality – Figure 15

Private debt deals This is similar to the leveraged loan market, except the debt does not normally trade on a secondary market. Market growth – Figure 9
Bond fund ETFs and mutual funds ETFs have expanded in popularity in all asset classes in the period, corporate bond funds are no exception. Market growth – Figure 10
Private Equity Credit quality – Figure 16

(Note: The fields in the above table are not meant to be mutually exclusive)

As the following charts from various sources indicate, all these non-bank mechanisms for providing corporates with financing have grown considerably since the last global financial crisis.

Figure 7 – Size of Collateralized Loan Obligation (CLO) Market – US$ billion

(Source: Citi, FT)

Figure 8 – Size of US Leveraged Loan Market – US$ billion

(Source: S&P, FT)

Figure 9 – Size of Private Debt Market – US$ billion

(Source: Bank of America, FT)

Figure 10 – Size of the Top Bond ETFs Targeting US Investors – US$ Billion

(Source: BitMEX Research Bloomberg)

(Note: The chart represents the sum of the market capitalisations of the following bond ETFs: iShares Core U.S. Aggregate Bond ETF, Vanguard Total Bond Market ETF, iShares iBoxx $ Investment Grade Corporate Bond ETF, Vanguard Short-Term Corporate Bond ETF, Vanguard Short-Term Bond ETF, Vanguard Intermediate-Term Corporate Bond ETF, iShares J.P. Morgan USD Emerging Markets Bond ETF, Vanguard Total International Bond ETF, iShares MBS Bond ETF, iShares iBoxx $ High Yield Corporate Bond ETF, PIMCO Enhanced Short Maturity Strategy Fund, Vanguard Intermediate-Term Bond ETF, iShares Short-Term Corporate Bond ETF, SPDR Barclays High Yield Bond ETF, iShares Short Maturity Bond ETF)

Corporate Debt Markets Conditions

As Figure 11 below illustrates, corporate debt levels have increased considerably since 2008, with gross debt of Russell 3000 companies now totalling US$11 trillion, compared to being just over US$8 trillion at the time of the last crisis. Corporations have taken advantage of low interest rates and the new investment products mentioned above, to borrow money at record levels.

However, as the red line on Figure 11 illustrates, Russell 3000 corporate balance sheet conditions still appear reasonably healthy, with an aggregate net debt to EBITDA of just under 2.5x.   Although this ratio has been increasing in the last few years, it is nowhere near as large as the c3.7x level which was present before the 2008 financial crisis. This perceived strength is caused by a few large tech giants hoarding cash and the strong economy boosting earnings. If the economy turns, corporate balance sheets could start to look unhealthy again, as earnings fall.

Figure 11 – Corporate Debt levels

(Source: BitMEX Research, Corporate Data, Bloomberg)
(Note: The figures consist of aggregate data for all the Russell 3000 companies.)

There is a considerable volume of corporate bonds set to mature in the coming years. This could exacerbate the impact of any liquidity crisis or stress in the fixed income sector. As our analysis shows (Figure 12), US$ 880 billion of corporate debt in the US will mature in 2019.

Figure 12 – Corporate Bond Maturity Wall – US$ billion

(Source: BitMEX Research, Bloomberg)
(Note: Figures are based on a database of around 6,400 US corporate bonds with an aggregate amount outstanding of US$5.7 trillion.)

Perhaps the most alarming indicator is the quality of the corporate debt. Figure 13 shows the credit rating distribution of the outstanding investment grade corporate debt over time.  At the end of 2018, almost 50% of the bonds had been rated at the lowest possible rating for investment grade securities, a far higher proportion than any time in the past 30 years. Figure 14 indicates that the situation from 2021 will get even worse, when the overwhelming majority of corporate debt maturing will be at the lowest investment grade rating.

Figure 13 – S&P Credit Rating Distribution of US Corporate Bonds Over History

(Source: Bloomberg, HSBC USD IG index constituents, including financial and non financial companies)

Figure 14 – S&P Credit Rating Distribution of US Corporate Bonds Outstanding By Maturity

(Source: BitMEX Research, Bloomberg)
(Note: Figures are based on a database of around 6,400 US corporate bonds with an aggregate amount outstanding of US$5.7 trillion.)

Evaluating the credit quality of some of the less conventional debt vehicles mentioned above, is more challenging. However, a recent report from Moody’s indicated that there has been a significant deterioration in the level of protection for investors in the leveraged loans market, as Figure 15 below illustrates.

Figure 15 – Moody’s Assessment Of The Covenant Quality For Leveraged Loans (US & Canada)

(Source: Moody’s, Bloomberg)
(Note: 5.0 is the weakest score, 1.0 is the strongest.)

Figure 16 – Average Total Debt To EBITDA Multiple Of Private Equity Deals

(Source: S&P, FT)

Low Volatility Environment

In our view, the unconventional monetary policies in advanced economies have squeezed investment returns and volatility, all while reducing borrowing costs; this has created an incentive for asset managers to use more leverage and take on more risk. At the same time the same policies have encouraged corporates to take on more debt. It is the fixed income sector, more than any other, which has been impacted by this low volatility. “Risk Parity” type investment strategies have become increasingly popular, where funds manage risk by constructing portfolios according to the risk (volatility) of each asset class and then using leverage to increase returns. The leverage mitigates the impact of lower returns from the higher weighting to the lower risk assets. This typically involves a larger weighting to fixed income, rather than equities, while incorporating more leverage to offset the low returns of these supposedly lower risk assets.

In February 2018, there was a sharp increase in volatility as the VIX skyrocketed and investment strategies focused on shorting the VIX, such as the Velocity Shares Daily Inverse VIX ETN, plummeted in value to almost zero. This was discussed in the March 2018 edition of the BitMEX Crypto Trader Digest. The victims of this were a small number of opportunistic investors looking for easy returns and the impact of the “volocaust” was limited on the rest of the financial system. However, in a way, the February 2018 event was a microcosm for what is happening more generally in fixed income markets. This time the more mainstream investors, are taking advantage of artificially low volatility and cheap borrowing costs. At some point the market will correct, and the impact of this will be far greater than in February 2018, when a multi-trillion dollar asset class unwinds, rather than one only worth several hundred million dollars.

The sequence of events can be described as follows, with various different factors contributing to exacerbate risk:

  1. Some catalyst occurs, causing a sharp increase in volatility.
  2. Investors will need to de-risk their portfolios, concentrating first in the most liquid markets, fixed income.
  3. In the most liquid markets, machines dominate trading and machines are likely to all withdraw liquidity at the same time.
  4. Fixed income markets become volatile, illiquid and dysfunctional, as investors rush for the exit.
  5. Securitised bond-based assets, such as CLOs and Bond ETFs, trade at a significant discount to their net asset values.
  6. The contagion spreads across other liquid asset classes, such as equities.
  7. Over the coming years the newly-established components of the debt machine begin to dry up; corporates struggle to refinance and the economy suffers.

