Bitcoin Cash SV – 6 block chainsplit

Abstract: On 18th April 2019, the BitMEX Research Bitcoin Cash SV node experienced 2 block re-organisations. First a 3 block re-organisation, followed by a 6 block re-organisation. In this brief piece, we provide data and graphics related to the temporary chainsplit. The chainsplit appears to be caused by large blocks which took too long to propagate, rather than consensus related issues. Our analysis shows there were no double spends related to the split.

Chainsplit diagram – 18 April 2019

Source: BitMEX Research
Notes: The above image indicates there were two valid competing chains and a non-consensus split occurred at block 578,639. Our node followed the chain on the left until block 578,642, then it jumped over to the right. About an hour later, it jumped back over to the left hand side. The chain on the left continued, while the chain on the right was eventually abandoned.

Chainsplit transaction data

Number of transactions
Main chain (within 6 blocks)
754,008
Fork chain
1,050,743
Overlap (within 6 blocks)
753,945
Eventual double spends
0

Source: BitMEX Research

Based on our analysis of the transactions, all the TXIDs from the forked chain (on the right), eventually made it back into the main chain, with the obvious exception of the coinbase transactions. Therefore, it is our belief that no double spends occurred in relation to this incident.

Timestamps of the blocks related to the split – 18 April 2019

Local ClockBlock TimestampHeightHashSize (MB)Log2 Work
11:39:4711:39:19578,638 000000000000000001ccdb82b9fa923323a8d605e615047ac6c7040584eb24193.187.803278
12:04:5112:04:37578,639 0000000000000000090a43754c9c3ffb3627a929a97f3a7c37f3dee94e1fc98f8.687.803280
12:28:0112:20:36578,640 00000000000000000211d3b3414c5cb3e795e3784da599bcbb17e6929f58cc0952.287.803282
12:43:4212:29:39578,641 0000000000000000050c01ee216586175d15b683f26adcfdd9dd0be4b1742e9e42.187.803285
12:59:2712:51:40578,642 00000000000000000a7a25cea40cb57f5fce3b492030273b6f8a52f99f4bf2a876.287.803287
13:05:1812:32:39578,640 000000000000000007ad01e93696a2f93a31c35ab014d6c43597fd4fd6ba959035.587.803282
13:05:1812:33:16578,641 0000000000000000033ed7d3b1a818d82483ade2ee8c31304888932b7729f6920.187.803285
13:05:1812:41:38578,642 00000000000000000ae4a0d81d4c219139c22ba1a8a42d72b960d63a9e1579141.087.803287
13:05:1912:56:37578,643 00000000000000000590821ac2eb1d3c0e4e7edab586c16d5072ec0c77a980dc0.887.803289
13:19:3613:14:22578,644 0000000000000000001ae8668e9ab473f8862dc081f7ac65e6df9ded635d338e128.087.803291
13:21:5613:18:07578,645 0000000000000000049efe9a6e674370461c78845b98c4d045fe9cd5cb9ea634107.287.803293
14:12:5413:15:36578,643 0000000000000000016b62ec5523a1afe25672abd91fe67602ea69ee2a2b871f23.887.803289
14:12:5513:43:35578,644 000000000000000003e9d9be8a7b9fc64ef1d3494d1b0f4c11845882643a64391.387.803291
14:12:5514:01:34578,645 0000000000000000052be8613e79b33a9959535551217d7fdacc2d0c1db1e6720.087.803293
14:12:5514:06:35578,646 00000000000000000475ab103a92eb6cb1c3c666cd9af7b070e09b3a35a15d660.087.803296
14:27:0914:24:37578,647 0000000000000000062bade37849ade3e3c4dfa9289d7f5f6d203ae188e94e4f77.087.803298

Source: BitMEX Research

If one is interested, we have provided the above table which discloses all the relevant details of the blocks related to the chainsplit, including:

  • The block timestamps
  • The local clock timestamps
  • The block hashes
  • The block sizes
  • The total accumulated PoW up to each block

With the above details one can follow what occurred in relation to the chainsplit and create a timeline.

