„Wait, on Krypton YOU can get paid to trade? I thought trading was costly.“

TL;DR: It’s all in the trading mechanism and how you use it. It’s an emergent phenomenon of Kyle and Lee’s concept of a fully continuous exchange which lets users express a preference for urgency.

On Krypton patience is a virtue and it all comes down to a choice of trading speed. Let’s walk through an example and see why!

Matching on CLOBs and AMMs

Suppose you want to buy ETH on a Central Limit Order Book (CLOB) or an Automated Market Maker (AMM). Whenever your aggressive trade hits the exchange, it executes against resting liquidity. You pay half the spread. On an AMM, you pay an additional price impact, which is quadratic in your trade size on a constant product AMM. On an CLOB, if your trade size exceeds the quantity available at the best ask, you pay an additional price impact. You also typically get front-run right up to your slippage tolerance or your limit price unless your trade is sufficiently small for the profit of a would be front-runner to swallowed by gas costs.

Now a seller comes in. It doesn’t matter if they arrive in the same block. They can’t match directly with you. Your trade is already implemented. They need to settle with a resting limit order (on a CLOB) or passive liquidity (on an AMM), paying half the spread, the price impact, and the front-runner.

Matching on Krypton

On Krypton, trades are spread out over time. If you are the only one except for the market maker, you need to buy at the price they are willing to sell at.

You could lower your limit price and wait for a seller to cross the spread. But that would reduce your expected fill, so you go with the original plan (see above).

If say 3 seconds after your trade, a seller comes in whose trading speed exceeds yours, say 0.03 WBTC/sec compared to your 0.02 WBTC/second, the market maker makes up for the gap of 0.01 WBTC/sec and the market clearing price moves to the market maker’s buy price.

The Continuous Batch Auction

In a continuous batch auction, every order that occurs at the same time fills at the same price. You pay the same low price the market maker pays. The seller who is less patient than you is paying you (and the market maker). On Krypton, patience is a virtue.

Over the course of your trade, you may switch back and forth between the one who pays and the one who gets paid depending on which side (buyers or sellers) have a higher aggregate trading speed and need to get part of the speed from a market maker.

On the fully continuous exchange many other virtuous properties beautifully emerge as well. It not just enables a true coincidence of wants but elegantly solves front-running and adverse selection. If you want to know why, check out our recent article on the history of DeFi trading mechanisms or go directly to the source and learn about the fully continuous exchange directly from its creators!

More Background (Archived):

• YouTube: https://www.youtube.com/@KryptonExchange
• GitHub: https://github.com/kryptondex
• Web: https://krypton.exchange/resources

Resources

• Toward a Fully Continuous Exchange
• Market Making on Krypton: A Primer
• Optimal Trade Execution on Krypton

Disclaimer

The information contained in this article is for general information purposes only. Any reliance you place on such information is strictly at your own risk. It is not intended to constitute legal, financial, or investment advice and does not take your individual circumstances and financial situation into account.

On August 2nd, 2022 Bloomberg reported that retail traders in the US are estimated to be losing $34B per year in bad trade execution. This is based on the study “The ‘Actual Retail Price’ of Equity Trades”

Professional asset managers are facing the same problem as retail traders and are implementing sophisticated ways to split trades over time to mitigate the cost of execution.

In their 2018 study “Trading Costs”, Frazzini, Israel, and Moskowitz offer a rare glimpse into the functioning and effectiveness of a state of the art trade execution algorithm based on a proprietary dataset from the asset management firm AQR.

The Hidden Cost of Trading

Have you ever wondered how much you are paying for your trades? Centralized exchanges typically state their commission while decentralized exchanges display their protocol fee. The actual, all-in cost of trading, is often known as slippage or implementation shortfall. It is the difference between the price at which your trade executes and the fair market consensus price at the time you submit your trade. Retail-facing trading venues, both centralized and decentralized are typically silent on the matter.

Krypton on the other hand aims to be fully transparent and objective about the total, all-in cost: before, during, and after your trade. It’s the first exchange that dares to tell its users how much they actually paid.

Here is what that means and how we do it.

Market Consensus Price and Implementation Shortfall

Implementation shortfall, sometimes known as slippage, is the difference between the fundamental value of an asset and the price at which you are able to buy or sell it, including all commissions and fees. A good proxy for fundamental value is market consensus micro price.

Micro price is a weighted average between the best bid and the best ask in an order book. The weights are the quantities available on each side. You can check it out on Krypton without placing a trade. Use it side by side with your DEX of choice to see how they perform!

Before Your Trade: The Power to Choose

Krypton empowers you with a choice between a trade-off between

• Expected implementation cost (execution cost),
• Variance of implementation cost (execution risk),
• Fill-rate risk.

Before placing your trade, Krypton illuminates the spectrum of outcomes in real-time based on your choice of trade parameters, the size of your trade, (no fear Krypton provides presets), and current market conditions.

Specifically, Krypton displays

• Expected time to completion
• Expected fill
• Expected total cost

as well as the distribution of implementation shortfall or slippage.

Going with the”Moderate”, Krypton estimates that our trade will fill in the next 2m and 11s. During that time, execution price fluctuates, creating execution risk.

Zooming in on the “Distribution of Slippage”, we see that most outcomes are between -0.30% and +0.38%. There is a good chance the trade will go in our favor while filling, leaving us with negative slippage. We could choose a different preset than “Moderate” or dial in the parameters ourselves. But 0.09% seems great.

This is because there is no front-running on Krypton and its economic mechanism makes the environment inhospitable to toxic traders, which means market makers don’t lose in arbitrage or adverse selection and do not need to recover such losses from us, the non-toxic trader.

Furthermore, there is the chance that we will partially match with a seller, bypassing the market maker altogether and creating a true coincidence of wants. If you are curious as to why that’s possible, head over here: https://medium.com/@kryptonlabs/how-krypton-beats-the-twap-310bf43718c3

During Your Trade: Uncertainty Resolving

As our trade implements, Krypton keeps us informed on its progress and cost so far and updates its prediction on the remaining part of our trade based on changes in market conditions. We can cancel the trade at any time upon which Krypton returns the unspent tokens.

After Your Trade: Realized Cost

Our trade is complete. We got our $1m BTC in a couple of minutes and paid 0.0983% all in.

That’s what we mean by full cost transparency before, during, and after your trade. Can your exchange do that?

More Background (Archived):

• YouTube: https://www.youtube.com/@KryptonExchange
• GitHub: https://github.com/kryptondex
• Web: https://krypton.exchange/resources

Resources

• Bloomberg
• The 'Actual Retail Price' of Equity Trades
• Trading Costs
• Toward a Fully Continuous Exchange
• Market Making on Krypton: A Primer
• Optimal Trade Execution on Krypton

Disclaimer

The information contained in this article is for general information purposes only. Any reliance you place on such information is strictly at your own risk. It is not intended to constitute legal, financial, or investment advice and does not take your individual circumstances and financial situation into account.

In 1971, Fischer Black predicted that sophisticated traders would execute large trades piecemeal over time to reduce price impact and hence expected implementation cost.

The Trade-Off

But how should you space out your trading activity over time?

If you trade fast, you cause higher expected price impact. If you trade slowly, you risk the price slipping away from where you wanted to trade. It can go either way and is called execution risk.

High execution from trading fast is the devil you know. Implementation risk from trading slowly is the devil you don’t know. You have to pick your poison.

Financial mathematicians Almgren and Chriss have formally analyzed this tradeoff. Their framework is the basis of modern execution algorithms. In their model, trading happens continuously as a flow.

Theory Meets Reality

But on existing exchanges, the optimal strategy is approximated using many small orders and requires fast, sophisticated, and expensive hardware and software. Executing strategies piecemeal on existing exchange venues means inheriting all the inefficiencies of the their underlying mechanism. In DeFi that cost can be substantial.
Here is why: https://medium.com/@kryptonlabs/intents-solve-the-hard-side-of-the-network-but-they-dont-solve-toxicity-defi-trading-mechanisms-e418aae738ac

On Krypton, continuous trading happens automatically; using a single action. The only thing traders need to do is to pick a trading speed cap, signaling their urgency as well as their slippage tolerance. The Krypton UI suggests all of these and illuminates the trade-off.