Of course we do not know what will be the main catalyst leading to increased volatility. It could be a geopolitical event, excessive levels of emerging market US Dollar-based debt, high levels of leverage in the Chinese asset management industry, passive ETFs, high frequency traders, the contraction of central bank balance sheets too quickly, a large unexpected corporate bankruptcy, the Eurozone debt crisis, even a catastrophic consensus bug in Bitcoin causing volatility ect ect…  

The point is, that whatever the particular event is, it doesn’t really matter. What does matter is the inherent instability and fragility of the financial system, driven by artificially low volatility and excessive leverage. Many may point their fingers at the particular catalyst after the event and blame it for the crisis, but that could be somewhat intellectually dishonest.


Banks are more crucial to the financial system and society than asset managers. If asset managers come under pressure, whilst some high net worth individuals may experience a write down in their assets; retail and corporate deposits should be safe; and therefore the coming crisis could be less intense than 2008. However, critically, the potential for government intervention to mitigate the impacts of the crisis may be more limited than in 2008.

Firstly and most obviously, the toolkit available to central bankers has been greatly diminished, with interest rates already low and their balance sheets already large. Secondly, and perhaps more importantly, is the political side of things. One cannot know for sure, but the typical people behind the Trump, Brexit, or the Yellow Vest movement may not be supportive of certain kinds of government intervention in financial markets.

In today’s more “populist” political climate, it may be more challenging to justify programs such as quantitative easing or other programs designed to increase asset prices at the relative expense of those earning median type salaries, who don’t own a large pool of financial assets. Therefore, in the next crisis, managing the perceived risk of a “political uprising” could significantly reduce the scope of action central bankers may be willing to take.

Keep in mind, there was also political opposition to central bank policy in the aftermath of 2008, with the rebellion peaking in around 2011. Another key difference this time is that available tools to those leading the rebellion then, such as social media, are now more developed. Political uncertainty in the West seems to have increased since 2008. If this uncertainty begins to interact with financial volatility, risks could be exacerbated.

As for when such a crisis will occur, we obviously do not know. In our view, the charts in this report identify a problem, but they do not seem to suggest that we are necessarily right on the precipice of a major crisis; it could be several years away. As for how to profit from such events, this is perhaps even more challenging than predicting their timing. Maybe one could construct a portfolio of VIX calls, long dated corporate bond ETF puts, index-linked government bonds, hedge funds specializing in volatility, gold and maybe to a lesser extent, even Bitcoin. Again, although one cannot know when these events will occur, perhaps now is a time to adjust one’s investment portfolio.

The BitMEX Insurance Fund

Abstract: In this piece, we explore why the BitMEX insurance fund is needed and how it operates. We compare the BitMEX insurance fund model, to the systems utilized by other more traditional leveraged market places (e.g. CME). We conclude that crypto-currency trading platforms which offer leverage and a capped downside face some unique challenges, when compared to traditional institutional trading platforms. However, the growth of BitMEX’s insurance fund provides a reasonable level of assurance to winning traders that they will be able to attain their expected profits.

(BitMEX Co-founder & CEO Arthur Hayes (left) and CME Chairman & CEO Terrence Duffy (right))

Leveraged Trading Platforms

When one trades on a derivatives trading platform such as BitMEX, one does not trade against the platform. BitMEX is merely a facilitator for the exchange of derivatives contracts between third parties. A key feature of the BitMEX platform is its leverage, where traders can deposit Bitcoin, then leverage it up, (in theory up to 100x) and purchase contracts with a notional position size far higher than the value of the Bitcoin they deposited.

The combination of offering both leverage and the ability for traders to trade against each other implies winners are not always guaranteed to get back all the profits they expect. Due to the leverage involved, the losers may not have enough margin in their positions to pay the winners.

Consider the following simplified example, where the platform consists of two customers trading against each other:

Trader A Trader B
Direction of trade Long Short
Margin 1 BTC 1 BTC
Trade execution price $3,500
Leverage 10x 10x
Notional position size 10 BTC 10 BTC
Current BTC price $4,000
Expected Profit $5,000 ($5,000)

In the above example, the winning trader A expects to make a profit of $5,000, which is greater than the amount of capital the loser, trader B, put up as collateral for the trade (one Bitcoin is worth $4,000). As such, trader A can only make 1 BTC ($4,000) in profits, perhaps making him/her feel slightly disappointed.

Traditional Exchanges

Traditional exchanges like the Chicago Mercantile Exchange (CME) do not share this problem to the same extent as crypto-platforms such as BitMEX. In traditional leveraged trading venues, there are often up to five layers of protection, which ensure winners get to keep their expected profits:

  1. In the event an individual trader makes a loss greater than the collateral they have in their account, such that their account balance is negative, they are required to finance this position by injecting more funds into their account. If they are unable or unwilling to do so, their broker may initiate legal proceedings against the trader, forcing the trader to provide the funds or file for bankruptcy. Each trader must use a broker, who may evaluate the balance sheet and capital of each of their clients, providing each client a custom amount of leverage depending on the assessment of their particular risk.
  2. In traditional derivative markets, traders are not typically given direct access to trading platforms. Instead, clients access the market through their brokers (clearing members), for instance investment banks such as JP Morgan or Goldman Sachs. In the event a trader endures losses and the debt cannot be recovered, the broker is required to pay the exchange and make the counterparties whole. From the perspective of the exchange, these brokers are sometimes referred to as clearing members.
  3. In the event of a clearing member default, the centralised clearing entity itself is often required to make the counterparties whole. In many circumstances clearing and settlement is conducted by a separate entity to the one operating the exchange. The clearing house often has various insurance funds or insurance products in order to finance clearing member defaults.
  4. In the event of a clearing member failing and the centralized clearing entity also having insufficient funds, in some circumstances the other solvent clearing members are expected to provide capital.
  5. Many of the larger clearing houses (and perhaps even the larger brokers) are often considered systemically important for the global financial system by financial regulators. Therefore in a doomsday scenario where it looks likely that a major clearing house could fail, it is possible the government may step in and bail out traders, to protect the integrity of the financial system. Traders and institutions often have massive notional positions (multi-trillions of USD) hedged against other positions or instruments, typically in the interest rate swap market. Therefore it is crucial that the main clearing houses remain solvent or the entire financial system could collapse.


CME is the world’s largest derivatives exchange, with an annual notional trading volume of over one quadrillion USD; it is over 1,000x as large as BitMEX. CME has several buckets of safeguards and insurance to provide protection in the event that a clearing member defaults. The funds are financed in various ways:

  • Contributions from CME
  • Contributions from clearing members
  • Bonds placed by clearing members, redeemable by the clearing funds in the event of member default

CME Clearing’s Various Safeguards and Insurance Funds (2018)

Base Financial Safeguards Package
Guaranty Fund Contributions $4.6 billion
Designated Corporate Contributions $100 million
Assessment Powers $12.7 billion
IRS Financial Safeguards Package
Guaranty Fund Contributions $2.9 billion
Designated Corporate Contributions $150 million
Assessment Powers $1.3 billion

(Source: CME)

In exceptional circumstances, CME also has the power to apply “assessment powers” against non-defaulting clearing members to help finance the cost of defaulting members when all the other insurance funds have been drained. The value of the assessment powers is capped at 2.75x for each clearing member guarantee fund per member default.