Conclusion
Our primary motivation for providing this information and analysis is not driven by an interest in Bitcoin Cash SV, but instead a desire to develop systems to analyse and detect these type of events on the Bitcoin network. Systems are being developed on our website, https://forkmonitor.info, to help detect chainsplits, caused either by poor block propagation or consensus related issues. This event on Bitcoin Cash SV is good practice for us.

As for Bitcoin Cash SV, the block sizes were particularly large during the period of the re-organisations. On the forked chain, the last two blocks were 128MB and 107MB respectively. On the main chain many of the blocks were over 50MB. Therefore, in our view, it is likely the large sizes of the blocks were the root cause of the re-organisations, as miners couldn’t propagate and verify these large blocks fast enough, before other blocks on different chains were found.

As for the implications this has on Bitcoin Cash SV, we have no comment. We will leave that to others.

BitMEX Partnership Announcement with Trading Technologies International

HDR Global Trading, the owner of BitMEX, has partnered with Trading Technologies International, Inc. (TT), a global provider of high-performance professional trading software. Through the partnership, traders eligible to trade at BitMEX now have access to market-leading trading tools via TT on all BitMEX products, including the flagship XBTUSD Perpetual Swap.

Arthur Hayes, CEO of BitMEX, said: “Like Trading Technologies, BitMEX is committed to providing innovative financial products and a seamless experience for sophisticated traders. By combining our robust technologies, this partnership will not only extend BitMEX’s unique services to Trading Technologies’ discerning clients, but advance our mutual vision to unlock access to cutting-edge cryptocurrency products.”

“This collaboration with BitMEX brings our award-winning trading software to a much broader cryptocurrency market. We expect this partnership will grow trading volume on BitMEX, not only through our existing clients, who want access to cryptocurrencies, but also through new users keen to leverage professional trading software and enjoy better trading experiences,” said Rick Lane, CEO of Trading Technologies.

The TT platform provides professional traders with direct global market access and trade execution through TT’s privately managed infrastructure spanning five continents. Designed specifically for professional traders, brokers, and market-access providers, TT incorporates a broad array of customizable tools to accommodate trading styles that range from manual point-and-click trading to automated order entry.

About Trading Technologies

Trading Technologies (www.tradingtechnologies.com, @Trading_Tech) creates professional trading software, infrastructure and data solutions for a wide variety of users, including proprietary traders, brokers, money managers, CTAs, hedge funds, commercial hedgers and risk managers. In addition to providing access to the world’s major international exchanges and liquidity venues, TT offers domain-specific technology for cryptocurrency trading and machine-learning tools for real-time trade surveillance.


XBTU19 and ETHM19 Auto-Deleveraging Events 2 April 2019

On 2 April 2019 between 04:44:34 UTC and 05:22:08 UTC, less than 200 positions were auto-deleveraged due to the sharp price movements of the underlying mark price on XBTU19 and ETHM19.

At the time of these auto-deleveraging events, the Insurance Fund allocated to these contracts was minimal. The Insurance Fund is allocated individually to each contract according to how many liquidations contribute to that specific contract (System Gains and Losses). In the case of expired contracts, BitMEX has a process in place to roll over the Insurance Fund allocated to these contracts into the next front month contract. With the recent expiry on the 29th March 2019, this process failed and front month contracts did not receive their reallocation, and the funds remained unallocated. As a result, a handful of users were auto-deleveraged upon large liquidations within these affected contracts.

BitMEX receives auto-deleveraging reports and was made aware of the unusually high rate of auto-deleveraging events, at which point we investigated the matter. We identified the root cause, corrected the allocation and put further controls in place to ensure that reallocation failures are automatically flagged internally.

For users that were affected, BitMEX will be reaching out to you personally to explain the situation and document your compensation. We compensated users based on the maximum potential profit that they would have made over the timeframe of these auto-deleveraging events. We exited these users at the best price of each contract: longs at 5,079.5 on XBTU19 and shorts at 0.03103 on ETHM19. BitMEX did not profit from these auto-deleveraged positions.

