Anatomy of a Killed Strategy8 min read
How a research workflow brought a strategy to life and promptly killed it.
S3 is the public research archive of Lance Ladia, documenting the birth of trading strategies from theory to implementation.
This archive exists as a result of disillusionment in the current trading space. Most research is either private by necessity or deceptively persuasive: selling certainty and perfect causality without room to be skeptical.
The process emphasizes epistemological honesty by design. Falsification over confirmation. Structural explanations instead of statistical accidents. Everything traded and published here follows the same principle:
Structural Theory. Systematic Rules. Statistical Evidence.
Forward-tested performance, published at the close of each month.
Next report September 2026
Reporting is pre-specified. The same figures publish every month, in the same order, whether or not they favor the strategies behind them.
Observations exploring markets, the human condition, and the space where the two intersect.
August 2026 · Method
How a research workflow brought a strategy to life and promptly killed it.
July 2026 · Research
A short-side opening-range reversal in E-mini Dow futures — 194 trades, a 59.79% win rate, and evidence consistent with 0DTE dealer hedging pressure at the open.
June 2026 · Research
In a sample of infinity, randomness produces fool’s gold: ideas that produce statistically compelling evidence with no proper substance behind the edge.
May 2026 · Creative
What happens when hairless and bipedal primates interact with one of the most extreme cognitive environments?
Open to opportunities in research collaboration, proprietary trading, and capital partnership.
Currently Trading own capital. Open to desk conversations.
Lance Ladia · August 2026
In June of 2026, I stopped trading a strategy that had produced a statistically significant edge across nearly two years. There were no breached risk limits or outlier drawdowns. It worked, and then it did not — an effect brief but strong enough that only a sequential test caught it.
What follows is the entire lifecycle of a strategy: the theoretical foundations, the pre-specified rules, the statistical evidence that justified inclusion in the portfolio, and the test that ended it. Exact trading parameters are withheld, but everything else in the process lies here.
One of my favorite behavioral finance tidbits is that fear is a stronger emotion than hope, and as such, the market will fall faster than it will rise. While I don’t know if such an idea has been tested before, the work of Kahneman and Tversky shows that financial loss is twice as painful as an equivalent financial gain, so I’d like to imagine that the fear and avoidance of experiencing that pain is commensurately strong.
If such an idea were true, then it could potentially be captured in a short-side, trend-following intraday strategy, particularly in the most active part of the day. The strategy that would be devised would have a psychological mechanism rather than an obligation-driven mechanism. However, during development, this narrowed down the candidates for markets where this idea would work best in. A strategy seeking to exploit psychological tendencies would be more pronounced where a greater number of participants are less informed. NQ futures sit on an underlying with an increasingly growing population of retail traders, visible in the explosion of retail options volumes, especially the more volatile 0DTE options.
I built a set of rules that classified early-session selling, sizing targets and stops according to the range of the opening minutes so that both scaled with realized volatility rather than arbitrary fixed distances.
Before stating any of the evidence, this was a behavioral hypothesis, not a structural one.
This will become important later. In Structural Edges & Statistical Accidents I hypothesized a distinction between two possible types of market anomalies: edges that exist because participants are obligated to act as a result of their mandates, careers, or job duties, and edges that exist because participants have converged on a behavior.
The first kind is durable because obligation doesn’t cease to exist. So long as the careers and goals of those who can move the market exist, this kind of edge should be more resistant to alpha decay. The second kind is contingent on the continued presence of the specific behavior, which decays when that behavior changes.
The imperative idea is that behavior has no timeline. There is no clock. Obligations change with new laws, enforcements, goals, or additions to the market. Behavior changes at random. Selling cascades by uninformed participants is unambiguously a behavioral mechanism.
The following was written down before data collection and not altered afterwards to change what the data offered:
The initial sample ran 22 months from March 2024 through December 2025: 192 trades, 119 wins and 73 losses. A win rate of 61.98%.
Mean win was +63.07 points against a mean loss of −63.19 points, a payoff ratio of 0.998, which was operationally 1.0. Symmetric by design.
Against a 50% null, the results yielded Z = +3.32, one-sided p = 0.00045. The Wilson 95% confidence interval on the win rate is [54.94%, 68.55%], which excludes 50% by a comfortable margin. At the observed effect size, the test had 95.8% power, so the sample was large enough to detect the edge it was looking for rather than merely large enough to fail to rule it out.
Because of the symmetric payoff ratio, the PnL was standardized to risk units, where the strategy returned +46R over the 22 months. Three of those months lost money and one finished flat.
Splitting the sample in half gives 62.50% across the first 96 trades and 61.46% across the second 96, showing the same edge at the same magnitude. With the remarkably stable month-to-month performance of the strategy, whatever the strategy was capturing, it had been capturing consistently for 22 months. No lucky streaks, no big win.
Alongside the primary claim, I theorized that gap-up conditions outperformed gap-down conditions because a gap higher (comparing the prior day’s RTH close versus the current day’s RTH open) would give overnight position holders an immediate profit to take, which cascades into more selling. It was wrong.
The direction was backwards. Gap-down mornings performed better, but the difference in performance was not statistically distinguishable in either direction (Z = −0.545, two-sided p = 0.586). The conditioning variable did not measurably matter.
While the gap-down sample looked better, using it as a justification to produce a gap-down-only variant would have produced a false positive. The evidence supports that the difference in the two buckets is noise, and acting on it would have fabricated the process.
The strategy was left as specified.