The Funnel

The “Funnel” depicts the spectrum of outcomes for a given choice of trading speed. The diagram below is from a calibration of the equilibrium model presented in “Market Making on Krypton: A Primer” to the BTC market. The horizontal axis represents the choice of trading speed while the vertical axis is trading cost in bps or basis points, where basis point equals 0.01%. The blue line in the middle is expected trading, while the green and the blue red line represent the boundaries within which 95% of all outcomes fall in a Monte Carlo simulation of Krypton.

Embrace the Funnel

Like gravity, death, or taxes, you cannot escape the funnel. But you can plan, optimize, and make it work for you. How to best do that depends on trade size, your risk aversion, and market conditions.

If you are curious, check out our recent paper and dive into the theory. It is what powers Krypton’s trading interface. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5086567…

More Background (Archived):

• YouTube: https://www.youtube.com/@KryptonExchange
• GitHub: https://github.com/kryptondex
• Web: https://krypton.exchange/resources

Resources

• Toward a Fully Continuous Exchange
• Market Making on Krypton: A Primer
• Optimal Trade Execution on Krypton

Disclaimer

The information contained in this article is for general information purposes only. Any reliance you place on such information is strictly at your own risk. It is not intended to constitute legal, financial, or investment advice and does not take your individual circumstances and financial situation into account.

The TWAP — A Metaorder Built on an Old Mechanism

A TWAP splits a trade into chunks of equal size. The implementation of these chunks proceeds on an evenly distributed time grid. Splitting into smaller and smaller chunks helps with price impact as it reduces the need to “walk the book” but it doesn’t help with the spread.

Every time even the smallest trade happens, the order still loses half the spread. This is more generally true of order types that are merely grafted on to an existing exchange mechanism. They invariably inherit all of the cost and inefficiency of that underlying mechanism.

Krypton — A New Mechanism Built from the Ground Up

Krypton is an entirely novel trading mechanism, based on a new type of order that defines the exchange itself from the ground up. It produces remarkable emergent phenomena, making it devoid of the artifacts of discretization which are the substrate for toxic trading strategies.

The economic mechanism itself was first conceptualized by Kyle and Lee in 2017. It is philosophically based on the idea by Fischer Black in his vision of a fully automated stock exchange. Krypton and DeFi are its practical realization.

Implementing Trades as Continuous Flows

On Krypton, trading happens continuously as a flow, like pouring a bottle of water. Market participants are required to reveal their urgency by specifying how much they are willing to move the price in their dis-favor in order to fill their trade faster.

This ingredient allows non-toxic traders to credibly signal their non-toxicity to the market by choosing a reasonably low trading speed. In game-theoretic terms, it allows for the existence of a separating Nash equilibrium where participants reveal their type.

The exchange itself is defined by the new order type, everyone has to use it, and it forces everyone to reveal their trading signal, which in turn permits everyone else to digest that signal and react before toxic traders can monetize their information advantage.

Patience Is a Virtue — Partial Netting Between Directional Traders and Negative Implementation Cost

On discrete exchange mechanisms such as AMMs and CLOBs, whenever part of your trade implements, you are trading against resting liquidity, paying their spread and price impact. If another directional trader arrives on the other side only a split second later, they need to match with resting liquidity on their other side, your side. But your trade is already out of the picture, making them pay or price impact and spread.

If you are the only directional trader on Krypton, you will of course execute against resting liquidity and pay both price impact and spread. Say the other trader comes in a split second later at a higher speed than you. Then they pay you to trade and here is why:

In a fully continuous exchange, everyone who trades at the same time trades at the same price. Since your speed is lower than the other trader’s speed, the market maker fills the gap and becomes the marginal trader. You trade at the same favorable price the MM does and get paid.

On Krypton, patience is a virtue: Continuous trading makes it possible to trade at negative cost.

Maximizing Opportunity — Fully Automated

As a bonus, Krypton varies the speed at which your trade fills over time, slowing it down when the price has moved against you and speeding it up when prices are more favorable. No intervention is required on your part. It all happens automatically for you at the exchange level.

More Background (Archived):

• YouTube: https://www.youtube.com/@KryptonExchange
• GitHub: https://github.com/kryptondex
• Web: https://krypton.exchange/resources

Resources

• Toward a Fully Continuous Exchange
• Market Making on Krypton: A Primer
• Optimal Trade Execution on Krypton

Disclaimer

The information contained in this article is for general information purposes only. Any reliance you place on such information is strictly at your own risk. It is not intended to constitute legal, financial, or investment advice and does not take your individual circumstances and financial situation into account.

Exchanges are two-sided networks. As in many such networks, there is an easy side and a hard side. The hard side is the supply side, i.e. liquidity providers AKA LPs or market makers AKA MMs. For a network to succeed and reach escape velocity, it needs to attract the hard side.

This is a walk down the history of decentralized trading mechanisms illuminating the “power of the final word” and Krypton’s implementation of the Kyle and Lee’s concept of a fully continuous exchange to solve it.

Automated Market Makers

The first wave of DeFi was dominated by AMMs. Order books were infeasible: Preventing stale quotes meant frequent on-chain transactions for each single MM. Gas costs would have killed the deal. Pooling liquidity and letting active traders pay reduced the technical cost at the expense of economic cost. The AMM was a testament to its time, a time before scaling solutions and scalable L1s, when blockspace was scarce, and when significant misalignment of economic incentives was deemed necessary to overcome technical limitations.

While automation in Uniswap v1 and v2 absolved LPs from any technical or trading expertise, it not just enabled toxic trading, it fundamentally required it for the AMM to operate. The AMM had no way of knowing what the fair price was unless LPs were ripped off to the max.

Toxic traders, known as arbitrageurs, would compete to buy block space from miners or validators to adversely select the liquidity pool in what is known as CEX-DEX arbitrage. Their willingness to pay equaled the maximal rent to be extracted from all LPs simultaneously, while the maximum bribe any single LPs was willing to pay was limited to the arbitrate losses in their own positions, unless LPs turned toxic themselves and ripped-off every other LP while withdrawing their own position.

LPs were indifferent between paying the bribe to protect their position while swiping all other LPs or being adversely selected themselves. The outcome was the same: Maximal bribe to the miners and maximal losses to LPs — the auction for block space is a zero-sum game.

Concentrated liquidity finally enabled LPs to convey price information to the AMM but the problem remained the same: The value of toxic trading equaled the max willingness of all LPs combined to pay to save themselves, once again resulting in maximal adverse selection.

The result was predictable: By mid 2022, almost every Ethereum node was running flashbots or a variation of it for additional income and 50% of trading volume on Uniswap v3 came through flashbots, either to protect their own positions or to steal from others.

Meanwhile, within the first year of its existence, Uniswap v3 lost LPs $100m in the ETH/USDC pool alone. One would expect fees to properly compensate LPs for these systematic losses. But these losses already account for fees. People heard the call and DeFi stopped growing.

To this day, monthly DEX trading volume has not reached the level of May 2021, the month Uniswap v3 was released.

Central Limit Order Books

Central limit order books or CLOBs make LPs and directional traders symmetrical. LPs are no longer at the mercy of simplistic formulas but can influence prices directly. In contrast to an AMM, quotes can move without value changing hands.

On CLOBs, market makers provide liquidity by placing limit orders that are resting until matched with an aggressive order from a directional trader. Similar to AMMs, market makers are giving others the option to buy, sell, or do nothing.

If the fair value of a token rises above the market maker’s best ask, a toxic trader colluding with a block producer can include a buy order on its own behalf to obtain the entire quantity available in the market place for less than the fair value because quotes are stale.

