Trading Reality — Markets in Production

Under the hood: Adverse Selection

Tibor S — Tue, 19 May 2026 14:00:49 GMT

When I moved to Spain, the apartment I had pre-arranged turned out to be further from the amenities than expected. I needed a car on short notice. So I went to a dealership, where I was shown their selection. Antonio — sales — quickly asked about my price range and started pointing to all the different models on display. Great price, very few kilometers. This one will serve you for decades! The previous owners handed it over in better condition than it came out of the factory!

Knowing less about cars than the average high schooler, I barely paid attention to what was being said, only registering mechanically what was being presented. I watched Antonio grow more and more compelled to impress. By the third loop through the 20-30 cars we’d narrowed down to, I realized there was one he kept skipping over. When I pushed him about it, finally, he admitted it was a “good car.” I closed the deal quickly, used the car for many years, and ended up selling it for more than I had paid for it.

I am not a bargainer, nor do I easily see through all sales tricks. But the words adverse selection lit up in front of my trading-geek eyes. This short story, adverse selection, and lemons all have something in common — and I will show you how.

Actuarial origins

In the last piece we covered three important market participant categories: the informed, the uninformed traders, and the market makers. We also showed that informedness is only spread-deep — that is to say, it can only be defined in relation to the market maker. This explains something intuitive: the market maker is trying to avoid being matched with informed traders, and adjusts their quotes accordingly to achieve a dual purpose. First, to skew the price in their favor whenever they do have to face the Informed. Second, to be matched as often as possible with uninformed counterparties to offset the losses caused by the former.

The market maker being picked off by an informed trader is a phenomenon called adverse selection.

The notion originates outside of finance, in the late 19th century, in the insurance industry. Insurers realized roughly a century and a half ago that if they followed pure statistical probabilities when underwriting claims, they would end up going under imminently. They identified an effect they termed anti-selection, or adverse selection — namely, that the people most willing to purchase their life insurance products were the ones likely to already suffer serious illnesses. The information asymmetry between insured and insurer left the insurer unable to predict claim probabilities correctly. Disaster — pun intended — was hard to avoid without coping strategies.

The same effect plays out with market makers, who may not be privy to the information that informed — or “toxic” — traders possess. So they developed several coping strategies.

One strategy is to stop offering the product to a specific target audience. To stop selling life insurance to those with terminal illnesses. Or to skip over the car that is priced correctly — shoutout to the Antonios and car dealerships of this world. For the market maker, this equates to refusing to quote to select counterparties. Not always readily available in regulated markets, but it very much happens in more fragmented financial marketplaces — selective quoting in CFD and interbank feeds is the obvious example.

Another option is to raise the price. Increase the insurance premium, up the sale price of the car, widen the quote. Enough to make the transaction financially viable. The reader will recognize that both strategies are widely followed in all the cases listed.

About a century after the insurance industry coined the term, Akerlof posited that this information asymmetry can have serious consequences. His example was close to home: sellers of used cars know whether theirs is a car with hidden issues; buyers do not. Rational buyers therefore adjust the price they are willing to pay downwards to take this into account. As a consequence, sellers of good cars withdraw from the market — finding this price too low. After just a few cycles of this we are left with: you guessed it — only Lemons.1

The mother of all costs: the Spread

The year was 2013. The country: Switzerland. The room set up for “Ultimate” Agile: a Kanban board, a sprint-board, a 3-min meeting board, and a room-sized whiteboard with never-ending post-its all pushing back on any and all deadlines. I know my strategy is the basis for the next unicorn, but whenever we test-execute it drifts... — I was told. This was familiar territory. This time it was at the strategy design phase. It works on all instruments, so we decided to start with spot FX. The alarm bells were deafening me. Let’s understand the underlying concept first. Statistical pattern deviation from noise. Ok.

So then the buy signal triggers, he said. And we buy at the bid. I know there is slippage, but...

Throat clearing. It was me, but it should have been the strategy author. I exhaled, inhaled, and again several times — I need to say this in a tone that is neither alarmed, nor surprised, nor demeaning, I thought to myself. What do you mean at the bid?...

The strategy was assumed to execute at the prevailing inside bid and ask, not even at the mid-price. So the discrepancy from the get-go was 2× the spread plus commission, plus slippage. It was a long conversation. And many more followed.