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

Update: Notice of Minor System Outages 29 March 2019

On 29 March at 12:00 UTC, BitMEX experienced a minor outage for approximately 15 seconds whereby all requests would have been load shed as the engine was blocked during settlement operations. The platform was back to normal after the 15 second outage.

At 20:13 UTC, the BitMEX website experienced a limited interruption in service to a small group of users. The issue was immediately identified and fixed. The API was not affected.

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

The Lightning Network (Part 2) – Routing Fee Economics

Abstract: BitMEX Research examines the market dynamics of Lightning network routing fees and the financial incentives for Lightning node operators to provide liquidity. We identify the interrelationship and balance between Lightning routing fees and investment returns for channel liquidity providers, as a major challenge for the network, rather than the computer science aspects of the routing problem. We conclude that if the Lightning network scales, at least in theory, conditions in wider financial markets, such as changing interest rates and investor sentiment may impact the market for Lightning network fees. However, regardless of the prevailing economic conditions, we are of the view that in the long term, competition will be the key driver of prices. Low barriers to entry into the market could mean the balance favours users and low fees, rather than investment returns for liquidity providers.

(Lightning strikes the city of Singapore) (Pexels)

Overview

We first wrote about the Lightning network back in January 2018, when it was mostly theoretical. Today, as the Lightning network transitions from abstract to experimental, we felt it was time to take another look. The primary focus of this report is to analyse the Lightning network from a financial and investment perspective, notably with respect to fees and the incentives for Lightning network providers.  We will not examine other aspects of the technology.

The routing problem

Critics of the Lightning network often point to routing as a major problem, typically making claims like its “an unsolved problem in computer science”. In general, we do not really agree with this characterization of the routing problem and do not see the computer science of routing to be a major challenge, finding paths between channels to make payments may be relatively straightforward and similar to other P2P networks, such as Bitcoin.

However, what we do think its a major challenge is the interaction or balance between the financial and economic aspects of liquidity provision and payment routing. Lightning network node operators need to be incentivised by routing fees to provide sufficient liquidity, such that payments can be made smoothly. Liquidity needs to be allocated specifically to the channels where there is demand and identifying these channels may be challenging, especially when new merchants enter the network. This balance between ensuring the network has low fees for users, while also ensuring fees are high enough to incentivise liquidity providers, is likely to be a significant issue. As we explain further in this article, the magnitude of this problem and the fee rates at which the market clears, may depend on economic conditions.

Lightning fee market dynamics

For onchain Bitcoin transactions, users (or their wallets) specify the fee for each transaction when making a payment and then miners attempt to produce blocks by selecting higher fee transactions per unit block weight, in order to maximise fee revenue. In contrast, Lightning currently appears to work the other way around, routing node operators set the fee and then users select a path for their payment, selecting channels in order to minimise fees. With Lightning, suppliers initially set fees rather than users. Lightning may therefore offer a superior fee architecture, as suppliers are providing a specialised service and it is more suitable that suppliers compete with each other over fee rates, rather than ordinary users, where the priority should be on simplicity.

In Lightning there are two types of routing fees node operators must specify, a base fee and a fee rate.

Two types of Lightning network fees

Fee type Description Convention
Base fee A fixed fee charged each time a payment is routed through the channelThis is expressed in thousandths of a Satoshi.

For example a base fee of 1,000 is 1 satoshi per transaction.
Fee rateA percentage fee charged on the value of the payment  This is expressed in millionths of a Satoshi transferred.

For example a fee rate of 1,000 is, 1,000/1,000,000, which is 0.1% of the value transferred through the channel. Equivalent to 10bps.

Investment capital

In order to provide liquidity for routing payments and to earn fee income, Lightning node operators need to lock up capital (Bitcoin) inside payment channels.

Two types of channel capacity

Description Creation
Inbound capacityInbound liquidity, are funds inside the node’s payment channels which can be used to receive incoming payments.

These funds are owned by other participants in the Lightning network.