To prevent adverse selection in a blockchain order book based on public price information, a market maker could bid against toxic traders for the right to cancel their stale limit orders, before the transactions containing the toxic order flow are executed on the blockchain.

The only way for a market maker to win would be to become toxic themselves and adversely select all of the other market makers in addition to revising their own quote. To beat out the toxic trader, they would have to bid their reservation value which is identical to their own.

Once again, the result would be indistinguishable from that of a toxic trader doing the adverse selection: Maximum losses to the LPs and the maximum bribe to the winning block producer.

But the ability to actively influence prices by market makers has one advantage: They can act before their quotes are going stale, revising their resting orders as the probability of being adversely selected rises above a threshold but before a toxic trade is profitable.

Active liquidity management in a CLOB is cut-throat, an arms race in trading technology. Don’t believe me? Just watch Sasha Stoikov’s take on the performance of his famous market making algorithm on Hummingbots! “Where Market Making Meets Market Microstructure” (min 39.06)

On CLOBs…
1.) To not get killed on gas fees, market makers must set extreme spreads unless they are on a very highly scalable blockchain.

2.) Playing the game involves an expensive tech arms race.

3.) Even then, they get adversely selected with 100% probability whenever their quotes go stale.

1.) and 3.) make blockchain-based order books less attractive than in CeFi. 2.) Prevents democratic liquidity provision. The solution to 1.) reduces decentralization (note that L2s include centralized components including the sequencer). The solution to 2.) means the winning market maker(s) must extract sufficient rents to cover their immense cost.

Intents

By reversing the roles, intents finally enabled LPs, the hard side of the network, to no longer systematically lose. Instead of LPs, now called solvers, making an offer that liquidity takers can accept, they would decide which trade requests to accept, giving LPs the final word.

But reversing the roles didn’t solve the problem of toxicity. It gave LPs the opportunity to turn toxic themselves. Similar to adverse selection faced by market makers on CLOBs and LPs on AMMs, directional traders are adversely selected in intent-based systems.

Here is how:
If a set of directional traders have buy and sell offers outstanding when the token experiences a significant upwards price movement, the off-chain counterparties simply accept to fill the sell orders and are ignoring the buy orders.

Directional sellers would like to cancel their outstanding off-chain intents to prevent adverse selection. Invalidating signed off-chain intents involves sending on-chain notification to the smart contract implementing the matching procedure that the offer is no longer valid.

Solvers have both incentive and opportunity to pre-empt that move by colluding with block producers. With multiple directional traders at risk, a toxic solver’s profits from bribing a block producer exceeds the value for any single directional trader to protect their trade.

In contrast to LPs, liquidity takers do not have the chops to bid in an out of band block auction for the right to rip off other liquidity takers. Even if there were a protocol that allowed liquidity takers to unionize, the outcome would be the same: max bribes and max toxicity.

Intents did not make trading less toxic or less costly. They merely shifted toxicity from directional traders to liquidity providers, imposing these costs on the easy side of the network directly rather than on the hard side to be passed on to and ultimately borne by the easy side.

The Power of the Final Word

AMMs, CLOBs, and intents share one feature in common: Each of them gives one side the final word to accept an offer from the other side or to withdraw their own offer. This control over finite value makes the ability to determine the order of operations valuable.

To defeat the power of the final word, Krypton implements Kyle and Lee’s idea of a fully continuous exchange which is itself based on a concept formulated by Fischer Black in 1971. Assigning value in continuous flows makes coming first irrelevant.

Kyle and Lee propose a simple change in perspective vis-a-vis standard economics: Instead of formulating demand and supply as functions mapping price to quantity, they connect price to trading speed. At the market clearing price, the aggregate rate of buying equals the rate of selling. If a trader wants to implement faster, they will have to accept moving the price more significantly in their disfavor. Additionally, requiring information based traders to reveal their preference for urgency signals their intention and allows everyone else to react before they can monetize their information advantage against others. Interestingly, Krypton’s implementation on blockchains renders transaction reordering within a block pointless as well, protecting users from front-running and sandwich attacks. Here is why:

On Krypton, blocks represent points in time while the inter-block period is a period of time. If a toxic trader colluding with a block producer could arbitrarily assign trades to transaction indices within a single block, including their own, it would not affect the outcome on Krypton. This is because all trades would be recognized to have been placed at the same point in time. In a continuous batch auction, everyone trade is matched at the same time fills at the same price. Hence, a would-be front-runner would simply be moving the price against themselves as much as they would move it against their target, rendering the attempt a losing proposition.

Blockchains as the Future Rails for Finance

Krypton is the first exchange that truly embodies the web3 ethos, by turning the empty mantra preceding the attract-extract cycle ‘Don’t be Evil’ into ‘Can’t be Evil’. As it turns out, a fully continuous exchange drastically limits the ability of even a monopolistic market maker to price gauge because directional traders can substitute expected cost for execution risk by trading more slowly, thereby netting directly with each other while bypassing the market maker.

According to our research “Market Making on Krypton: A Primer”, The market maker still makes ample profit on the volume they trade. But this volume is just a small fraction of the total market. With a low number of liquidity providers, the majority of directional traders do not need a market maker.

By removing the toxic party rather than just reassigning roles, Krypton drastically lowers the cost of trading in DeFi, allowing blockchains to unleash their potential to becoming the future rails of our economy.

More Background (Archived):

• YouTube: https://www.youtube.com/@KryptonExchange
• GitHub: https://github.com/kryptondex
• Web: https://krypton.exchange/resources

Resources

• Toward a Fully Continuous Exchange
• Market Making on Krypton: A Primer
• Optimal Trade Execution on Krypton

Disclaimer

The information contained in this article is for general information purposes only. Any reliance you place on such information is strictly at your own risk. It is not intended to constitute legal, financial, or investment advice and does not take your individual circumstances and financial situation into account.

Market makers face an interesting set of challenges, which is why one of the things we’re building as part of the Krypton exchange is a market making algorithm (which we refer to as the market making bot). This tool will help market makers rapidly adjust to market information and make decisions about liquidity provision and inventory management.

Liquidity provision is a thorny problem, partly because market makers are always facing the probability of trading against an informed party (someone who has a short-term informational advantage). Trades arising from informed traders are referred to as “toxic orders,” and being on the losing side of this kind of trade is known as adverse selection in finance/economics.

Adverse selection exists in both DeFi and traditional finance and is a significant source of hidden costs for both market makers and, ultimately, for “normal” traders.

That’s where VPIN comes in.

Quick intro to the model

For a market maker, there are several things happening at once that have an impact on how they should provide liquidity (i.e., what quantity of an asset at what range of prices). Four academics came up with a tool that helps to quantify the level of toxic order flow, known as the Probability of Informed Trading (PIN).¹

This metric is a critical factor in determining the range at which market makers should provide liquidity to the market. Here is a very brief overview, followed by an extension of the model for high-frequency data, which is of immense use to us today–and which provides some shocking insights into how DeFi exchanges perform.

Probability of Informed Trading

For a given security (or asset) at a given price, there are four unknown factors at play in the PIN model:

• The probability of good news hitting the market
• The probability of bad news hitting the market
• An arrival rate of informed traders
• An arrival rate of uninformed traders

Quick note: Informed/uninformed doesn’t mean smart/not smart. An informed trader has information that is material to the fair value that the rest of the market hasn’t had access to yet. Someone engaging in arbitrage qualifies as an informed trader.

In order to make money, market makers have to set a bid-ask spread for the asset in question, and in this model the spread they choose is based on those four factors.²

That means there’s an optimal range given all the available information.

The authors behind the PIN model used these factors to define a breakeven bid-ask spread (i.e., a spread at which the market maker won’t lose money). From there, they derived a variable they called PIN–the Probability of Informed Trading.³

PIN describes the probability of toxic order flow into the market that the market maker has to deal with. Basically, it gives a real-time estimate of the conditions under which market makers are providing liquidity.