It isn’t controversial to say that the biggest and most significant cost to trading is the spread. What others term slippage is generally just the difference between the spread at the time of the order intent and the time of it arriving to the market. The former is a virtual spread that drives the decision. The latter is the spread and price in effect.

A strategy developer needs to understand exactly what the spread is, and what it isn’t. Why it exists. What it means for a strategy. How it interacts with your system.

The spread is considered to comprise three structural components:

Order processing costs — exchange, settlement, and other transaction fees.
Inventory costs — the price of the maker having to hold excess positions.
Adverse selection — the cost of the maker being picked off by more informed players.

In modern markets, adverse selection accounts for 50-80% of the spread. The other two have been compressed by technology — exchanges run leaner, settlement is cheaper, processing is automated. What is left is the cost of the maker not knowing who you are. So you are paying the cost of what the market maker has to defend against. Namely, their inability to identify you, or any other market participant.

How Adverse Selection reaches all

Adverse selection affects both limit orders and market orders. A market order surrenders price for timing. A limit order surrenders timing for price. The trade-off sounds straightforward.

Most developers I’ve watched come at this from the wrong angle. They treat the order-type choice as a tactical question — limit if there’s time, market if there isn’t. The realization takes a while: the choice doesn’t decide whether you pay the spread. It decides how the spread comes back for you.

The market order’s timing guarantee in particular deserves more scrutiny than it usually gets. It assumes what you see as the market price is not a mirage. That the price won’t move significantly by the time you hit the market. That this instrument’s volatility is within the range you expect on the micro time scales. That is a bigger bet than it appears, which is why most professional and systematic trade designers rarely use full market orders. They use marketable limit orders when they need to be the aggressor.

The deeper observation is that adverse selection affects both order types — through different delivery mechanisms.

On a limit order, you are the one quoting a price. You get filled when someone hits your bid or lifts your offer. But who chooses to hit you? I have some bad news and more bad news. Most likely it will be an informed trader. That means you lose. Assume instead that it is incidentally an uninformed trader that wants to dip their paw into the book while you are there. Do you think the market maker — with state-of-the-art infrastructure, fee structure, incentives, and near-perfect microstructure algorithms — will let you lead the queue to face the uninformed player?

On a market order, you are the one crossing the spread to demand execution. The maker moves their price to protect against you and everyone else trading in your direction. They have to — that is their function. The cost lands on your fill price, and you call it slippage. When there isn’t much slippage, well, it was just too obvious for them that you were not informed to begin with.

Either way, you are paying for the maker’s uncertainty about who you are.

No free order type

The common framing that limit orders capture the spread and market orders pay the spread is simply untrue. Limit orders do not avoid the cost. They transmute it. The spread you didn’t pay shows up as adverse fill selection. The trade-off between price certainty and timing certainty simply becomes: which form of adverse selection you choose to bear.

If you haven’t ever done the exercise to check all-passive (limit) versus all-aggressor (market) PnL for several trading hours, I highly suggest you do it for any market or instrument. What you find will surprise you and make you understand the point viscerally: sometimes the aggressors out-trade the limits, sometimes the other way around. There is no consistency. No determinism.

This matters to you. It sure matters for your strategy. If your signal has urgency, the slower fill rate of limit orders will likely erode it. If you think you can operate without urgency, posting limits could be profitable — but be sure to check your assumptions about infrastructure, costs, and latency, because your competitors are of the most sophisticated type. And overall, if your signal is genuinely informed — predictive of near-term price movement — both order types will cost you, just differently.

Understanding which form of adverse selection your strategy is exposed to, and navigating that interaction deliberately, is paramount if you want to succeed and be consistent in trading.

In the next piece I will turn to a related practitioner discussion — the notion of a good trade and a bad trade, why I used to push back on it, and what changed my mind.

The Hand of the Maker

Tibor S — Fri, 08 May 2026 19:31:58 GMT

If you want to make it in trading — and in building trading systems in particular — the most important concepts to understand are not trends and reversals, not breakouts and fakeouts, not regime detection and volatility clustering. The single most important set of concepts revolves around market participant behavior. But not in the naive sense of trading psychology, which is a form of hype that circulates mostly in non-professional circles. What matters is the structural makeup of market participants, and how that structure changes.

