FTX Monthly Digest

Tristan Yver

November 2020

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Contents

1 FTX in November 3

2 P2P Lending on FTX 5

3 The Institutional Marginal Buyer is Changing Crypto’s MarketStructure 63.1 What and why I’m writing: . . . . . . . . . . . . . . . . . . . . . 63.2 Marginal Spot Seller? Marginal Spot Buyer? Derivatives in Con-

trol? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63.3 Identifying Marginal Sellers . . . . . . . . . . . . . . . . . . . . . 73.4 Identifying Marginal Buyers . . . . . . . . . . . . . . . . . . . . . 83.5 Institutional Buying’s Effect on Market Structure . . . . . . . . . 13

4 Market Quality 144.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144.2 The physical limits of trading . . . . . . . . . . . . . . . . . . . 144.3 Let’s go down in order to go up: Demystifying the CPU 101: . . 144.4 Load experienced by matching engines . . . . . . . . . . . . . . 164.5 How to achieve scalability and stability from an architecture point

of view ? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174.5.1 Dilemma: . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.5.2 Sell side: . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184.5.3 Buy side . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.6 A few thoughts about how to solve these dilemmas . . . . . . . 20

5 FTT 21

6 Readers’ Questions 21

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1 FTX in November

November has been a busy month for FTX. We’ve rolled out an assortment ofproducts, upgrades, and new feature requests. We continue to work on feedbackprovided by the community and are constantly looking for innovative new waysto serve our customer base. From NOV 1st - NOV 30th we had $67.8B in overallvolume and a healthy $4.75B in spot as we see our spot markets grow. A 250%increase from the month of October. FTX also saw its highest volume day ever- clocking $7b.

Spot Margin Trading Up to 10x spot margin trading has arrived to FTX!Youcan even withdraw margin borrowed assets. For further detailed informationplease see Spot Margin Trading Explainer.

Spot Market Lending

FTT Staking Staking FTT gives the following benefits:

• Increased referral rates: referrers that stake FTT are paid a higher fractionof their referees’ fees

• Maker fee rebates: stakers get maker fee rebates (in addition to the stan-dard FTT fee discounts)

• Bonus votes: stakers get bonus votes in our polls (in addition to thestandard number of votes, based on FTT held and trading volume)

• Increased airdrop rewards: stakers get increased SRM airdrops (and po-tentially later other airdrops and yield)

HOT Listings FTX has listed several coins and stocks in November. Stocks(can be traded up to 101x leverage) - ARKK, TSM, AMD, SQ, PYPL, MSTR,MRNA, ZM, NIO, TWTR, UBER, NVDA, BILI, BYND, GOOGL, TSLA,BNTX, PFE, BABA, SPY, AAPL, AMZN, FB and NFLX. Coins - SNX, FRONT,WAVES, OMG, MATIC, LEO, OKB and HT.

FTX Podcast October was an exciting month for the FTX Podcast withguests including Dan Matuszewski and CL founder of CMS Holding and cryptotwitter cat, Matt Ballensweig Head of Lending at Genesis Trading and NodarJanashia founder of Zapper,Fi.

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**The Content of the entirety of this digest is for informational purposesonly, you should not construe any such information or other material as legal,tax, investment, financial, or other advice. The content of this digest written byexternal authors do not reflect the views of FTX.

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2 P2P Lending on FTX

Our P2P lending market goes hand in hand with our recently launched spotmargin trading. When a trader enters into a leveraged spot position, the fundsto enable the leverage need to be sourced from somewhere; often exchangeswill set the rates themselves and lend to their users. However, this is lessthan ideal as it means that borrow rates are set by a single party instead ofdetermined by the market, as a result borrowers may be overpaying for thoseloans. This also affects lenders, they either can’t lend at all or can only lendat a size and rate determined by the exchange. With FTX’s P2P lending, theborrow rate is determined by the market; lenders choose the minimum interestrate they would accept to lend out their funds and then every hour an auctionoccurs which calculates the total demand for borrows and finds what interestrate would satisfy that demand in full, then charges borrowers that rate for thefollowing hour. In and of itself, spot margin and lending is a proven, successfulproduct in this market with hundreds of millions of dollars currently tied up inloans on exchanges. However, we’ve taken it several steps farther.