This means what? Conceptual check-in

Thinking about this conceptually, the higher the probability of informed trading, the more likely the market maker is to be adversely selected–that means being on the losing side of a trade to an informed market participant (like someone engaging in arbitrage).

To some extent, adverse selection is a cost of doing business for the market maker and is taken into account in bid-ask spreads. The market maker simply needs to make sure that they recuperate those expected losses from other traders through the bid-ask spread.

However, getting this wrong can be extremely costly to market makers, so when the probability of being adversely selected increases, market makers need to set even wider bid-ask spreads in order to protect themselves.

The more informed trading there is in a market, the harder it is for market makers to make money.

Basically: The more informed trading there is in a market, the harder it is for market makers to make money. At some point, they’ll want to leave the market altogether to avoid losses, right?

Keep that point in mind for this next section.

High-Frequency Version

The PIN model has been around since 1996, and has been validated repeatedly in academic finance. In 2010, Easley, Lopez de Prado, and O’Hara proposed a high-frequency estimate for PIN, which they called VPIN, or Volume-Synchronized Probability of Informed Trading. It uses a novel method of trade volume classification called (creatively) Bulk Volume Classification.⁴

Essentially, where PIN assumes that information events happen regularly in standard calendar time, VPIN assumes that information events happen regularly in what is known in finance as volume time. So information events would occur once per some fixed amount of trade volume, instead of once per some certain amount of time.⁵

This is useful because there are periods in the market where not a lot happens in terms of information or toxic order flow. This makes calendar time less useful, as you could end up with a lot of dead zones. So, it can make sense to count “time” in terms of trading volume instead.

The point of this is that the updated model is able to estimate toxic order flow based on volume imbalances and trade intensity. It came to fame because it can provide a read on the pressures facing market makers in situations with high volatility.

For example, the 2010 Flash Crash.

A flash crash is a rapid, extremely deep fall in security prices over a very short time period, and in 2010 there was a trillion dollar flash crash that lasted for 36 minutes and affected the S&P 500, the Dow Jones Industrial Average, and the Nasdaq Composite. All of these indexes experienced massive declines before rapidly recovering, creating a massive swing in prices.

Here’s a visual of what happened:

Interestingly enough, about an hour before the crash, the creators of PIN/VPIN found that the stock market hit some of the highest order flow toxicity readings in recent memory.⁶ Here’s a VPIN reading of the S&P 500 leading up to the crash.

Widening out the historical context, you can see how VPIN fluctuated throughout the financial crisis period, with a notable spike for the Flash Crash.

Let’s put this all together

Remember how we wrote earlier that when order flow toxicity rises, market makers will want to leave? When market makers withdraw liquidity, either by widening bid-ask spreads or pulling their liquidity entirely, overall liquidity falls.

That, in turn, means that the proportion of toxic order flow increases relative to the total volume of trading in the market. Which drives even more market makers to leave.

This creates a feedback loop, and can give rise to liquidity-driven crashes like the 2010 Flash Crash. The VPIN authors have identified hundreds of crashes that were the result of this kind of scenario.

So what does this have to do with crypto?

Now, some research has indicated that VPIN isn’t just useful in the high-frequency trading context: it can provide meaningful insights into other trading situations.⁷

We’ve been talking about traditional trading platforms here, which use an order book mechanism.⁸ Centralized crypto exchanges also use order book systems, so out of curiosity we decided to analyze trade flows using the VPIN metric.

Then we decided to compare that to DeFi. On average, trade toxicity is about 3.88x higher in DeFi than CeFi.⁹ The market on a leading AMM is so toxic in fact that LPs get ripped off by informed traders one more than every third trade on average.

Why is it so bad? In DeFi, the predominant trade mechanism today is the automated market maker, or AMM. You can read more about the mechanics of AMMs vs. traditional order books here.¹⁰

One of the key shortcomings of the AMM is that it is inherently dependent on informed trading. That’s because prices on an AMM can only be updated via executed transactions on the exchange, so anyone who can see prices from other platforms has more information about price action than the AMM itself.

Looking at all this, it’s almost surprising that anyone provides any liquidity to AMMs at all.

Without a toxic trader, the AMM does not know what the right price is–the liquidity pool must be adversely selected in order for the AMM to provide realistic prices. In other words, they are highly susceptible to toxic order flow.

This isn’t new information, however. Previous research has shown that liquidity providers for the ETH-USDC pair have lost about $100 million on an activity that should, to put it bluntly, be profitable. Looking at all this, it’s almost surprising that anyone provides any liquidity to AMMs at all.

What does this have to do with Krypton?

As you can see, order books aren’t in and of themselves a solution to the issue of informed trading–whether in traditional finance, centralized crypto, or DeFi, all of these exchanges are susceptible to toxic trade flows.

How to fix that?

From a market maker’s perspective, it would be really helpful to a) be able to rapidly determine if an incoming trader is an “informed” trader (i.e., bringing toxic order flow to the market), and b) be able to respond rapidly to that information.

In a traditional trading environment, there is always some percentage of informed trading–it might vary in proportion and spike at times, but in our view there is something of an unholy alliance between exchanges and toxic traders. People in general lose, but exchanges themselves benefit from high order volume, so it’s the kind of problem that’s just allowed to exist in the background (or which gets justified by talk of price discovery, which we can go into another time).

One of the things that makes Krypton special is the fact that informed traders are essentially forced to telegraph that information in the selection of trading speed.

One of the things that makes Krypton special is the fact that informed traders are essentially forced to telegraph that information in the selection of trading speed. We capitalize on the one weakness of both arbitrage and front-running: they are time sensitive activities. You have to move fast to fully monetize your opportunity.

On Krypton, informed traders can be spotted much more rapidly. Since quantity only starts getting transferred when prices and trading speeds jump, the possible damage to market makers (and other market participants) doesn’t have a chance to get done. This stands in stark contrast to existing exchanges like AMMs and CLOBs, where the full quantity has already been transferred by the time the market interprets the signal.¹¹

We’ll have more data on our stress test results very soon that demonstrate how effective Krypton is at protecting market makers in this situation.

Bonus: This is an example of how DeFi is good for finance

The SEC estimated that an order-tracking system that achieves these goals would cost about $4 billion to implement and $2.1 billion per year to maintain.¹² Researchers have long thought that using supercomputers (such as those at national laboratories) could help by tracking trade flow and flashing a “yellow light” during dangerous surges so that regulators or exchanges could have time to react.

We are well beyond this at this point: Not to toot our own horn here, but Krypton has built such a thing–including the realtime market maker response algorithm–with our first funding round of $7 million.¹³

This technology has the potential to completely transform financial markets.

This technology has the potential to completely transform financial markets. By completely obliterating the ability of informed traders to pick off market makers and worsen pricing for uninformed traders, we’re creating a more cost-efficient market for everyone. That means no more hidden costs to executing transactions, rebalancing portfolios, and achieving market returns (or even long-term outsize gains).

In other words, what we’re trying to do here is make markets more efficient–meaning less waste in the form of hidden costs to market participants. By making transactions as frictionless as possible, we’re not just trying to save you money, we’re trying to open up the future for all kinds of activities in finance.

In DeFi, we have the opportunity to truly deploy a better market design, so that’s what we’re here to do.

Here at Krypton, the endnotes are considered the afterparty of an article:

¹ Here’s the original paper, please let us know if you need help accessing it.

² In practice, the severity/impact of good or bad news (think variance) and inventory risk also play a role, they’re just not part of this model. For those interested, the analysis of inventory risk as a driver of the bid-ask spread predates information-based explanations by a number of years. Two papers (here and here) by Ho and Stoll are considered seminal in that area. Here is what has become the standard approach in marketing-making on CLOBs, which simply ignores the risk of adverse selection.

On Krypton, both adverse selection and inventory risk are considered in our market making model (what we usually refer to as the “market making bot.”)

