The safe-haven hunt in 2022 : is Bitcoin the answer?

By Chadi El Adnani, Crypto Research Analyst @SUN ZU Lab

July 2022

Is bitcoin a safe-haven? Powered by SUN ZU Lab

We revisit in this article a question studied by Sun Zu Lab in 2020 during the financial crisis caused by the Covid-19 pandemic: did bitcoin resist the market downturn better than other assets in 2022, and would it have been strategically interesting for investors to shift some of their positions to bitcoin before the downturn? the answer to these questions is still negative, as it was the case in 2020. We study in this analysis the behavior of various assets over the year 2022, specifically bitcoin, ether, equity markets (S&P 500), bond markets (US interest rates) and gold.

This result does not constitute an absolute answer to the intrinsic value of digital assets, but rather provides insights into a specific situation.

Economic context

2022 has been marked so far by the Russian invasion of Ukraine which, added to an economic context already weakened by the Covid-19 pandemic, has accentuated the slowdown of the world economy which could enter a long period of stagflation (low growth and high inflation). According to World Bank figures, global growth is expected to fall from 5.7% in 2021 to 2.9% in 2022, significantly lower than the 4.1% figure announced last January. We cannot help but notice the very visible parallel with the 1970s stagflation period. The comparison is striking: persistent supply disruptions that fuel inflation (Russian gas shortages, the effects of the prolonged severe lockdown in China on various value chains, etc.), coupled with the end of a long period of very accommodating monetary policy in major advanced economies: key interest rates close to 0%; Fed balance sheet in excess of $8.5 trillion (35% of US. GDP, an all-time record), etc. Added to this are projections of a slowdown in global growth and the fragility of emerging and developing countries in the face of the urging need to tighten monetary policies to curb inflation. 

In this unique context, we wanted to put ourselves in the shoes of investors to understand whether it is in their best interest to move from one asset class to another, and especially whether cryptos represent an effective safe haven in times of crisis. We model the S&P 500 by its most liquid ETF: Spider (ticker SPY, NAV as of 06/17/22: $378 billion). Gold is modeled by its most liquid physical ETF (ticker GLD, $63 billion NAV). U.S. government bonds are modeled by the iShares 7Y-10Y ETF (ticker IEF, $18 billion NAV). Prices are closing prices and all execution issues are neglected. Access considerations are also neglected: the products used here are easy to access, anyone can open a securities account with an online broker or a crypto exchange in a few days. Data is extracted from Yahoo finance. 

Finally, the « safe haven » concept reflects the idea that certain assets, financial or otherwise, provide a safe haven in the event of economic and financial turmoil. We often find in this category the US and German bonds, due to the almost absolute confidence in the strength of their economic fabric and their ability to always repay their debts. The Japanese Yen has often been considered a safe haven as well.


Without further ado, here is the relative performance of the 5 assets during 2022:

Bitcoin performance

The numbers speak for themselves: only gold managed a stable performance in 2022 (0.2%), while BTC or ETH under-performed the bond and equity markets (-55.8% and -70.5% respectively). 

Another way to answer the original question is to calculate the “flight to quality” percentage of investors who switched asset classes. Indeed, this type of behavior is extremely common; cautious investors forecasting the increase in interest rates and inflation at the beginning of the year might have chosen to move some of their positions to safer assets, such as gold, US sovereign bonds, or even Bitcoin? Let’s look at the results:

Bitcoin performance vs benchmarl

The red curve reads as follows: investors choosing to switch their position on 12/31/21 from gold to Bitcoin would have realized a loss of 56% between 12/31/21 and 6/17/22 (compared to a situation where they would have remained invested in gold). The graphs show exactly how much gain/loss was received for switching, depending on when the switch took place.

It appears that the choice to “pivot” from another asset to BTC has never paid off in 2022. Gold, on the other hand, has well played its role as a safe haven, providing positive gains at almost any time of the year on S&P > gold and bond > gold pivots.


Rather than analyzing the assets’ volatility as defined by the classic financial formula, i.e. the annualized standard deviation of daily returns, let’s look at a more intuitive measure: the intra-day variation. We compute the 30-day moving average of daily amplitudes for the five assets.

The graph shows that over the year 2022, Bitcoin and Ether have varied on average between 3% and 8% from their highest to lowest price on a single day. In contrast, the S&P 500, gold and bonds only vary by 1% to 2.5% (or even less). Investors should keep in mind then that the amplitude of movements in crypto markets is 4 to 5 times greater than in traditional markets, which requires careful monitoring to deal with these risky assets.

Bitcoin's volatility


Finally, let’s analyze liquidity as characterized by daily volumes. This is a post-trade measure of liquidity, i.e. the liquidity that has been achieved through transactions. We could also study a pre-trade measure: the liquidity available before execution in the exchanges’ order books , which is a little more complex to compute.

The graphs below show average trading volumes (30-day moving average) in 2022, with 1-month volatility (annualized standard deviation) as the second axis:

The S&P 500 ETF is one of the most liquid instruments in the world, with $30 to $60 billion traded every day. The causal relationship between volatility and volume is immediately apparent.

Liquidity of Bitcoin by SUN ZU Lab

For the gold ETF GLD, we have daily trading volumes between $1 and $3.5 billion, but the causal relationship is still perfectly visible.

Liquidity of Bitcoin analysed by SUN ZU Lab

Regarding Bitcoin, we have first of all a confirmation of its very volatile character, with annualized volatility varying between 40% and 80%. This graph also raises discrepancies already made by Sun Zu Lab in 2020: it appears that the volatility/volume causality is still not respected, with peaks of volatility in February-March that are not accompanied by any increase in volume, and on the contrary periods like April when the volume seems to grow by itself!

The two circled areas on the chart are anomalies, likely due to the fact that the officially announced volumes are greatly overestimated and that the magnitude of phantom volumes varies over time. The S&P 500, gold and the bond ETF, not included here, show a rather stable “structural” liquidity: when the volatility peaks stop, the average volume goes back down to a base level.


The answer to the question “is BTC a credible alternative to the volatility of traditional markets?” is negative, again, over the year 2022. Unquestionably, gold and sovereign bonds are still the “safe haven” they have been for a long time. On the other hand, the liquidity of bitcoin (and ether) is still problematic; the volumes traded do not follow the same logic expressed historically by investors in the traditional markets.

Questions and comments can be addressed to or

About SUN ZU Lab

SUN ZU Lab is a French Fintech that aims to become the leading independent provider of digital asset market quantitative analytics tools and services. Leveraging the founding team’s 70+ years of experience in international capital markets and trading technology, SUN ZU Lab provides crypto professionals with unprecedented liquidity analytics in the form of quant reports, dashboards, real-time augmented data feeds, and bespoke studies.

Demystifying the Terra debacle

Terra Luna debacle

By Stéphane Reverre and Amine Mounazil

A stablecoin is a cryptocurrency whose value is “pegged” to the price of a reserve item, the US dollar being the most commonly used reserve asset. Stablecoins appeared for the first time in 2014, and have been gaining popularity since. Today, they are considered the backbone of the crypto ecosystem by providing an “on-risk” and “off-risk” alternative for cryptocurrencies. Moreover, their adoption is driven by the demand of traditional financial institutions and the larger crypto sector for a standard approach to leverage blockchain technology while avoiding related risks.

Thus, stablecoins address one of the fundamental issues with many mainstream cryptocurrencies: their extreme volatility, which makes them inadequate for real-world transactions. Altough competition is nothing new among stablecoins, “de-pegs” are an unwelcome and novel eventuality as different projects fight tooth and nail to source the liquidity required to keep their coins close to the promised pegged value ($1 most of the time).

In this piece, we first give a brief overview of stablecoin issuance, followed by an analysis of stablecoins from a macroeconomic standpoint, before introducing quantitative insights about the recent activity that led UST to depeg and spread contagion in the digital asset market.

A brief overview of stablecoin economics

To better understand the issuance of fiat-backed stablecoins, consider each stablecoin protocol as a financial organization similar to a bank with assets and liabilities. The reserve assets (monetary units or investment securities) and liabilities (issued tokens) are matched 1:1.

However, things are different in the case of crypto-collateralized stablecoins because of the volatility of the collateral. If the value of cryptocurrency reserves falls, the system may become undercollateralized. As a result, if liabilities are in dollar-equivalent, the 1:1 backing will not hold. One way to solve this problem, and to keep the entire system safe, is to make reserve assets significantly larger than liabilities (over-collateralize). Although their exact mechanisms differ, this is how the Maker (DAI) and the Synthetix
(sUSD) protocols manage their risk.

As for algorithmic stablecoins, they are based on the idea that maintaining the value of the stablecoin over time is possible through the right set of incentives offered to market participants, in response to market conditions. In other words, the protocol itself contains provisions to defend the peg directly, as in the case for Celo Dollar (cUSD) and TerraUSD ($UST).
When it comes to non-custodial stablecoins (i.e. stablecoin that do not hold physical collateral), there are five key hazards:

Each of these can cause the stablecoin value to fall, possibly to zero. Because they are not formally backed by collateral, algorithmic stables like $UST are especially vulnerable to collateral risk. When there is a loss of confidence, such as when the general crypto markets falls, it may lead to a bank run or “death spiral”.

