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Crypto Market Structure and Best Execution.

Comprehensive guide on best practises & implementation of trade executions and building trading technologies

What is Best Execution?

Best Execution is the ability to buy or sell a desired amount of something at the lowest or highest possible net price.

Due to the many variables and difficulty in measurement most people define it in terms of the trading process utilized. Those variables include:

●         Bid/ Offer spread or the different  bid between bid and offer
●         Fees paid (to exchanges brokers etc)
●         Market impact cost the movement from beginning to end a trade
●         Opportunity  cost ( costs for waiting to trade particularly if once trading intentions have been leaked to the market

What is Meant by Trading Process?

Trading  can be as simple as:

●         Calling  one dealer and agreeing to buy or sell (or the digital equivalent of trading or on a screen)
●         Giving an order to an agent, and letting them decide, where and how to trade

Trading can be as complicated as:

●         Trading on several exchanges simultaneously while comparing quotes with those provided to you by dealers.

Examples of POOR Trading Process (for a large order)

Calling multiple dealers to request a quote at the same time for the same order or placing the entire order onto an exchange where the public can see it

➔         Both create market impact before trading even starts, resulting in very expensive trades

Trading manually on individual exchanges, breaking the order up by hand

➔         Exchange prices diverge and converge rapidly, making it impossible for manual trading to be optimal

Importance of Fees Relative to Spreads

In equities, the fees charged by exchanges to remove liquidity are less than 30% of the tick size, but in crypto, the fees can exceed 100 times the tick size. The following table puts this in context by showing the average spread posted on several top exchanges alongside their fees: (Spreads calculated by sampled data from CoinRoutes).

What is Best Execution
What is Meant by Trading Process?
Examples of Poor Trading Process (for a large order)
Importance of Fees Relative to Spreads
Screenshot 2022-09-27 at 10.56.11 AM.png

Notes on Spread Data

●         Fees are a very significant driver of trading costs, being larger than bid offer spreads most of the time.
●         While they vary, bid-offer spreads on exchanges are between 1 and 5 basis points considering their top of book.
               ○     This table doesn’t show this, but this is roughly 1 Bitcoin on average
●         The fee differential between maker and taker is an important reason why CoinRoutes uses mostly passive orders. There are other reasons which will be explained later.

Characteristics of the Consolidated Order Book (Bitcoin)

These summary statistics for buying or selling 1000 bitcoin were produced from CoinRoutes Cost Calculator on Sunday afternoon.

Screenshot 2022-09-27 at 11.02.37 AM.png
Notes on Spread Data
Characteristics of the Consolidated Order Book (Bitcoin)

Characteristics of the Consolidated Order Book (Bitcoin)

For comparison, here are statistics for 2500 bitcoin a couple of hours later on Sunday, produced from CoinRoutes Cost Calculator.

Characteristics of the Consolidated Order Book (Bitcoin)
Screenshot 2022-09-27 at 11.05.25 AM.png
Notes on Book Depth Data
Importance of Exchange Variance
What is microstructure?
Overview: market structure issues in market liquidity
Implications for Trading & Trading Technology
Myths Debunked By Data

Notes on Book Depth Data

●         Exchange liquidity is quite robust & potentially cost effective

○         It is cheaper to trade 1000 bitcoin using smart routing than retail tends to pay

●         To SEE exchange liquidity requires software to evaluate THOUSANDS of individual price levels

○         Most systems, not built for crypto, are limited in the size of the book they can process.

●         Beyond 2000 Bitcoin, the liquidity does start to thin out

○         This puts a premium on patience (minutes/hours) for working large blocks; It turns out that algorithmic trading outperforms smart routing overall as well, but this explains why market makers charge so much for very large trades.

Importance of Exchange Variance

The following graph shows Bitcoin’s best bid and offer among 6 major exchanges for Sunday, May 31st. Notice;

1.  The best bid is normally above the lowest offer, but only for a few basis points
2.  Exchanges rotate among each other for which one has the best bid and best offer, statistically, there is a strong element of mean reversion, meaning that order placement should always have pan-market data available.

Myths Debunked By Data

Crypto Market Structure is just like FC or Equities

➔         Not true, fees relative to spreads, the importance of depth, & fragmentation make it very different.