³ If you’re interested in this sort of thing, it’s worth noting that the PIN model is based on earlier research in which a market maker updates their expectation of fundamental value based on observed patterns of order flow. In game theory, this concept is known as a Perfect Bayesian Nash Equilibrium. The market maker draws inference about fundamental value via Bayesian updating. If you’d like to learn more about Bayes formula and this concept, this is a cool intro–you can adjust the probabilities and visually see how they impact the outcome. We recommend it: Thomas Bayes was a chad and Bayesian updating is definitely… wait for it… based.

⁴ We won’t get into these details here, but as you might imagine, there is an immense amount of academic literature on this. Here’s the original VPIN paper (you can download without an account, the button is on the right of the download page), and consider this paper that applies the Bulk Volume Classification algorithm to European stock data.

⁵ The creators of VPIN offer a nice intro to these differences here, in an article that explores some suggested improvements to the model. The intro in particular is a little more intuitive than many academic papers.

⁶ It’s important to note that there have been academic disputes about whether this VPIN reading of the flash crash is accurate and whether Bulk Volume Classification itself is truly better than other more common volume-based metrics, like the standard tick rule. A joint CFTC-SEC study found that VPIN’s results were consistent with the events in the 2010 Flash Crash. In any case, we won’t dive into the methodology discussion here because we think these results are credible and that in general VPIN is a useful tool within a larger toolkit.

However, we raise this point because we think it’s important to keep in mind that these kinds of disputes are common in both statistics and finance. While it may not make things as simple as one might like, any statistical exercise is informed by the assumptions, tools, and data that we use. That means all of them are subject to limitations and error. In other words, choose your tools wisely, don’t rely too heavily on just one, and always remember that statistics is not a faith-based field of study. There are and should be discussions about methodology and outcomes.

With DeFi data, we do not need to use a trade direction classification algorithm, as crypto trades come with an aggressor flag. In other words, we know whether it was a buyer or seller who initiated the trade (we know the direction of the trade).

⁷ Fun sidenote: this article investigates VPIN with respect to a Bitcoin crash back in 2013.

⁸ Self-serving sidenote: as you can see, an order book is not in itself a solution to predatory trading.

⁹ Uniswap v3 vs. Binance.

¹⁰ If you’re looking closely, you may be wondering why Binance’s VPIN seems so much better than traditional equities. There’s a number of possible factors at play. First, crypto charts can be compared pretty easily, but comparing crypto to TradFi might introduce apples-to-oranges issues. For example, there are likely differences in the type of information that can be exploited and how (e.g., in TradFi latency matters, while in crypto the ability to buy out blockspace matters), and on traditional exchanges researchers have to infer the directionality of informed trade flows, whereas in crypto this info is available. We may of course also be seeing differences in measurement and methodology.

¹¹ You can read more about the damage market makers experience on existing exchanges here.

¹² You can read about the story behind the SEC’s cost estimate here.

¹³ Obviously, scaling our systems for a multi-trillion dollar market might take a little more dough.

Krypton is dedicated to the idea that functional markets are fair markets, where you can get the trade you want at a cost that makes sense. Our academic roots are the foundation of our novel approach to solving the challenges of building a decentralized financial system, and we’re proud to offer a truly novel approach to exchange architecture with the Krypton DEX.

In finance, being able to profit from information without taking any risk is something of a holy grail. This is why you’ve heard stories about the lengths that hedge funds and high-frequency traders will go to in order to gain just a little bit of advantage–whether it’s laying fiber-optic cable through mountains or becoming best buddies with an insider at a public firm.

There are a lot of ways to do it, but in this article we’ll focus on the two that are most relevant to DeFi, and explain how Krypton solves for these problems–and make no mistake, it is a problem. Even in traditional finance, the market inefficiencies that result from these trading behaviors are extremely costly, and it’s even more expensive in DeFi.

(Quick note: This article goes into a fair bit of detail. If you have any questions at all, please drop a comment or reach out to us via Twitter. Good communication is important to us, so your feedback would be really helpful.)

The Basics of Information Advantage

At Krypton, we group traders into three general categories:

• “Regular” traders, or liquidity takers, are market participants looking simply to buy or sell.
• Market makers, or liquidity providers, do as their name suggests–provide liquidity into the market by acting as a standing counterparty to liquidity takers in an order book, with actively managed quotes at slightly differing prices (the bid-ask spread), or, on an AMM, by providing tokens to a liquidity pool with prices being determined by a simple, predictable formula.
• Finally, informed traders employ tactics like statistical arbitrage and front-running to gain advantage over other market participants. Their actions drive up the cost for everyone else. As such their activities are typically characterized as toxic or predatory.

The first information-based strategy we’ll talk about is front-running, which happens when an informed trader has access to incoming trade data and is in a position to place their own trades to take advantage of it (we’ll get into a detailed example of how this works later on).

In DeFi, front-running is related to price slippage: Because miners or validators (block producers) can see incoming order flow data and squeeze in their own orders, you end up paying your maximum slippage tolerance on every single trade. This is an added cost to trading.¹

Informed traders may also take advantage of short-term information signals that spot actual or potential short-term mis-pricing of assets. In DeFi, you’re most likely to see this as mis-pricing between trading venues, namely between AMMs and centralized exchanges (more on this in a moment).

In DeFi, informed trading gives rise to impermanent loss for liquidity providers. Impermanent loss is described as adverse selection in traditional finance, which describes the risk/cost to market makers of being on the losing side of a trade against a more informed trader.²

Adverse selection is an important factor in determining bid-ask spreads, which is the difference between a market maker’s quote to buy a security (the bid) and the quote to sell a security (the ask). The wider the spread, the worse for regular traders like you and me.

Why Should We Care About This?

Price slippage and wider spreads affect all traders in the market–basically, the cost of front-running and adverse selection is passed on to the rest of the market.

Impermanent loss acutely affects liquidity providers. Here’s an analysis of liquidity provider losses for ETH/USDC pools on Uniswap v3. By this estimate, even after accounting for the fees liquidity providers earn, in total they have lost over $115 million (as of this writing) in this single trading pair alone. They eat these costs because they have no way to update their bid-ask spreads–not a great business model for LPs.

What Does Krypton Do About It?

The key innovation at Krypton was the realization that all of these problems stem from one source: The way that trades are executed. Let’s get into it.

First, it’s important to note that there are two parts to a trade: The assignment of value (the determination of who is entitled to what) and the settlement of the transaction (the actual transfer of value). For our purposes, settlement occurs via an ERC-20 token transfer, i.e., via the transfer function. This isn’t something we need to change or influence.

However, the assignment of value is something we can and should influence, as this is what ultimately impacts the price. At Krypton, what we’ve adjusted is the speed at which a trader becomes entitled to their tokens. This is a subtle but incredibly powerful innovation that stops all of the above issues in their tracks.

How the Krypton approach is Different

In a traditional trade, the assignment of value–the exchange–occurs instantly, in a single burst. I sell you X amount of Y token, and bam, that full amount no longer belongs to me and now belongs to you. Essentially, we are either not trading at all, or we’re trading a lump sum in a flash.

Every single trade happens in this way, so visually a series of transactions would appear as a bunch of individual bursts over a period of time.

At Krypton, we use a continuous flow system, so each individual trade isn’t executed in a discrete burst but rather in a flow over a period of time. You can liken this to pouring water from one bucket to another versus throwing a ball to someone nearby. Once the ball leaves your hand, it’s all gone, but it takes time to pour the water.

A key feature of this approach is that the transaction requires an agreed-upon speed. That also means that our supply and demand curves look different — rather than clearing at a price vs. quantity, the Krypton market clears at a price vs. trading speed.

How Does This Work in Practice?

Let’s walk through how this works with an example. First, consider the mechanics of your trade on an AMM or a central limit order book:

Suppose I want to buy a token and I know that right now the price is $100, and that there is some price impact from my trading activity (i.e., the token price is affected by the transaction³). Let’s say in this example my “buy” trade will drive up the price by $5, so if I were to start buying at the current market price of $100, the price would rise to $105 by the time I’m done trading, with an average price of $102.50.