If the payment channels are closed, these funds will not return to the node operator.
An inbound balance is created in one of two ways:

* When another network participant opens a payment channel with the node

* When the node operator makes a payment via an existing channel
Outbound capacityOutbound liquidity, are funds inside the node’s payment channels which can be used to make outbound payments.

These funds are owned by the node operator and part of their investment capital. The node operator may consider the opportunity costs of other investments, while considering the total outbound balance.

If the payment channels are closed, these funds will return to the node operator.
An outbound balance is created in one of three ways:

* When the node operator opens a payment channel with another network node

* When the node operator receives a payment via an existing channel

* When payments are routed through the node and fees are received

Graphical illustration of a channel’s inbound and outbound capacity

(Source: Bitcoin Lightning Wallet)
(Note: The orange balance is the inbound capacity, while the blue balance is the outbound capacity)

The operation of the Lightning fee market

Becoming a successful routing node is harder than one may think. At the time of writing, according to 1ml.com, there are 7,615 public Lightning nodes. However, it is likely that only a few hundred of these nodes are doing a good job providing liquidity, by managing the node, rebalancing channels and setting fees in an appropriate manner.

Node operators may need to:

  • Adjust both fee rates and the base fee, monitor the impact of the adjustments and calibrate for the optimal income maximising settings
  • Analyse the network and look for poorly connected Lightning nodes with high payment demand, such as a new merchant
  • Analyse the fee market, not just for the network as a whole, but the high demand low capacity routes you are targeting
  • Constantly monitor and rebalance ones’ channels, to ensure there is sufficient two way liquidity
  • Implement a custom backup solution for the latest channel states, to protect funds in the event that the node machine crashes

Currently, there are no automated systems capable of doing the above functions. If this does not change, specialist businesses may need to be setup to provide liquidity for the Lightning network. However, just as with liquidity, the challenges in overcoming these technical issues do not necessarily mean payments will become difficult or expensive. These technical challenges may simply adjust the equilibrium market fee rate. The more difficult these problems are to overcome, the higher the potential investment returns will be to channel operators and the greater the incentive will be to fix the problems. It will be demand that drives Lightning’s success, not the challenges for node operators.

In order for Lightning fee markets to work, node operators may need to adjust fees based on the competitive landscape, this could be based on algorithms or be a manual process, aimed at maximising fee income. In an attempt to emulate what may eventually become standard practise, BitMEX Research experimented with modifying the fee rate on one of our nodes over a three month period, as the below section reveals.

Fee rate experimentation

BitMEX Research decided to conduct a basic experiment to try and evaluate the state of the fee market, even in the Lightning network’s current nascent state. We set up a Lightning node and regularly changed the fee rate to attempt to determine which rates would maximise fee revenue, just as node operators may eventually be expected to do as the network scales.

Our basic non-scientific analysis from one node is illustrated in the scatter chart below. It appears to indicate that fee rates do currently have an impact on a lighting node’s fee income. The daily fee income appears to quickly accelerate as one increases the fee rate from 0 till around 0.1 bps. Once the fee is increased above this rate, average daily fee income appears to gradually decline. Therefore, based on this experiment, it appears as if the revenue maximising fee rate is around 0.1 bps, which is certainly very low when compared to other payment systems. However, of course, this is only the fee for one hop, a payment may have multiple hops. At the same time, the current Lightning fee market is barely exists, indeed BitMEX Research may be one in only a handful of Lightning nodes that has significantly experimented with economic revenue maximising behaviour by changing fees. Once the network scales and other parties try to maximise revenue, fee market conditions are likely to be very different. This exercise should therefore only be considered as an illustrative experiment, rather than anything particularly revealing about lighting fee markets.