Most Bitcoin liquidity is OTC, not on exchanges

➔         Perhaps OTC is the gateway, but exchange liquidity is both significant and publicly available.

Smart Order Routing is Easy, Just Take the Best Price

➔         Not really, the smartest routing uses mean reversion, fee differentials, fill probabilities to optimize order placement.

Implications for Trading & Trading Technology

●         Be aware of relative trading costs and where liquidity is available
●         Demand liquidity only when you need to—. If you need immediate execution for a short term strategy, it is reasonable to pay for it. Otherwise, the market is priced better for liquidity providers.
●         Data access, while trading, is critical – ensure that, even when trading on single venues, your order placement is informed by a consolidated view of the market.

Using these concepts, CoinRoutes has delivered less than 2 basis points of slippage from the consolidated midpoint of hundreds of millions of dollars of orders through our system.

Overview: market structure issues in market liquidity

The behaviour of prices and even the viability of markets depend on the ability of the trading mechanism to match the trading desires of sellers and buyers. This matching process involves the provision of market liquidity. The role of the market maker in providing liquidity is widely recognised, but liquidity can also arise from other aspects of the trading mechanism. In particular, rules and market practices governing the trading process, such as how trading orders are submitted and what trading information must be disclosed, can affect the creation of liquidity. This raises the question of whether changes in market structure can enhance the provision of liquidity. Is there a “Golconda exchange” that provides optimal liquidity?

What is microstructure? 

Issues related to market liquidity are part of a broader analysis of the microstructure of markets. Market microstructure refers to the study of the process and outcomes of exchanging assets under a specific set of rules. While much of economics abstracts from the mechanics of trading, microstructure theory focuses on how specific trading mechanisms affect the price formation process.2 Much of the microstructure literature has focused on the price-setting problem confronting market intermediaries. The Walrasian auctioneer provides the simplest (and oldest) characterisation of the price-setting process. The auctioneer announces a potential trading range, and traders determine their optimal order at that price. If there are imbalances in traders’ demands and supplies, a new potential price is suggested, and traders then revise any orders. No trading takes place until a market-clearing price is found. The London gold fixing loosely resembles the Walrasian framework, but most other markets differ dramatically. In particular, specific market participants play roles far removed from the passive one of the auctioneer. Demsetz (1968) was one of the first economists to analyse how the behaviour of traders affects the formation of prices. Demsetz argued that while a trader willing to wait might trade at the single price envisioned in the Walrasian framework, a trader not wanting to wait could pay a price for immediacy, ie liquidity. This results in two equilibrium prices. Moreover, since the size of the price concession needed to trade immediately depends on the number of traders, the structure of the market could affect the cost of immediacy and thus the market-clearing price. The price-setting problem examined by Demsetz has been investigated more formally using inventorybased models. These models view the trading process as a matching problem in which the market maker - or price-setting agent - must use prices to balance supply and demand across time. There are several distinct approaches to modelling how prices are set by market makers: Garman (1976) focused on the nature of order flow; Stoll (1978) and Ho and Stoll (1981) examined the optimisation problem facing dealers; and Cohen, Maier, Schwartz and Whitcomb (1981) analysed the effects of multiple providers of immediacy. Common to each of these approaches are uncertainties in order flow, which can result in inventory problems for the market maker and execution problems for traders. An alternative approach to modelling the behaviour of prices focuses on the learning problem confronting market intermediaries. Starting with Kyle (1984, 1985), Glosten and Milgrom (1985) and Easley and O’Hara (1987), market structure research has given greater attention to the effect of asymmetric information on market prices. If some traders have superior information about the underlying value of an asset, their trades could reveal what this underlying value is and so affect the behaviour of prices. The key to extracting information from order flows is Bayesian learning. Each trader has a prior belief about the true value V of an asset. Traders observe some data, say a trade, and then calculate the probability that V equals their prior belief given that these data have been observed. This conditional probability incorporates the new information that traders learned from observing the data, and is hence their posterior belief about V (Graph 1). The posterior then becomes the new prior, more data are observed, and the updating process continues.