The robustness of a stablecoin depends on its ability to maintain the peg by reducing the spread between its “market value” and its “theoretical value“, i.e. the value of the currency the coin is pegged to. This ability is supported by two elements: the quality of market makers and the depth of the orderbook.

Stablecoin macroeconomics

In legacy markets, a currency peg is a means of securing a currency’s stability by tying its exchange rate to another currency. A notable example is the peg of the Chinese Yuan to the dollar, managed by the Chinese Central Bank, or the Swiss franc, whose peg to the Euro snapped in 2015. Currency pegs allow governments to develop a stable trading environment free of volatility, and to effectively operate an active control of monetary flows (e.g. balance of payments, foreign direct investment etc). As a general rule, government’s foreign currency reserves must be large in order to maintain a peg. This is because, if the government has to appreciate/depreciate its own currency, it may have to do so in the open market with its own reserves, in addition to traditional tools such as raising/decreasing interest rates.

A stablecoin is in essence a digital asset designed to keep its value by being pegged to a fiat currency such as the dollar or the euro, a commodity such as gold or silver, or another crypto currency. To keep the peg, the money supply of stablecoins is extended and contracted. When the price of a stablecoin rises in relation to the peg, the stablecoin’s money supply expands. Similarly, if the price of a stablecoin falls in relation to the peg, the stablecoin’s money supply contracts.
One recurrent risk for stablecoins, or any pegged currency, is the threat of an attack aimed at breaking the peg and profiting from price discrepancies (Soros’ 92). This risk has been present in legacy markets long before the emergence of blockchain technology and decentralised finance (DeFi), and is illustrated in modern economic theory by the concept of the “impossible trinity”. The impossible trinity (or unholy
trilemma) states that an authority (say a central bank) can only have two of the following at the same time, but never all three:

  1. Free capital movement (i.e. absence of capital controls): citizens of a country can diversify their assets by investing overseas, thanks to capital mobility. It also invites foreign investors to invest in the country by bringing their resources and expertise.
  2. A fixed foreign exchange rate (i.e. a peg): a fluctuating currency rate, which is sometimes influenced by speculation, can be a cause of larger economic unpredictability. A steady rate also makes it easier for households and businesses to participate in the global economy and develop long-term plans.
  3. An independent monetary policy: when the economy is in a recession, the said authority can raise and lower interest rates, and when the economy is overheated, it can cut the money supply and raise interest rates.

Source : Wikipedia

We turn now back to our crypto eco-system. It is crucial to understand the tokenomics behind both assets before diving into a quantitative analysis of the $LUNA and $UST debacle. The underlying protocol, Terra, operates with two tokens ($LUNA and $UST), and incorporates a virtual automated market maker (AMM). The objective is to keep those tokens in balance to maintain the $UST stablecoin’s peg. In addition to the “algo” part, Terra is supported by the Luna Foundation Guard (LFG) and its reserves.

Market participants can mint (e.g. create) $UST on Terra by burning (e.g. destroying) an equal dollar-amount of $LUNA and are incentivized to do so. Consequently, the price of $LUNA rises as the demand for stablecoins rises: the change in $UST demand dictates how much $LUNA must be burned. As this amount is subsequently burned, supply decreases.

As it happens, $UST’s adoption since the end of 2021 has been parabolic, thanks to Anchor, Terra’s lending and borrowing protocol, which offered annual percentage yields (APY) as high as 19.5% on deposits. This, of course, has resulted in a reduction of $LUNA’s supply, which decreased by 5% in January 2022 alone:

Market activity quantitative insights

Now that we are somewhat familiar with the tokenomics of these protocols, we can better understand the market behavior by first reading market activity then looking at anomalies and microstructure analytics on [include venues & pairs].

Earlier this year, the Luna Foundation Guard (LFG) raised $1 billion through a sale of $LUNA (its native token) to form a $BTC reserve, in order to maintain Terra’s stability and fund future developments. The establishment of a $BTC reserve was meant to reduce the possibility of a death spiral. So, instead of having to mint $LUNA to arbitrage the price of $UST, users can now exchange $UST for $BTC on Terra.

Swap BTC and UST

In April again, the LFG acquired an additional 37,863 $BTC for ~$1.5 billion, while the Anchor APY was reduced to 18%. During the same period, a massive $UST sell-off happened on Curve and Binance resulting in a small depeg that was aggravated by the withdrawal of roughly $2 billion from Anchor. The unfavorable timing has led many participants to speculate this was a coordinated attack to drive the stablecoin to depeg in order to either cripple the Terra ecosystem or trigger some sort of contagion that would result in $BTC to dip.

Historically, every known stablecoin has broken its peg at some point. Moreover, statistical evidence* shows that $BTC volatility is statistically stable with a finite theoretical variance, whereas stablecoin volatility is statistically unstable and responds to $BTC volatility synchronously.

(*) “On the stability of stablecoins”, Journal of Empirical Finance, vol. 64, 2021, Grobys et al. – Source

During the crash, the biggest volatility peak was observed at the beginning on May 9th at 6:20 pm. Many sale trades were observed, bringing the price down but immediately the market reacted and pushed the price back up, hence the high volatility. This selling pressure lasted exactly 1 min. The charts below show market activity for LUNA and UST on FTX, Kraken and Gemini over a few days prior to the crash (from May 5th to May 9th). Markets are still in “rebound mode” i.e., they tend to come back up even after severe down-moves, especially for UST:

Terra Luna price on FTX
Terra Luna price on Gemini
Terra Luna price on Kraken
Terra Luna price on Kraken UST

After May 9th, this “rebound” behavior disappears entirely, markets just “give-up” for LUNA, and UST trades at levels much lower than its peg value:

Terra Luna price on Gemini

When zooming closer at periods where activity is very surprising (to the point where our filters identify them as “anomalies”), here is one example of what we find on May 9th:

Terra Luna traded price and net traded volume with anomaly number 1

The biggest orders were sent in the middle of the crash. At 19:07 and 21:57:

Terra Luna traded price and net traded volume at 19:07
Terra Luna traded price and net traded volume

Following the crash, we observe a strong desynchronization between prices on FTX and Gemini, as can be seen below, where prices differed by more than $8 for several minutes. In addition, Gemini suffered a dramatic 9-fold decrease in market share on the LUNA-USD pair:

Terra Luna traded price on 05 10 2022
Terra Luna traded price on 05 10 2022 22:51
Terra luna rolling revenues

At the beginning of the crash, Gemini had a book twice as full as that of FTX, which can explain the large trades and other strange events observed on FTX. Logically, it was easier for manipulators to move the price on the market where the book was the thinnest. Following the crash however, the liquidity available in Gemini’s order book was almost non-existent.

Tether ($USDT), the crypto market’s largest stablecoin, also displayed symptoms of stress as Terra’s UST stablecoin imploded. It abruptly lost its $1 USD peg in early morning trading, falling as low as $0.95 before going back to $1.


On May 13th, block production on Terra was temporarily paused to prevent second-order consequences on network stability and governance caused by hyperinflating supply. Trading of $LUNA pairs was also suspended on some exchanges like Binance and Coinbase as it traded at less than a tick’s value.

In an effort to preserve the community and the developer ecosystem, Do Kwon (CEO of Terraform Labs, the company behind Terra) came up with the Terra Ecosystem Revival Plan. In order to significantly strengthen the liquidity around $UST peg, the LFG Council voted to loan $750M worth of $BTC to trading firms to help protect the $UST peg, and to loan $750M UST to accumulate $BTC as market conditions normalized. In addition, the foundation is looking to use its remaining assets to compensate current and past holders of $UST.

Distribution of LFG’s Reserve Assets as of 16 May 2022

On the 16th of May, Do Kwon published the Terra Ecosystem Revival Plan 2, where he imagines a fork in the Terra chain that will lead to a new chain without the algorithmic stablecoin. While the entire crypto-currency market has mixed opinions on this new experiment, it is clear once again that more scrutiny is needed to protect the small holder and protect the market from counterparty risk.

About SUN ZU Lab

SUN ZU Lab is a leading independent provider of liquidity analysis for investors already active or crypto-curious. We provide quantitative research on the liquidity of all digital assets to help investors improve their execution strategies and source the highest level of liquidity at the lowest cost.

Our product line includes research reports, software tools, and bespoke developments to fulfil the needs of the most demanding digital investor.

Liquidity Primer: an overview of Liquidity in Crypto Markets

The various types of crypto liquidity powered by sun zu lab

By Chadi El Adnani, Crypto Research Analyst @SUN ZU Lab

All seasoned investors know this for a fact: the first and foremost characteristic of a financial instrument is its liquidity. In its 4 th annual global crypto hedge fund report published recently, PWC confirmed that liquidity is indeed the most common consideration for crypto funds when choosing a trading venue (cited by 39% of respondents, see below). This percentage is significant and far above the next consideration, trading opportunities, at 18%. In the wake of this finding, we felt an overview of market liquidity with a focus on crypto markets was a good idea. Too often is the concept of liquidity overlooked or taken for granted, whereas in real life it is not only critical but difficult to quantify or elicit. In this article we provide general foundations about what liquidity is, its variations and manifestations

What are the most common considerations for crypto funds when choosing a trading venue?

