The “Vanilla” Baseline: Price-Time Priority
Let’s start with the standard model that most crypto and equity traders are familiar with. Consider the following top-of-book bid level ($100), where Alice arrived first:

• Alice: 5 lots

• Charlie: 5 lots

• Bob: 10 lots

• Total Bid Depth: 20 lots @ $100

Below them, Dan is bidding for 30 lots at $99.

Now, imagine Josh enters the market and tries to sell 5 lots at $99. What happens?
In a standard “Vanilla” CLOB, the answer is simple: Alice buys 5 from Josh at a price of $100.

The principles here are:

1. Price-Time Priority: Makers are rewarded for improving the market. The first maker to form a new price level (Alice) is rewarded by being filled first.

2. Taker Benefit: The taker (Josh) gets filled at $100, even though he was willing to sell at $99.

This is the baseline. But exchanges can, and do, change these rules to incentivize different behaviors. Let’s look at the alternatives.

Variation 1: The Pro-Rata Model
In some markets, specifically CME STIRS (Short-Term Interest Rate) markets, the exchange might decide to split the fill among everyone at the price level. Using our example above, if the exchange used a 1:2:2 split, Alice, Bob, and Charlie would share Josh’s sell order proportionally to their size. Some venues use a hybrid called GTBPR (Good-Till-Best-Price-Rata), like Eurex German debt, where Alice might get a slightly larger allocation for arriving early (e.g., a 2:1:2 split), but size still dominates time.

Why do exchanges do this?
First, a technical caveat: if someone fires a single lot into a pro-rata book, the exchange obviously can’t split one contract. It usually defaults to the front of the queue. Pro-rata really kicks in for larger flows. But why design a book this way? Usually, it is because the product’s tick size is too large relative to volatility. The market barely moves, and the exchange refuses to decrease the tick size (TradFi exchanges are notoriously horrific at updating specs with the times). In this environment, market making devolves into a pure speed game to get to the top of the queue. It becomes inherently monopolistic. By switching to pro-rata, the exchange incentivizes participants to join the back of the level with size, rather than racing to the front. Personally, I’ve never been a fan. In a thick, time-priority queue, the middle of the queue is actually the most dangerous place to be - you face the risk of adverse selection from a massive informed order without the ability to cancel quickly. Pro-rata mitigates this, but it feels like a band-aid solution. Why not just decrease the tick size?

Variation 2: Going Dark
Now, let’s get into the funkier constructions: Dark Orders. These are common in FX and US Equities, though rare in crypto. The premise is simple: If a firm like Citadel is generating alpha, they don’t want to broadcast their intent to the world. If they display their orders on a lit book, other traders can observe the strategy, front-run it, or cannibalize the alpha.

Dark orders allow traders to rest liquidity without it appearing in the market data.

• The Upside: Takers (like Josh in our example) might try to sell at $99, but suddenly get filled at $101 because a dark bid was resting there. This provides unexpected price improvement.

• The Downside: It opens a can of worms regarding fairness. If the whole book goes dark, price discovery breaks. Who gets to use these orders? Do we show the matches in the data feed?

The logical extreme of this is the Bilateral Feed, which is how much of the FX market works. You only show your price to whitelisted counterparties who agree not to leak your data. The public CLOB is wide and full of sharks; dark mechanisms are the shield.

Variation 3: Icebergs and the CME “Hack”
Icebergs are a “lite” version of dark orders. You show a small tip (e.g., 1 lot) and hide the rest. When the tip fills, the exchange immediately reloads another lot from your hidden size to the back of the queue. The trade-off here is severe: You lose queue priority. Because standard market data (Level 3) usually allows sophisticated traders to infer when an iceberg has reloaded, you are effectively signaling your presence while sitting at the back of the line. The adverse selection risk is often higher than just showing the full size.

The CME Edge Case
Years ago, there was a fascinating edge case on the CME involving icebergs.
CME would disseminate fill messages to the private feed (involved traders) slightly faster than the public feed.

• Front-of-queue orders knew a trade happened first but didn’t know the total size.

• Back-of-queue orders knew last, but they knew how many orders in front of them had vanished.

HFTs realized that by using a 1+1 Iceberg (show 1, hide 1), they could get the best of both worlds. The private fill message for their first lot would tell them exactly when the trade occurred and give them information advantages over the rest of the book. CME eventually updated their specs to kill this, but it remains a classic example of how microstructure quirks create alpha.

Variation 4: Quote Domination (The “Anti-Dark” Order)
Finally, let’s look at the weirdest interaction: Quote Domination, found on the SIX Swiss Exchange. In our original example, Josh sells 5 @ $99. In a normal world, he gets filled at the maker’s price ($100). In a Quote Domination world, Josh gets filled at 99.
The exchange differentiates between “Orders” (retail/normal flow) and “Quotes” (approved Market Makers). If a Quote is interacting with an Order, the execution happens at the Quote price. This seems hostile to the Market Maker, but it comes with lower fees. The exchange is essentially saying: “If you want cheap fees, you have to act like a true Market Maker and absorb flow at the crossed price.”