Screenshot 2022-09-27 at 11.15.26 AM.png
Market structures

What we have learned The information-based approach has greatly enhanced our understanding of the behaviour of markets and by extension the nature of market liquidity. Perhaps the greatest insight of this approach is how information affects quotes and spreads. Information-based models highlight the role of market parameters such as the size of the market or the ratio of large to small trades in the adjustment of prices. This in turn provides an explanation for the existence of bid-ask spreads even in competitive markets, without reference to explicit transactions or inventory costs. Inventory-based explanations of the bid-ask spread are problematic because empirical evidence of inventory effects in financial markets is weak. Another important conclusion is that prices ultimately converge to their true, full-information value; in the limit markets are strong-form efficient.3 This follows from the Bayesian learning process. It is not entirely clear, however, what market efficiency means in a dynamic setting. Given that some traders have superior information, prices along the adjustment path do not exhibit strong-form efficiency, and indeed there can be very great differences in the speed with which prices move toward full-information levels. Markets with greater volume, for example, adjust faster (in clock time) to information. The time between trades, in particular the tendency for transactions to cluster, also appears to affect the adjustment of prices. The time varying process by which transactions arrive has important implications for econometric modelling of market volatility. Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models and Autoregressive Conditional Duration (ACD) models have come to be widely used for analysing price and transactions data, respectively.

Finally, much has been learned about the information contained in specific trades. Different types of trades seem to have different information content. Similarly, trades in different markets seem to have different information content. What we still do not know For all that we have learned, there remain several puzzling issues concerning the trading process. Foremost is what determines volume. While empirical research has identified a strong link between volume and price movements, it is not obvious why this should be so. Volume may simply be a consequence of the trading process; whereas individual trades cause prices to change, volume per se may not affect prices. Or as seems more likely, volume could reveal underlying information, and thus be a component in the learning process. Pfleiderer (1984), Campbell et al (1991), Harris and Raviv (1993), Blume et al (1994), and Wang (1994) have examined this informational role. A second set of issues revolves around what the uninformed traders are doing. It is the uninformed traders who provide the liquidity to the informed, and so understanding their behaviour can provide substantial insight and intuition into the trading process. Information-based microstructure models typically assume that uninformed traders do not act strategically. Yet, if it is profitable for informed traders to time their trades, then it must be profitable for uninformed traders to do so as well. Admati and Pfleiderer (1988, 1989), Foster and Viswanathan (1990), Seppi (1990) and Spiegel and Subrahmanyam (1992) among others have applied a game-theoretic approach to modelling the decisions of uninformed traders. A common outcome with this approach, however, is the occurrence of multiple equilibria. Another open question is what traders can learn from other pieces of market data, such as prices. Neither sequential trade models such as Glosten and Milgrom (1985) nor batch trading models such as Kyle (1985) allow traders to learn anything from the movement of prices that is not already in their information set. But in actual asset markets the price elasticity of prices appears to be important. Technical analysis of market data is widespread in markets, with elaborate trading strategies devised to respond to the pattern of prices. Finally, microstructure theory has not yet convincingly addressed how the existence of more than one liquidity provider in more than one market setting affects the price adjustment process. Much of the literature assumes the existence of a single market-clearing agent. However, alternative mechanisms could arise that divert order flow away from the specialist. Multi-market linkages introduce complex and often conflicting effects on market liquidity and trading behaviour. Indeed, it is not even obvious whether a segmented market equilibrium is sustainable. Current models of liquidity, for example, suggest that securities markets may have an inherent disposition toward being natural monopolies. Further research in this area is particularly important given the rapid increase in the number of electronic exchanges in recent years.

Market structures 

Markets are currently structured in a myriad of ways, and new market-clearing mechanisms are arising with surprising frequency. All trading in a particular security can be directed to a single specialist, who is expected to make a market in that security. The New York Stock Exchange (NYSE) is the best known example of such a market structure (Table 1). Alternatively, dealers can compete for trades, buying and selling securities for their own account. Traditionally dealers competed in a central location, such as the London Stock Exchange or NASDAQ, but competition need not be centralised. Bonds, for example, trade primarily through bilateral negotiations between dealers and customers. A still third trading mechanism is the automatic matching of orders through an electronic broker. Today the majority of trading in the global foreign exchange market takes place over electronic exchanges such as Reuters and Electronic Broking System (EBS).