Lightning node daily fee income versus the feerate

(Source: BitMEX Research)
(Lightning fee income data charts – notes and caveats:
* Daily data from 31st December 2018 to 24th March 2019
* Data from one Lightning node
* The base fee was 0 across the period
* The investment return data excludes onchain Bitcoin transaction fees, when including the impact of fees all but the most optimal fee rate buckets would show a negative investment return
* The data includes both weekdays and weekends, in general Lightning network traffic is significantly lower at weekends
* The fee rate was changed every day at around 21:00 UTC. The fee rate was reduced each day and then jumped up to the top of the fee rate range after several says of declines, to begin the next fee rate downwards cycle. The reason for this was that some wallets (e.g. mobile wallets) did not always query the fee rate each time it attempted to route a payment through the node, therefore when increasing the fee rate, many payments would fail. For example, when opening a channel from a mobile wallet to the Lightning node, then increasing the fee rate and immediately attempting to make a payment, the payment often failed as the wallet attempted to pay with a fee which was too low. In our view, in order to Lightning network fee markets to work, node operators may need to regularly change fees and therefore wallets may need to query fee rates more often
* Channel rebalancing occurred manually, once every two weeks. Approximately 30 minutes was spent on each occasion

* The Lightning node was running LND and the software was updated to the master every two weeks
* Approximately 30% of the channels (by value) were opened using the autopilot, the other 70% were opened manually
* The investment return was calculated by taking the outbound channel capacity of the network each day, annualising the investment return based on the daily fee income and then calculating a simple average based on all the days with a fee rate inside the particular range
* The data is based on one node only and its particular set of channels, the experience for other node operators may be very different
* We tried to use our public node for this experiment, however the fee income was too sporadic, with some network participants regularly paying well above the advertised fee rates by considerable amounts, making the data unreliable

* Unfortunately we needed to use a log scale for both axis. With respect to the fee rate we were unsure of which rates to charge, even which order of magnitude to set, therefore we tried a wide range of fees, from 0.0001% to 0.5% and a log scale was appropriate. At the same time, the daily fee income was highly volatile, ranging from 0 satoshis to over 3,000 satoshis. Therefore a log scale was most deemed appropriate. As the network develops and becomes more reliable and fee market intelligence improves, a linear scale may be more appropriate)

Fee incomes and investment returns

In addition to daily fee income, one can also consider the annualized investment return associated with running a lighting node and the various fee rates. This is calculated by annualising the daily fee income and dividing this number by the daily outbound liquidity.

The highest annualised investment investment return achieved in the experiment was 2.75%, whilst the highest fee bucket investment return was almost 1%. This seems like a reasonably attractive return for what should in theory be a relatively low risk investment, at least once the ability to backup lighting channels in real time becomes implemented. Existing Bitcoin investors could be tempted by these returns and provide liquidity to the Lightning network, or alternatively US dollar based investors could buy Bitcoin, hedge the Bitcoin price exposure using leverage and then attempt to earn Lightning network fee income.

Lightning node annualised investment return by fee bucket

(Source: BitMEX Research)

Of course, liquidity providers in the current Lightning network are not likely to be motivated by investment returns.  Current node operators are likely hobbyists, with the overwhelming majority of node operators making losses when considering the onchain fees required to open and rebalance Lightning channels. Although this hobbyist based liquidity probably can sustain the network for a while, in order to meet the ambitious scale many have for the Lightning network, investors will need to be attracted by the potential investment returns.

Lightning network fees and economic conditions

A 1% investment yield may seem attractive in the current low yield environment, however the Lightning network may initially have difficulty attracting the right commercial liquidity providers. Investors in this space are typically looking for a high risk high return investment, which appears to be the opposite end of the spectrum for the relatively low risk low return investment on offer for Lightning liquidity providers. Therefore a new type of investor, one that fits this profile, may be needed.

If the Lightning network reaches a large scale, it is possible that the highly liquid investment product, with stable low risk returns, is sensitive to economic conditions.

Consider the following scenario:

  1. The federal reserve base rate is 1.0%
  2. Lightning node operators are typically earning an annualised investment yield of 1.5% on their outbound balance
  3. Due to robust economic conditions and inflationary pressure, the federal reserve open market committee increase interest rates from 1% to 3%.
  4. Due to the more attractive investment returns, Lightning network node operators withdraw capital from the Lightning network and purchase government bonds
  5. Due to the lower levels of liquidity in the Lightning network, users are required to pay higher fees to route payments and the Lightning network becomes more expensive

However, if Lightning network liquidity is large enough for the above logic to apply, Lightning would have already been a tremendous success anyway.