Source: PWC 4 th annual global crypto hedge fund report 2022

What is liquidity ?

We focus in this article on market liquidity, which is a different concept from monetary liquidity: a company’s ability to meet its current liability commitments. Liquidity is defined as the ability to buy or sell large quantities of an asset without significant adverse price movement. It is an important factor that investors need to assess before executing their trades, since it is a clear constraint on how quickly they can gain access to the market and subsequently how fast they can lock in a profit from a particular asset.

There exists different types of market liquidity:

We identify at SUN ZU Lab three different types of market liquidity: transaction liquidity (post-trade), order book liquidity (pre-trade) and invisible liquidity. Those notions are additive, in the sense that all three exist at the same time for a given instrument. Yet there is a timing dependency between them: invisible liquidity needs to become visible (pre-trade) before it can be consumed (post-trade).

Derivatives markets, whether in crypto or TradFi, enjoy far more liquidity than spot markets. The Bitcoin futures market, for example, saw average monthly turnover of $2 trillion at its peak, a far greater figure than BTC spot markets’ volumes. Liquidity is not only variable in time, it is also distributed in space across multiple venues. Our data shows for example that BTC trading volumes are fragmented across exchanges, as seen in the graph below (limited to 5 exchanges, and in reality there are many more creating an even higher degree of fragmentation).

Crypto Liquidity | turnover repartition powered by SUN ZU Lab

Source: SUN ZU Lab data

Transaction liquidity:

Transaction liquidity refers to liquidity that has been expressed through actual trading volumes. This is an important indicator as high trading volumes usually imply less difficulty to buy or sell large quantities. Among the most liquid markets we can cite the forex market, thought to be the most liquid in the world as major currency pairs are traded by governments, banks, and even individuals. The stocks and commodities markets are very liquid as well, although intuitively no large cap or bond will ever be as liquid as a national currency.

Source: data from Yahoo Finance

In the graph above, we show daily trading volumes in 2022 for the S&P 500’s most liquid ETF, Spider SPY, as well as BTC-USD and ETH-USD. The S&P 500 ETF is one of the most liquid instruments in the world, with $30 to $60 billion traded every day. Data shows that Bitcoin and Ether’s daily trading volumes vary in range between $10 and $40 billion.

These figures however need to be taken with a grain of salt due to various sources reporting that officially announced crypto trading volumes are greatly overestimated with the magnitude of fake volumes varying over time.

Order book liquidity (pre-trade):

Order book liquidity represents the total nominal (price * quantity) visibly offered across all available trading venues. This liquidity materialises as different buy/sell quantities sent by investors at different prices. Across all buy orders, the best price is referred to as “best bid”, and the “best offer” on the other side across selling orders. The mid price is quite logically the middle of those two.

A visible order book represents the underlying supply and demand of an asset in the form of individual bid and ask orders. To illustrate this, here is a chart compiled by SUN ZU Lab showing how average liquidity aggregates around mid-price for BTC-USD on three main exchanges: Bitstamp, Kraken and Binance-US on the 22 nd of June, 2022.

Crypto Liquidity | Average market depth powered by SUN ZU Lab

Source: SUN ZU Lab data

The chart reads as follows: each bar represents the average quantity of BTC (in number of BTC) offered for buy/sell orders. For example, there were on average 200 BTC offered for sale at 80bps from mid-price on the three exchanges against around 210 BTC bid offers at -80bps from the mid-price.

Order books communicate information about investors expectations and appetite. In particular there appears a new concept to qualify liquidity: the bid-ask spread, which is the difference between the highest price a buyer is willing to pay for an asset and the lowest price a seller is willing to accept. The magnitude of this spread gives a good indication about an asset’s liquidity. For example a large spread indicate poor offer and/or demand, and incidentally may drive the volatility higher. Conversely a narrow spread is an indication of a deeper market where investor’s interest is high, and volume potentially abundant for buyers and sellers to execute their trades. Bid-ask spreads for Bitcoin, for example, used to be higher than 10% in the early days of crypto, but they have dropped massively to as low as 10bps on the main exchanges as crypto adoption, investor interest and trading volumes increased over time.

Invisible liquidity:

We refer to invisible liquidity as all forms of liquidity that is not captured in public trades or order books. Note for example that liquidity exposed to a limited set of investors would qualify here as invisible liquidity (more on this below).
In general terms, the taxonomy of “invisible liquidity” is extremely difficult to establish as it depends on the structure of the market. In markets where OTC activity is high, those trades and the interaction of brokers with their client are compartments of invisible liquidity.
One could argue that those compartments divert liquidity away from other pools, yet not all investors have access to it. In markets where OTC is not developed, those liquidity pools simply do not exist.

In the world of crypto, we distinguish the following categories:

Non-communicated, invisible orders: quantities available for trading that are yet to be communicated by investors to the markets. There is virtually no way to quantify these volumes as they only exist in a theoretical state in strategy books of portfolio managers.

Communicated, invisible orders: we put in this category for example trading activity of dark pools, also known as Alternative Trading Systems (ATS). These are private marketplaces where investors place buy and sell orders, without the venue disclosing available prices or volumes. Liquidity has found its way to the marketplace, it is just not visible (by anybody).

Communicated, partially-visible orders: quantities available for purchase or sale, expressed through an Indication of Interest (IOI), which refers to an investor’s non-binding interest in a security, usually communicated to a broker. In traditional markets, IOI are heavily regulated because, depending on applicable rules, they constitute visible or invisible liquidity. The regulators’ particular interest on this
compartment stems from the fact that liquidity visible only by a few agents is easily manipulable.

DeFi liquidity: this is liquidity placed on decentralized exchanges (DEXs) without being incorporated into order books. We will dive more in detail into how liquidity pools in DEXs work in another article.

On-Chain Liquidity: centralized exchanges (CEXs) rely on the “order book” mechanism to enable off-chain transactions: aggregate positions are “written” on chain only when investors transfer their positions out of exchanges (and de facto reclaim ownership of those on their wallets). Pure on-chain transactions today account for a relatively small percentage of total volumes. Decentralized exchanges (DEXs) are another form of on-chain liquidity, that relies on smart contracts to execute trades automatically, recorded directly (and immediately) on blockchains.

Data from Chainalysis shows that DEXs have surpassed CEXs in terms of on-chain transaction volume in January 2021, with a $175 billion volume sent on-chain to CEXs from April 2021 to April 2022, vs. a $224 billion volume sent to DEXs during the same period.

Peer-to-Peer Liquidity: P2P is a type of crypto exchange trading that allows traders to trade directly with one another without the need for a centralized third party to facilitate the transactions. This method allows exchanging parties to select a preferred offer and trade directly without using an automated engine to execute transactions.

Liquidity risks

The main liquidity risk associated to markets is for investors not to be able to enter/exit their positions due to a lack of sellers/buyers offering a fair price. One of the markets where this type of risk is the most visible is the real estate market. During times of economic turmoil or bad real estate market conditions, it could become impossible to find a buyer at the right price even though the property may have obvious value. A perfect example for this is the experience of NBA superstar Michael Jordan in trying to sell his Chicago mansion that has been on the market for 10 years already! The over-equipped luxurious house was originally listed for $29 million, before the owner was obliged to cut the price nearly in half overtime to try and match market expectations.

There is also another psychological effect that comes into play with illiquidity; the longer an asset is listed for sale, the more potential buyers are keen to second-guess their decision as the lack of interest and competition over the asset drives its value lower.

All of this has been extensively studied in traditional finance, and has become known as the “liquidity premium”: there is a clear relationship between price and liquidity. The higher the liquidity, the higher the price. This is the reason why real-estate prices are often subject to discount when economic conditions are under stress. We provide links to research articles about this subject in the appendix.


Liquidity is one of the most important concepts for individuals, institutional investors as well as exchanges and market makers. Despite holding high-value assets, any of these entities may experience a liquidity crunch if such assets cannot be sold within a short period. This is turn create heavy pressure on prices.

Top crypto projects’ tokens such as BTC, ETH, BNB or SOL appear to have reached a reasonable level of liquidity, but this is not the case for the vast majority of tokens that are only listed on one or two exchanges, with very low daily trading volumes and market depths. Above all, liquidity of the entire crypto market is still nowhere near liquidity levels visible in traditional markets.

Questions and comments can be addressed to or


About SUN ZU Lab

SUN ZU Lab is a leading independent provider of liquidity analysis for investors already active or crypto-curious. We provide quantitative research on the liquidity of all digital assets to help investors improve their execution strategies and source the highest level of liquidity at the lowest cost.
Our product line includes research reports, software tools, and bespoke developments to fulfil the needs of the most demanding digital investor.