Based on the size of the insurance funds in the above table, CME has around US$22 billion in various insurance funds. This represents around 0.002% of CME’s annual notional value of trading.

BitMEX and other crypto-currency trading platforms that offer leverage cannot currently offer the same protections to winning traders as traditional exchanges like CME. Crypto-currency is a retail-driven market and customers expect direct access to the platform. At the same time, crypto-trading platforms offer the ability to cap the downside exposure which is attractive for retail clients, therefore crypto-exchanges do not hunt down clients and demand payments from those with negative account balances. Leveraged crypto-currency platforms like BitMEX offer an attractive proposition to clients: a capped downside and unlimited upside on a highly volatile underlying asset. But traders pay a price for this, as in some circumstances there may not be enough funds in the system to pay winners what they expect.

BitMEX Insurance Fund

In order to mitigate this problem, BitMEX developed an insurance fund system, to help ensure winners receive their expected profits, while still limiting the downside liability for losing traders.

When a trader has an open leveraged position, if their maintenance margin is too low, their position is closed forcefully (i.e. liquidated). Unlike in traditional markets, the trader’s profit and loss does not reflect the actual price their position was closed on the market. On BitMEX if a trader is liquidated, their equity associated with the position always goes down to zero.

Example trading position
Direction of trade Long
Margin 1 BTC
Bitcoin price (at opening) $4,000
Leverage 100x
Notional position size 100 BTC = $400,000
Maintenance margin as percentage of notional position 0.5%

In the above example, the trader has a 100x long position.  If the mark price of Bitcoin falls 0.5% (to $3,980) the position is liquidated and the 100 Bitcoin position needs to be sold on the market. From the perspective of the liquidated trader, it does not matter what price this trade executes at, whether its $3,995 or $3,000, either way they lose all the equity they had in their position, they lose the entire one Bitcoin.

Now, assuming there is a liquid market, the bid/ask spread should be tighter than the maintenance margin. In this scenario, the liquidations result in contributions to the insurance fund (e.g. if the maintenance margin is 50bps, but the market is 1bp wide), then the insurance fund will rise by almost as much as the maintenance margin when a position is liquidated. Therefore, as long as healthy liquid markets persist, the insurance fund should continue to grow at a steady pace.

The two graphics below attempt to illustrate the above example. In the first chart, at the time of liquidation, market conditions are healthy and the bid/ask spread is narrow, at just $2. As such, the closing trade occurs at a price higher than the bankruptcy price (the price where the margin balance is zero) and the insurance fund benefits. In the second chart, at the time of liquidation the bid/ask spread is wide. The closing trade occurs at a price lower than the bankruptcy price, therefore the insurance fund is used to ensure the winning traders receive their expected profits. This may seem like it would be a rare occurrence, but there is no guarantee such healthy market conditions will continue, especially in times of heightened price volatility. In these times, the insurance fund can drain much faster than it builds up.

Illustrative example of an insurance contribution – Long 100x with 1 BTC collateral

(Note: The above illustration is based on opening a 100x long position at $4,000 per BTC and 1 Bitcoin of collateral. The illustration is an oversimplification and ignores factors such as fees and other adjustments. The bid and offer prices represent the state of the order book at the time of liquidation. The closing trade price is $3,978, representing $1 of slippage compared to the $3,979 bid price at the time of liquidation.)

Illustrative example of an insurance depletion – Long 100x with 1 BTC collateral

(Notes: The above illustration is based on opening a 100x long position at $4,000 per BTC and 1 Bitcoin of collateral. The illustration is an oversimplification and ignores factors such as fees and other adjustments. The bid and offer prices represent the state of the order book at the time of liquidation. The closing trade price is $3,800, representing $20 of slippage compared to the $3,820 bid price at the time of liquidation.)

The BitMEX insurance fund currently sits at around 21,000 Bitcoin or around US$70 million based on current Bitcoin spot prices. This represents only 0.007% of BitMEX’s notional annual trading volume, of around one trillion USD. Although this is slightly higher than CME’s insurance funds as a proportion of trading volume, winning traders on BitMEX are exposed to much larger risks than CME traders through the following:

  • BitMEX does not have clearing members with large balance sheets and traders are directly exposed to each other.
  • BitMEX does not demand payments from traders with negative account balances.
  • The underlying instruments on BitMEX are more volatile than the more traditional instruments available on CME.


In the event that the insurance fund becomes depleted, winners cannot be confident of taking home as much profit as they are entitled to. Instead, as we described above, winners need to make a contribution to cover the losses of the losers. This process on BitMEX is called auto-deleveraging.

Auto-deleveraging has not occurred on the BitMEX Bitcoin perpetual swap contract since March 2017. In early March 2017, the SEC disapproved the Winklevoss’ application for the COIN Bitcoin ETF. On that day, the market dropped 30% in five minutes. The sharp price drop depleted the insurance fund entirely. Many XBTUSD shorts were ADL’d (Automatic Deleveraging) and their profits were capped.

Although the BitMEX insurance fund has grown considerably since then, crypto-currency trading is a volatile and uncertain industry. Despite the current healthy periods of reasonably high liquidity, sharp movements in the Bitcoin price going forwards is a possibility, in our view. One cannot be certain that ADL’s won’t occur again, even on the BitMEX Bitcoin perpetual swap contract.

Insurance Fund Data

Although the absolute value of the insurance fund has grown, as the charts below show as a proportion of other metrics from the BitMEX trading platform, such as open interest, the growth is less pronounced.

BitMEX Insurance Fund – Daily data since January 2018

(Source: BitMEX)

BitMEX Insurance Fund as a proportion of the BitMEX Bitcoin perpetual swap open interest – Daily data since January 2018

(Source: BitMEX)


Assuming the insurance fund remains capitalized, the system operates under a principle where those who get liquidated pay for liquidations, a losers pay for losers model. While this approach may be considered somewhat novel, in a way there is a degree of fairness to it, that isn’t present in some alternative models mentioned above. It begs the question, why should traders who do not engage in risky leveraged bets have to pay for those that do?


Although 21,000 Bitcoin inside an insurance fund, worth around 0.1% of the total Bitcoin supply may seem large, BitMEX cannot offer the same robust guarantees to winning traders, compared to those provided by traditional leveraged trading platforms. While the insurance fund has achieved a healthy size, it may not be large enough to give winning traders the confidence they need in the volatile and unpredictable bumpy road ahead in the crypto-currency space. Given such volatility, it’s not impossible that the fund is drained down to zero again.

Notice of API Timeouts 8 February 2019

Between 05:40 and 07:11 UTC today, a subset of the requests to the BitMEX REST API experienced slow API responses and eventual API timeouts due to resource contention at the API layer. Upon detection via our internal alerting mechanisms we identified the cause and mitigated the immediate impact within a few minutes. There is currently no ongoing issue and there was no impact to the trading engine or user data during this time.