In addition to demand for loans from spot-margin traders, lenders on FTXget the benefit of additional borrow demand from futures traders. Due to ourreal-time settlement of PnL, winning traders can use USD PnL immediately totrade or withdraw to their own wallet. Conversely, if a losing trader is usingBTC, USDT, or other cryptos as their collateral, they might run a negativeUSD balance which means there is a shortage of USD. In the past we handlednegative USD balances by charging 0.1%/day to any amount in excess of $30k,but with the introduction of the P2P lending market, we now allow users toborrow USD from the lending market to cover their negative USD balance,thereby providing consistent borrow demand to lenders and allowing tradersto pay market interest rates for this USD instead of a rate set by FTX. Thisborrow demand should entice more lenders onto the platform, which can createa positive reinforcement cycle: more liquidity for spot-margin traders allowsfor them to trade larger sizes at lower rates, the increased demand for borrowsbrings in more lenders, providing additional liquidity for traders, so on and soforth.

Good so far, right? It gets better. Not only can users borrow funds to tradeon leverage and prevent collateral conversion to cover negative USD balancesfrom futures trading, users can also use our P2P lending market to withdrawassets they don’t hold! For example, you hold lots of BTC that you don’t wantto sell, but you also want to yield farm on Serum Pools with ETH and USDC.All you have to do is deposit your BTC to FTX and submit a withdrawal forETH and USDC to your wallet, the system will automatically borrow the fundsfor you in the background and send them to your wallet! When you deposit theETH and USDC back into your wallet, the loan will automatically be returned.So not only can you utilize borrowed funds for trading on FTX, you can utilizeborrowed funds where ever the highest yields or anywhere you please.

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3 The Institutional Marginal Buyer is ChangingCrypto’s Market Structure

Matt Kaye is Managing Partner at Blockhead Capital, a long/shortcrypto hedge fund founded in 2017: @Matt Kaye

3.1 What and why I’m writing:

In the last edition of FTX’s monthly digest read here, Z @SplitCapital laid outa compelling case for how bitcoin’s 2017 coin-margined derivatives are no longerthe driving force behind price discovery. In his write up, Z timelines the riseand demise of Bitmex and goes further to highlight how decentralized financeis stealing market share away from centralized incumbents.

It’s my intent to take the foundation laid by Z to build a case for howbitcoin’s market structure is changing and will change given the trends we’reseeing emerge in today’s capital flows.

3.2 Marginal Spot Seller? Marginal Spot Buyer? Deriva-tives in Control?

As a capital manager I ask myself 3 simple questions every day and every night:

• Who is the marginal spot seller here?

• Who is the marginal spot buyer here?

• Are derivatives in control of price discovery?

When answering these questions, I attempt to build a compelling case forwhichever question I think I have the best answer to, allowing me to take (or nottake) a directional position. What should immediately stand out to readers ofZ’s write up, and a conclusion I agree with, is that derivatives are not currentlyin control of price discovery. This leads us to try and answer the remaining twoquestions.

Allow me to take a moment to cover some basic terminology. If you’recomfortable with the concepts above then feel free to skip the paragraph below:

Since price is a function of supply and demand we want to try and answerthe question, at any given time/price level, is there more demand or supply?By knowing the answer we will know whether price will go up or down. Periodscharacterized by outsized supply are known to have a marginal seller, whileperiods of outsized demand are known to have a marginal buyer. If we wereto conclude that derivatives are in control of price discovery, then we wouldbegin to ask ourselves a completely different set of questions, which I will notbe covering in this piece.

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3.3 Identifying Marginal Sellers

In a bull market, the relationship between price and sellers is such that as priceincreases so does selling. However, the relationships is not inherently bearishand price can continue on an upward trajectory while selling increases. It’s notuntil there are more sellers than buyers that price will begin a downward trend.Below you can clearly see the trend of bitcoin holders adding to their positions(buying) when price is at lows and reducing their positions (selling) when priceis on its way to highs.

Certain bitcoin stakeholders are required to sell bitcoin periodically withlittle regard for price, as 2nd order factors drive their decision to sell. The bestexample of this is bitcoin’s primary natural seller, miners. Miners must sellbitcoin in order to fund their mining operations with fiat. Of course, bitcoinhas other natural sellers, however they are not always sellers and can easily benatural buyers. Market makers fall squarely into this category as they deploya myriad of strategies to capture profit in the form of spread and/or premium.To illustrate this, market makers who are short volatility may need to buy orsell spot bitcoin in order to hedge their book’s delta exposure, while arbitragefocused strategies may favor longing a future and selling spot to profit from amarket inefficiency. It is difficult to gauge market maker positioning, however,should you be able to gather the necessary information you will find yourselfwith a significant edge in understanding the near-term directionality of an asset.