There is little value in obsessing about the fear and resulting actions a retail trader goes through upon detecting a pattern, when retail traders make up a minuscule percentage of the market anyway — and even that portion is diluted by stacking on top of some larger player who has already purchased the flow from the Robinhoods of this world.

In the last piece I closed with the question of why good signals have a front-loaded shape. The answer starts with who is on the other side of those signals — and that is where this piece picks up.

Let’s focus on two participant categories whose implications cannot be overstated: they will determine how you trade, how much you trade, what you can trade, and when you trade. Many readers will be familiar with informed and uninformed traders. Few realize that informedness only makes sense relative to a third participant — the market maker. So that is where we have to start.

Market making, in practice

In the early 2000s I was CTO/COO of an HFT shop that did what we called 5-5 for years (a tongue-in-cheek nod to hedge fund fee structure). With a headcount of just five people, we matched 5% of Nasdaq OTC volume. As part of the incessant pursuit to chisel away unit costs, we had a seat at the exchange, were a registered market maker, and were a self-clearing broker-dealer.

Without getting into the distinction between designated and primary status, registered market makers are required to quote two-sided continuously during regular trading hours, within a defined band of the National Best Bid and Offer. The point is that being more than just an Electronic Liquidity Provider — quoting voluntarily, withdrawing when unprofitable — came with obligations, and those obligations came with allowances. The rules differ slightly for options, but the concept is the same.

One point worth emphasizing because it tends to be missed: a market maker is not obligated to always sit at the inside bid and ask. They are required to quote a certain percentage of the time and within a defined band of the prevailing best bid/ask. They are designated as providing liquidity, which is itself considered a service. That liquidity is not a guarantee, and market makers compete with all other participants for profit. They are neither the villain of the market story nor a benevolent provider of public good — just another competing participant.

So why spend this much time on market makers if what we want to focus on is informed and uninformed traders? Because informedness is defined relative to the market maker. Not as a pure concept. As a structural relation. And as I will show, the distinction between informed and uninformed is not an incidental property of who happens to be in the market. It is not a “designation” of who is smart and who isn’t. It is a structural and unavoidable feature of how markets operate.

A flash history of informedness

The distinction between informed and uninformed traders has been discussed since at least 1971, when Bagehot — the pseudonymous Jack Treynor — framed market making as a contest between liquidity providers and traders with superior information. The formal academic framework arrived with Kyle (1985) and Glosten-Milgrom (1985). What this body of work established is that the distinction is structural, not behavioral. It is integral to how markets operate, not an incidental fact about who happens to be trading on a given day.

Your intuition that a big institution’s block trade, or a super-pod order from the Millenniums and Citadels of the world, would be termed informed — while a retail order is uninformed — is not really the correct distinction according to this framing. And yet, this remains a valid way to think about informedness. At least as a starting point. Size enters the story too: Kyle’s 1985 model is built on the idea that informed traders strategically choose how aggressively to trade because their order size and timing leak information to the market. The bigger the trade, the more the maker can infer, the more the price moves before the trade completes. Size is not what defines informedness, but it is one of the signatures by which informedness gets detected. At our HFT shop we ran algorithms tuned specifically to detect block trades hiding behind VWAP/TWAP slicing — the size signal worked in both directions.

The Uninformed come to the rescue

The above intuition breaks down when you push it further, however. What if all the retail-like flow disappeared, one might ask — and you can argue this is already partially the case given the retail order flow business operating upstream of public venues. Would the markets become more efficient? Would informed-on-informed be the new reality?

No, and the reason is structural. Uninformed flow isn't just the contrast that defines informedness — it's what makes the market work. The literature is precise on this: Black (1986) called noise traders necessary, and the no-trade theorem (Milgrom and Stokey, 1982) shows why. If everyone were rational and informed, the fact that someone wants to trade with you would tell you they think they have an edge — so you should refuse. Markets seize up. Liquidity disappears. I saw this at the HFT in the small: we refused to trade with players whose cancel/replace patterns suggested they were reading the same signals we were. The theorem isn't abstract — it's how a desk actually operates when it suspects the other side knows what it knows.