Fixes for the underlying root cause of the issue have been identified and are being worked on as a priority. We will follow up with another announcement once these are live. We have also increased the sensitivity of our system monitoring to detect and resolve potential similar issues much sooner. We apologise for any inconvenience this may have caused.

The Mystery Of The Bitcoin Nonce Pattern

Abstract: We note that the distribution of nonce values in the Bitcoin block header does not appear to be random, with unexplained gaps emerging, where nonces occur less often. We then speculate on why this may be the case and provide charts illustrating the phenomenon. Although in our view the explanation for this is likely to be benign, it remains a mystery.

Overview and Recent Tweets

The Bitcoin nonce forms part of the block header, which is used by miners to provide entropy as part of the Proof of Work process, to try and find a hash meeting the difficulty requirement. Although it may depend on how mining software and hardware is configured, in theory the distribution of the nonce values should be random. In 2009, when Satoshi is presumed to have been a significant miner, the nonce value followed a particular pattern, as we discussed in an earlier piece.

On 4th January 2019, @100trillionUSD tweeted a graphic illustrating the nonce value distribution for Bitcoin. It seemed to show that the nonce value was random from mid 2010 to the start of 2016, after which point four mysterious regions appeared, where nonces occurred less often.

A few days later, on 7th January 2019, @khannib noted that Monero also appeared to have an unusual nonce value distribution. The Monero hardfork, which may have prevented the usage of ASICs, appears to have made the distribution random again, perhaps indicating ASICs cause the pattern.

On 23rd January 2019, TokenAnalyst dug further into the Bitcoin nonce value distribution pattern, by colouring the nonce values for the relevant mining pools.

A further tweet from TokenAnalyst implies that Antpool is a major cause of the unexpected nonce value distribution, while Bitfury and Slushpool have nonce values which perhaps do not significantly contribute to the “white spaces”.

New Nonce Value Distribution Scatter Charts

We have replicated the above analysis, producing similar scatter charts (starting in 2018); in an attempt to shed more light onto the issue.

We have also produced an individual scatter chart for Antpool,, F2Pool, Slushpool and Bitfury. The charts appear to agree with TokenAnalyst’s data, in that the “white spaces” are more clearly visible for Antpool, than for Slushpool and Bitfury. Although, with respect to Slushpool the white spaces are still visible, but they are more faint. Bitfury may not have found enough blocks for one to see a clear pattern. A statistical analysis may also be possible, although the human brain interpreting these scatter charts may be just as useful compared to some forms of statistical analysis.


Bitcoin nonce value distribution – All nonces (Since 2018)

(Source: BitMEX Research)


Bitcoin nonce value distribution – Antpool (Since 2018)

(Source: BitMEX Research)


Bitcoin nonce value distribution – (Since 2018)

(Source: BitMEX Research)


Bitcoin nonce value distribution – F2Pool (Since 2018)

(Source: BitMEX Research)


Bitcoin nonce value distribution – Slush (Since 2018)

(Source: BitMEX Research)


Bitcoin nonce value distribution – Bitfury (Since 2018)

(Source: BitMEX Research)

Bitcoin Cash ABC

Bitcoin Cash ABC also shares the same nonce value distribution pattern as Bitcoin.

Bitcoin Cash ABC nonce value distribution – (Since 2018)

(Source: BitMEX Research)



It is possible that covert AsicBoost may be either a contributing factor or cause of the pattern. The pattern began to emerge around the time many speculate covert AsicBoost started; and this pattern could be a quirk in the implementation of covert AsicBoost, which requires the manipulation of the nonce. However, the usage of covert AsicBoost is believed to have stopped in Bitcoin in 2018, when this pattern continued. Although it’s possible that despite covert AsicBoost itself being stopped, the quirk in the firmware remains.

In the below chart we have looked at the nonce value distribution for blocks mined using overt AsicBoost. Again, the pattern remains visible, albeit faintly. This may suggest the pattern has nothing to do with covert AsicBoost, but it’s far from conclusive.

Bitcoin nonce value distribution – Overt AsicBoost blocks (Since 2018)

(Source: BitMEX Research)


For now, the unusual nonce value distribution on Bitcoin remains a mystery. The community may wish to dig into this issue further and conduct more analysis, such as reviewing mining pool software and ASICs in more detail. We would guess this is nothing more than a meaningless anomaly with a benign cause; however a mystery in Bitcoin like this could be intriguing to some analysts.

Update: fix for root cause of last week’s issue

In response to last week’s post, yesterday we successfully released an enhancement to our internal market data distribution component’s re-subscription logic. This addresses the root cause of the previous week’s issue and along with the additional safety mechanism to prevent impact to the trading engine deployed at the time, we don’t anticipate a reoccurrence of last week’s issue.

Tracking US$24 billion Of Tokens ICO Makers Allocated To Themselves

Abstract: This is our third major piece on ICOs. In our first piece in September 2017 we focused on the interrelationships between ICO team members. In our second piece, in October 2018, we tracked the Ethereum balances in the ICO treasury accounts. In collaboration with TokenAnalyst, this piece focuses on the treasury balances of the ICO tokens themselves, on the Ethereum network. This report is based on tokens where the team controlled holding’s were worth an astonishing US$24.2 billion on issuance (in reality liquidity was too low for this value to be realized). Today this figure has fallen to around US$5 billion, with the difference primarily being caused by a fall in the market value of the tokens, alongside US$1.5 billion of transfers away from team address clusters (possibly disposals).

(Source: BitMEX Research)

(Note: A reminder of the various interconnections between ICO team members, from our September 2017 interactive graphic)

Team controlled token holdings (Own tokens) – summary data

US$ billion
Value of ICO coins allocated to token teams 21.5
Issuance to team post ICO 2.7
Total issuance to team controlled wallets
Coins leaving the team address cluster (Perhaps sales) (1.5)
Profits/(losses) due to token price changes (12.0)
Net impact of Noah (token burn) (4.4)
Net Impact of EOS (1.2)
Current team holdings 5.0

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018, based on data for 108 tokens)

Of the US$24 billion worth of tokens ICO project teams issued to themselves, 54% of the value has been lost due to coin price reductions. The peak valuation of the team holdings of their own tokens, using the individual price peak for each coin, is over US$80 billion. This larger figure implies US$70 billion of “losses” from the peak. Although peak valuation highly is dubious due to a lack of liquidity and most of the tokens were granted to the teams essentially for nothing, therefore classifying these price movements as losses may not be appropriate. Unlike ICO investors, the teams did not have an offering price or initial investment. However, some trading activity occurred at these ridiculously high valuations, therefore we believe it’s still interesting to consider these figures, while bearing these caveats in mind.

Based on current illiquid spot prices, the ICO teams still appear to own around US$5 billion of their own tokens, money they essentially got from nothing, depending on ones view. At the same time the teams may have realized gains of US$1.5 billion by selling tokens, based on coins leaving team address clusters. Although this figure may also be an overestimate, as coins could have left the team address cluster for a variety of reasons.