Screenshot 2022-09-27 at 11.17.58 AM.png

Actual markets do not conform to simple structures. Indeed, they typically involve more than one structure. What is important, therefore, is not the operation of any specific trading mechanism, but rather the rules by which trades occur. These rules dictate what can be traded, who can trade, when and how orders can be submitted, who may see or handle the order, and how orders are processed. The rules determine how market structures work, and thus how prices are formed. Since rules can affect the behaviour of prices, liquidity might also naturally depend on how a market is structured. Indeed, liquidity concerns may dictate the structure of the market. Drawing on the extensive body of research investigating the interaction between market structure and liquidity, the remainder of this paper focuses on two critical issues in the creation of liquidity: the impact of limit orders, and the effects of transparency.

Limit orders A wide variety of order types are found in securities markets. The most familiar type is a market order to buy or sell one round lot at the prevailing price. Other orders, such as “market-at-close”, “fill-or-kill” and “immediate-or-cancel” allow traders to control the timing, quantity or execution of their trades. By far the most common alternative type of order is a limit order specifying a price and a quantity at which a trade is to transact. Limit orders specify a price either above the current ask or below the current bid and await the movement of prices to become active. If the market is rising, the upward price movement triggers limit orders to sell; if the market is falling, the downward movement triggers limit orders to buy. Limit orders thus provide liquidity to the market. Limit order traders receive a better price than they would have if they had submitted a market order, but face the risk of non-execution and a winner’s curse problem. Whereas a market order executes with certainty, limit orders await the movement of prices to become active, ie a limit order is held in a “book” until either a matching order is entered or the order is cancelled. Moreover, because once posted their prices do not respond to the arrival of new information, limit orders are more likely to be executed when they are mispriced. Foucault (1999) finds that in deciding whether to submit a market order or post a limit order, traders’ main consideration is the volatility of an asset. In a volatile market, the probability of mispricing an asset is higher, and so limit order traders quote relatively wide bid-ask spreads. This raises the cost of market order trading, thereby increasing the incentive to use limit orders rather than market orders. But as a result of fewer market orders, the execution risk associated with limit orders increases. Order size may also influence investors’ choice between market and limit orders. Seppi (1997) concludes that small retail and large institutional investors prefer hybrid markets such as the NYSE, where specialists compete with limit orders to execute market orders.4 Mid-size investors, on the other hand, might prefer pure limit order markets such as electronic exchanges. According to Seppi, specialists will undercut limit order prices at the margin. Such undercutting lowers the probability that limit orders will execute, thus resulting in reduced depth in the book. Evidence in Sofianos (1995) of a U-shaped relationship between specialists’ total revenue and trade size suggests that specialists do indeed provide relatively more liquidity to small and large trades. The composition of order flows is a dynamic process, with investors’ preferred order type changing in response to developments over time. Goldstein and Kavajecz (2000) examine the behaviour of liquidity providers on the NYSE during periods of extreme volatility. They find that following a precipitous drop in equity prices, traders abandoned limit orders in favour of floor brokers. In particular, whereas specialists maintained narrow spreads and normal depth, liquidity drained out of the limit order book. Similarly, in foreign exchange markets, trading tends to move from electronic ordermatching markets to dealer markets during periods of market stress. Such dynamics raise the question of whether dealer markets handle information more efficiently than pure limit order markets. Another issue relating to limit orders is whether they can provide enough liquidity for every type of trade. The experience of limit order markets suggests not. For example, on the NYSE, the Toronto Stock Exchange and other exchanges with features of limit order markets, a substantial proportion of block trades - trades of 10,000 shares or more - are submitted to block traders or “upstairs market makers”, who form a syndicate of buyers to take the other side of the trade. One reason for using block traders rather than limit orders is that large transactions might be interpreted as signalling new information, and so move prices against the seller. Limit order systems are constantly evolving as new technologies are developed, and indeed OptiMark designed an electronic trading system that was supposed to minimise the impact that large orders had on price. OptiMark’s system ensured that orders remained anonymous until executed in full and was initially lauded as presaging the transformation of institutional trading. Despite the system’s advantages, however, it was poorly received by brokers and OptiMark ran into financial difficulties in mid-2000. Finally, there is the question of how much information about the limit order book is optimal. On the NYSE and a number of other exchanges, orders held in the specialist’s book are not