Noise traders break the deadlock. Their flow has no information in it, which gives informed traders something to trade against and lets prices catch up to reality. The uninformed lose on average. That’s the cost of having a market at all. It is this informational asymmetry that makes the price tick.

Definition. Please.

It is dead simple. If the Market Maker loses when trading against you, you are informed. If they make money, you are uninformed. Don't try to beat the market. Beat the Market Maker first.

He who Makes the vocabulary

The language has evolved. From 1971 to today the terms have drifted: uninformed became noise trader, informed became toxic flow. Notice how the names have become pejorative for both groups. Uninformed flow used to be a structural necessity; now it's noise. Informed flow used to be the trader earning a return on their information; now it's toxic.

The vocabulary tells you whose seat at the table got the loudest voice. That seat belongs to the third participant we have been circling: the Market Maker.

Some readers will recognize toxic order flow from a different marketplace than this piece centers on: CFDs. Colloquially called FX. Liquidity providers in that business throw the term around a lot, especially when they want to upcharge or restrict access. This is no coincidence. Informed, uninformed traders and market makers are not exclusive to centralized markets. They are emergent features of any market where someone quotes both sides. The vocabulary ports because the structure does.

Why this matters for trading systems

Some of the cleanest, most reliable strategies I have worked on were ones we eventually traced back to a specific class of participants doing a specific thing. The strategies we couldn't trace? Those went away first. Half-life detection is great, but it's only a "half-solution".

Every trading system makes assumptions about participant mix, whether the developer knows it or not. The strategy that worked in backtest assumed a certain composition of flow. The execution model that produced a clean equity curve assumed a certain receptivity from the makers on the other side. When the mix changes — and it does, regularly — the assumptions degrade. Quietly.

You can build trading systems without thinking this way. Plenty of people do. But if you arrive at a persistent edge without ever asking whose behavior this is exploiting, you have most likely found a participant-structural pattern without knowing it. Which means when it goes away — and it will — you won't know why.

Adverse selection is one of the mechanisms that turns participant structure into the actual cost of trading. And it defines the spread. Now we can explore it substantively, and that is what my next piece will do. And with this we add very important concepts to our toolkit — ones that will survive any trend, strategy, and possibly even you and me.

References

Bagehot, W. [Treynor, J.] (1971). The Only Game in Town. Financial Analysts Journal, 27(2), 12-14.

Milgrom, P., & Stokey, N. (1982). Information, Trade and Common Knowledge. Journal of Economic Theory, 26(1), 17-27.

Black, F. (1986). Noise. Journal of Finance, 41(3), 528-543.

Front-loaded Value in Trading

Tibor S — Fri, 01 May 2026 09:40:24 GMT

Good trades realize most of their value early in the holding period; bad trades do not. And early doesn’t scale with the holding period.

I have seen this across trading systems, in the widest range of assets and markets. The implications should make you pause. Pause. Repeat: bad trades have no shape. Good trades have a specific one. This affects everything — signal discovery, sizing, exits, and your ultimate PnL touchpoint: Executions.

A recap

In my previous post I took you through my Quant Room struggles to convey how executions rewrite strategy results completely, and how often I had to go through the same exercise. To gather the data, build the argument, confront the enthusiasm of a Strategy Builder with facts on the ground. The same facts.

It took dozens of cumulative samples before I started to wonder. The effect could not be explained by a combination of spread, commission, and slippage costs plus the core statistical mechanics. It wasn’t cost of execution. It was the shape of the value when the signal was the right one.

The shape

The first time I started to suspect was when I had to do the same exercise back-to-back-to-back, reviewing five very different strategies within two days. Momentum. European equities. Spot and FX futures with a carry component. A dollar-neutral US stocks portfolio. Medium-frequency day trading. They should have had nothing to do with each other. But my analyses from the previous four were still on my desk. The sheets were still on the Desktop. I could not unsee it. It was glaring at me.

In good signals, the extractable value rises sharply from entry and is mostly realized in the first part of the holding period. The initial jump is sudden, followed by a plateau or modest decay.

This shape is the cumulative average across the system’s good trades, not any individual trade. Yet the same shape surfaced again and again across the systems, in their positive-outcome trades. Regime and asset class adjust the exact shape, but not the pattern.