Transaction Cost Analysis: the answer to all hidden costs behind crypto trading (1/2)

Transaction Cost Analysis by Sun ZU Lab

By Chadi El Adnani, Crypto Research Analyst @SUN ZU Lab

The “odd-eighth quotes” phenomenon

To introduce this article about Transaction Cost Analysis, we first revisit a very famous historical case. Before decimalization, the US stock market tick size used to vary by increments of 12.5 cents. This means that all prices were quoted in eighths of a dollar (ex. $12 or $12 1/8). However, on May 26 and 27, 1994, several US national newspapers reported an article that first appeared in the Journal of Finance (“Why Do NASDAQ Market Makers Avoid Odd-Eighth Quotes?”, by Christie, Harris and Schultz) raising the question of the width of the range of prices quoted by NASDAQ market makers. The researchers had found that the prices quoted to investors were almost systematically 1/4 ($0.25) apart, thus avoiding the minimum allowable 1/8 spread ($0.125)

To get a first grasp of the implications of this situation, we need to highlight that the range quoted by market makers is a good indication of the maximal margin they intend to capture. So by quoting a stock at $12 – $12 1/4 (i.e. they are willing to buy at $12 and sell at $12.25), instead of the allowed $12 1/8 – $12 1/4 range, market makers are refusing to decrease their margin bellow $0.25. So what would prevent market makers from avoiding odd-eighth quotes on 70 out of 100 actively traded NASDAQ securities, including Apple Computer and Lotus Development? The authors found no explanation and concluded that the situation was probably the result of implicit collusion among the participants to maintain wide spreads.

Surprisingly, On May 27, dealers in Amgen, Cisco Systems, and Microsoft sharply increased their use of odd-eighth quotes leading effective spreads to fall “magically” by nearly 50%, this pattern was repeated for Apple Computer the following trading day. Noticing these weird trends, the same authors published a new article a few months later to reconduct their investigation (“Why did NASDAQ Market Makers Stop Avoiding Odd-Eighth Quote?”). Using individual dealer quotes for Apple and Microsoft, the authors found that virtually all dealers moved in unison to adopt odd-eighth quotes.

These strange findings led thousands of individual investors to file a class-action lawsuit against 30 brokerage firms, including Merrill Lynch, Goldman Sachs and Morgan Stanley, which agreed in 1997 to pay about $900 million in what was called at the time the biggest settlement ever of a price-fixing lawsuit. 

What is Transaction Cost Analysis?

What the researchers behind the article have provided is part of what is known nowadays as Transaction Cost Analysis (TCA). Transaction costs have become a relevant issue in Europe for example since 2007 with the implementation of MiFID (Markets in Financial Instruments Directive). The introduction of the best execution obligation gave TCA a central role, aiming for investment firms to “take all reasonable steps to obtain the best possible result when executing orders for their clients”. Moreover, MiFID’s Article 21 of Level 1 and Article 45 of Level 2 require investment firms to provide their clients with “appropriate information” about their execution policies. Clients wishing to select a firm to deal with from among a competing group need to have sight of the relevant firms’ execution policies in order to evaluate whether a particular investment strategy is suitable. By requiring ex-ante disclosure of the execution policy, MiFID addresses clients’ information needs; they should always be able to pay the lowest possible net cost (or receive the highest possible net proceeds).

Transaction Cost Analysis (TCA) is the study of trade prices to determine whether trades are arranged at favourable prices (low prices for purchases and high prices for sales). The difference between the cost of the transaction at the time the manager decided to execute it and the actual cost is at the heart of TCA, including all operating charges, spreads, commissions and fees. The resulting differential is called “slippage”.

There are usually 9 components identifiable when dealing with transaction costs, they can be classified as per the following chart:

Implicit transaction costs by SUN ZU Lab

Implicit transaction costs:

Market Impact and various Opportunity costs detailed as follows:

Explicit transaction costs:

Brokerage Commisions, Market fees, Clearing and Settlements costs, Missed Trade opportunity costs, Bid-Ask spread detailed as follows:


The same transaction costs exist for crypto trading as well but suffer from a severe lack of transparency. The Markets in Crypto-assets (MiCA) regulation in its latest version comes however with a “best execution” obligation that should force digital assets service providers down the same path as their TradFi peers. This obligation should come with extensive quantitative analytics of order execution prices, leading to increased competition among trading venues and liquidity providers. When that day comes, institutional and retail crypto investors should be able to rely on Sun Zu Lab’s advanced and completely independent liquidity analytics to provide much-needed transparency to the crypto ecosystem. 

In the next part of this article to be published, we will look at pre and post-trade TCA and analyse how this process actually works and how it helps an investment manager save money.

Questions about this article can be addressed to or


About SUN ZU Lab

SUN ZU Lab is a French Fintech that aims to become the leading independent provider of digital asset market quantitative analytics tools and services. Leveraging the founding team’s 70+ years of experience in international capital markets and trading technology, SUN ZU Lab provides crypto professionals with unprecedented liquidity analytics in the form of quant reports, dashboards, real-time augmented data feeds, and bespoke studies.

What is Tokenomics and why does it matter?

A brief historical perspective to better understand the stakes behind tokenomics

Let’s start this article with the famous experience of Philip II, King of Spain in the 16th century, with the Eldorado discovery and the massive rise in inflation that followed throughout the entirety of Europe!  

In the 16th century, Spain conquered Latin America and discovered an immeasurable wealth within gold and silver mines. The kingdom hit the jackpot and its financial deficits appeared long behind it. This wasn’t the case nevertheless; the problem came from the fact that the Crown of Spain was over-indebted to many European creditors, leading the massive silver and gold discoveries to only make a quick passage through Spain before enriching the coffers of its French and Dutch neighbours. The European market ended up being flooded with coins, so that the immense Spanish wealth was diminished relative to other European kingdoms.

In the end, the excessive amount of silver and gold imported, and above all distributed, in Europe caused an important devaluation of what Philip II could think of as his Eldorado. A better financial management could have allowed him to preserve his reserves of invaluable minerals and thus be able to develop on a more important scale of time his fabulous treasure.

This story shows the importance of the quantity put into circulation on the valuation of an asset. This analysis works perfectly for the cryptocurrency market as well, any analysis of an asset’s ecosystem requires careful attention to the notions of quantity in circulation, total quantity and inflation management.

Inflation and the importance of tokenomics

While the media usually describes inflation as a rise in the price of everyday consumer goods, it is in reality the value of money that tends to fall rather than prices getting higher. This notion of inflation is at the heart of tokenomics, a merger of “token” and “economics” used to refer to all the elements that make a particular cryptocurrency valuable and interesting to investors. In this regard, there exist two predominant models: deflationary and inflationary tokens.

This is the model used by Bitcoin, i.e. a fixed total supply and less and less money issued over time. Many cryptocurrencies are governed under this model, like Solana, Litecoin, Tron, and many others alongside the king of cryptos.

In the case of Bitcoin, for example, a block is mined about every 10 minutes, rewarding the miner 6.25 BTC (when Bitcoin started it was 50 BTC per block, then 25, 12.5, 6.25, etc). The reward is halved every 210K blocks, leading to a halving every 4 years with the 10 minutes mining-time per block assumption. Without changes to the protocol, the final Bitcoin will be mined around the year 2140.

Many blockchains have been coded without incorporating a limited amount of token issuance. This choice can be made for a variety of reasons, usually involving the use to be made of the blockchain in question. The Ethereum protocol for example operates under this model. However, some mechanisms are put in place to limit inflation, or even to create a deflationary system.

This is the objective of the implementation of future updates of the Ethereum network: while the annual rate of ETH tokens issuance is currently equal to nearly 4.5%, the switch from Proof of Work to Proof of Stake should allow developers to reduce this rate to less than 1%. The network introduced as well a burn mechanism, meaning that part of the fees paid by Ethereum users in the future will not be returned to validators, but will be removed altogether. This could not only achieve a balance with the issuance rate, but potentially lead to a decrease in the number of tokens in circulation in case of high network usage.

Both models have their strengths and weaknesses with good justifications behind their use. For example, the Ethereum white paper indicates that a stable issuance rate would prevent the excessive concentration of wealth in the hands of a few actors/validators. Whereas Bitcoin’s deflationary system, as previously stated, allowed for the growing development of its ecosystem by paying miners large amounts of Bitcoin when it was not worth the tens of thousands of dollars it is worth today.

More generally, it is always a good idea to look into a project’s tokenomics before getting involved. This can help answer questions like:

Main differences between deflationary and inflationary tokens:

Solana case study

Let’s take a deep dive into one of the most prominent blockchains’ tokenomics. Solana has a native token called SOL that has two primary use cases within the network:

The Solana team distributed tokens in five different funding rounds, four of which were private sales. These private sales began in Q1 2019 and culminated in a $20 million Series A led by Multicoin Capital, announced in July 2019. Additional participants included Distributed Global, BlockTower Capital, Foundation Capital, Blockchange VC, Slow Ventures, NEO Global Capital, Passport Capital, and Rockaway Ventures. The firms received SOL tokens in exchange for their investments, although the number of tokens allocated to investors was not disclosed.