Lastly, holders/traders in profit become natural sellers the further into profitthey go (not many have the stomach to hold onto large unrealized profits).Hopefully it’s obvious at this point that when the combination of sellers (miners,market makers, and holders/traders) outnumber buyers, price goes down, and

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we would subsequently characterize the market as having a marginal seller. Assuch, being able to identify marginal sellers can be a lucrative exercise.

3.4 Identifying Marginal Buyers

If you’re going to take anything away from this piece, I genuinely hope it’sfrom this section. Contrary to the rule that governs the relationship betweenprice and sellers, the relationship between price and buyers is such that buyersbecome more emboldened and confident as price goes up, resulting in morebuying. For the sake of argument, we can view the number of bitcoin sellers asfinite to the number of people who currently hold bitcoin, however the numberof bitcoin buyers is capped only by the population of the world’s total eligiblebuyers (which is far from saturated). This is important as it’s the driving forcebehind bitcoin’s price appreciation even in the face of increased selling. So, whois bitcoin’s marginal buyer sending prices higher? The answer is institutionalbuyers in the spot market and it is my opinion that this trend is only justbeginning.

Institutional buyers are different from crypto-native funds in that they mustabide by stricter mandates and regulations. As such their choices in venues arelimited. We see their footprint in regulated venues such as LMAX and CMEwhich focus on serving institutional clients.

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Additional institutional buy pressure is observed in the demand for Grayscale’sbitcoin trust (GBTC) which offers bitcoin exposure to traditional institutionsand investors who are unable or unwilling to buy and hold bitcoin directly.GBTC’s nature is such that when there is increased demand for the product,the share price’s premium to NAV increases. This results in market makersand crypto-native funds arbitraging the premium by purchasing spot bitcoinand contributing it to the trust. After the lockup period, the spot contributorcan realize any premium to NAV which may still exist. As GBTC premium toNAV increases so does the resulting buyside demand for spot bitcoin comingfrom those who want to capture the spread. The most extreme form of this iscurrently being observed in Grayscale’s Litecoin product.

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Clearly GBTC inflows are resulting in increased bitcoin spot buying. How-ever it’s worth noting that capturing GBTC’s premium can later result in bitcoinspot selling as the trade unwinds and the premium is captured (dependent onthe trader’s desired base currency and willingness to take directional risk).

It’s also helpful for us to try and frame marginal buyers and marginal sellersto the regime in which they are participating. We can examine high timeframeon-chain metrics to help us understand where we are in bitcoin’s price cyclerelative to previous price cycles. For example, both a measure of long-termholders (HODL Waves) and Reserve Risk provide evidence that we are indeed at

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the end of the accumulation phase and at the relative beginning of the parabolicbull market.

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Willy Woo (@woonomic) recently published a thought that I felt capturedthe cyclical time of the market quite well:

“The re-accumulation phase of this bull market has coincided with thelongest and deepest depletion of BTC inventory from spot exchanges in bit-coin’s 12 year history.

This depletion has lasted 10 months thus far, doubling last cycle’s 5 monthspan. Similarly, the volume of coins scooped off exchanges and into cold storagereflects 19% of inventory vs. 11% in the last cycle. With this in mind, 2021 isexpected to be wildly bullish.”

While not a perfect measure, the number of active entities transacting onbitcoin’s blockchain provides more support for the existence of widespread insti-tutional buying. 2020 has seen a steady increase in the number of new entities,however the metric of total active entities is still shy of 2017’s all-time high.This discrepancy between price and number of active entities suggests thatlarger sized intuitions are driving the market as opposed to a larger number ofsmall retail participants.

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3.5 Institutional Buying’s Effect on Market Structure

The takeaway is simple, so long as there is a marginal spot buyer, the priceof bitcoin will go higher and the role of derivatives and technical analysis willcontinue to be diminished. Successful participants will adapt to learning howto track market flows and in doing so will learn to identify marginal buyers andmarginal sellers.

What was a mean reverting asset is now entering a phase trend followingvertical accumulation in which institutions, who are relatively insensitive toprice because of their long term outlook, compete with one another to acquiretheir share of bitcoin’s available supply in an effort to outperform gold. When westop to understand the macro factors pushing traditional allocators into bitcointhen we can fairly conclude that institutional allocation to bitcoin will increasefor at least the next 6 months barring a shock to the current global approachto both monetary and fiscal policy. Those who do not adapt to bitcoin’s newmarket structure will likely be left behind. Adapt or die.