Bad trades, on the other hand, wander, bleed, spike, and revert. The more you accumulate, the more distinct the pattern appears compared to good signals.

So being front-loaded is a feature. It itself is a diagnostic about the quality of the trade.

One caveat: this is a post-event observation. The shape only emerges in retrospect, which limits its direct use in real-time signal generation. And to be clear about what I’m describing — the front-loading is disproportionate, not absolute. There is real value in the rest of the holding period; you can’t just cut early and assume the trade is done. What you can say is that the early window carries more of the value than its share of the holding period would suggest.

For a long time I had the observation without the implication. I knew the shape was there; I didn’t yet see what it meant for the rest of the strategy. That came later, and it came through analysis.

Why this matters for execution

A two-day holding period trade may have most of its value accrue in the first few hours. But importantly, a disproportionate fraction of that will be packed into the very first minutes. Counterintuitive, but a logical consequence of the pattern described — the same disproportionality recurses within the early window itself.

Slippage and timing

This reframes what slippage actually is.

The traditional view treats it as a uniform tax on returns, a few basis points subtracted from the edge regardless of when in the trade they’re paid. Under the front-loaded shape, that view is wrong. Slippage is an edge-dampener, and the path it dampens is the good-signal path. The same basis points cost more when paid in the window where the value is concentrated.

Timing the entry becomes impactful for any strategy. A late entry isn’t just a delayed entry — it’s a different trade. The shape has begun without you. What is left to capture is the reduced part of the curve, while the fixed costs and the bad trades continue to drag you down at full strength.

This is why post #1’s claim that execution rewrites strategy results isn’t a marginal-cost story. It’s a shape-of-alpha story.

A note on what this isn’t

I want to clarify this has not been derived from first principles, nor is it a claim that this will consistently repeat for all asset classes in all markets, now and in the future. The mechanism — fast information assimilation, real edges being narrow in time, the difference between a real signal and one that’s mostly noise — is suggestive but underdetermined. These are observations, and empirical assertions.

So this is not an academic claim, even as the related literature has been documenting related shapes — particularly in how price impact decays after large orders — for decades. Even as analysis of informed and uninformed market participants has led to convergent conclusions (more on this later). What I would like to offer is the trader-side mirror of those observations, framed in a way practitioners can use.

For readers who want to go deeper into the underlying market mechanics from the academic perspective, here are the foundational papers and a few accessible follow-ups I would recommend (skip if short on time):

The question

There is a question that we have been brushing against without pinning it. Why does this shape exist? Why does information get absorbed in a way that front-loads the value? Why front-loaded for good signals and not for bad ones?

The answer lives in the domain of market participants and information availability. Information becomes available to all or to a few in waves — waves that are difficult to conceal for long. It is the sudden availability of this information that underpins the effect we described so far.

The market participants who are in on it and those who only see the effects after the fact play a different role in price discovery.

This is exactly the topic of my next piece: the informed and uninformed traders, and how their interaction determines most of the market mechanics relevant to a trading system developer.

Execution IS the Strategy

Tibor S — Mon, 27 Apr 2026 21:14:24 GMT

Most trading strategies do not fail because of signal quality. They fail because execution is treated as an implementation detail.

In this article I want to show how true that is — not by listing everything that can go wrong, but by pointing to the few things that consistently matter.

A most human trading-exec anecdote

When I was hired as an Executions Specialist at a multi-billion dollar hedge fund, I used to sit — politely, patiently — through meetings and presentations from quants and portfolio managers. It would quite often play out the same way.

They would present their stellar performance charts, methods, equations, market references — everything you would expect. These were serious people. Ivy League PhDs, experienced PMs, people who had clearly put in the work.

At some point I would ask a simple question:

“May I ask what the execution assumptions were?”

Almost without fail, something would shift in the room.

“We don’t care about execution — we have average holding times of 2 full days and above!”

You could hear the resounding irritation. So I learned not to push it in the room.

I would wait for the meeting to finish, then ask for a private conversation. Same question, this time without the audience. Polite, dry: gather the data.

Then I would go back, collate the information, and do the work — sit there, shut everything else out, run the analysis properly, knowing already that this might not go well. I had my intuition, and my internal statistics. Then another private conversation. Because the result was rarely subtle.