This creates headaches for MMs:

• They can’t easily cross the spread without paying higher “Order” fees.

• They risk missing better prices on deeper levels.

• If a normal order lands while their quote is in-flight, they lose the price improvement they would have received in a standard CLOB.

Conclusion
Microstructure is rarely as simple as “highest bid meets lowest offer.” Whether it’s Pro-Rata distributions, Dark pools, Icebergs, or Quote Domination, exchanges design these rules to manipulate participant behavior, usually to balance the needs of liquidity providers against the need for volume. Understanding these nuances determines whether your alpha actually captures the spread, or just gets run over.

Below, I examine the paper by Jez (@izebel_eth) to tease out the problems this causes perps exchanges, and how we’ve fixed them at QFEX.

The paper talks about how @tradexyz gets the oracle index price for their XYZ-100 perp from CME Nasdaq-100 (NQ) futures, but needs a bit of a preamble. Why bother doing this at all? The underlying index (Nasdaq-100) is easy to find, and it’s hard to imagine that it’s legally more risky to consume Nasdaq-100 directly than NQ futures.

The answer: NQ futures trade 23 hours daily / 5 days weekly, whereas the underlying stocks only trade 16 hours daily / 5 days weekly. To the unaware, this is a bit weird. you can trade indices (via futures) long after (and before) the stocks in those indices themselves. Having oracle pricing for longer is worth the headache of deriving from the futures prices, because perps are dangerous to the exchange when the underlying is closed.

What can go wrong? Broadly, a well-capitalized trader can move the market a large amount away from its fair price (see the post below for an onchain example), capitalizing on the resulting liquidations. Usually, unless they can do this very quickly, they will be penalized for this via funding payments; but how do we even decide the funding payment when the underlying is closed?

According to their docs, @tradexyz pays funding based on a synthetic oracle price derived from the perps book, but made slightly ‘sticky’ to the last known close price. This stickiness is hard to calibrate and imposes a belief that moves during market close should always revert. On the other hand, QFEX does not pay funding during close, adhering to the principle that this ‘mean-reversion’ mindset isn’t always right. What if it’s a genuine move? Then the market could ‘gap’ at the next open, and the exchange would take the risk of not being able to liquidate losing users fast enough: a decidedly worse outcome.

QFEX and @tradexyz also both set minimum and maximum prices based on the last known open price. While not an ideal solution, it does prevent egregious moves while the market is closed.

As for the calculation itself, eagle-eyed readers might question the need for fixed constants in the calculation of the @tradexyz spot index. What if dividend payout changes, like it did during Covid? The answer is that the market will correct by changing the price of the perp to compensate. At QFEX, we use the daily close prices of the index and future, with an adjustment for the price change in the future: a much simpler and cleaner approach, that mirrors how we did it when I was working as a market maker at Tower.

Finally, the situation is more bleak for stocks. Although the paper talks about deriving a stock price from a future, futures don’t exist on single US equities, so this discussion is purely hypothetical. To make matters worse, stocks are not 24/7 and only open during market hours, compounding the issues discussed above.

While AMMs are ubiquitous in crypto exchanges, there is nothing inherently blockchain-native about them. Many different order matching models have been tried and tested across time, but liquid markets are almost exclusively Central Limit Order Books (CLOBs), across all asset classes. Why?

A quick recap: markets exist to enable genuine end users (hedgers, speculators and so on) to find each other with minimal cost and friction. A secondary objective is clear, transparent pricing; this enables economic information to flow quickly and without friction, which is the cornerstone of any market economy.

Amongst many equals, one participant is special: the market maker. In a CLOB, market makers constantly place buy and sell orders to match genuine end buyers and sellers that arrive at the market at different points in time, want to transact in different sizes, or don’t know what price it is fair to transact at. As long as they buy, on average, at prices lower than they sell, they make a profit regardless of market direction.

This is part of the beauty of CLOBs - by incentivizing this correctly, we create a competitive structure that effectively commoditizes market function into a labour- and capital-intensive business, and creates better outcomes for all genuine participants.

In the early days of DeFi, a narrative began to emerge that CLOB market making PnL is value-extractive, and should be centralized and distributed amongst the community. Sounds like communism, right?

This was no doubt fueled by many of the predatory practices of some crypto trading firms, which were certainly not making markets. AMMs also conveniently solved new exchanges’ cold-start problem: by inviting their community to contribute cash towards a liquidity provision, they in effect were letting them invest in a community-backed market making firm which the protocol couldn’t fuel themselves. With just a single market maker in place, there is no need for a CLOB to track the priorities of different market makers, either.