The risk free rate of return

In some ways, if the Lightning network matures, one can even think of the investment returns from running a Lightning node as Bitcoin’s risk free rate of return, or at least a rate of return free from credit risk. In traditional finance this is often the rate investors earn by holding government bonds, where the government has a legal obligation to pay the principal and coupon and a means to create new money to pay the holders of the bonds, such that the risks are near zero. In theory, all other investment projects or loans in the economy should have a higher return than this risk free rate. The same could apply to Bitcoin, with Lightning node liquidity providers return rates being considered as the base rate within the Bitcoin ecosystem.

In the future, if most of the technical challenges involved in running nodes have been overcome and there are competitive fee setting algorithms, this Lightning network risk free rate could ultimately be determined by:

  • Conditions in wider financial markets – higher interest rates could mean a higher Lightning network risk free rate
  • The demand for Lighting network transactions – more demand or a higher velocity of money, should increase the Lightning network risk free rate

Conclusion

Whether specialist hedge funds and venture capital investors will have the same enthusiasm about becoming Lightning network liquidity providers, as they did for the “staking as a service” business model for proof of stake based systems in mid 2018 remains to be seen. While the investment returns for Lightning network liquidity providers do not yet look compelling, with the network in its formative stages, we do see potential merit in this business model.

In our view, the Lightning network can easily scale to many multiples of Bitcoin’s current onchain transaction volume without encountering any economic fee market cycles or issues, all based purely on hobbyist liquidity providers. However, if the network is to reach the scale many Lightning advocates hope, it will need to attract liquidity from yield hungry investors seeking to maximise risk adjusted investment returns. Should that occur, unfortunately the network may experience significant changes in fee market conditions as the investment climate changes over time.

However, it is relatively easy to set up a node, provide liquidity and try to earn fee income by undercutting your peers. Where the balance is ultimately struck between the operational channels of running nodes, the extent of liquidity provision and the investment returns, we obviously do not know. However, if we are forced to guess, based on the architecture and design of the Lightning network, we would say the system is somewhat rigged towards users and low fees, rather than liquidity providers.

The Road to $10K

Did you take your losses like a champ, or bottom tick the market with your market close order? The first quarter of 2019 witnessed depressed volumes, volatility, and price. The local lows of late 2018 have not been retested; however the market chop makes me feel like I’m at the Saudi embassy.

The repair of crypto investors balance sheets is not done yet. Losses must be digested, and the unlucky masses must wage cuck a bit longer to get back in the game.

All is not lost; nothing goes up or down in a straight line. 2019 will be boring, but green shoots will appear towards year end. The mighty central bank printing presses paused for a while, but economic sophists could not resist the siren call of free money. They are busy inventing the academic crutches (here’s looking at your MMT), to justify the next global money printing orgy.

Do not despair. CRipple is still worth more than zero. And Justin Sun’s new age religion TRON, paired with the Pope CZ, tells us there are those still willing to eat shitcoins with a smile.

Electric Cars and Sand Schmucks

While Bitcoin is an innovative technology, the technical merits of the protocol do not exist in a vacuum. The world’s monetary situation is very important. It determines how willing investors are able to suspend disbelief and believe crypto fan boys and girls.

Throughout 2018 the omnipotent Fed began reducing the size of its balance sheet and raising short term interest rates. The world still beats to the tune of the USD. Financial institutions and governments require cheap dollars, and the Fed happily obliged since the 2008 GFC.

Tech VC funds won’t admit it, but cheap dollars are key to their business. How else can you convince LPs to continually fund negative gross margin businesses, until they “scale” and achieve profitability? Everyone wants to become the next Facebook.

When investing in government bonds yields zero or negative, desperate investors will do whatever it takes to obtain yield. Tesla is a perfect example. Lord Elon is a master at creating open-faced pits, and torching his investors’ money in them. Tesla does not belong on the Nasdaq, but rather as a speciality flavour at the New York Bagel Co.