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4 Market Quality

@Releive of : thekingfisher.ioand scalnyx.com

4.1 Introduction

The health and maturity of a market can’t be estimated only through priceand volume. Its actors and their relationships have a significant impact on thedirection an industry tends to take. We’ll consider the exchanges, major marketmakers, and common market participants and how their interactions are shapingthe crypto-markets.

In this article, we’ll go through the physical limitation of exchanges matchingengines, how this impacts the broader crypto market and a few propositions thatcould make cryptos a fairer and more efficient market.

4.2 The physical limits of trading

For thousands of years, humans have worked to make their markets more re-silient, efficient and ever faster. From logistics networks to online payment pro-cessing systems, the hunt for latency reduction is as old as markets themselves.What happens when an industry faces a roadblock it can’t push through? Thediscovery of Silicon and its applications allowed us to push the speed of infor-mation propagation close to the speed of light. ... We can’t make it any faster.Having worked on the Euronext matching engine, I gained an understanding ofhow to get the most of hardware’s physical limits and develop faster MatchingEngines (ME).

The ME is the heart of any exchange. Highly skilled marathon men have aslow but strong heartbeat that can do a lot, every heartbeat is optimized to givethe best efficiency and speed in bringing the nutrients to tissues and muscles.Likewise, the ME is responsible for efficiently and speedily resolving tradingoperations, while making sure the resulting information is relayed to the correctpart of Exchanges’ infrastructures, in a reliable and resilient way. Latency ofME is going down years after years thanks to exchanges’ teams working hard,while some old leaders start getting based and sit on the trophy losing more andmore customers everyday.

“If you don’t go up, you go down”

4.3 Let’s go down in order to go up: Demystifying theCPU 101:

The mid-2000s marked the end of the Moore’s law era, when upgrading tohigher-frequency hardware brought automatic performance enhancements. In-stead, the industry turned to a model where performance gains come fromadding more execution units (cores). But leveraging multicore architectures

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requires extra development and testing efforts, and a naıve approach of addingmore threads often falls short of scalability expectations. Why?

“One Central Processing Unit (CPU) is really good at one task, many CPUsaren’t good as a team by default”.

A CPU is composed of a processing unit and a memory unit. One CPU coreis organized to execute instructions at its frequency speed. For example, a 3GHz CPU processes each instruction at 0.333 ns (1 000 000 000 ns = 1 sec). Thesemiconductor technology (in nanometers) determines the physics boundaries.

The reality is much more complex. We are assuming that instructions anddata are already stored in the CPU physical registers that run at the samespeed as CPU frequency. In reality, Instructions and data are stored in a multi-layer cache memory architecture (L1/L2/L3). The more it is close to the CPUprocessing unit PHYSICALLY ON THE CHIP, the fastest and smallest it is.So the L1 cache is the fastest and the smallest. Figure 1 shows the multicoreCPU microarchitecture and the corresponding size/speed of each cache level.

Figure 1: Multicore CPU Microarchitecture

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To understand what makes latency long and cycles disappear, we have tounderstand the fundamentals of the Universal Scalability Law applied to themulticore CPU system. The main parameters that impact scalability are:

• Core resources contention: due to hyperthreading for example. Severalsoftware threads want to access simultaneously to the same executionunits, and thus processing is serialized and prioritized.

• Cache memory contention: it occurs when there is contention from thesame core on its private cache memory (L1/L2) in case of operating systemcontext switch, or when the problem size is too big to be stored in theprivate cache memory. This phenomenon is called cache thrashing. Inmulticore, cache thrashing occurs on the shared L3 cache (or LLC lastlevel cache) between the cores. Hence, the impact is several hundreds ofnanoseconds lost in latency. The solution shall be an intelligent softwarethat improves the data locality in-cache processing in order to also improvethe memory bandwidth utilization while keeping the CPU core busy doinglocal computation; this is a very difficult problem, and there is no easysolution today.

• Cache coherency: it is a hardware mechanism that allows for core tocore seamless communication whenever there is a software synchronization(mutex, barrier, etc...) or access to the same memory area by both cores.A simple cache coherency (one cache line) costs at least 600 cycles.