The initial distribution of SOL tokens was as follows:

Source: compiled by SUN ZU Lab from web sources

According to Messari data, vesting schedules were as follows: Solana’s three pre-launch private sales all came with a nine-month lockup after the network launched. The project’s public auction sale (held in March 2020) did not come with a lockup schedule, and the SOL tokens distributed in that sale were fully liquid once the network launched. The founder’s allocation (13% of the initial supply) was also subject to a nine-month lockup post-network launch. After the lockup period ends, these tokens will vest monthly for another two years (expected to fully vest by January 2023). This last clause is a good protection for investors as team members’ tokens are locked-up for a longer period. The Grant Pool and Community Reserve (both overseen by the Solana Foundation) contain ~39% of the initial SOL supply combined. These allocations began to vest in small amounts since Solana’s mainnet launch.

Inflation stands at an initial annual inflation rate of 8%. However, this inflation rate will decrease at an annual rate of 15% (“dis-inflation rate”). The inflation decrease is thus non-linear and much more important in the first years. Solana’s inflation rate will continue to decrease until it reaches an annual rate of 1.5%, which the network should reach in about ten years or 2031. 1.5% will remain the long-term inflation rate for Solana unless the network’s governance system votes to change it.

Proposed Inflation schedule curve – Source:

Major identified issues with current projects’ tokenomics

Using the Solana example, we can see that more than 50% of the tokens in circulation are concentrated, during a long period after the project’s launch, in the hands of the core team, VCs and early investors. This is hardly an exception to Solana as similar distributions are very common within the blockchain ecosystem projects. Can we talk seriously about the benefits of blockchain decentralization with such capital and governance concentration, without forgetting technical knowledge concentration as well?

Blockchain projects often come with varying lock-up periods that can last from less than a year to five years for early investors and the founding team, who usually cash out their investments after this period. What we identify as a significant issue after the end of the lock-up period is the huge and asymmetric risk-return transfer between this first group, which realized a pretty good return on their initial investments and are completely de-risked at this stage, and retail investors joining the project at a stage where core decision-makers are no longer incentivised to ensure the well-functioning of the project.

Cryptocurrency projects often use ICOs (Initial Coin Offering), among other fundraising techniques, to raise funds through the issue of crypto-assets in exchange for either fiat currency or an established cryptocurrency like bitcoin or ether. The issuing entity usually accounts for digital assets collected as an intangible asset, or as a financial instrument in the case of stablecoins for example as they are redeemable for cash. The accounting for tokens distributed on the other hand depends on the promise given to investors under the terms of the ICO, which could include: free or discounted access to the entity’s goods or services for a specified or indefinite period of time; a share of the profits of the entity or access to an exchange through which it can transact with other users of the exchange in buying goods or services. Digital asset projects may also offer equity tokens, which are a type of security tokens that work more like a traditional stock asset, giving their holders some form of ownership in their investments. The use of these equity instruments remains restricted nevertheless, raising the question of the rights and guarantees given to retail investors in particular in exchange for the funds given to the cryptocurrency project?   

What we refer to as double-dipping practices, in this case, relates to VCs investing in cryptocurrency projects and realising important capital gains on their equity shares as well as digital token holdings. This privilege is almost unique to the cryptocurrency ecosystem, raising some questions again about asymmetric information advantages against retail investors: compared to traditional VC funding, crypto VC investors enjoy a double economic as well as governance advantage, having control over token and equity.


Tokenomics is an important aspect of cryptocurrency which covers almost anything to do with the token. Professional as well as retail investors should spend a lot of time studying a project’s tokenomics before investing to be well aware of the financial and governance rights attributed to them via the token purchase. There is an absolute need in our view for regulation on this particular topic to evolve in order to provide better transparency and eventually protection levels for investors.

This article has been written by Chadi El Adnani – Crypto Research Analyst @SUN ZU Lab

About SUN ZU Lab

SUN ZU Lab is a leading independent provider of liquidity analysis for investors already active or crypto-curious. We provide quantitative research on the liquidity of all digital assets to help investors improve their execution strategies and source the highest level of liquidity at the lowest cost.

Our product line includes research reports, software tools, and bespoke developments to fulfil the needs of the most demanding digital investor.

Questions can be addressed to or

Index Liquidity: what does it mean?

Indexing has been a popular strategy on traditional markets, indeed recent history shows index funds and ETFs have received massive inflows and registered a growth in assets superior to actively managed funds.

Thus it is no surprise that indexing is starting to emerge on digital assets. It is a way for investors to diversify their exposure away from the most heavily traded assets while keeping a rigorous methodology to avoid arbitrary decisions. Trakx is a leading provider of such indexing solutions.

However, investing under an indexing strategy doesn’t necessarily mean more liquidity. Indeed an index is often nothing more than the sum of its parts. Assessing index liquidity means therefore looking in-depth at the underlying basket. This is our specialty at SUN ZU Lab, and the purpose of this article is to introduce our methodology in the context of one of Trakx’s indices, the TOP 10 DEFI Index with the following composition:

Our intention was to establish the fundamental liquidity profile of the index, enabling investors to tailor their execution strategy to the reality of the marketplace.

What are the fundamental characteristics an investor will want to know?

If you were to invest in any kind of security your first question would be to assess how much capacity is available without causing adverse price movements. Is that capacity compatible with your allocation? If not, you will not be able to build the exposure you want.

Investing in an index means buying the underlying basket and tracking changes through time. Those changes are governed by written documentation issued by the index sponsor. For example, if you were to invest $100,000 in an index with 4 equally weighted components, you would have to invest $25,000 in each of them. Each component has its own characteristics. Buying $25,000 in one of them may require digging significantly into the order book, while buying another may simply require hitting the first offer on the market. Furthermore, an optimized execution strategy needs to factor in the ability of an intermediary, for example, a specialized broker, to provide better prices than might otherwise be available. This would be the case for instance if the broker was able to find an aggressive seller when you placed your order.

We submit that there are really two principal measures of liquidity to help investors with their allocation on a given asset (or indeed index): capacity and slippage.

The notion of capacity answers the following question: how much volume is needed to absorb the exposure that I want to take (or unwind)? The same question can also be formulated differently: under reasonable constraints on my participation in the market, what is the addressable liquidity?

Slippage analysis is focused on the measure of price deterioration vs. execution size and is trying to answer the following question: what is the price deterioration associated with the exposure I want (i.e. how much do I have to pay to find adequate liquidity for my exposure)?


Our liquidity analysis for Traxk Top 10 DEFI index looks at those measures (and several more incidentally). For example, the following chart shows the capacity for a 20% participation at a point in time (here daily for the second half of March):

For each asset, the maximum addressable size is calculated under a constraint of 20% participation in the market. From this figure, a total capacity for the index is inferred based on individual asset weights. For instance, the figure of $40M on March 23rd reads as follows: buying $40M of the index on that day is the maximum to execute less than 20% of the day’s volume on each index component. As can be seen from the chart, the figure is fairly constant throughout the period, and the average is displayed in the legend: $38.4M.

Naturally looking at daily volumes may be inadequate for smaller exposures, and intraday figures would be useful. We do provide those as well, in the form of an hourly capacity:

The calculation is identical to the daily one, except the time interval is set to 1 hour. The chart suggests that in this timeframe, a 20% cap in participation will result in a $1M addressable liquidity. This figure may seem small, but it shouldn’t be interpreted for more than what it is: a passive observation of market behavior. If a market participant was actually entering the market at one point to build his exposure, he would most certainly attract more liquidity above and beyond the one passively recorded here.

To complete the participation picture, we thought it would be useful to know which one of the index components is responsible for constraining the overall liquidity. If all components had similar liquidity at all times, this question wouldn’t be relevant, but naturally, this is not the case. Individual variations mean that one asset may be the “limiting factor” at one point in time but not before or later. To provide an intelligible picture we built the following chart:

It shows over the considered timeframe the individual participations for the least liquid index components, for a $10M size. We limited the number of assets to 3 daily to avoid information overload. For example, on March 21st, a $10M order would have resulted in a 7% participation for MKR, 4% for RUNE, and 4% also for UMA. All other tokens had lower participation on that day (and hence are not shown here).

The picture gives the following insights:


The other quantitative measure of liquidity is slippage. It is a measure of price deterioration when actively buying or selling a financial instrument. The idea is quite simple: if the mid-price of an asset is $100 when you start buying, and if your average price is $100.3, then slippage for this particular execution is 0.3%.

Slippage is a very “local” measure, it is extremely dependent on the execution strategy deployed, and on the circumstances of the moment. The same order may face very different conditions depending on when and where it is executed. Do you access liquidity on one single venue, on several, and if so which ones? Do you aggressively consume liquidity available in the order book or patiently place your orders and wait for matching orders?

To address those questions, we present the following chart, showing an intraday $500K market impact for the index:

It shows the expected price deterioration for a $500K order over a 2-hour execution window, on both sides of the order book. As can be seen, the impact here is quite reasonable at around 10 bps. As was said before, this figure shouldn’t be taken for more than what it is: if an investor was to enter the market, there is no way of telling how much more liquidity he would find. Along the same lines, for an order larger than $500K, the estimated impact cannot be considered linear (i.e. $1M order would not necessarily create a 20 bps impact).