common knowledge, although the specialist may choose to allow traders to view the book. By contrast, on electronic exchanges the order limit book is usually transparent. Madhavan and Panchapagesan (2000) find that on the NYSE the ability to observe the evolution of the book conveys valuable information to the specialist. In particular, specialists use information from the order book to set a more efficient opening price than the price that would prevail if all orders - both market and limit orders - were considered. Coppejans and Domowitz (1999) examine a pure limit order market and conclude that the trading process is influenced only by the flow of orders, not the stock of orders on the book. The book is not irrelevant; flows, after all, are changes in stocks. But in a market with an open book, the book per se does not appear to contain information on the value of the asset being traded. While helping us to understand how price formation occurs in actual markets, the results of these empirical studies do not imply that one particular market structure provides for more efficient price discovery than another. The experimental methods discussed below offer more meaningful insight into such hypothetical questions. Transparency As the information-based microstructure models demonstrated, the information available in the trading process can affect the trading strategies of market participants. It thus follows that the market equilibrium depends on the degree of transparency, ie the ability of market participants to observe the information in the trading process. Consider the previous discussion of the limit order book. If the book were known only to the market maker (as on the NYSE), then the market maker, as well as the informed and uninformed traders, would behave differently than if the book were common knowledge (as in the market examined by Coppejans and Domowitz). The openness of the book is but one of many differences in the degree of transparency across markets. The breadth of trade data reported and even the timeliness of the reported data can also differ tremendously. Some markets such as bond dealers provide only pre-trade information, meaning that quote data are made available but not transactions data. Other markets require post-trade transparency, ensuring that the price and quantity of trades are observable. The NYSE and NASDAQ, for example, are required to report immediately all quotes and trades. At the other extreme, trades handled “off board” - trades executed outside of the United States after US markets close - need not even be acknowledged. Differences in transparency may play a significant role in the creation of liquidity. As a factor in traders’ strategic decisions, transparency can influence their willingness to participate in the trading process. In 6 BIS Papers No 2 the United Kingdom, for example, the Financial Services Authority allows the reporting of large trades to be delayed for a period of time because it believes that immediate disclosure would expose market makers to undue risk as they unwound their positions and so discourage them from providing liquidity. Transparency is also a crucial consideration in the competition among markets for trading volume, and thus in the prospects for further fragmentation of liquidity. Bloomfield and O’Hara (1999, 2000) use laboratory experiments to address some of these issues. Their experiments include multiple dealers operating under varying degrees of transparency and traders with differing trade motivations. A key finding is that low-transparency dealers are more likely to set the highest bid and the lowest ask (inside quotes) in early rounds of trading, in order to capture more order flow (Graph 3). The information learned from the order flow allows low-transparency dealers to quote narrower spreads than their more transparent competitors and to avoid money-losing trades. This informational advantage declines with repeated rounds of trading because lowtransparency dealers reveal their information through their choices of quotes. Moreover, as trade progresses and individual dealers learn from trade outcomes, spreads for all dealers decline (Graph 3). Trading gains follow a pattern similar to spreads. Wide spreads in early rounds result in large gains because traders in need of liquidity are forced to buy at high prices and to sell at low prices (Graph 4). Gains then decline in concert with the decline in spreads. Notably, neither high- nor low-transparency dealers earn money at outside quotes, ie bids that are lower than the highest bid and asks that are higher than the lowest ask. Even though trades at outside quotes are executed at more favourable prices, dealer profits are eliminated by the higher likelihood of transacting with an informed trader. At inside quotes, the proportion of total trades coming from informed traders is approximately 10%, but at outside quotes, the proportion rises to 70% (Graph 4). Liquidity traders’ preference to transact at the best available quote results in this higher degree of adverse selection at outside quotes. Interestingly, traders do not behave strategically in these experiments. Concern about the possible impact of a trade in one round on prices in future rounds might be expected to lead traders to pay a premium to conceal their trades by trading with low-transparency dealers. Recall that this concern was one of the motivations behind the design of the OptiMark trading system. As Graph 4 shows, however, informed traders are as equally likely to transact with low-transparency dealers as with hightransparency dealers. In this setting, transparency seems to have a greater impact on dealer behaviour than on trader behaviour. 