Liquidity provision, however, is brutal work. By placing orders for others to trade against, the market maker is constantly selling options to other market participants to trade against. He will only trade if somebody fancies their price. What does the taker know here that our market maker doesn’t? Did the price just move somewhere else? Maybe there is a big buyer coming that he doesn’t know about? This is a constant problem known as adverse selection that is exploited by arbitrageurs. Market makers are constantly in competition to refine their models and pick up the best flow, which unwittingly provides end users with the best service and spreads.

So how do AMMs avoid these problems? They don’t!!

An AMM is a market maker who is always quoting (in CLOB terms) a two-sided market, but with nobody working full-time on avoiding adverse selection. When the price of an asset moves, arbitrageurs pick off the AMM and move the price back in line. The service of market making shifts to arbitrageurs, along with the PnL - there is no saving! Unlike a CLOB, where market making is done by professional firms who can recalibrate, AMMs lose money for their community backers any time there is any volatility. Last cycle, protocols tried to explain this as ‘Impermanent Loss’ - a psyop that has now mercifully mostly been debunked.

Showing prices is necessarily brutal, labour intensive work, and anyone who tries to tell you otherwise is putting you in a barrel to be shot by those who know. Optimal market models must reward liquidity providers for their service, and encourage competition to get the best possible deal for takers: something CLOBs do best. By design, AMM-based markets are destined to end badly for their backers.

The market for swaps is mind-bogglingly massive, but its off-exchange nature has many problems:

• Heightened Counterparty Risk

• Fragmented Liquidity

• Uncompetitive Pricing

Crypto markets solved these problems years ago with perpetual futures. Let’s examine how perps on traditional assets revolutionize finance for both institutions and retail.

Sounds Complex?

Over the Counter (“OTC”) markets involve bilateral trading between two parties, under their own custom legal framework.

A hedge fund seeking leveraged exposure to negotiates all terms directly with its bank: leverage ratios, funding rates, margin requirements, and settlement procedures.

Sounds complex? It usually is.

The International Swaps and Derivatives Association tried to standardize the paperwork via the ISDA Master Agreement, but in reality, the cost and work required to trade swaps leave it out of the reach of all but the biggest financial institutions.

Perps democratize this key institutional investing strategy with a common contract spec, tradeable on an exchange.

House of Cards

Remember 2008 and the GFC? When Lehman Brothers collapsed, it didn't just hurt Lehman's direct counterparties. It created a cascade of failures because everyone was connected through bilateral derivatives contracts.

Here's what actually happened: Bank A had swaps with Lehman. Bank B had swaps with Bank A. Bank C had swaps with Bank B. When Lehman failed, Bank A couldn't pay Bank B, so Bank B couldn't pay Bank C. The whole system was one giant house of cards.

Central clearing de-risks everything.

With futures (including perps), everyone trades against a central counterparty through a process called novation.

When someone fails, credit risk then gets socialized between the pool of traders, and the CCP’s (Central Clearing Counterparty, not China) guarantee fund covers the loss and the system keeps functioning.

This is similar to what crypto exchanges do with their “insurance fund”.

CCPs also allow you to offset positions against counterparties. If you are cleared by a CCP and you buy AAPL from Bank A then sell it to Bank B, you end up with no position (because both trades face the CCP, and so cancel out). Bilaterally, you’d have two positions: one long, and one short.

The result is an 80-90% reduction in gross exposures. Lower exposure means less capital required, which in turn means lower costs. Lower costs mean better prices for everyone (apart from the banks making money by ripping off customers bilaterally).

There was a regulatory push to increase CCP usage after the GFC, including in the Dodd-Frank Act; nowadays, approximately 75% of OTC derivatives are cleared. However, that still leaves trillions of dollars of uncleared OTC risk globally, stacking up, waiting to topple.

Shadow Pricing

When you trade an equity swap with JPMorgan, here's what happens:

• You: “What’s your price for 10x leveraged exposure to TSLA?”

• JPMorgan: “3.2% funding rate, 50 basis points spread.”

• You: “Is that competitive?”

• JPMorgan: “trust us - it is.”

The only way of checking is to go ask a second market maker, and then a third.

Exchange-traded perpetual futures flip this completely. Every quote is visible - you can literally sit and watch the order book (which I’d recommend - it’s hypnotic). Every trade is reported.

Transparency is everything.

OTC derivatives lead to another pricing issue: they’re "customized". This sounds great - until you realize what it actually means. No two contracts are identical, so you can't trade them efficiently.

Exchange-traded derivatives are the opposite: identical contracts for everyone create fungibility that enables efficient trading.

"Customization" is a great feature when you're buying a suit. When you're trading derivatives, it’s a bug.

Liquidity Begets Liquidity

Trading volumes mean opportunity for market makers, who add liquidity. This creates a virtuous cycle as ever more customers come to trade on the most liquid venues. The more trading that happens in one venue, the tighter the spreads and deeper the order book becomes as market makers jostle for position.