This all occurs in a single transaction on the blockchain. Now, in a single block there can be many transactions. Let’s say for the sake of simplicity that my trade occurs in block 1,000 at transaction index 50.

If someone could see that trade go into the memory pool and place the exact same trade as me at transaction index 49, then they would start buying at $100, and drive up the price to $105. By the time my trade pops up at transaction index 50, the price is already $105 and by the time I’m done, the price will be $110.

So, in this situation, instead of buying at an average price of $102.50, I bought for an average price of $107.50.

If that same trader could then get another transaction in at transaction index 51, they could start selling at $110 (the new market price). Say when they finish selling the price has dropped back down to $105, for an average price of $107.50.

The results: My average cost went up by $5 and they scored an average profit of $5.⁴ This maneuver, called sandwiching, artificially pumps up the price for the trader in question at the expense of me and other market participants.

This is a huge problem in DeFi. The order in which transactions enter a block are determined by a block producer, and today the majority of block producers run a modified Ethereum mining client that helps to maximize opportunities to profit in this way (Flashbots being far and away the most prominent).⁵

But on Krypton, the ordering of transactions within a block doesn’t matter.

This is because of the way that Krypton interprets the passage of time on the blockchain: Krypton views transactions in the same block as having occurred at the same point in time. Every trade that happens at the same point in time has to execute at the same price.⁶ Thus, if someone tries to front-run you by getting their transaction at an earlier index in the same block, they end up moving the price against themselves as much as they move it against you.

In the example above, the trader who comes in at transaction index 49 and pushes the price up to $105 thus moves the price for both of us.

In other words, it makes this kind of front-running unprofitable.

But wait, you might be saying, what if they try to censor my trade from appearing in a specific block and put their trade into that block instead? To do that, they’d have to buy out the entire block via Flashbots to make this work, which is likely very expensive or even practically impossible.

Depending on my trading speed and the settlement cycle, this trade would have to work very hard and pay potentially a very high cost just to front-run a portion of my trade. Furthermore, when my trade arrives in the next block, they need to immediately cancel their trade and put on a trade in the opposite direction. But they would have to do so at a lower speed since they would otherwise drive the price to a point where their sandwich is losing. In addition, I can drive down their gains and lower my losses by simply reducing my trading speed.

What we’ve done here is architect a system that just makes it really expensive to pick off these kinds of profits, which means it’s an unappealing if not outright impossible place for a front-runner to be.

How Does Krypton Solve Impermanent Loss?

In both traditional finance and in crypto, market makers (or liquidity providers as we call them on AMMs) act as a kind of instant-counterparty to a trade. The way they make money is through the bid-ask spread: the difference in the price at which they’re willing to buy and the price at which they’re willing to sell.

Market makers are as a general rule always available. On an order book exchange, they keep active bids/asks in the order book. On an AMM, the liquidity provider pledges their tokens and transactions are executed according to a simple pricing formula (often of the constant product type).⁷

Sometimes, a sophisticated, informed trader obtains and processes some kind of price-relevant information a short time before others do so, and this trader will want to trade against the market maker/liquidity pool before they can update their prices. That way, they can monetize that information before the market price reflects it.

Market makers know this is always a possibility, so in setting their bid-ask spreads they account for the risk of trading against someone more informed. In practice, this means bid-ask spreads are often higher than they should be.

But what if a market maker could protect themselves against informed traders and thus offer better pricing to the rest of us?

This is another place where the Krypton approach shines. For an informed trader, speed is critically important because it’s usually only a matter of time before someone else (or the whole market) finds out about it. So, if the price is going to rise from $100 to $105, informed traders have an incentive to buy up all resting inventory priced below $105 in order to get the maximum quantity (and thus profit) possible.

On Krypton, that means they’d need to buy fast. They’d choose the highest trading speed they can get in order to obtain the highest number of tokens they can in the shortest amount of time. But this drives up the price.

High trading speed also alerts the market that something is going on. Even if a market maker has no idea what the information is, they can see that someone needs to trade very fast and is happy to massively move the price against themselves to do so. This is a dead-giveaway that someone is out to monetize short-lived information. The market maker can thus respond by widening out the bid-ask spread and lowering their cap on trading speed.

This way, even if the informed trader was able to monetize information for a block–or even a few blocks–they’d get stopped relatively quickly. Again, at best, only a portion would be transferred at a disadvantageous price. That means market makers are protected in exactly those situations that are typically the most costly to them: when a large chunk of price-relevant information comes out in a very short amount of time.

On Krypton, due to its continuous-flow trading, the moment that market makers notice a price and trading speed jump, the damage to them only begins to get done — they can react.

In all traditional trading mechanisms which trade at infinite speed, when the public observes that prices have massively jumped and a large quantity has been transferred, it is already too late for the market makers. The damage has been done. What Krypton effectively does is to turn trading speed into a publicly digestible signal and forces toxic traders to send that signal which market makers can interpret as a code red for adverse selection to close their city gates until the zombie horde has passed.

Krypton’s trading mechanism creates a major disincentive for adverse selection based on a short-lived signal. It just doesn’t make sense to do informed trading on a platform where you can’t effectively monetize it.

What This Means for DeFi

For the vast majority of traders, a continuous flow trade that executes over a period of 1 second or 10 seconds or even 5 minutes isn’t going to make a difference in terms of the information you’re trading on. But where it does make a difference is in the costs you pay to get that transaction. By making it unprofitable to carry out front-running and information-based trades, Krypton protects other market participants from the costs of these actions.

That means a more fair and efficient exchange in which the market price and the price you pay are almost the same.

This is a system that could work in any kind of centralized or decentralized setting. But the benefits to DeFi in particular are huge: With the ability to trade efficiently and without the risk of predatory actors, we hope to unlock the incredible potential of DeFi for other applications that rely on functioning exchange in order to work, like asset management.

We can’t wait to build that future with you.

¹Of course, this assumes that the profit from front-running exceeds protocol and gas fees. Since gas fees are coming down by virtue of scaling solutions, you can expect ever more trades–even small ones–to be front-run.

²AMMs are particularly susceptible to this risk because prices on an AMM only update through executed transactions on the AMM–they have no way of “learning” the market price other than through the liquidity pool buying or selling at unfavorable prices. In other words, liquidity providers are basically guaranteed to be the victims of adverse selection.

³Price impact should be differentiated from price slippage: Price impact is what you expect you would get given the shape of the demand or supply curve (or, the state of the order book or the state of the liquidity pool on an AMM) if your trade were executed right away.

⁴The difference between the price I thought I’d get ($102.50) and the price I got ($107.50) is described as price slippage.

⁵In traditional finance, latency advantage is associated with high-frequency traders, who try to capitalize on information by being a few milliseconds (or even microseconds) faster than others.

⁶That’s because we use a continuous batch auction (we will address this another time as it is beyond the scope of this article).

⁷We won’t get into this in detail here, but please let us know if you’d like to learn about it!

Krypton is dedicated to the idea that functional markets are fair markets, where you can get the trade you want at a cost that makes sense. Our academic roots are the foundation of our novel approach to solving the challenges of building a decentralized financial system, and we’re proud to offer a truly novel approach to exchange architecture with the Krypton DEX.

Why We Like the BA

One thing that brings me a lot of joy about Krypton is that it is both internally regulating and offers a better deal for consumers. We think this is a really great example of what crypto (and DeFi) can offer the world.

So with that in mind, we are a company that welcomes sound, reasonable regulation of our industry. But that in turn requires sound, reasonable conversation about the things that make crypto (and Krypton!) novel relative to what’s come before. It requires clear definitions about what things are and a common understanding of how things work, what distinguishes DeFi from CeFi, and so on.

But effectively sharing all this requires education and intelligent communication–particularly with the lawmakers who can have so much influence on our industry.