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Institutional Cryptocurrency Trading Best Served by Hybrid Trading Model | Market Structure

In conclusion, economic experiments provide disquieting evidence that transparent markets may be less liquid than markets with weaker reporting requirements. Transparency reduces the information content of specific trades and so reduces dealers’ incentive to compete for orders. As a result, bid-ask spreads in transparent markets tend to be wider than those in less transparent markets. This accords with the experience in actual markets. Spreads on Instinet, for example, are frequently narrower than those on NASDAQ. The “Golconda exchange” may be less transparent than some of the markets that currently dominate global trading.

Institutional Cryptocurrency Trading Best Served by Hybrid Trading Model | Market Structure

I. Introduction

Given all the hype surrounding the expected acceptance and adoption of digital assets by true institutional investors, many trading platforms have been funded, built and gone live. While there may be several different business models that attempt to best solve the workflow requirements and trading needs for large institutional order flow, none seem yet to have particularly reached a velocity that can responsibly or reasonably attract large ticket sizes and manage the flow in a way that matches the protective and price sensitive nature of mature capital market asset classes like equities and foreign exchange.

One biggest hurdle has been fitting the model within current and future legal parameters, handling custody of assets, providing a proper institutional lending market (cross asset prime brokerage), and with that a coherent and viable margining system for institutional flow.

There are a few different models that seem to be the most prevalent. Of these, probably just a handful have so far attracted any volume, as they currently exist. OTC desks and manual trading, or off exchange, without the benefits and assistance of electronic systems, were out of the gate the primary sources for institutional flow. Exchanges, whether an institutional offshoot of a legacy retail platform, or some of the newer platforms, got into the game, providing many to many, order book driven markets that function by attracting markets from a market maker, whether that’s a high frequency firm (HFT), or any entity that can post the appropriate size and meet KYC/AML. While this may help meet a small piece of the actual demand, there isn’t the depth or market structure to accommodate large order flow. Add to that issues that arise with more nefarious trading that occurs in unregulated environments, it’s clear that while this may provide some support, it is not sufficient — yet — to take on what is required for any influx of large order size.

Separately then, there is a strong contingent of what can possibly best be described as companies that function as an order execution management system. I think that they have done an overall good job of providing solutions that help mitigate cost and execution. Execution in this model is derived by sending orders to many and variant liquidity points, whether that be an exchange or standalone market maker, like the HFT’s described above. Using algorithms, they can route orders globally, finding the best price for their client. With some algorithmic functionality (TWAP, VWAP, LWAP, etc.) and a cost structure where they can, either via an omnibus account or not, establish trade accounts at all these entry points, post the needed collateral, and then take client funds directly. By doing this, they can pass down cost savings they negotiate with exchanges and other liquidity points. Nonetheless, their clients still will have a capital charge that can’t be allocated among their various trading silos if they engage in activity other than cryptocurrency (see Prime Brokerage services, netting equity, FX, etc.) and still pay fees on top of that.

The scope of this article is strictly around what I consider to be an optimal methodology to trade this market, at reasonable prices, with minimal slippage and deep liquidity, and outperform the alternatives.

II. Risks / Issues with current exchange market structure and solutions via smart order routing:

II. Risks / Issues with current exchange market structure and solutions via smart order routing:
Screenshot 2022-09-27 at 11.24.27 AM.png

Cryptocurrency markets remain susceptible to too many forms of market manipulation and bad faith trading activity. This may also be true in other markets, like equities and FX, but those markets have an advanced infrastructure and a form of co-opetition that does not exist in crypto. Spoofing is still a problem in crypto. Same with front running and other practices. In the example here, a market maker can post a fairly generic and relatively wide market on an exchange designed to attract institutional flow. Without any special market structure, and even with some of the more generic order types (Iceberg, VWAP, etc.), execution is challenging. Once the aggressor tips their hand, the information can be used to sweep markets elsewhere, drive up prices (or down as the case may be) and prevent the original interest from completely their order. Even using the smart order routing (and accompanying tools like VWAP, etc.) will have the effect of chasing liquidity, moving markets, and allowing the smartest and fastest (in as much as that’s possible with the current market infrastructure) to scoop up profits and take advantage of any low hanging fruit.