Like before we wonder also about the intraday “limiting factor”: which token(s) is(are) responsible for this impact? We answer this question along two distinct axes. First, we look at the 3 tokens with the largest capitalization in the index to get a sense of slippage on them. Second, we look at the 3 least liquid. The following charts show the results:

The largest cap tokens are indeed not limiting factors here, if anything the impact on those (5~10 bps) is smaller than on the index as a whole. The 3 least liquid are indeed responsible for the impact of the index, at 10~20bps each.

Note by the way that the least liquid according to the impact metric (RUNE, MKR, COMP) are similar to those considered least liquid when looking at trading volume (RUNE, MKR, COMP).


What actionable conclusion is an investor to draw from this analysis? Well, firstly he or she would be able to quantify the current addressable exposure in the marketplace. This is quite important information, as it enables sizing up execution. Secondly, he or she can extract a first-order estimate of market impact for the considered index. This is also a critical factor, if only because it is helpful when benchmarking providers of execution services, whatever the form (algo, block, direct-market access, etc.)  Finally, we believe our research can help Trakx refine its indexing methodology, to reflect not only capitalization but also liquidity, another key characteristic to promote truly investable indices.

Because digital assets are still in their infancy, those metrics should be monitored closely as they evolve almost daily. To enable investors to keep close contact with the market, SUN ZU produces an impact analysis at least twice monthly, and on-demand if necessary.

Price discrepancies in the world of digital assets

What a strange market!

We at SUN ZU Lab are striving to gain a deep understanding of how the markets for digital assets function, to help our clients achieve their investment strategy through better execution. Based on our long (very long!) experience of traditional markets, we have started looking closely at liquidity and arbitrage. The interested reader will find a sample of our efforts here.

We subsequently turned our attention to the basic behavior of trading venues, trying to understand if there was something special there compared to what we knew of traditional well-established exchanges. Thus we started looking at small-scale data to try and detect unusual patterns. Because we aimed at being as systematic and unbiased as possible, we devised automated filters to isolate those events.

The result is our latest weekly “market anomalies” report, which presents events selected because of a sharp price movement or heavy volume. For each of those events, we look at price discrepancies and/or volume patterns. Well, the results are quite surprising, here are three examples abstracted from our report dated March 26th for bitcoin.

Price anomaly #1: -1.53% on 3/21/21 between 09:43 and 09:44 UTC

By looking at the most significant price movements over a 1-minute interval we can detect periods where markets are moving “fast”, and those moments are very good candidates to detect unusual price or volume patterns.

As an illustration, below is the 1-minute charts of BTC prices and volumes on 5 venues, between 9:43 and 9:44 on 3/21:

To get a better sense of price discrepancies, we chart the maximum price difference in the interval between the lower and higher price:

It is fairly clear that during this period, prices on the different venues were far from being “aligned”. In another of our reports, we look at price discrepancies and arbitrage opportunities at a macro scale, but here at the tick level, we suspect there are opportunities to be grabbed.

Or are there? Let’s have a closer look at the strangeness of this picture:

I have numbered 4 surprising patterns:

  1. a small blip of Kraken vs. other exchanges: what is happening on Kraken at that precise time? BTC is trading there several times $100 below other markets. What is unexpected is not the fact that one venue experiences sharp movements independently from the others. The surprising part is that none of the other venues moves in sync. We strongly suspect that there are many arbitragers out there sharpening their trading algorithms to specifically identify and exploit those situations. Why was there no change anywhere but on Kraken? One possible explanation is that there was not enough time to capture that opportunity. Well, this is unlikely because as it happens all venues traded repeatedly during a window that lasted several seconds. Another explanation is that bid/ask spreads were very wide and trades occurring on one side of the spread appeared to be out of sync. Investigating that idea requires tick-by-tick order book data, and a little bit of work (but do not worry we are on it). Another explanation could be that some of the trades on Kraken were fake trades, or that they happened outside the regular bid/ask spread if they were “pre-negotiated” (a particular status that indicates a block trade on traditional exchanges). In any case, we would need a little bit more information from the exchange to be able to conclude.
  2. Coinbase price seems to be lagging: as surprising as it looks, this type of pattern is quite straightforward to explain. It results from the lag in order book positioning on Coinbase. The bid/ask spreads on other exchanges adapts more rapidly, which means that prices tend to lag and adjust at the last moment (i.e. the next transaction) on Coinbase. Again confirming this initial intuition requires tick-by-tick order book data, but nothing worrying there.
  3. Bitfinex at $100 to $150 discount compared to other markets: well, that’s a big one. The discount on Bitfinex appears 20 seconds before but stays stubbornly high. It appears and doesn’t disappear for some time (see #2 anomaly below where it exists with the same magnitude). A discount like this would rarely appear on traditional markets (stocks, derivatives, bonds, etc) because market participants would arbitrage it away sooner rather than later. If it persists as it does here, it is not your typical liquidity discount. Indeed, it is associated with Bitfinex, other exchanges keep trading in sync (more or less). Therefore the underlying reason is to be found with the specifics of Bitfinex: are there trading constraints that would induce investors to severely discount BTC on that platform compared to another? Indeed our research confirms that this discount appears quite regularly, and never turns into a premium. Investors should therefore be mindful and investigate further the underlying reasons that could create a recurring anomaly like this (as will we!).
  4. $80 price difference between Kraken on one side and Bitstamp/Binance on the other: whereas Kraken was at the bottom before, it is now trading higher than the others. Coinbase is lagging, Bitfinex is still at a discount, but Bitstamp/Binance and Kraken repeatedly trade at a $100 difference for 6 to 7 seconds. Are those real arbitrage transactions or “visual effects” due to lagging or fake transactions? As before we need tick-by-tick order books to conclude, and possible further information from the exchanges if everything else proves insufficient.

For the sake of curiosity, below are two more anomalies abstracted from our report. The reader will find the Bitfinex discount again, and long, recurring inter-venue price discrepancies. NB: the last anomaly is one detected through our volume filters, hence labeled “volume anomaly”. Volumes on Bitfinex appear as extreme outliers, accompanied by price dips that have a very small impact on other venues.

Price anomaly #2: +1.11% on 3/21/21 between 09:44 and 09:45 UTC

Volume anomaly #1: 706 BTC on 3/21/24 between 22:15 and 22:16

Well, we knew digital asset venues were not so closely integrated, but here we have a very detailed view of anomalies that are curious and result in significant price discrepancies. To track some of those anomalies regularly, we will produce a weekly report incorporating the above results (and more!) for BTC as well as other digital assets. Investors seriously curious about cryptocurrencies and digital assets may find in those a way to monitor market efficiency and as it evolves through time and across trading venues.

If you want to follow our research and efforts to shed light on all things related to the liquidity of digital assets, register for a 30-day free trial on our research, consult our website, or contact us. Feedback and comments are always welcome!

Our take on the liquidity of digital assets

A more systematic approach to liquidity monitoring

We at SUN ZU Lab have a deep curiosity about digital assets. Like everyone else, we follow bitcoin’s saga, with a volatility which, after all, is probably regarded more as a source or opportunity than a risk to be curtailed.

As we have written before (here and here), if digital assets were to constitute only a new source of liquidity, they would be an invaluable development in modern finance. Whoever has spent time on capital markets knows that liquidity is an elusive target at best. After decades of electronic trading, intense innovation, and regulatory adjustments, attracting and retaining liquidity remains as difficult as ever. Exchanges and trading venues worldwide know it firsthand, their primary responsibility is indeed to attract liquidity, one way or the other. Many have adopted “market-making” schemes whereby they basically waive fees and sometimes actually pay for participants to contribute orders in their central order book.

Liquidity is defined as the ability to rapidly buy and sell a product for significant size without adverse impact on its price. Note that this definition is not limited to financial products. Real estate for example is a very illiquid asset class. There is a constant trade-off between the time it takes to conclude a transaction and price improvements each party is willing to concede.

Liquidity is a very desirable feature on any developing market, and digital assets are no exception. If the price of bitcoin catches the front news almost every day now, reported trading volumes are also a big part of the story. Taken at face value, those suggest that bitcoin is at least as liquid as some of the most heavily traded traditional instruments (see here and here for a more detailed analysis on the subject).

Even though this is most certainly not true, in great part because volumes are massively manipulated (an x10 factor is commonly hypothesized), the bottom line is that public attention for bitcoin and more generally digital assets is bound to translate into more and more trading activity i.e. more liquidity. In fact, most of it may not even be visible: as investors stand by for the right price to buy or sell, they do not necessarily register their interest with a broker or on the order book of an exchange.

Where is liquidity to be found? There are dozens of venues, on-chain, off-chain, centralized, decentralized. Which venue should you turn to to find the most liquid market? Should you trade on several markets, if so which ones? What price and/or size can you expect if you include 1, 5, 15 markets in your shopping list?

Those are fundamental questions for investors, retail and professional alike. The marketplace is evolving so quickly and in so many different places that it is virtually impossible to follow all relevant developments. That’s where we at SUN ZU Lab, want to help. We started out with a mission: promote a truly institutional standard in the analysis of digital asset liquidity. Our quantitative research is meant to offer a dissected view of liquidity in time and space, so interested parties can get a sense of macro and micro trends to make better-informed decisions for their trading flow.