The BA spends a lot of time and resources talking to policy makers and helping to drive a productive conversation for our industry. The organization itself is stacked with talent, but it is also really cool how many people are participating in the conversations that the BA is facilitating.

When we were in DC for the BA Policy Summit in November there were dozens of protocols, Layer 1s, VCs, and policy makers in one room talking about where we’re going as an industry and what can be done to help support sustainable growth of crypto in the US.

Of course there are still a ton of unanswered questions and different points of view. We’re in a nascent industry that is still, in many ways, defining itself. We’re really excited to get to participate in those conversations and are eager to contribute to them.

Why Should You Care About This?

We know that regulatory talk isn’t everyone’s cup of tea. But whatever your thoughts about crypto and regulation, there are two pretty practical reasons to welcome it.

The first is language. Regulation gives us a set of shared definitions and basic principles. At its most simplistic, regulation helps everyone understand what stuff is and what that means.

Just by way of example, here’s an important one: Is a token a security or an asset (or both)? Does it depend on context? Can the same token be two different things? (is there a Shrodinger’s token??? (I’m sorry))

Those kinds of questions might not interest everyone reading this, but for a business in our space it’s very useful to have clarity on these issues. Well-defined answers to questions like these impact our community rewards, our own economic incentives, our employee compensation, and of course our legal bills :)

The second is legitimacy. While it’s still up to every individual business or protocol to act in a legitimate way, being regulated helps to confer legitimacy on the industry as a whole. It makes crypto something that is a) more broadly understood, and b) more easily welcomed. That, in turn, makes crypto more accessible to more people.

Again, that might not interest everyone. But if you truly believe that what you’re doing has value for a lot of people, then it’s worth asking what a lot of people want. And in our view, when it comes to finance, legitimacy really helps in fostering adoption. (Ask Anna about the history of private equity if you want to talk more about this)

Of course, as noted previously, it’s still up to individual businesses to act in a legitimate way. And regulation isn’t a panacea by any means. Sometimes regulation is irrelevant, or even counterproductive.

But in our view, the best way to get good regulation is to participate in the conversation from the outset–and to hear from people who have different points of view and who know more than you about policy and law. So as you can imagine, for us, applying to join a group like the BA was a total no-brainer.

Where Krypton Fits In

At Krypton we’re looking at the long term. We are trying to build something that can be a critical part of financial infrastructure for a very long time.

We want to give ourselves every opportunity to succeed in a way that’s good not just for our business, but for any business that uses Krypton as part of their own infrastructure and for any individual who wants to do the best they can with their resources.

In short, we want to be a great example of the good that is possible in DeFi. And we think we can do that, especially if we can contribute to and learn from the people at the BA. It’s an awesome organization and we’re really thrilled to be part of it.

Krypton is dedicated to the idea that functional markets are fair markets, where you can get the trade you want at a cost that makes sense. Our academic roots are the foundation of our novel approach to solving the challenges of building a decentralized financial system, and we’re proud to offer a truly novel approach to exchange architecture with the Krypton DEX.

Check us out at krypton.exchange, join our Discord, or follow us on Twitter for more information about our upcoming release and to contribute to our lively discussions about DeFi.

The downfall of FTX provides an interesting window into some of the pitfalls of centralized finance (or CeFi). In the 72 hours leading up to Tuesday, November 8, 2022, FTX saw about $6 billion in withdrawals, and as of this writing FTX has suspended withdrawals and a proposed takeover by Binance has fallen through.

It would be easy to treat this as just another story about why crypto is dangerous. But this is much deeper: This is the story of why centralized finance is dangerous.

What Centralized Systems Have in Common

There are two key issues at work in this situation, and these issues have arisen repeatedly in finance history:

1. A lack of visibility into activity on centralized exchanges, and
2. A lack of proof that what is going on matches what is being said about what’s going on

In the simplest terms, big blowups in finance (and the fallout that sometimes follows), happen because the risks taken exceed the risks managed. Regulatory rules attempt to mitigate those risks, especially if they involve consumer assets.

Unfortunately, regulations are generally put in place in response to blowups, not in anticipation of them.

Just one example, it was only after the Financial Crisis that new rules about capital reserves intended to avoid another one (known as Basel III) came down. Regulation is often backward-looking like that–it’s not the fault of regulators, it can just be really hard to see how bad risk exposures can be until after they come to light.

But that lack of visibility is not random: It’s a result of centralization and Points 1 and 2 above.

Let’s talk about that.

How does excessive risk exposure happen ?

First and foremost: Incentives without rules. Traders and centralized financial institutions have a profit motive, and that profit motive creates incentives to aggressively value assets and aggressively de-value liabilities, or to simply push the limits of whatever risk management model is in play. After all, you do not get paid on the risks you didn’t take.

When there are rules, there are at least limits to what an institution can do in pursuit of that advantage. But when the rules are vague, as they are in crypto, there might be very few limits–or even none.

The news is changing every day, but it’s clear there was an excessive amount of risk commingling between FTX and Alameda, and it’s unclear what level of preferential treatment Alameda had with FTX. Whatever is going on, one look at the financials by Binance was apparently enough to get them to scrap their offer.

The thing is, this isn’t new, historically speaking. These types of situations have played out in various forms across the financial industry over a very long period of time.

That brings us to reason number two: A lack of transparency. Centralized institutions are, in many ways, black boxes–they can reveal what they want when they want to, and they can basically use whatever methodology they’d like in order to justify what they’re saying. Again, in the absence of rules, the ability to remain opaque is heightened.

Combine this ability to be secretive with very strong incentives to be self-interested, and these institutions have every reason to behave as aggressively as they want. (Again, at least until a regulator with power comes along and says no).

When institutions have the opportunity to fudge the truth for the benefit of their own short-term self-interest, they will do so. Maybe not every time, and maybe not after regulation comes down, but that’s a bit of a trite consolation to anyone who hasn’t been able to access their own assets.

Maybe the next regulatory wave will close some loopholes, but what good will that do when centralized institutions find yet another way to accidentally engineer another collapse?

The cat and mouse issue of regulatory oversight has been playing out for decades. But as long as the incentives and the opacity are there, the opportunity for this kind of mess remains.

How DeFi Solves These Problems

One of the core tenets of decentralized finance is trust minimization. You don’t have to trust that I’m doing what I say I’m doing, or that I’m not taking advantage of the incentives that any sane profit-motivated trader would want to take advantage of.

You can check. Anyone can check.

Coming from the traditional financial industry myself, I personally found this a sort of strange concept at first. Trust surely lies at the heart of all exchange, doesn’t it?

But just because it used to be that way (with hundreds of years of failures to show the downsides), it doesn’t mean it has to continue to be that way.

In DeFi, we have a level of transparency that is simply unheard of in any centralized setting.

Because it’s transparent, there is no place to hide. The kinds of shenanigans that were going on at FTX, and that have gone on throughout financial history are out in the open for everyone to see. Indeed, we at Krypton have spent a lot of time talking about the problems that still need to be solved in DeFi (namely, in our case, to eliminate predatory trading and give people better trade execution).

But one of the reasons we have those data points to talk about in the first place is because the data is they’re there for us to see.

So do “bad” incentives exist in DeFi? Yes, of course. They exist everywhere. But traditional finance tries to solve the problems that arise from these incentives with regulation; DeFi solves them by shedding light on what was heretofore in the dark.

There’s more to “trust” than risk management — there’s also control.

Bank runs and withheld assets are nothing new in centralized finance–we just tend to forget about this when things are going well and everyone is happy. And then we are filled with dread when things are going poorly and people can’t access their funds.

This is another place where DeFi gives something that centralized finance simply cannot provide: Full control over your own assets.

In DeFi, you do not place permanent custody of your tokens with a third party who can withhold access to the wallet that bears your name. You control your own wallet.