Data Caveats & weaknesses in calculation methodology

  • The liquidity of many of these tokens is low and therefore the US Dollar values may be gross overestimates, this applies to both the initial allocation, current value and the value of any losses. In some cases, the value of tokens given to the team, for instance with projects such as Veritaseum or Noah, were almost comically large relative to the real trading volume in the coins. Therefore it can be considered unrealistic to value the team holdings based on the exchange price of the tokens.
  • The challenge and uncertainty involved in producing this dataset surrounds the allocation of the tokens to the team address cluster. TokenAnalyst conducted this allocation. The methodology used was imperfect and we have not dug into individual projects. The data was obtained by analysing the token smart contracts and transaction patterns on the Ethereum blockchain and applying machine learning type techniques to establish a team controlled address cluster for the team of each project. The data is therefore a probabilistic estimate and is likely to be inaccurate at the individual project level. However, the primary motivation for this report was to produce macro data about the team holdings of ICO tokens on Ethereum. Although this analysis has produced results which are far from perfect, we believe one can draw reasonable macro conclusions from the analysis.
  • As mentioned above, our analysis is based on reviewing smart contract data and transaction patterns, not documents and policies of individual projects. Therefore, it’s possible we included tokens as part of a team balance, although in reality they are held as part of another form of reserves, escrow or some other category, where it’s inaccurate to attribute the coins to the team’s own funds.
  • The data assumes the issuance date is the same date as when the first price data appeared on Coinmarketcap, this may not be a reliable assumption.

Summary data

Value of coins issued to team controlled address clusters (own tokens) – US$ million – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Token data up to Dec 2018, data based on prices at the time(s) of issuance)

Loss in value of team controlled holding (own tokens) – US$ million – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018)

Proportional loss in value of coins in team controlled address clusters (own tokens) – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018)

Value of coins transferred out of team controlled address clusters (Own tokens) – US$ million – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018. Huobi and Qash are exchanges and the tokens appear to have been sent to their respective platforms. It is possible the above figures represents sales/”cashing out”, although there could be other reasons for the transfers)

Current value of coins in team controlled address clusters (Own tokens) – US$ million – Top 10

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018)

The raw data – Team holdings of own tokens – US$ million

Token Value at ICO Post ICO issuance Transfers away from team cluster Loss in value Current value
VERI 4,762 0 (15) (3,196) 1,552
NOAH 4,478 0 (4,423) 55
KIN 980 0 (0) (703) 277
AGI 863 0 (27) (814) 22
POLY 842 0 (17) (727) 99
HT 643 0 (366) (29) 248
GNO 636 0 0 (533) 103
QASH 617 0 (177) (300) 140
MKR 596 0 (46) (445) 105
TEL 452 0 (8) (408) 36
ITC 334 0 (7) (323) 4
ZRX 333 0 (9) (155) 169
ZIP 266 0 0 (226) 41
BLZ 256 0 (32) (207) 17
GTO 241 0 (67) (157) 17
BNB 219 110 0 118 447
BTO 198 0 (28) (165) 5
ICX 160 0 (79) (67) 14
ETHOS 153 0 (15) (123) 16
TNT 152 0 (10) (133) 9
CENNZ 143 0 (6) (121) 15
AST 141 0 (24) (104) 13
KEY 132 0 (2) (124) 6
BIX 118 2 0 (85) 35
CVC 117 0 (1) (75) 41
FSN 100 0 (6) (75) 19
OCN 100 0 (31) (64) 5
DEW 95 0 (1) (87) 7
SRN 89 0 (15) (69) 4
MDS 88 0 (8) (75) 5
EDO 83 0 (11) (58) 15
ABT 76 0 0 (71) 5
WTC 69 0 (50) 17 37
INS 68 0 0 (66) 2
PPT 65 0 (55) (5) 5
IHT 65 0 (2) (58) 5
CPT 65 0 (0) (43) 21
SPHTX 64 0 0 (60) 4
DRGN 58 0 (47) (2) 8
MCO 54 0 (89) 72 37
XYO 54 0 (6) (23) 25
RCN 54 0 0 (48) 6
DPY 47 0 (23) (22) 2
THETA 45 0 0 (30) 16
MANA 41 0 (95) 127 73
R 40 0 0 35 75
APPC 35 0 (24) (9) 2
CMT 33 0 (1) (25) 8
FUEL 32 2 0 (29) 5
CREDO 31 0 (0) (6) 25
DMT 31 0 (17) (12) 2
POWR 30 166 0 (154) 42
LRC 30 8 0 (21) 17
WPR 26 0 0 (24) 2
AMB 24 0 0 (17) 7
RNT 22 0 (1) (15) 7
ENG 22 0 0 (12) 10
COB 22 0 (10) (5) 7
GTC 20 126 0 (141) 6
REN 19 0 (3) (13) 3
DENT 19 635 0 (564) 90
UTT 19 0 (0) (11) 8
AE 13 0 (19) 6 0
DATA 11 0 (3) (6) 3
BRD 10 17 0 (21) 7
SNGLS 8 0 0 (3) 6
LEND 6 0 (7) 3 2
RLC 6 0 (5) 2 3
PLR 6 3 0 (4) 5
HVN 5 0 (5) 0 1
CVT 5 11 0 (8) 9
LYM 5 0 (4) 0 2
SAN 5 0 (7) 5 4
GNT 4 0 (12) 31 23
KICK 3 2 0 (4) 1
DGD 2 0 (5) 5 3
EDG 2 0 (29) 28 1
ENJ 2 0 (0) 1 2
RHOC 1 14 0 (13) 1
ARN 0 6 0 (6) 1
ELF 0 45 0 (40) 6
PAY 0 142 0 (132) 11
DAI 0 1 0 0 1
HPB 0 134 0 (119) 15
CRPT 0 3 0 (2) 1
HOT 0 7 0 0 7
SALT 0 95 0 (92) 3
NAS 0 71 0 (50) 21
NGC 0 12 0 (11) 1
CPC 0 12 0 (9) 3
GVT 0 3 0 (2) 2
SNM 0 14 0 (11) 2
BTM 0 9 0 (1) 8
QRL 0 7 0 (6) 2
NULS 0 71 0 (52) 19
POE 0 58 0 (54) 4
TEN 0 29 0 (15) 13
MTL 0 188 0 (177) 11
WINGS 0 18 0 (15) 3
SPANK 0 106 0 (93) 13
OMG 0 195 0 (154) 41
STORJ 0 133 0 (85) 48
BAT 0 38 0 14 52
VIBE 0 10 0 (8) 2
IOST 0 218 0 (185) 34
Total 21,513 2,723 (14,805) (4,396) 5,035

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Price data up to Jan 2019, token data up to Dec 2018)

Conclusion & summary data

This analysis highlights the lack of standards and transparency in the ICO market, especially when it comes to the allocation of tokens to the founding team’s wallet. Teams were often able to mint, burn, buy, and sell (their own) tokens at will, without analysts being able to easily track what is occurring. We would often see tokens in exchange clusters, and it was hard to tell whether the token project “paid” the exchange to list tokens or the token project just transferred their treasury to the exchange to cash out.