Exchanges concentrate liquidity in a single venue. All trading interest pools together, creating deeper markets and better execution for everyone.

Meanwhile, OTC markets fragment liquidity across thousands of bilateral relationships. Each dealer-customer relationship is essentially a separate market. There's no network effect, because there's no network.

It's like shopping at a farmers’ market vs Costco. Who has time to haggle over every artisanal loaf or heritage cucumber with individual vendors? Costco, meanwhile, has already done the heavy lifting of bringing buyers and sellers together in one place. 🌭

The Betfair of the CFD world

For retail, CFDs (contracts for difference) have existed in traditional markets for decades, allowing traders to speculate on price movements without owning the underlying asset. But they've always been traded bilaterally with brokers - the same OTC structure that creates all the problems we've discussed above.

Perps enable open-ended exposure to underlying assets, just like CFDs (check out my company QFEX). However, they operate through a standardized contract format that trades continuously on exchanges, regardless of time horizon, leverage amount, or position size.

They take the economic function of CFDs and move them onto exchanges - like Kalshi does to Draftkings, and Betfair does to William Hill.

Imagine a world where every CFD provider was competing for your business on the same platform. That’s what a perp exchange looks like. Traditional CFD providers hate this because it eliminates their ability to charge massive spreads and customize terms in their favor.

Let There Be Light

OTC derivatives were designed for a shadowy world of bilateral relationships and hand shakes behind closed doors.

Exchange infrastructure was designed for electronic trading, automated risk management and clearing, and maximum liquidity concentration.

The result is markets that are more liquid, more transparent, and more efficient than their OTC equivalents. For traders, this means better execution, lower costs, and reduced risk. For the financial system, it means greater stability and resilience.

It’s the first Friday of the month, 8:29 AM ET. All over the world, institutional traders are staring at their screens like they are extras in the Apollo 13 command room. The poor intern is confused at the respite from the non-stop bullying. The senior rainmakers are too busy sweating to care.

The Bureau of Labor Statistics, or BLS, is about to release a number that can move trillions in assets, in seconds.

That number is Nonfarm Payrolls (NFP). And if you're not watching it, you're missing the most important 60 seconds of monthly market action.

The Most Important Economic Indicator

Nonfarm Payrolls (NFP) measures how many people got hired or fired in the U.S. last month, minus farmers and a few other categories the government decided not to count.

It's the best real-time snapshot we have of whether the US economy is creating jobs or destroying them.

The reason this matters is because jobs drive everything. People with jobs spend money. People spending money keeps companies profitable. Companies staying profitable keeps the economy growing. The Fed watches this data obsessively because employment is literally half their mandate (the other half being price stability).

The NFP report contains several components that traders worship like ancient prophecy:

• The Headline Payroll Number: Net jobs added or lost. This gets the most attention and usually drives the initial market reaction. Consensus expects around ~150k-200k monthly additions in a healthy economy.

• Unemployment Rate: The percentage of people actively looking for work who can't find it.

• Average Hourly Earnings (AHE): How much workers are getting paid per hour. This is the Fed's favorite inflation early warning system. Rising wages often lead to rising prices, which in turn lead the Fed to think about inflation.

• Revisions: The BLS revises the previous two months' data, and these revisions can be massive. The July NFP revised June’s number from +147k to +14k, a -133k swing that sent bonds rallying and equities dropping as traders realized the economy wasn't as strong as they'd thought.

• Industry Breakdown: Where the jobs are being created or destroyed. Services growing while manufacturing shrinks tells a different story than broad-based growth across sectors.

NFP = Signal

It moves everything in the market.

1. Currencies: The dollar moves on interest rate expectations. Strong NFP typically strengthens the dollar because higher rates make dollar assets more attractive. Weak NFP does the opposite.

2. Equities: Here's where it gets interesting. High payrolls can be good (healthy economy) or bad (Fed tightening fears). Low payrolls can be bad (recession fears) or good (Fed easing hopes). The market's reaction depends on which narrative dominates at the time.

3. Commodities: Gold hates strong NFP because it reduces safe-haven demand and raises rate expectations. Oil usually loves strong NFP because it signals economic growth and energy demand. Usually...

4. Bonds: NFP directly influences Fed policy expectations. Strong jobs = potential rate hikes = bond yields up = bond prices down. Weak jobs = potential rate cuts = yields down = bond prices up.

Here's the key insight that separates professional traders from everyone else:

NFP trading isn't about whether the number is high or low. It's about whether the number beats or misses expectations.

Markets theoretically operate on the ‘efficient market’ hypothesis - current asset prices take into account all known information. If consensus expects 150k jobs and we get exactly 150k, prices shouldn't move because that outcome was already reflected in asset values.