4.4 Load experienced by matching engines

Taking into account CPUs’ limitations, exchanges have to manage their opera-tions. First, exchanges have to manage their traders’ positions. That exposesthem to liquidity risks when mass liquidations occur during highly volatile mar-ket episodes. Exchanges tend to manage the liquidation of those positions inways to avoid ripple effects on other market participants when mass liquidationshappen. They also need to enforce market rules and backlog trades, to ensurethe resiliency and consistency of their services.

The 99th percentile represents the top 1% highest latencies values for queriesevaluated within a given time interval for a given task. A resilient and efficientpiece of software will be considered by the 99th percentile that will become theadvertised latency threshold. Exchanges’ Matching engines, regardless of howwell coded they are, will always be limited by those latencies, limiting the totaltransaction throughput for their users. If an exchange has a capped amount oftransactions per second.

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This adds up to a couple thousand operations per matching instruction, forthe ME to manage. They also have constraints for business enquiries related toserving many instruments and handling millions of messages per second.

4.5 How to achieve scalability and stability from an archi-tecture point of view ?

The ME is a highly-interconnected workflow computational profile, combiningdata from multiple sources, where the frequency of communication betweenmodules and their inter-dependencies are high and dynamic. Threads synchro-nizations and scheduling needs to be done dynamically and quickly.

Reactive software is a design philosophy and a paradigm shift that englobesbuilding both large-scale reactive microservices and fine-grain reactive applica-tions (one process). Based on asynchronous message-passing design, there exista plethora of concurrent programming models that allow for building a reac-tive software from the ground up. The actor model is one such battle-provenprogramming mode.

Actors are a very efficient concept, supporting the whole development toproduction lifecycle. By being directly mapped to functional concepts, actorsshorten the distance between business and functional architectures; they encap-sulate the logic at a level granular enough for splitting work between developers;they are directly usable concepts for testing; and they allow administrators todecide the topology dynamically, based on available hardware and applicationload.

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The ME receiving those operations will distribute the needed computationsbetween the available CPUs’ cores. If a core is full, the data is sent either toan upper cache level, to motherboard sockets, or in the worst case, back to theRAM and sent to hardware resources (this has the highest cost because it needsto reach other components).

Every single time a core has to sync to balance load; they pay the “latencyfee”. The more cores you add, the more the data can be spread out but themore latency fees you will pay. This fee increases exponentially. This is mostlywhy exchanges can’t really just scale by buying more servers, and also why theycan be victims of their own success. The critical point of latency hits whenentropy kicks in, spreading data to many cores and they just make promises toeach other to wait for data to execute instructions.

The strategy used by engineers is to dedicate each core to one specific roleand never do anything else, make sure they never get too much data or instruc-tions, and avoid as much as possible any sync and randomness from the inputs.To achieve this, they will either have to spend money on research and/or investinto software to manage memory allocation and core control. And especiallyhave rigorous engineers not wasting any bytes of CPU time.

4.5.1 Dilemma:

The exchanges and the market as a whole, find themselves in a dilemma: in-crease the load at the cost of making their speed and latency limitations moreapparent.

4.5.2 Sell side:

From the sell side (exchanges) point of view, you spend a lot of money devel-oping solutions to better handle entropy. You need talented developers thatunderstand the latency dilemma including the entropy generated by having agrowing customer base (trading software, algo traders, market makers). At somepoint, the design limits are reached by physics rules. Considering that, any mis-take stacks and can quickly become painful, this ends up limiting developmentcapacity until innovation is killed due to the fear of creating new latency costs.Exchanges eventually have to reduce the risk of ME malfunctions due to whatcan be similar to a Distributed Denial of Service made by legitimate marketparticipants.

To mitigate those risks, they apply rate limits and prioritize which instruc-tions bring them the best business opportunities. CPU and latency is physicsand math. Research, rigor and notions of “fairness” are up to humans discre-tion. If you slow down everyone, you make the market more inefficient for themajority of traders. It then gives an advantage to the ones with resources (lowlevel coding engineer, capital to deploy, research team, volume). Another pointworth mentioning, each operation, for legal reasons, has to be saved even if it’sto cancel the order a couple millisecond later. HFT (High frequency trading)

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firms put a high load on exchanges with mostly wasted resources at the cost ofthe exchange.

4.5.3 Buy side

On the buy side (customers’ point of view), an unlimited ME allows for betterstrategies, more competition, more efficient markets, better arbitrage opportu-nities between exchanges, and a fairer market for low volume traders that canaccess liquidity at the same pace as everyone else.