The market disagrees with my Tesla melancholy, investors continue to line up to eat Elon’s sexy Tesla hot shit cakes. Can you blame them, after you are fully invested in the S&P500 where else will you be able to show alpha to your investors?

Another example of this free money folly is the Vision Fund.

  1. Top tick the “Value” your investments while still on the Softbank’s books.
  2. Find a group of schmucks from the sand (That’s where the former Deutsche credit boys come in, “Be Bold”)
  3. Sell your mark-to-fantasy private Unicorns into the vehicle populated by your sand schmucks
  4. Take your cash and payout to your Japanese investors as dividends.

These entities thrived while the Fed held rates at 0% and reinvested their treasury and MBS roll off. TSLA hit its all-time high in mid-2017. Since then Elon has struggled to generate enough buzz to keep his stock elevated. I’m sure he isn’t thrilled that bondholders are due close to $1 billion in cash because the stock price failed to scale $360.

The Vision Fund’s sand schmucks also got cold feet. They baulked when the fund proposed to invest an additional $20 billion into the We-Broke company. The check size got sliced down to $2 billion.

When dollars get scarce suddenly investors discover value investing all over again.

The height of crypto silliness in December 2017 occurred just before the Fed embarked on its quantitative tightening. The 2018 pain train spared no crypto asset or shitcoin.

But things are a changin’. The Fed couldn’t stomach a 20% correction in the SPX. In the recent Fed minutes, the dot plot now shows no rate increases for the rest of 2019. The Fed will start reinvesting its runoff in the third quarter. We are only a hop, skip, and a jump away from an expanding Fed balance sheet.

Beijing knows China must rebalance its economy away from credit-fueled fixed asset investment. However, Xi must not have the political cojones to push this sort of painful change through. Therefore, the PBOC said “fuck it” to any attempt to reign in credit growth. The two most important central banks are creepin’ back into a super easy credit regime.

Easy money will manifest itself in other higher profile and more liquid dogshit before crypto. 2019 will feature an IPO beauty pageant of some of the best cash destroying businesses. Uber, Lyft, AirBnB, and possibly the We company all are rumoured to IPO this year.

Lyft is apparently oversubscribed for its upcoming IPO. Oh baby, this is going to be a fun year.

If these beauties can price at the top of the range, and trade above the IPO price, we know that party time is back. Crypto will be the last asset class to feel the love. Too many people lost too much money, in too short a time period, to immediately Fomo back into the markets.

Get Excited

Green shoots will begin to appear in early Q4. Free money and collective amnesia are powerful drugs. Also after two years of wage cucking, punters should have a few sheckles to rub together.

The 2019 chop will be intense, but the markets will claw back to $10,000. That is a very significant psychological barrier. It’s a nice round sexy number. $20,000 is the ultimate recovery. However, it took 11 months from $1,000 to $10,000, but less than one month from $10,000 to $20,000 back to $10,000.

Melissa Lee peep this. $10,000 is my number, and I’m stickin’ to it.

BitMEX Research Launches Ethereum Node Metrics Website – Nodestats.org


Abstract: BitMEX Research is delighted to announce the launch of a new website to monitor the Ethereum network, Nodestats.org. 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. Nodestats.org 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

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

The main purpose of Nodestats.org 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

Nodestats.org 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 Nodestats.org 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 Nodestats.org 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 Nodestats.org 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 Nodestats.org 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 UsageNodestats.org takes a reading from the machines every 5 seconds, related to how much memory is being utilized by the Ethereum client.

All the machines Nodestats.org 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 Nodestats.org 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 Nodestats.org 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 Nodestats.org 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 Nodestats.org 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.

Conclusion

Like its sister website, Forkmonitor.info, Nodestats.org 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, Nodestats.org 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?)

Overview

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.

Conclusion

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

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.

Auto-deleveraging

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)

Incentives

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?

Conclusion

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, BTC.com, 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 – BTC.com (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)

 

AsicBoost

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)

Conclusion

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.