But if consensus expects 150k and we get 200k?

That's a 50k "beat" that wasn't priced in. Equities might rally on economic strength, or sell off on Fed tightening fears. Bonds will likely sell off as rate expectations rise. The dollar will probably strengthen.

If we get 100k instead? That's a 50k "miss" that triggers the opposite reactions.

The magnitude of the surprise often matters more than the absolute level of the number.

Respect the Volatility

NFP represents one of the purest examples of information-driven market volatility in modern finance. It's a real economic data point that directly influences central bank policy, released at a specific time when all market participants are watching.

This creates a perfect storm for large price movements across all asset classes. Currencies can move 100+ pips in seconds. Bond futures can gap multiple ticks. Equity indices can easily swing 1-2% in minutes.

Whether you're trading it or just trying to understand why markets occasionally lose their minds on Friday mornings, NFP deserves your attention. It's a monthly reminder that markets are ultimately driven by real economic fundamentals, even in an age of algorithmic trading and central bank intervention.

Here's why tokenized equities are fundamentally broken.

Fundamentals

Tokenized equities are blockchain tokens that represent ownership in traditional stocks.

The pitch sounds reasonable at first.

The issuer buys e.g. shares through normal markets, issues blockchain tokens backed by those shares, and lets you trade the tokens on crypto exchanges.

Crypto exchanges give access to 24/7 trading and near-instant cross-border settlement, theoretically democratizing access to markets for people who wouldn’t otherwise have access. We can even do it on DeFi (if we’re into that) while maintaining exposure to real companies.

Yet the tale of the tape is bleak.

FTX had tokenized stocks - we know how that ended. Binance quietly discontinued them shortly after launch. Mirror Protocol created synthetic stocks on Terra, which was nearly as spectacular a failure as FTX.

Every major attempt has either collapsed, been abandoned, or exists in a state of zombie-like irrelevance.

Tricky Arbitrage

Traditional markets work because arbitrage keeps prices aligned. If trades at $150.00 on NYSE and $150.01 on Nasdaq, arbitrageurs will eliminate that penny difference in microseconds. The end user doesn’t care where they trade; the market handles any fluctuations for them.

The key part: they can settle at Nasdaq with stock bought on NYSE, seamlessly. The U.S. securities clearing system just works.

The analogue in the tokenized world, however, is what Nathan Allman from Ondo Finance diplomatically calls a "high-friction minting and redemption processes". In other words, it’s expensive.

Let’s see what effect this has on spreads:

Say trades at $150.00 on Nasdaq, and my favourite tokenized version $xAAPL trades at $150.01.

So, I buy on NYSE… but to complete the arb, I need to take this stock to the protocol, and ask them to mint me an $xAAPL to sell.

Even assuming this process is instant, it’s not cheap - I can expect to pay anywhere between 10 bps (that’s 0.1%) and 1% for the privilege, depending on the stock and protocol.

Let’s say it costs 0.5%. This effectively means that until $xAAPL trades above $150*100.5% = $150.75, there is no profit in it for me. And for the other side, I can’t arb until it’s below $150*99.5% = $149.25. This is a massive spread of 1%: far wider than spreads I see on NYSE or Nasdaq.

ETFs and ADRs

If the above mechanics seem familiar to you, it’s because they are exactly how ETFs are formed.

A company like Vanguard or Invesco takes a (basket of) stock(s) and sells units of that basket to us.

ADRs are similar: copies of single US stocks listed outside the US for local investors, with a simple conversion back to the original. ADRs made Jane Street what it is today.

This solves the cross-border problem that we described above.

Indeed, S&P 500 ETFs are very popular in Europe and Asia, and are even structured in tax-efficient ways for their local populace, like UCITS in Europe. By embracing different regulatory regimes, ETF issuers deliver a better outcome for investors.

Crypto tokens, meanwhile, usually don’t benefit from any tax-favorable structures.

Well What About the Other Problems?

Like 24/7 trading?

Or DeFi venues?

DeFi Users Don't Care About Stocks

The DeFi ecosystem evolved around entirely different primitives: yield farming, governance tokens, automated market makers, and protocol-native assets.

DeFi users who want leverage trade perps on $ETH or $BTC, not tokenized shares.

DeFi values decentralization, permissionless access, and crypto-native innovation.

Tokenized equities offer none of these benefits - they're centralized, regulated, and fundamentally tied to traditional infrastructure, but without any of the liquidity or reliability that makes traditional markets work.

Traditional Users Have Zero Incentive to Switch

Tokenized stocks don’t offer leverage: they’re for investors, not traders. And if you’re going to buy a stock to hold for multiple years, you can afford to wait for the market to open.

Aside from the liquidity concerns, investors also have to face counterparty risk from the tokenization platform.

A close friend of mine had a huge token position on FTX shortly before the blow up. He thought he was safe: the tokens were theoretically backed by a Swiss company, with real shares.