When the number of customers increases, the ME slows down, and excludesmost traders. Buying servers in the geographic vicinity of the exchanges’ serversallow certain market participants to send specific price patterns, which is ofparticular interest from a regulatory and exchanges viewpoint. During highvolatility periods, HFT will mostly consume competitors’ liquidity and absorbnon-HFTs’ passive orders that make it through the orderflow. Takers tend toact as informed traders and their order flow can be considered toxic for othersparticipants.

One example that most traders already experience, was a HFT strategy send-ing orders at a rate of 6,000 orders placed and cancelled per second. Meaningthat each quote has a life shorter than 1.6 millionths of a second - the amount oftime it takes for light to travel about 1,500 feet. Anyone further than half thatdistance has no chance of executing against these quotes. This is a commonlyused strategy called quote stuffing, possible because a market participant is inthe same datacenter as the exchange. This leads today to large hidden spreads,created when aggressive orders hit the exchange.

Questions arise about market rules only the sell side can apply with incentivefrom the regulators or self regulations to improve the quality of their services.Supporters of HFT will say they improve liquidity by reducing the spread andthe possibility of other malicious strategies, like spoofing, to occur.

Others will say that such speed is unfair. Non-HFTs find themselves sand-wiched between rate limits, statistical arbitrage strategies, and ghost quotes,which reduces the possibility for small participants to win. On top of highlyskilled participants, new products like altcoins quantos have emerged in thelast years and are the most concerning at the time of writing this article. TheICO craze and most writing and educations about the liquidity trap of thecryptocurrencies market have lead traders to prefer leveraged products withimproved liquidity instead of low liquidity spot coins/tokens, as detailed in theprevious digest of June from Clarens Caraccio

¡¡ Being long “Inverse” is riskier than being “short”. As the price goes down,the position accumulates more and more delta and puts the trader at a greaterrisk of liquidation ¿¿. A new wave of traders are turning to highly leveragedproducts with the same low underlying liquidity on spot, but this time exposedto negative gamma on “long” positions when using non-USD collateral.

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Market makers seek neutral positions, their rates of taker orders are mostlydependent on their inventory. On higher volatility and without proper incentivesthey won’t bother executing an optimum liquidation of their inventory in weaklimit order books with controlled intensity. They remove liquidity against in-formed traders to avoid potential losses themselves. Leveraged participants endup getting their positions taken out by the exchanges risk engine just becausethe underlying index’s order books have been withdrawn.

The Bitcoin and altcoin markets aren’t regulated by any generic “fair rules”regarding latency, spreads, collateral requirements or fee structure. But allexchanges apply rate-limits based on volume. These limits are designed mostlyto protect matching engines against the high load of demand and provide anefficient cryptocurrencies market.

The rise of new tools to quickly trade into these limits and capture theavailable liquidity allow non systematic participants to also capture these inef-ficiencies with faster execution to trade against market makers but this entersdirectly in conflicts with them. The role of quoting low liquidity caps is tediouswork that needs incentives, now they also get hunted, so they will complainabout the 99th percentile degradation and use it as an argument to complainabout the matching engine latency.

4.6 A few thoughts about how to solve these dilemmas

A point on how exchanges incentivize market makers. Legacy markets offeringout-sized rebates was the industry’s first incentive program to implement a depthof book requirement. Executed volume in accordance with market quality ruleswill align the interests of market makers and exchanges, improve the overalltrading environment for investors and all participants in marketplaces.

We suggest the following points as ways to further improve the market’squality, exchanges execution and their MEs performances.

Market will always have pitfalls sown by delicacies, some have created thecentralised world we know today that we are trying to disrupt, let’s not repro-duce errors of the past for ideology and let’s try to build together the best of theworld with best practices in mind. The higher the quality of a market the lowerthe explicit costs will be for the investors, therefore ensuring a better quality ofexecution.

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5 FTT

FTX has burned over 8.3% of the circulating supply of FTT and the insurancefund holds 5.25 million FTT, almost 5% of the circulating supply. Removing13.27% of the circulating supply of FTT from the ecosystem has been achievedin a year and few months. When looking at the graph below, comparing feesbetween exchanges, it is a noteworthy accomplishment. Another exciting oc-currence for all FTT holders is that they will receive SRM airdrops for holdingthem on FTX. Find the details here.

Figure 2: Fee comparison between exchanges

6 Readers’ Questions

e-mail : research@ftx.com with questions you would like answered about thecryptocurrency and blockchain ecosystem. We shall select certain questionsand dedicate a section to them in next months digest.

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