You might release tokens into a smart contract that carries out a transaction or other agreement, but the nice thing about a decentralized smart contract is that you don’t have to trust that someone will enforce it. You don’t have to rely on the promise of one person that everything is going to be okay. A smart contract executes on its own, without any intervention. If you want your tokens back, you’ll get them back.

And again, all of this is public. Everything can be validated and verified.

This is ultimately why we’re building Krypton as a DeFi protocol, and it’s why we’re using Chainlink’s decentralized oracle nodes to provide our computing power. We believe that decentralization is ultimately safer for consumers and simply provides for better-functioning financial markets.

The Future of Finance

This situation just illustrates again why DeFi has such enormous potential to change finance for the better.

Is there still work to be done? Yes, of course. Our first step as a company is to eliminate the profitability of predatory trading and thus make DeFi the home of the best, most cost-effective trade execution in finance. From there, we hope to be part of the vanguard in helping DeFi become the global standard in trading.

Look, this week has been brutal for crypto (and it’s only Wednesday as of this writing). But it also highlights the topography of the crypto landscape, and the significant advantages that DeFi has over centralized systems, both in crypto and in traditional finance.

If you haven’t thought about DeFi before, take a look, compare it to what you know, and ask yourself why these same stories keep playing out in centralized financial institutions.

Then join us in building the future of finance.

About Krypton

Krypton is dedicated to the idea that functional markets are fair markets, where you can get the trade you want at a cost that makes sense. Our academic roots are the foundation of our novel approach to solving the challenges of building a decentralized financial system, and we’re proud to offer a truly novel approach to exchange architecture with the Krypton DEX.

Check us out at krypton.exchange or follow us on Twitter @KryptonProtocol for more information about our upcoming release and to contribute to our lively intellectual discussions about DeFi.

Trading is complicated enough when you’re just punching numbers into a black box: Understanding exchange mechanics and the differences between traditional finance, centralized crypto exchanges, and decentralized finance makes it even more complicated.

One of the core value propositions of Krypton is an advancement on order matching in DeFi–but to really get what that means, it’s useful to have an understanding of how orders are matched in the first place.

Here’s a little history on trading mechanics in both traditional finance and crypto, and why we believe that what we’re doing is going to help DeFi so much.

How TradFi exchanges work

In traditional finance, your typical exchange functions as a Central Limit Order Book (CLOB), which collates buy and sell orders. In a CLOB, when a new order is entered, the system checks if it can be matched against an existing order. If it can, it’s executed; if it cannot, it’s entered into the database until it can be offset. These are all limit orders, which means you note that you want to buy or sell a security at a certain price or better.

So, for example, if you want to buy shares of the hot new shipping company Odysseus at $100, you could put in a limit order that tells the market you’ll pay $100 or better (which, in this case, means $100 or less). Say there’s a resting order to sell Odysseus at $99–when your order hits the CLOB it will be executed at $99.

This is a clear win for you, unlike the naming decision behind your new investment.

CeFi CLOBs

In the crypto world, the major centralized exchanges function as CLOBs. So, if you want to buy the Odysseus token this time, you’ll enter a buy order, and your CeFi exchange will match that against resting orders or enter it into the database to be matched in the future. Same idea.

Now this next part will become important in a moment:

CeFi crypto is sort of a compromise. It offers traders access to cryptographic coins and tokens that exist on decentralized blockchains while being under the custody of the exchange (rather than the trader) which mirrors/resembles the structure in TradFi and facilitates regulatory oversight.¹

What goes on behind the scenes in the CeFi world is that the centralized exchange (CEX) requires each user to open an account with them. In other words, the centralized exchange maintains custody of your tokens. It’s a lot like a regular brokerage account in traditional finance. So, when you make a trade, the centralized exchange either credits or debits the account you’ve opened by the appropriate amount traded.

What Happens in DeFi

One major question in DeFi is how to allow people to trade directly with one another’s wallets without the need for a centralized, trusted intermediary.

“This is why banks have such nice buildings, right? It’s not because they want to spend money on buildings; it’s to create confidence in them as an entity in order for people to transact through them.” — Sergey Nazarov²

This is actually a really complicated problem to solve. In fact, it hits at one of the main limitations of a CLOB, which requires a buyer for every seller and vice versa. If no one happens to be buying when you’re selling, you can’t execute the transaction. Ideally, there is also a market maker in the middle to bridge gaps in order flow and manage liquidity .

These two challenges were particularly thorny before scaling solutions, and ultimately gave rise to AMMs.

Instead of matching your order to another individual order, AMMs match your order to a liquidity pool, which in practice means that there’s always someone to trade with. Your trade is carried out by a computer program, which runs a simple formula to determine pricing. This program is written as a Smart Contract on top of a blockchain like Ethereum, so if it’s open-sourced, the program is trustworthy in the sense that it’s predictable and verifiable.

There are a few key advantages of this system.

• You don’t need to match individual orders, which means you don’t need to wait for a counter-party in a thinly-traded market.
• Because there is no requirement for market makers, your operational requirements are simplified.
• Finally, the computational cost is low. Particularly in the early days of DeFi, anything other than simple computation would be too costly to be feasible.

So What’s the Problem?

Because of their structure, price discovery on AMMs only occurs if a trade occurs. The AMM works on behalf of the liquidity pool, but it’s very simplistic and cannot process outside price information in the way a traditional market maker would.

The impact is that AMMs can be easily taken advantage of by informed traders — and it’s not difficult to become an informed trader because true market prices can be gleaned from other exchanges, namely CeFi order books.

Some more detail on a few of the most pronounced costs that result from this:

AMMs are susceptible to “Sandwich Attacks,” a form of Miner Extractable Value (MEV).

Since the outcome of a trade with an AMM is entirely predictable according to the AMM’s formula, any third party can manipulate the token trading ratio (e.g. token price). Given the knowledge of a new incoming trade like a buy order at a new price, any front runner simply has to issue a new buy order in front of this new price, pushing the price the incoming trade has to pay up. The original trade executes, pushing the price even higher. Then the front-runner can execute a sell order immediately after at this even-higher price, collecting the difference immediately.

This is exacerbated in a world of lower gas fees.

With advent of scaling solutions, gas fees will continue to drop, which it makes it profitable to front-run or sandwich ever smaller trades. Recent academic research shows significant untapped potential for predatory traders to exploit these opportunities. Together, this implies that the problem of predatory trading is bound to get worse.

With the rise of Flashbots and its competitors, it’s very easy to take advantage of this.

Flashbots Auction is a private communication channel between miners and searchers which allows those searchers to directly bid on their desired transaction order. Flashbots maximize miner payoffs and allows for price discovery on the value of a given MEV “opportunity” (take a look at how much it has cost markets here).

That the simplicity of AMMs is exploited by miners is a disadvantage of the mechanism, not an issue of miners or MEV. That simplicity also gives rise to other costs and market inefficiencies, which we’ll cover in future articles.

What’s next?

AMMs were an awesome way to introduce the world to DeFi. Unfortunately, problems like these make it difficult for DeFi to scale with the use of AMMs alone. This is why we created Krypton — a novel approach to the classic order book that protects traders from these shenanigans and helps everyone achieve best trade execution.

What do you think about this article? Did you know about the different ways that markets match orders, and did any of this surprise you?

[1] Obviously, whether all this is right or true or good or bad depends on your point of view. We are partial to DeFi, as we are a DEX, but we think anyone in the DeFi space can learn a lot from the things people like CeFi. The reasons we’ve heard generally include security, regulatory oversight, and usability.

[2] From an interview with Lex Fridman https://www.youtube.com/watch?v=TPXTmVdlyoc

Krypton is dedicated to the idea that functional markets are fair markets, where you can get the trade you want at a cost that makes sense. Our academic roots are the foundation of our novel approach to solving the challenges of building a decentralized financial system, and we’re proud to offer a truly novel approach to exchange architecture with the Krypton DEX.

Check us out at krypton.exchange, join our Telegram community or follow us on Twitter for more information about our upcoming release and to contribute to our lively intellectual discussions about DeFi.