Even better, a US data release on D-day had caused them to pump big time.

He found a group in the same boat, and they spent weeks trying to find the owner of their shares. The conclusion? An empty office building in Zug, the name of the holding company nowhere to be seen. There was no backing.

The Future is Perpetual

Tokenized equities take efficient, liquid markets and make them worse for investors. For traders who want leverage, platforms that deploy perps to offer crypto UX in traditional market liquidity are the answer.

We need QFEX. No blockchain required.

The concept of leverage stretches back thousands of years, emerging from humanity's fundamental need to manage uncertainty and risk.

4000 years ago, farmers in ancient Mesopotamia had a problem: how could they ensure the price of their crop today, when harvest season was 6 months away? Their solution: create tokens to represent future commodity deliveries. These tokens could be exchanged for gold, or other commodity tokens, creating the world's first futures market. Traders quickly realized that, unlike with bartering, they could speculate on the movement of prices for assets they didn't even own: they could sell assets they didn't have today, or buy more than they could afford, based on the promise of future delivery.

Leverage was born.

Even better, Aristotle later documented how the philosopher Thales used what he called a "financial device of universal application" to profit from olive harvest predictions. Thales used his knowledge of the stars (as good an investment strategy as any) to predict the following year’s olive harvest. He secured rights to olive presses during winter when demand was low, then rented them out at harvest time when demand spiked, essentially creating the first recorded options trade.

Japan's Revolutionary Approach

The world's first official futures exchange emerged in Japan, with the Dojima Rice Exchange in 1697. Rice was the foundation of Japan's entire economic system: land values, samurai payments, taxes, but most importantly SUSHI were all rice-based.

The Dojima Exchange introduced several concepts that continue to define modern leverage trading. Contracts were fungible (standardized and interchangeable), allowing them to be traded freely between multiple counterparties. The exchange also implemented margin requirements and daily settlement - innovations that wouldn't appear in Western markets for a century.

By the mid-18th century, futures trading volume exceeded the physical rice market: a sign of things to come.

The Chicago Revolution

Though it may be hard to believe, things were looking even bleaker in 19th Chicago than they do in the age of 'Chiraq'. The grain market was in chaos: farmers often burned grain for fuel rather than accept below-cost prices, while winter scarcity created consumer hardship.

Twenty-five Chicago businessmen established the Chicago Board of Trade (CBOT) in 1848 to address these issues.

The CBOT's solution - standardized "to arrive" contracts - allowed market participants to control large positions with relatively small capital outlays. A farmer could hedge an entire harvest with a fraction of its value, while speculators could amplify returns by trading contracts worth multiples of their invested capital. The CBOT remains a globally important exchange to this day.

The Financial Revolution: Beyond Commodities

The 1970s marked the futures market's expansion beyond its agricultural roots. When the Bretton Woods system collapsed in 1971, floating exchange rates created unprecedented currency volatility. The Chicago Mercantile Exchange launched the world's first financial futures in 1972: currency contracts that allowed traders to leverage foreign exchange movements.

Interest rate futures followed in 1975, with Treasury bond futures becoming the world's most actively traded contracts by 1977. Those scenes of sweaty men in brightly-colored jackets started right about now.

Why Derivatives Dominate

Every mature market today has significantly more derivatives trading than spot trading, and this isn't coincidental - it's a mathematical inevitability. Leverage provides several crucial advantages:

1. Capital Efficiency: Traders can control large positions with minimal capital, freeing resources for other investments or risk management strategies. This is how hedge funds generate such massive returns: they lever their capital to take larger positions.

2. No Physical Delivery: Producers and consumers can hedge price exposure without tying up capital in physical inventory.

3. Price Discovery: Markets are a voting mechanism for the fair price of assets. Giving people the ability to vote with more than their bankroll lets them vote depending on their confidence level, which leads to more efficient price discovery.

A mathematical side-effect also emerged: the presence of expiry dates meant traders could make risk-free profits if contracts traded out of line with underlying assets. If the futures price moved above the spot price, traders could sell the future, buy the asset, and hold both until the future expired. This arbitrage mechanism keeps derivative prices aligned with spot markets.

Roll Risk and Fragmentation

However, traditional futures create new problems in modern markets. The vast majority of traders don't need their contracts to expire; they're trading for price exposure, not physical delivery. When I’m trading oil futures, I don’t want 20,000 barrels of crude at my doorstep.

Expiries creates four major inefficiencies:

1. Operational Burden: Traders must constantly "roll" positions from expiring contracts to new ones. This creates operational risk: many firms have accidentally taken delivery of commodities they never intended to own.

2. Transaction Costs: Every time traders roll their position, they pay exchange fees and market-maker spreads, creating hidden costs that compound over time.