III. Solution

The solution is not to execute ‘externally’, meaning, to go in search of liquidity. Rather, in my view, the solution is to keep the liquidity ‘internal’ or contained within one exchange. But one exchange with the proper market structure and necessary liquidity pools to meet the demands and needs of different traders.

With that in mind, a hybrid of sorts, based off two key trading structures, work hand in hand to produce the best liquidity for large order sizes. This helps guaranty that when a trader needs to execute immediately on a price, that price will be firmly there. Likewise, when a trader needs to trade considerably large size, there is a market structure that will not simply facilitate that transaction, but also ensure it’s done so in a safe, risk mitigated fashion.

These two trading structures form two distinct liquidity pools that exist side by side within one overall ecosystem.

The 2 structures that work best in this hybrid model are an ‘order driven’ market and a ‘quote driven’ market.

Within the order driven market, the structure is more complex than simply an exchange style order book, implementing order types and algorithms that are already common to the digital asset market, like Iceberg and TWAP, VWAP. The quote driven market on the other hand is an adjunct and offshoot of the order driven market, as the first may not at all times provide the firm quotes / orders required to speedily and efficiently execute an order, particularly in fast moving markets.

Let’s dive in with more specifics:

1. Order Driven Market

It is an order book type platform that, in this example, uses conditional orders alongside more traditional order types to facilitate a safe and orderly market for large tickets.

  1. Can be a dark pool or some combination, like dark for the taker, and lit for the maker.

  2. In this environment, the maker can get the benefit of the conditional order type while the taker can display prices and aggress in a dark environment. By supporting this market structure, the order driven platform doesn’t compete or create pricing issues with the quote market. The two structures, while they sit side by side, can’t be compared to one another or arbitraged.

2. Quote Driven Market

It is an ESP (Executable Streaming Prices) Platform with LP’s (liquidity providers) streaming different liquidity pools to their quote requester, and where all quotes are always firm (not conditional) and disclosed.

Drawing a comparison to the current plethora of OEMS’s that exist — they have become prevalent because in some ways, partly technological and partly building efficiencies of market liquidity, it is easier for the provider to use some smart routing system and algorithms to source liquidity than it is to build a complete ecosystem that attracts the liquidity onto the platform and contains all the trading interests in a centralized, safe, liquid, deep environment.

IV. Order Driven Markets — using a conditional order type. Many to Many.

The 2 structures that work best in this hybrid model are an ‘order driven’ market and a ‘quote driven’ market.
1. Order Driven Market
2. Quote Driven Market
IV. Order Driven Markets — using a conditional order type. Many to Many.
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We need to begin this conversation by presenting an understanding of a conditional order type, describe why it is such a useful tool for large institutional flow, and then discuss why it best functions when sitting side by side with a quote drive (ESP) marketplace.

A conditional order can somewhat be more understood as an order that will need a “Firm-Up” or be “Firmed- Up”. It is not sent as a firm order because the market maker gets a firm up request from the system. This puts a delay into the order, possibly a few milliseconds, possibly a few seconds or more. The maker can then check against their own metric and make sure a trade is suitable before allowing it to go through. This workflow allows traders to work orders across multiple trading venues and in a more or less de facto way, aggregate all that flow and liquidity into a centralized book and not risk double execution. In this way, they can consider the large order that comes into an order book and check that against market conditions and their interests in the sum of all the liquidity points, venues or exchanges anywhere around the globe where they may have placed their own liquidity, and confirm they are able to manage the large size.

The key thing to remember about a conditional order is that it is not a firm order. When an order is matched, the maker has some time to decide if they want to firm up or not. This window can be predefined in a system’s rule book. And while there are many advantages to this order type, most specifically to ensure liquidity for large orders, they need to be implemented in a way that remains fair to the taker as well. The concept is to promote the taker’s need to execute a large block, not disenfranchise them so they are not getting best execution.