We had to start somewhere, so we decided to launch our product on BTC, including for the time being 3 markets: Coinbase, Kraken, and Bitstamp. We will be producing 3 reports: one on liquidity, another one will look at market integration (i.e. the existence of price discrepancies between different venues), and the third at what we call market anomalies (i.e. events for which price or volume is an outlier). [Register for our newsletter if you want to hear from us and receive a free sample!]

Let’s look here at the liquidity report. It is a quantitative dissection at liquidity over a 2 week period and will be produced twice a month. Our intention is to present rigorous information to qualify and quantify liquidity dynamics both intraday and over the observed period.

Here is the list of indicators we look at, abstracted from the report over the last 2 weeks in January:

Price chart with volume-at-price histogram

This chart is a necessary introduction, we absolutely need to have a sense of how the market has moved over the period, and at what price points have traders concentrated their activity. The volume-at-price add-on on the right side is an elegant way to present this information.

Daily traded volume and intraday traded volume, by venue

Those two charts are fairly standard representations of volumes. We present both views to determine whether there are intraday patterns that have developed over the observed window. In particular, we could expect higher volumes when Asian and/or US markets open. Quite naturally we need to address also the question of the relative partition between exchanges:

Volume partition by exchange
Intraday and daily annualized volatility, by venue

Similarly to volumes, we plot both the intraday and daily volatility to monitor whether intraday patterns developed. Despite the fact that the charts are very similar on distinct venues, we decided to keep them all, to make anomalies between markets stand out, should some come up.

Intraday and daily mean trade size, by venue

The average trade size is a very important feature of liquidity dynamics. It is dependant on a number of factors, such as the type of investors (retail, institutional), the pricing structure of the exchange (fixed vs. variable fees, caps on per-order fees), time of day, etc. We found in our research that infrequent large trades tend to alter the readability of the average, therefore we decided to include the median as well:

Intraday and daily median trade size, by venue

Believe it or not, we think there are many more questions that we should be asking. For example: how does trade size vary with volatility? with the bid-ask spread? We will absolutely incorporate the relevant metrics to answer those questions in due course.

Participation indicators, by venue

Those two charts give two measures of market participation. First, we answer the question: how much volume does it take on a given day to fill a $10M order? As you can see it takes anywhere from 1% to 3% of the daily volume, on each exchange taken independently. Practitioners in traditional markets advise their clients to never cross the 15%~20% threshold. In the case of BTC on those three exchanges, roughly 10 investors with a significant order would be enough to “drain” available liquidity. The second chart is an examination of the same question, with a shorter time frame (1 hour), and a smaller size ($100K). Markets seem to be amply capable of absorbing repeated $100K orders.

Intraday arket impact, by venue

This chart is a slightly different take on market impact. It answers a different question: how much adverse price movement would a $100K market order create? This gives a measure of market depth, which as you can see is fairly stable in time but quite different across venues.

Intraday ratio between traded and available volume, by exchange

By definition, each trade “consumes” part of the visible liquidity. What ratio of the liquidity available is consumed, and how fast is it replenished? The chart gives an answer intraday. As can be seen, there seems to be a somewhat constant rate of consumption and replenishment, something which is somewhat surprising. Again, we will absolutely look at this in deeper detail in due course.

Volume available at best limits, by venue

This chart presents a view of the total size available at best limits, in each of the individual order books. It is useful to monitor absolute levels of “aggressiveness” from market participants, and patterns in the overall migration, if any, from one venue to the next.

Intraday mean price and time between transactions, by venue

Those two charts present an introduction to order book velocity. Each venue has its core client base, and those may have very different trading habits. Those habits are also constrained by venue-specific factors such as commissions, API throughput, matching engine performance, etc. Investors may gain valuable insights by qualifying each venue’s reactiveness and specific velocity.

Limit Order Book distribution at +/- 400 bps and +/- 100 bps from mid-price, by venue

Those charts present the distribution of each order book around mid-price, for a depth of 100 bps and 400 bps. There is a wealth of information to be extracted from those distributions. Here are two examples.

First, they indicate how participants think about risk beyond the first limits: are they placing a lot of orders away from mid? if so, are those significant compared to the best limits?

Second, how does each venue compare? Am I more likely to find liquidity at 50 bps from mid on Coinbase or Kraken? What about 100 bps?

Market makers, speculators, institutional investors have very different trading strategies. Market makers may be interested in price discrepancies, which would lead them to participate symmetrically from mid-price. Institutions may be more buyers or sellers at different times. In each case, they would presumably occupy one side more than the other. Although the averaging in the above charts provides a lot in readability, it is also the source of much “compression”. We have a lot more to say on order book dynamics, and in fact, it will be the subject of an entirely new report soon!

Popular wisdom would have that a trending market shows asymmetry in liquidity. Well, unfortunately, this is not quite as simple as that. The graph above represents the order book imbalance by trading venue. Order book imbalance is defined as (Qa — Qb)/(Qa + Qb), where Qa is the total quantity, available at 100 bps from mid on the offer, and Qb is the total quantity available at 100 bps from mid on the bid.

Imbalance oscillates between -1 and +1: a market with no bid (Qb = 0) has an imbalance of +1, and -1 for a market with no offer (Qa = 0). An imbalance close to 1 means the order book is dominated by offers and vice-versa.

It is very apparent that the three exchanges have very different imbalances. At 100 bps market depth, Coinbase is mostly an “offer” market (with variability), whereas Kraken and Bitstamp are mostly “bid”.

Average and Intraday market depth, by venue

Total visible liquidity is a very natural question: how many bitcoins are available in each order book? Those two charts present two different answers to that question. On the left, absolute size at 100 bps, on the right intraday depth at 10bps. Those can help provide first-order estimates of impact: 100 BTC can be executed with a max impact of 50 bps; at any time, an order of 10 BTC should not be filled more than 10~15 bps from mid.

Intraday and daily weighted spread, by venue

Of course, no study of liquidity would be complete without spread analysis. Above are time series of the weighted spread, showing intraday and daily dynamics. Unsurprisingly the weighted spread deteriorates quickly with volatility (early days in the chart). The charts below enable comparison between a simple spread and a measure weighted by the total quantity on best limits (bid + offer):

Spread and weighted spread distributions, by venue. NB: for enhanced readability Y-axis is logarithmic.

It can be seen quite easily that exchanges have very different order books.

Now this is a long list of charts and tables. You may get a sense that a lot of it is repetitive. We would argue that it is not, quite the contrary. As was said earlier liquidity is an elusive parameter, and in the world of digital assets it is bound to evolve very quickly. Monitoring and understanding it appears to us an important prerequisite for traders already active in the space, or even for “crypto curious” i.e. investors or institutions interested in the subject but not yet in a position to transact.

Is Bitcoin a safe haven in times of crisis?


Bitcoin and digital assets in general have been presented by some as credible alternatives to institutional finance as we know it today, with its strengths and especially its weaknesses — widely exposed and documented with the 2008 crisis. The idea of a mechanized trusted third party has a certain appeal. Removing human arbitrariness and intermediaries in the management of money would make it possible to get rid of what is perceived as cumbersome state control and to guard against the stranglehold of the financial industry, which is accused of taking a large share of profits and caring far too little about consequences. Gone are the days of lax inflationary policies steered by a central bank subservient to political power. Not to mention the possibility of keeping one’s savings right under the nose of bankers and tax officials.

There is much to be said for a world in which dehumanized algorithms, complete anonymity and decentralized governance have become the norm, a world that is unlikely to deliver on the promises its proponents put forth, both in terms of efficiency and equity — not to mention questionable transparency, which is high on the list of criticisms from institutions today. But some proposals are worthy of attention and, at the very least, of consideration. One of them is the following: are digital assets likely to offer true storage of value, independent and uncorrelated to traditionally available assets (shares, bonds, gold, etc.)? The example of countries with failing institutions is often cited, such as Venezuela or Nigeria. Bitcoin would be a way to hedge against immediate monetary turbulence, delirious black market exchange rates etc. With the coronavirus crisis, this question extends to developed economies: would it be in the interest of an individual or institutional investor to transfer part of his savings to bitcoin in anticipation of a collapse of traditional assets or even the financial system as a whole?

Let’s eliminate immediately the hypothesis of the collapse of the system: if such a catastrophe were to occur, the consequences would be sufficiently devastating to take with them the very existence of a “safe haven”, a compartment protected from the ambient deflagration. Instead, we will try to answer the question of the allocation between digital currencies and traditional financial assets.

Examining this idea requires some work, which we will carry out in a very basic way. The question can be rephrased as follows: “if I had chosen to invest part of my assets in certain digital assets, would I have done better than traditional markets? ». We consider this problem through the examination of three elements: the performance of digital assets, their volatility and their liquidity.

Performance is used to determine whether or not an investor would have avoided a decline or even benefited from a rise if he had “pivoted” to a digital asset. Volatility gives an idea of the carry of a position, i.e. its variation over time: is it really a smooth ride? Liquidity is a slightly more technical notion: when the market panics, is it still possible to carry out transactions or, on the contrary, does everyone run away?