3. Front-Running: Market makers can predict roll flows, creating invisible costs as they position ahead of predictable trading patterns. This was the basis of entire trading strategies that I created in my past life as an HFT trader.

4. Liquidity Fragmentation: Market makers have finite risk appetite, so listing correlated products across multiple order books decreases liquidity on each, increasing transaction costs for all but the most sophisticated traders.

The cost of rolling futures for market participants runs into many billions of dollars every year.

Perpetual Futures: The Evolution of Leverage

Cryptocurrency markets solved these problems with perpetual futures: contracts without expiry dates that maintain leverage benefits, while also eliminating roll costs. Instead of expiring, perpetual contracts use funding rates - periodic payments between long and short positions, that keep contract prices aligned with underlying assets. Perpetual futures, or “perps”, now make up over 95% of volumes in crypto markets.

This innovation represents leverage trading's natural evolution. Perpetual contracts provide continuous price exposure without operational complexity, transaction costs, or liquidity fragmentation of traditional futures. They offer pure leverage - the ability to amplify returns and manage risk, without the mechanical inefficiencies that plagued earlier systems.

Deploying more capital - steady lads (Do Kwon, 2022)

The Future of Leverage

From clay tokens to perpetual contracts, the history of leveraged trading is a story of innovation. Every stage of the journey has expanded leverage's benefits, while reducing its costs and complexities.

Today's perpetual futures represent leverage in its most refined form to date: continuous exposure, minimal costs, and maximum efficiency. As QFEX soldiers on with its mission to bring perpetual futures to traditional assets, we're witnessing the next chapter in leverage's 4,000-year evolution.

The Great Divide

Futures contracts have become the dominant force across most asset classes. In commodities, oil futures trade around 50 times the volume of physical oil. Interest rate derivatives overshadow the underlying bond markets by 4:1, with $579 trillion in notional value. Currency futures and forwards represent the backbone of international trade and investment.

The absence of futures in U.S. equities, meanwhile, is something of a historical accident: when futures markets developed in the 19th and early 20th centuries, they focused on commodities and agricultural products. By the time financial futures emerged in the 1970s, regulatory boundaries had already been established. Stocks fell under SEC jurisdiction as securities, while futures remained with the CFTC.

As a consequence of this dual regulatory mandate, equity derivatives in the U.S. were forced under the options framework. The gap in the market - of viable futures on single stocks - persists to this day.

The Hidden Cost of Options Complexity

The dominance of options in U.S. equity markets creates significant inefficiencies that most traders don't fully appreciate. While options serve important purposes for sophisticated strategies, the vast majority of equity options volume represents something much simpler: traders seeking leveraged exposure to stock price movements.

Consider the typical retail trader buying call options on a popular stock.

They're not implementing complex volatility strategies or seeking asymmetric payoffs. They don’t care, or likely even know about, the additional risks that trading options poses (the ‘Greeks’ - theta, vega, gamma, rho etc). They simply want to amplify their returns with limited capital. Retail users are forced into an options framework that introduces unnecessary complexity and costs.

Options markets also suffer from liquidity fragmentation. A stock trading at $100 might have active options at strikes $90, $95, $100, $105, $110, and beyond, each over multiple expiries. Each strike represents a separate market with its own bid-ask spread and liquidity profile; S&P500 options have around 2000 associated order books. But market makers have capital and risk limits, that they now have to spread across the liquidity landscape; the result is wider spreads and higher trading costs. Futures, meanwhile, typically only have one active order book at a time.

The Pricing Uncertainty Problem

Options present market makers with fundamental challenges that don't exist with futures, including complex Greeks calculations and future volatility estimates.

Futures pricing, by contrast, is far more straightforward. Futures strategies can be left to fully automated systems that quote tight spreads and require minimal human effort to run. Human options traders, meanwhile, demand higher transaction costs for their intensive labour. And no wonder - Fischer Black and Myron Scholes were awarded the Nobel Prize for Economics for working out the fair price of an option, but you don’t need a Nobel Prize to understand how to price a future!

The complexity also creates barriers to entry.

Futures are intuitive. If you think a stock will rise, you buy a future and profit from the increase. Options require understanding of strike prices, expiration dates, implied volatility, and time decay. This complexity keeps many potential participants out of the market.

The Perpetual Solution

Cryptocurrency markets have pioneered the next evolution: perpetual futures.

For equity markets, perpetual futures represent an opportunity to provide what most options traders actually want: simple, efficient leverage. Instead of choosing between dozens of strike prices and expiration dates, traders can access direct exposure to stock price movements with transparent pricing and concentrated liquidity.

The benefits extend beyond simple leverage.

Institutional investors managing large portfolios can hedge more efficiently with equity perps, avoiding the cost of rolling their futures monthly or quarterly. And this isn't a new idea: swaps, an off-exchange version of perps, already trade massive sizes on Swap Execution Facilities in the United States. By bringing the swaps market on-exchange, perps greatly benefit the end user with increased liquidity and price transparency.