To be fair, perhaps we could improve the analysis by spending more time reading the specific documentation of the individual projects and by speaking to the teams involved. This would have resulted in a more robust dataset.

But one thing about ICOs that many people often overlook, is that ICO teams often make profits in two ways from the issuance:

  1. Selling the newly issued tokens (often for Ethereum), and,
  2. Issuing themselves their own tokens.

Our October 2018 report focused on the former, while this report focuses on the latter. The summary table below combines the figures from both of our reports.

ICO team profits US$ billion
ICO process
Ethereum Raised 5.4
Own tokens issued to founding teams 24.2
Total raised 29.6
Changes in coin price
Ethereum profits/(losses) – Mostly realised 0.8
Own token profits/(losses) – Mostly unrealised (17.6)
Total profits/(losses) post issuance (16.8)
Total ICO team profits 12.8

(Source: BitMEX Research, TokenAnalyst, the Ethereum blockchain, Coinmarketcap (for token prices))
(Notes: Ethereum price data to October 2018, Own token price data to January 2019)

Although, as we have repeatedly explained, there are many inaccuracies and assumptions involved in producing the data. Based on our methodology, it appears as if ICO teams have profited by almost US$13 billion from this ICO process. In our view, this money was made incredibly easily, with very little work, accountability or transparency. Therefore, ICOs have proven to be an extremely attractive way for project founders to raise funds. The results for investors of course, have not been as attractive.

The ICO cycle now appears to be dying down to some extent and it’s much harder to raise funds than it was in late 2017. But with so much money made and lost, the events of 2017 and early 2018 are not likely to be quickly forgotten. Entrepreneurs will remember the success (and keep trying to raise money) while investors will remember the pain. A repeat of this cycle within a few years is therefore less likely than many may think.

Two unrelated minor outages on 9 January

On 9 January BitMEX experienced two unrelated minor outages.

At 02:44:10 UTC the WebSocket API saw a degradation in performance for a minute where 7% of commands sent by clients failed. Connections continued undergoing a 1% failure rate of commands until servers recovered at 02:47:00 UTC.

Clients may have also seen an increase in response times for some market data REST endpoints up during this period. This was due to a rolling restart of the API servers that occurred in too tight of a timeframe.  

In addition, at 05:48:10 UTC and 06:10:10 UTC, BitMEX experienced minor outages for approximately 30 seconds whereby requests to the trading engine were load-shed as the engine was busy. During these times, clients would have observed a lack of updates over the WebSocket API for the same reason. The outages were due to data replay complications during a regularly scheduled market data distribution component restart.  

There was no data loss during these events and an additional safety mechanism to prevent a similar situation from impacting the trading engine has already been deployed. The root causes have also been identified and we are currently working on permanent fixes to prevent a recurrence. Updates will follow in the future.

We apologise for the inconvenience. If you have any questions, please contact customer support.

Atomic Swaps and Distributed Exchanges: The Inadvertent Call Option

Abstract: In this piece, we look at a common problem facing both distributed exchanges and cross-chain atomic swaps: what we call the “inadvertent call option.” Non-custodial fully-distributed trading systems often inadvertently create an American-style call option, rather than the more simple desired operation of exchanging one asset for another. We review how this same issue applies to some specific distributed trading platforms like Bisq and particular cross-chain atomic swaps constructions. We then look at how IDEX solves this problem, but then requires users to trust the platform operator, to some extent, by removing some benefits of distributed exchanges. We conclude that despite the added complexity, in some circumstances it may be better to embrace the call option feature as a viable product, rather than ignoring or fighting it.


Alongside distributed stablecoins, distributed exchanges (DEXs) are often seen as one of the two holy grails within the cryptocurrency ecosystem. However, similar to distributed stablecoins, the challenges involved in DEXs are often underestimated. In this piece, we focus on one specific challenge with distributed exchange systems: instead of allowing simple exchange, these systems often inadvertently produce American-style call options.

The Theory Of The Inadvertent Call Option

When trading one cryptocurrency asset for another in any fully non-custodial system, one party must act first and the second party must follow. In theory, at some point, this second party then has optionality: – he or she can either follow through and complete the trade, or take no action and stop the trade. In the time interval between the first party taking the necessary action and the second party being required to act, if the price of the token the second actor is attempting to buy falls in value, or the price of the token he is selling increases in value, he could refuse to complete the trade. This means the following:

  1. The trader who acts first has written an American call option on the spread between the two assets.
  2. The trader who acts second has purchased an American call option on the spread between the two assets.

These exchanges can either happen atomically or as two separate transfers. Let’s consider Alice buying Bitcoin from Bob, using Litecoin.

Description Call option problem
Atomic trading

Either both the Litecoin transaction and Bitcoin transaction occur, or both transactions fail

(e.g. Cross-chain atomic swaps).

One party must act first and then the second party can decide to execute both trades or not. This decision can be influenced by price changes in any of the two assets in the intervening period. This provides the second party to act with optionality.
Non-atomic trading

It is possible for one of the transactions in the trade to succeed and the other to fail. In this case, typically a quasi-custodial mechanism such as multi-signature escrow, is required to prevent cheating for at least one side of the trade

(e.g. Bisq-type platforms).

One party must act first and then the second party can decide to execute his part of the transfer or not. Failing to execute the second transfer could result in either:

  • The second party successfully stealing funds from the first party
  • The third party escrow agent reversing the first transaction

Either way, the second party has optionality.

As the table above illustrates, whether the trading is atomic or not, the same optionality principle applies.

One may think this is an insignificant issue, as the time periods can be short or the option value may be low; however, this is typically not the case: the option period is often around 24 hours and cryptocurrency prices can be very volatile. This high volatility is typically the reason traders wish to exchange the tokens in the first place. Therefore, the option value can be significant and impact trading.

It may be possible to mitigate or solve this problem by using more steps involving more deposits, but we have not yet observed a system achieving this. The other way to mitigate the problem is via the reputation of individual traders and a distributed web of trust-based systems, with traders revealing a form of identity. Traders can then lose reputation if they cancel trades based on price volatility. However, this may greatly increase the complexity of the systems, as functioning sybil attack resistant distributed reputation systems are also challenging to construct.

Below we examine three differently constructed distributed exchange type systems (or quasi DEXs) and explain how the call option problem arises.

Case Studies


Summary table

Type Non-atomic
Optionality period 24 hours (up to 8 days)
Escrow Multisignature escrow for the trader selling Bitcoin only

Bisq (previously known as Bitsquare) is a peer-to-peer application, which enables one to buy and sell cryptocurrency with fiat money, as well as trade between crypto-tokens. Bisq is essentially a DEX, as traders connect to each other over a peer-to-peer network and transact with each other directly.

Bisq Daily Trading Volume (USD)

(Source: Coinmarketcap)

Screenshot from the Bisq platform

(Source: BitMEX Research)

Below we explain some examples of potential trading activity on Bisq and describe the resulting options.