The conditional order is posted into a many to many order book.

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Graphical representation of consolidated liquidity with order driven market and conditional order market structure:

Using a market structure that implements the conditional, the market maker can, without risk, place their interest for a large order. They have the liquidity at other venues (or through internalization, etc.) that guaranty their ability to safely fill the order. If prices change, then the order is not firmed up. If good, they execute. The market will shift in the direction of the flow, but after the fact. The institution with the sizable interest gets done. The market maker can provide the full liquidity knowing they have a safe exit (presumably at some profit).

V. Quote Driven Market — all quotes are always firm

ESP (executable streaming prices) is very common in other markets, notably foreign exchange. So too is RFS (request for stream) and RFQ (request for quote). One main difference between ESP and RFQ is that ESP comes through an electronic mechanism like an API, with Fix being the most common (in FX) while RFQ, while similar in workflow, can be a more manual process. ESP is continuous whereas RFS is sent after a request is made.

ESP is designed to be a one to many market structure whereas an Order Driven Market is a many to many order book market. Typically, in an ESP market structure, there is a single market taker asking for liquidity and receiving quotes from multiple market makers. These liquidity providers have direct API connectivity into the trading venue. With the OEMS platforms mentioned earlier, there is direct access to this type of liquidity, but it is fashioned around taking a firm order from the taker and sending it out to various sources via many connections and routers to be executed rather than the efficiencies of pooling all that liquidity into one platform. Foreign Exchange is a perfect example of where trading venues have built great success by creating their own liquidity pools. In the ESP model, the outbound order is firm. It is the price at which that trader wants to be executes. The feed comes directly into the venue, sits side by side all the other streams, and is visible and transparent to the taker. If the taker uses a GUI, they can visually see all the quotes streaming in and then select the best one. If all activity is via API, from both maker and taker, the same workflow exists. The taker will aggress on the best price at the moment.

V. Quote Driven Market — all quotes are always firm
Screenshot 2022-09-27 at 11.33.31 AM.png

In the quote driven market, there is not the same level of transparency. Only the trader asking for the quote will ever see that price, not the market as a whole. And typical in this structure, opposing market makers don’t see their competitors quote. They show their best price to the taker and get executed or not. Through subsequent market activity they may be able to determine if the other market maker traded the client interest, and to a degree, they can understand the price that traded, or a close approximation. On smaller activity, it may be that there is no evidence of the flow. So, while order driven markets may offer more transparency (if lit) and a sense of the depth of the market, the quote driven market guarantees a fill at all times.

Graphical representation of consolidated liquidity with quote driven market and tiered liquidity structures:

Nothing leaves this liquidity pool. Market makers can push/pull liquidity from other venues, liquidity sources, but given the rules supporting this system and the smaller notional amounts that will be quoted, it’s designed to be unlikely and lacks incentive to drive the market away elsewhere.

Graphical representation of consolidated liquidity with quote driven market and tiered liquidity structures:
Screenshot 2022-09-27 at 11.35.07 AM.png

VI. Why a hybrid model, combination of both, is the best structure

The hybrid doesn’t aim to squeeze one trader type into both categories, but rather to create separate liquidity pools that are each the most beneficial for a specific trader type. Active traders, who are constantly in and out of the market, either trading off of systematic signals and momentum, looking for small price discrepancies or market inefficiencies, etc., may require firm prices to act upon their strategy. What is critical for this trader is that there is a firm price with a reasonably narrow spread that they can rely on.

Systematic traders can’t be in a position where they don’t execute on a trade. When their signals have them enter or exit a position, they need to know they can get their trade done. It is better to have a little slippage than to miss a level entirely.
When it comes to trading a large order, a conditional order will draw in that depth of liquidity. These traders benefit from an order book model, which lets them work bids and offers in a many to many environment, but potentially without the need for a conditional order type. That has a more specific function.

The institution who is looking to execute on a large order needs the market maker to post that size and benefits from a market structure that uses the conditional order type. The market maker can confidently show a price for that amount knowing they will be protected from disruptions or price movements in a high volatility market.






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VI. Why a hybrid model, combination of both, is the best structure
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