The Path Forward

The technology exists. The demand is proven in crypto. The only barrier is regulatory inertia that protects an inefficient status quo. As markets evolve toward greater efficiency, the artificial separation between equity options and futures becomes increasingly untenable.

Perpetual futures on equities would provide what markets need most: instruments that allow participants to express their views efficiently, without unnecessary complexity. In a world where leverage has become the dominant force across all asset classes, equity markets remain trapped by an options framework designed for a bygone era.

The question isn't if equity futures will eventually emerge - it's when.

For traders forced to navigate the complexity and costs of options when they simply want leveraged equity exposure, change can't come soon enough.

Enter Leveraged ETFs.

They promise simple exposure: 2x the daily return of the S&P 500, 3x the movement of gold miners, or -1x the performance of Tesla. Their marketing is straightforward, but performance is almost always underwhelming. The mechanics create a hidden tax that compounds over time.

These instruments suffer from what options traders call the 'short gamma problem': they're forced to buy high and sell low to achieve daily rebalancing. This process systematically destroys value.

Perpetual futures offer the same leveraged exposure without forcing this daily structural flaw. Let's examine the mathematics of rebalancing, and the path-dependent nature of compound returns, to unveil why.

The Rebalancing Trap

Leveraged ETFs must maintain their target leverage through daily rebalancing. For example, a 2x leveraged S&P 500 ETF (like ) must maintain exactly 2x exposure to the S&P 500 at the end of each trading day. When the market moves, this creates a mechanical trading requirement that often works against the fund.

Here's an example: I invest $100 in a 2x leveraged ETF when the underlying index $XYZ is at $100. The fund buys $200 of $XYZ exposure.

Day 1: $XYZ rises 10% to $110

• My ETF gains 20%, so it’s now worth $120

• The fund's $XYZ exposure increases to $220 (a 10% increase on $200)

• But to maintain 2x leverage at the end of the day, it needs $240 of exposure (2x my $120 ETF position)

• Required trades: Buy $20 more $XYZ exposure at $110

• The fund now has $240 exposure, which is double my ETF position of $120. It has rebalanced.

  

Day 2: $XYZ falls 10% from $110 back to $99 (a fall of $11)

• Fund’s starting exposure: $240 (from end of Day 1)

• Fund’s exposure after 10% decline: $216 (a fall of $24)

• My ETF loses 20%, so it’s now worth $96

• To maintain 2x leverage, the fund needs an exposure of $192

Required trades: Sell $24 of index exposure at $99 to rebalance.

So in order to keep the required amount of leverage each day, the fund bought $20 at the high ($110) and sold $24 at the low ($99).

The underlying index ended at $99 (down 1%), so one might expect my position to be down 2% (worth $98).

In fact, it’s worth $96. I’ve lost $2 from buying high and selling low as the fund rebalanced to maintain my leverage. This is the ‘short gamma problem' of leveraged ETFs. The more volatile the underlying asset and the more frequently I rebalance, the more money I lose.

The Gold Miners Debacle

The gold mining sector provides a perfect case study in leveraged ETF destruction of value. The three key indexes for this study are:

1. (VanEck Gold Miners ETF): The underlying index

2. (Direxion Daily Gold Miners Bull 3X): 3x leveraged long

3. (Direxion Daily Gold Miners Bear 3X): 3x leveraged short

Over a volatile 7-trading-day period in March 2020 (during the COVID pandemic), GDX was virtually unchanged (-0.2%). However, both NUGT and DUST lost over 5% in value. The daily rebalancing in a volatile, up-and-down market systematically destroyed value in both long and short directions.

Traders here might think something is fishy - and they'd be right. There's an opportunity here in predicting the fund's rebalancing trades in the underlying index. I'll defer to the legendary ’s article to explain this properly.

Even Direxion (the ETF providers) themselves caution investors that the NUGT and DUST funds “should not be expected to provide three times or negative three times the return of the benchmark’s cumulative return for periods greater than a day.” In other words, leveraged ETFs track the move of the underlying each day; over periods of more than a day, they systematically eat away at returns.

Perpetual Futures Are Better

Perpetual futures eliminate the daily rebalancing problem. Users seeking long-term leveraged exposure to equities could rebalance their leverage far less frequently, reducing the effective volatility by allowing the underlier time more time to increase in value and negating this short gamma problem.

To put it in concrete terms, if I buy exposure to the S&P 500, there's a 50% chance it is lower tomorrow. If I wait a year, that drops to 25%, using average S&P 500 performance over the last 100 years (it's even lower if I just use the last 20 years).

By rebalancing yearly, I make buying low and selling high during my rebalance far, far less likely.

The choice is clear: accept the gamma tax of leveraged ETFs, or embrace the clean leverage of perpetual futures.