A word on the notion of “safe haven”. This concept captures the idea that there are assets — financial or otherwise — that provide safety in the event of financial turbulence. The perception of security can be linked to objective or subjective criteria. When investors bought US and German debt in the sovereign crisis of 2012–2013, the perception of security was linked to an objective factor, the economic resilience of those economies and the very high probability that governments would be able to meet their commitments. Conversely, the status of gold as a safe haven has no basis in reality today. When world currencies were pegged to gold in the Bretton Woods world, gold was the ultimate security since it was the assurance of being able to buy any currency at a fixed price. In the modern world, where exchange rates are floating, possession of gold does not provide any real safety. We are in the realm of self-fulfilling prophecies. Everybody thinks that gold resists better when the markets fall, so investors buy it as soon as tensions appear, and bingo we see a posteriori that the predictions have proved to be correct. Another important point is that the use of safe haven assets is limited in time. Nobody takes a position on gold to protect a portfolio over 15 years. Anyone who is significantly invested in gold for 15 years does so to diversify their risk in an allocation strategy, not to protect themselves from volatile periods. A safe haven plays a role episodically, mainly when the markets are nervous. It is therefore at this time that one must look closely, and this is why the recent crisis offers a particularly relevant point of observation.

A quick methodological note: the S&P 500 is modelled by its most liquid exchange-traded fund (ETF), known as the Spider (ticker SPY, $257 billion in assets as of 4/24/20) Gold is modelled by the sum of the two largest physical ETFs (tickers GLD and IAU, $80 billion in assets). US government bonds are modelled by the iShares 7Y-10Y ETF (ticker IEF, “only” $21.5 billion in assets). The BTC/USD and ETH/USD data are from, volumes were divided by 10 to account for the numerous and documented manipulations of various exchanges on their data. The factor 10 is arbitrary but regularly appears as a working hypothesis in studies on the issue (but in fact the absolute value of the volume is of little importance here). The prices are the closing prices, and of course all execution issues are neglected. Access considerations are also neglected: the products used here are easy to access, anyone can open a securities account with an online broker within a few days. Access to digital assets is not yet so democratized. All this is therefore very simplified.


The performance we are interested in is the one surrounding the recent crisis. An investor who has been carrying bitcoin or ether for a long time is doing so for reasons that are not our concern here. Here is the relative performance of the 5 assets between 01/23/20 and 4/22/20:

The charts speak for themselves: the crisis reached its climax on March 12th, after the US President announced, quite unexpectedly and without any consultation, the closure of US borders. All assets drop sharply, except bonds, which have always been considered safe among the safest. Surprisingly, gold is also falling significantly. It appears that bitcoin or ether would not really have been an insurance policy. The BTC holds better over the few days before the crash, but drops lower immediately afterwards.

Another way to answer the same question is to ask what was the “safety premium” for investors who made the right choice? Prior to March 12th, investors were given a free hand to choose the best investment if they sensed upcoming turbulence. This behavior, called “flight to quality”, is widespread. Before mid-March the pandemic was beginning to show its effects in China, nervousness about the impact and spread of the disease was beginning to show. A prudent investor could have chosen to transfer some of his positions to assets considered safer, such as gold or US sovereign bonds. The outcome would have proved him right… The following graph shows the premium he would have captured:

Precisely, the green curve reads as follows: an investor who would have transferred his position on 01/23/20 from the S&P 500 to US bonds would have captured a 25% gain between 01/23/20 and 04/22/20 (compared to a situation where he would have remained invested in the original support). In concrete terms, he would have avoided the massive decline around March 12th. If he had taken this decision later, at the end of February or the beginning of March, he would still have gained 10%. The graphs show the exact premium captured for having changed support, depending on the date the change takes place.

It appears that the choice to pivot from S&P 500 towards BTC (blue curve) only pays off on March 12th, when the BTC loses 37%. Pivoting from gold to the BTC would have been even worse off (yellow curve).

Note that after the crash, the decision to stay invested in the original support or to pivot is of little importance: from about March 26/27th, the curves are rather undifferentiated. Moreover, the bond > gold pivot (orange curve) turns out to be winning at almost any time…


To examine volatility, we could use the financial version, i.e. the annualized standard deviation. But for a change we will use a more intuitive measure, the intra-day variation. The following graph shows the 90-day moving average of the daily high-low amplitudes of the 5 assets:

NB: the starting date is arbitrary, it was chosen to visualize about 5 years of data.

This is an intuitive rather than a financial measure of price variability, which can be understood as follows: since May 2018, on a “normal” day, the BTC has varied on average by 4% to 8% between its highest and lowest price. In contrast, the S&P 500, gold and bonds only vary between 1% and 2% (or even less). In March, we see a significant increase in these figures, which corresponds to higher intra-day volatility, i.e. the effect we want to measure.

Indeed, carrying a BTC or ETH position is not a smoot ride compared to other asset classes: the amplitude of movements is 4 to 5 times greater. Incidentally, dealing in BTC requires careful monitoring compared to traditional assets that have the good taste of moving less quickly and whose markets are closed several hours a day.


Finally, let’s look at liquidity, characterized by daily volumes. This is an ex post measure of liquidity, precisely that which has been expressed in the form of transactions. We could also look at an a priori measure, the liquidity available before execution in the order books of exchanges, but that is a more complex task…for another time. The graphs below show average trading volumes (90-day moving average) over a period of about 5 years, with 1-month volatility as the second axis:

The S&P 500 ETF is one of the most liquid instruments in the world, with $20 to $30 billion traded daily. The causal relationship between volatility and volume is immediately visible. Same presentation for gold:

The ETFs under consideration only trade around $1 billion a day, but the causal relationship is still perfectly visible. NB: the volatility seems much more uneven than that of the S&P 500, but this is an optical effect related to the smaller scale.

Here is finally the same graph for the BTC:

If we had to (re)confirm the much more volatile behavior of the BTC, here it is: volatility episodes are frequent, acute and independent of those of traditional markets. But this graph raises many questions.

Compared to previous ones, it immediately appears that volatility/volume causality breaks down. Some peaks in volatility are not accompanied by any increase in volume, and conversely the volume seems to grow by itself…

The two areas circled on the graph are anomalies, presumably explicable by the fact that the officially announced volumes are vastly overestimated and that the magnitude of the “ghost” volumes varies over time. Both the S&P 500 and gold (and the bond ETF, not included here) show a fairly stable “structural” liquidity: when volatility eases, the average volume falls back to a floor. The fact that it falls very sharply on BTC between May 2018 and March 2019 suggests that the “true” floor is much lower. Similarly, in the second half of 2019, volumes return to average levels for the S&P 500 and gold. Nothing comparable for BTC (or ether), growth is exponential. It is not justified by any measure of volatility, nor by any other market event or media coverage.


To the question “Is BTC a credible alternative to the volatility of traditional markets?” it is fairly easy to offer a negative for the recent crisis. It is certainly an isolated point — but absolutely significant in view of the magnitude of the crisis. There is no doubt that gold and sovereign bonds are still the “safe havens” they have long been. On the other hand, the liquidity of bitcoin (and ether) is still problematic, and does not exhibit an explainable pattern in view of the proven behavior of the investment community as expressed every day elsewhere.

Liquidity and order book distribution

When we formed the team to launch SUN ZU Lab, we had mostly one question: how liquid are crypto-assets, and if there is indeed liquidity to be found, to what extent is it different in quantity or quality from what we have seen in traditional markets for 25 years?

“Liquidity is defined as the ability to buy or sell an asset for large size without significant adverse price movement.”

This straightforward question is not so simple and has many ramifications. Indeed if you can qualify and quantify liquidity, then you should be able to execute better (i.e. with smaller adverse price movement). Therefore whoever starts with order book analysis should be ready to go all the way to real-time “smart” order routing.

We are not the ones at SUN ZU to shy away from a challenge like this, so we decided to take the first step and share our results regularly. Among the many insights we will explore and comment, we would like to start with a fundamental element: order book distribution.

The chart below shows how liquidity aggregates (those are excerpts from our first public research report available here) around mid-price. To get a sense of this, we plotted the distribution of orders in the BTC/USD order book for three exchanges (Coinbase, Bitstamp, and Kraken) and averaged the results for the first half of September:

Order book distribution at +/- 400 bps from mid-price. Data from

This chart reads as follows: each bar represents the proportion (in %) of orders found at the given distance from mid-price (mid = (bid + ask) / 2). for example a 0.7% bar at -150 bps indicates that 0.7% of all orders in the price interval [-4%; +4%] can be found 1.5% below the mid-price. By construction, for each exchange, the sum of all bars equals 100%.

In fact, the above chart is the superposition of three individual pictures:

The benefit of layering the three graphs is to present an aggregate view of liquidity, and its partition across the three exchanges, with a single scale and a clear color-coding.

If we focus on a smaller interval, say [-1%; 1%], we get the following distribution:

Order book distribution at +/- 100 bps from mid-price. Data from

As before, this image is the results of layering three individual charts:

What’s the point of those charts? Why did we choose to aggregate data across exchanges and average through time? Well, the intent is to measure behavioral patterns from investors, on individual exchanges, and across the entire market (represented here by only three exchanges, more are coming). Here are a few points of interest:

Welcome to the wonderful world of liquidity analysis!