Example 1: Acquiring Bitcoin with USD on Bisq

Alice wishes to purchase 1 BTC from Bob, using U.S. dollars:

  • Step 1: Bob places 1 BTC in a 2 of 3 multisignature account. The three signatures belong to Bob, Alice, and a third-party arbitrator. This is Bob’s offer, which includes a price (e.g. US$3,800 per BTC).
  • Step 2 – Alice can accept Bob’s offer by paying a small refundable deposit into another multisignature account. The fee is set by Bob (e.g. 0.01 BTC).
  • Step 3 – Alice has 24 hours to conduct a bank wire transfer, paying US$3,800 into Bob’s account. If there is no dispute and the wire transfer occurs, Alice receives the 1 BTC and her deposit back. If no wire transfer occurs, Alice loses the deposit and the 1 BTC is returned back to Bob. Any dispute is mediated by the third-party arbitrator.

The above represents Alice buying Bitcoin; however, when considering the economic incentives involved, since Alice can back out of the trade with limited consequences, one could consider that, after step 2, she has acquired the following American-style call option:

Call option details
  • Underlying: Bitcoin
  • Quantity: 1
  • Strike: $3,800
  • Time to expiry: 24 hours
  • Premium: 0.01 BTC

Therefore, when Bob determines the value of the deposit Alice is required to pay, in theory he should consider Bitcoin’s volatility and use options pricing systems to ensure Alice is unable to acquire the option for a cheap price. Based on the prices currently available on Bisq, it appears many of these options are undervalued.

Example 2: Acquiring Bitcoin with Monero on Bisq

Alice wishes to purchase 1 BTC from Bob, using Monero (XMR):

  • Step 1 – Bob places 1 BTC in a 2 of 3 multisignature account. The three signatures belong to Bob, Alice, and an arbitrator. This is Bob’s offer, which includes a price (e.g. 80 XMR per BTC).
  • Step 2 – Alice  can accept Bob’s offer by paying a small refundable deposit and a fee set by Bob (e.g. 0.01 BTC).
  • Step 3 – Alice has 24 hours to do an on-chain Monero transfer. If there is no dispute and the transfer occurs, Alice receives the 1 BTC and her deposit back. If no wire transfer occurs, Alice loses the deposit and the 1 BTC is returned back to Bob. Any dispute is mediated by the arbitrator.

Again, the above example may be considered as Alice buying Bitcoin; however, when considering the incentives, since Alice can back out of the trade with limited consequences, one could consider that she has acquired the following American-style call option:

Call option details
  • Underlying: Bitcoin
  • Quantity: 1
  • Strike: 80 XMR
  • Time to expiry: 24 hours
  • Premium: 0.01 BTC

If one is trying to capture the benefits of buying a call option with a low premium, this Monero trade may be more beneficial than the U.S. dollar version, since the Monero price is more volatile, and therefore the value of the option is higher. Since the Monero price is more volatile than Bitcoin, it may be more economically correct to conclude that Alice has acquired the following put option, rather than a call option.

Put option details
  • Underlying: Monero
  • Quantity: 80
  • Strike: 0.013 BTC
  • Time to expiry: 24 hours
  • Premium: 0.01 BTC

As a trader, if one wants to take advantage of this structure, one could purchase these Monero puts for a low premium and then hedge the exposure by going long Monero on a centralised platform. However, Bisq has small position limits and therefore the size of the profit-making opportunity is limited.

Although it may make marketing the platform more challenging, it might make Bisq more robust to rebrand these trades as options and encourage sellers of Bitcoin to set the deposit price such that it’s consistent with the premium payment for the equivalent option based on the price volatility of the assets involved, for example by using the Black–Scholes model.

Cross-Chain Atomic Swaps

Summary table

Type Atomic
Optionality period 24 hours (or whatever the parties set as the lock time period)
Escrow None

We believe cross-chain atomic swaps were first described by TierNolan on the Bitcointalk forum in May 2013. Cross-chain atomic swaps allow users to exchange one asset for another atomically, such that the entire process either succeeds or fails. This allows for no risk for either party losing out by only one of the two transfers completing.

The following illustration describes the on-chain atomic swap process.  It is based on a swap between Alice and Bob, with Alice exchanging 1 Bitcoin for 100 Litecoin belonging to Bob.

Cross-chain Atomic Swap Construction

# Actor Description
1 Alice Alice picks a random number X.
2 Alice Alice creates a transaction sending 1 BTC to Bob.

Transaction 1

The transaction can be redeemed when either:

  1. Bob signs it and X is known, such that the hash of X is a necessary value.
  2. Both Alice and Bob sign it.
3 Alice Alice creates and signs a transaction sending the 1 BTC output of transaction 1, back to herself.

Transaction 2

The transaction is time locked for 24 hours.

4 Alice Alice sends transaction 2 to Bob.
5 Bob Bob signs transaction 2 and returns it to Alice.
6 Alice Transaction 1 is broadcast to the Bitcoin network.

7 Bob Bob creates a Litecoin transaction sending 100 LTC to Alice.

Transaction 3

The transaction can be redeemed when either:

  1. Alice signs it and X is revealed, such that the hash of X is the necessary value.
  2. Both Alice and Bob sign it.
8 Bob Bob creates and signs a transaction, sending the 100 LTC output of transaction 3 back to himself.

Transaction 4

The transaction is time locked for 24 hours.

9 Bob Bob sends transaction 4 to Alice.
10 Alice Alice signs transaction 4 and returns it to Bob.
11 Bob Transaction 3 is broadcast to the Litecoin network.

At this point, Alice has the optionality.
If the LTC/BTC price ratio increases, she could continue the swap process. Or, if the LTC/BTC price ratio falls, Alice can end the process here.

Call option details
  • Underlying: Litecoin
  • Quantity: 100
  • Strike: 0.01 BTC
  • Time to expiry: 24 hours
  • Premium: 0
  • Type: American
12 Alice Alice spends the output of transaction 3 to herself, revealing X. Alice now has 100 LTC.

13 Bob Bob spends the output of transaction 1 to himself, using the X Alice provided above. Bob now has 1 BTC.

(Source: BitMEX Research)

As the table illustrates, although one is attempting to structure an atomic swap, similar to Bisq, it has inadvertently resulted in an American-style call option. The same issue appears to apply to either multi-currency routing via the lightning network or off-chain lightning-based cross-chain atomic swaps, during the construction of the channels. It could be possible to solve these issues with more steps and a longer series of deposits, although this added complexity may make implementation more challenging. Just as for Bisq above, it may be more appropriate for cross-chain atomic swap developers to embrace the call options and make it the product, rather than to try to brush this problem under the carpet or solve it with added complexity.


Summary table

Type Atomic
Optionality period n/a
Escrow Partial escrow for both sides of the trade with IDEX, with a sunset clause

IDEX is an exchange platform using the Ethereum network. Traders deposit funds into an Ethereum smart contract, where the signature of both the traders and the IDEX platform is required to submit orders, execute trades, or make payments.