Sharpe ratios north of 4 would drop to around 1.2 under optimistic assumptions. Under more conservative ones, the strategy would simply bleed. The clearest case I remember was a cross-asset carry, EUR-pegged — and it was very much not the only one.

And this was NOT high-frequency trading. But: Multi-day holding periods. Highly liquid instruments. Top-tier market access. Competitive commissions.

And still — execution didn’t just “adjust” the result.

It rewrote it.

Where things break

Most systematic trading strategies follow a familiar path: signal discovery, backtesting, deployment. Execution is either assumed, simplified, or treated as neutral. This is where things start to break.

Slippage — if it is considered at all — is often treated as a fixed adjustment. A number. A parameter. But it is not. It is variable. Context-dependent. State-dependent.

And this leads to the first structural trap.

Trap #1 — The better the signal, the harder it is to execute consistently

There is a counterintuitive truth about fills that most people miss: a good trade is contested. Informed traders on the other side of a real edge will not let you in cheaply. A bad trade is often uncontested — the book gives way, slippage is small, you fill at touch.

Which means cheap, easy fills should make you question your signal. Slipped, partial fills, adverse selection — the things that feel like the market punishing you — are often the market confirming that something is actually there.

There is a structural reinforcement of the same effect: strong signals also tend to appear in transitional regimes — news, dislocations, crowded inventory — where spreads widen and execution gets worse for everyone. But the cleaner version of the trap is the one above.

What is assumed to be independent — signal and execution — is not. They interact. And that interaction is rarely in your favor.

This is the first of several traps I’ll be writing about. Subsequent posts will cover risk-layer distortion, the market-selection trap, and the one nobody wants to hear about: the cost of being right.

Execution and risk

An experienced trader will place a lot of emphasis on risk. But if execution assumptions are wrong, the entire risk layer is distorted. Entry is not where you think it is. Exit is not where you think it is. Drawdowns are not what you modeled.

The problem is not just that execution adds cost. It is that it quietly reshapes your risk profile — sometimes by hurting you, and sometimes by helping you for the wrong reasons.

Here is a real one: A single tick of slippage caused a missed exit. The position stayed on. The next entry — one that should never have fired if the exit had triggered correctly — missed a big winner. The rest you can imagine. Double the drawdown, strategy abandoned, and my post-humous analysis revealing the real drift. This happened not to one but to a dozen different trading systems I was tasked with overseeing. So as a trader you need to be ready, firm and honest.

What execution actually is

Execution is much more than most people think. It is not just slippage. It is not just the trading platform. It is the market you chose, the venue, the latency, the commissions, the placement, the size of the order, the available liquidity, the competition for the same trade.

In one word: Reality.

Take “trading gold” — a useful example, because it sounds like a single thing and isn’t. Gold futures, micro futures, OTC, CFD products — each has a different book, a different liquidity profile, a different microstructure, and even different price behavior. They are not interchangeable. And execution is where that difference shows up first — usually painfully, and usually after you have already committed.

Why it gets ignored

Execution is unsexy. It’s often treated like the last administrative step — the final turn before you “get your license” to trade.

But it’s not a formality. It’s a traffic light. It should tell you green, yellow, or red. And in practice, it should very often tell you: No.

That “No” is not a failure. It is information.

Implications

Execution is market selection. It is cost. It is timing. It is not a layer on top of the strategy — it is part of the strategy. It feeds directly into risk. It operates at the level of micro, even nano-level details. And those details accumulate.

If you have a great “strategy,” you may have signal. You may even be 10–40% of the way there, depending on what you’re doing. But that’s not the end. And it shouldn’t feel like the end.

A red light from execution is often the most valuable answer you can get. It means either:

you are onto something real but not yet implementable,
or you have been disciplined enough to hit a wall before carving a hole in your wallet.

Closing

Years later, I still think back to those private conversations after the meetings. The walk back to the desk. The dry presentation of numbers that nobody had asked for. A Sharpe of 4 becoming a Sharpe of 1.2, then sometimes a flat line.

Without execution, a strategy is just a thesis. With execution, it becomes either a position or a lesson.

Execution is not downstream of the strategy.

It is where the strategy actually becomes real.