After a certain time horizon, users can withdraw funds from the smart contract without a signature from IDEX, which protects user deposits in the event that IDEX disappears. Order submission, order cancellation, and order matching is conducted off-chain on the IDEX servers, to allow for a fast and seamless user experience. The events are then submitted in sequence to the Ethereum blockchain and are only valid with a valid signature from the users. Therefore, IDEX is unable to steal user funds or conduct trades without user authorisation.

According to Dex.Watch, IDEX is the global number one Ethereum-based DEX, with an approximate market share of 50%. IDEX-type platforms are in many ways more advanced than the exchanges above as they can solve the call option problem by having both party’s funds partially held in escrow during the trading period.

IDEX Daily Trading Volume ( USD)


Although IDEX cannot steal user funds or conduct trades without authorisation, the order of events is determined centrally by IDEX. IDEX could fail to execute an order in a timely manner as well as front run orders or fail to execute an order cancellation in a timely manner. Therefore, while users are protected from some risks common in centralised exchanges, in practise they are still exposed to many of the risks most often talked about with respect to typical centralised exchanges. However, we still consider IDEX type platforms to potentially be a significant improvement compared to the fully centralised alternatives.  IDEX also has other limitations such as one can only trade Ethereum-based assets and the platform is eventually constrained by Ethereum network capacity.


In some ways Bisq’s model is more ambitious than IDEX and cross-chain atomic swaps. IDEX limits itself to tokens that exist on the Ethereum network while atomic swaps only deal with some cryptocurrencies. In contrast, Bisq attempts to handle fiat currencies such as the U.S. dollar. While solving the call option problem may be possible using Ethereum smart contracts or more complex lightning network constructions when, fiat currency is involved, it may be impossible to solve.

Of course atomic swaps and an IDEX type platform could work with US dollars if there is a working distributed U.S. dollar stablecoin. This illustrates how the two holy grails, distributed stablecoins and distributed exchanges, are interrelated. In a catch-22 type situation, each can only function robustly if the other exists.

Without a distributed stablecoin, in our view, when trading fiat currency for cryptocurrency through distributed systems, the use of call options could be inevitable. Bisq is potentially a useful distributed onramp into the cryptocurrency ecosystem; however, rather than trying to solve the call option problem, perhaps Bisq should embrace it. Maybe an effective onramp into the cryptocurrency ecosystem could be via American-style call options. While this may not be easy, it may be the only way to structure a robust highly censorship-resistant way in.


Two sides of the coin: the bifurcated near-future of money


A digital society requires digital cash. You hear the word cryptocurrency a lot. But there’s a very big difference between a truly decentralised cryptocurrency like Bitcoin and what could be called centralised ‘e-money.’

As Bitcoin today officially heads into its second decade of existence, this is a ripe moment to familiarise yourself with some of the fundamental changes in modern money, including the ways people store and transmit value, that I think you can expect to see in the near future

We Gave Them an Inch, Now They’re About to Take a Mile

The first type of new money I believe we’re going to see is centralised e-money. This descends directly from the current system, taking government (fiat) currency and updating it for the digital age. It’s a natural — and I imagine inevitable — synthesis of the existing central bank system and our increasingly corporatised economy.

The keystone phenomenon that makes e-money possible is the way in which we as a society have grown accustomed to handing over our entire private lives to corporations. We’ve done so in exchange for entertainment and convenience, and we’ve certainly received ample supplies of both. It’s only a small step now, however, to our accepting (or being forced to accept) the corporate issuance of money and the further diminution of privacy that comes with that.

The clearest glimpse into where e-money is heading is probably WeChat Pay, which has now practically eradicated cash in China. The WeChat Pay system works like this: using QR codes and mobile phones, merchants deduct credits from your WeChat wallet, which is connected directly to your bank account. Instantly, while standing at a market stall, Chinese renminbi (CNY) is debited from your account, and credited to the merchant’s account. They get their money, you take your dumplings, and the friction and annoyance of using physical cash evaporates.

As someone who travels around China frequently, I actually love WeChat Pay. However, as someone who built a career in banking and now makes his living in Bitcoin, I also know the privacy limitations of centralised payment systems.

The various mobile payment systems now offered by major players in different parts of the world differ in their details. But in some cases, they know almost everything about you: what goods and services you purchase, as well as where and when you purchase them, which can presumably be linked to all the other data they have on you.

At the same time, we’ve seen our governments in the West, when the spirit moves them, lean hard on our corporate friends to cough up our personal information. Unsurprisingly, the corporations tend to comply with these requests. We have also witnessed private sector payment networks and crowdfunding platforms kick people out for having too close an association with offending ideas or speech, or for being bad actors. Not all of this is necessarily unreasonable, but who gets to draw the line? They do.

Furthermore, monetarily, you can see where this leads: whether it happens gradually or suddenly, at some point central banks and governments, in accord with their nature, may start directing the monetary functions of corporations in a more hands-on way. The way they would do it, I expect, is by deputising commercial banks and large social media companies, who shall become nodes on a payment network, with the authority to participate in the e-money system and earn transaction fees.

Significantly, the payment network’s rules can be enforced instantly and flawlessly via code. The only place left in the system for inefficient or corruptible humans to participate will be at the apex of the network, where the authorities can issue credit directly to people, tax every transaction immediately, and determine who can and can’t be part of the network. In theory, your entire financial existence can be governed this way.

Thankfully, That’s Where Bitcoin Enters the Conversation

Although such a monetary system as I’ve just described may or may not be warehoused on a blockchain look-alike, make no mistake: it is centralised, top-down, and censored (meaning you can be barred from using it if you fall afoul of the centralised powers).

Bitcoin, by contrast, is decentralised, peer-to-peer, and censorship resistant. Bitcoin runs via a network of voluntary, independent, and self-interested actors, who neither demand nor require any favours or permissions; a few basis points in transaction fees is literally all they want from anyone — and all they’re allowed to take. And while the public address of any Bitcoin wallet, and its transaction history, are visible to all, no personally identifiable information is contained in any transaction.

Which means that Bitcoin, or something like it, is perhaps society’s best hope for a private form of electronic money. And privacy, I argue, is an important part of a well-functioning society. For moral and even psychological reasons, citizens deserve the ability to keep certain details about their lives to themselves.

To sum up: for a long time, physical cash has been the best form of money with respect to privacy. But armed with a more efficient and transparent form of e-money, government after government will gradually make physical cash obsolete. Sooner than you think, cash will not be an option for privacy, or for anything else. And private citizens will come to appreciate the inherent value of Bitcoin, as their ability to discreetly hold and transfer value evaporates once cash goes the way of the dodo.

Grounds for Optimism in General

Bitcoin is still very much an experiment. However, after 10 years of operation, the Bitcoin protocol has not been hacked — despite offering what’s effectively the biggest ‘bug bounty’ in software history. Bitcoin is an amazing achievement of disparate private individuals working together towards a common goal.

As I consider how a community of people collectively created an alternate monetary system, I am greatly optimistic about what other aspects of our global society we can improve through a collective, decentralised effort.

And I say this even in the face of the various centralising forces currently being marshalled: humanity’s bifurcated monetary future will be better than our monopoly monetary past, as some money becomes more convenient while other money becomes far more private.