Position sizing used fractional Kelly, which carries an assumption: that outcomes are independent and identically distributed. If wins and losses cluster, drawdowns run deeper than binomial arithmetic guesses and a fractional-Kelly size that is conservative is quietly over-betting. The runs test below examines the independence half. The other half — that the win rate stays where it was measured — is what section 06 turns out to be about.
The assumption is testable using a Wald–Wolfowitz runs test on the 192-trade sequence:
No detectable serial dependence. The point estimate leans towards slight alternation, which is far safer than clustering. The conditional split says the same thing: the probability of a win after a win was 59.32% against 65.75% after a loss, a difference of Z = −0.889, and the lag-1 autocorrelation of the outcome series is −0.064.
At n = 192, the runs test reaches 80% power at an autocorrelation of roughly 0.204. Weaker dependence around 0.10 would have escaped detection 72% of the time. Based on the size of the data, it is most appropriate to conclude that no serial dependence strong enough to matter for sizing was detectable.
Simulating the drawdown distribution at the levels of dependence: at zero autocorrelation, the 99th-percentile drawdown is −14R, at 0.10 it is −17R, and at 0.20 it is −21R.
Against that, the realized maximum drawdown across the sample was −7R. The independence-implied distribution has a median drawdown of −6R, a 95th percentile of −10R, and a 99th percentile of −12R, which puts the observed figure at the 46th percentile.
From January through June 2026, the strategy took 51 trades and won 21 of them, giving a win rate of 41.18%.
Against the prior sample, the difference is real: Z = +2.672, two-sided p = 0.0075. The Wilson interval on the 2026 win rate is [28.75%, 54.83%], excluding the historical 61.98% entirely.
Precision matters here given the small sample: a 41% win rate over 51 trades is not significantly below 50%, and at this sample size, the test has only 52.8% power to detect the original effect. The data does not support the claim that the edge turned negative, but what it does support is narrower and sufficient: live results are no longer consistent with the edge at the magnitude it was tested at.
That is why fixed-sample testing is the wrong tool for the job. Waiting for enough trades would mean funding the decay for the course of the entire year. A sequential probability ratio test was used that was evaluated after every trade and stops as soon as the accumulated evidence favors one hypothesis over the other by a pre-set margin.
Running the historical win rate as H1 against a coin-flip null at α = β = 0.05 puts the stop boundary at a log-likelihood ratio of −2.94.
The boundary was crossed on June 11 2026, at the 47th trade of the year. The strategy was killed at the end of that month, giving an extra two losses. Because June was the worst month of 2026, a robustness check was run on 2026 excluding June, which gave 19 wins across 42 trades. The comparison against the prior sample remains significant at p = 0.046. The conclusion is independent of the worst month.
The shape of failure was unpredictable. As is the nature of behavioral edge decay, which decays without a structural reason you could have identified in advance.
This is what I believe occurred. There was no regulatory change to point at, no expiry-calendar shift, no alteration in market structure that I can identify — the edge simply ceased to exist on no schedule.
The strategy followed an epistemologically honest research process: correctly pre-specified according to a possible theory, correctly sized, correctly monitored, and correctly stopped. The hypothesis was still the fragile kind. The process worked as intended, which was to monitor fragility and risk, rather than trying to hammer and overfit edges to be as durable as possible. The process emphasizes that it can only monitor when an edge has stopped paying out as intended.
What would put it back in the book? The pre-specified sample was extremely unlikely to occur by chance, so more research would have to be done on why the effect existed and why it ceased. Going past that, rebound in performance is weaker evidence, simply the same behavioral hypothesis with a longer sample, though this time around there is a newfound skepticism to the performance of the strategy.
While the research workflow had provided what had seemed to be an independent return stream to a portfolio of other systematic strategies, this one had proved to fail, but the safeguards of the process through sequential monitoring prevented paying dues over the course of the year.
Lance Ladia · May 2026
The European Securities and Markets Authority (ESMA) requires that CFD brokers based in Europe must post the percentage of clients that trade with them and lose money. Between 74 to 89% of all traders with European brokers lose money.
Brokers are not interested in seeing their clients fail, rather the opposite. The profitable client is the client that trades more, and is eventually the client that will pay more commissions to the broker.
One can argue that the infrastructure to trade has never been better: many brokers are offering extremely tight spreads, with low margins required, and the accessibility for knowledge, thanks to the internet and social media, has never been more free.
Why then, after improved infrastructure, is the failure rate so drastically high? Contrary to popular belief, institutions do not see to it that the retail trader fails. What I propose in this article, is that the individual retail trader, despite meaning well, is actually beating themselves out of the market. And unfortunately, the current trading education space does little to mitigate the beatings. It has put the cart before the horse, and at times, monetized a cart with wheels that do not work.
Do you remember your first trade? I do: it was on a small cap stock named GRRR. I was on the lucky end of a limit up market where I made my first $100 trading stocks. The euphoria from that trade was unimaginable. I imagine, if you won your first trade as well, that there is an equally strong euphoric experience from your end. If you didn’t win your first trade, I imagine that you might not be here at all, and you will have said to hell with the market and gone about your day.
I remember there was a day I sat in an 9AM college class and had been staring at the market, waiting for it to open. I found a biotech company with the ticker RNAZ and once again found myself on the lucky end of a limit up market where I made my first $1000 trading stocks. I immediately walked out of the class. I vividly remember the walk back to my dorm room, and I felt as if at any moment, a group of personal servants would pop out from the bushes and start rolling red carpet where my feet would land on my triumphant walk back to a twin bed where I slept in for the day after leaving class.
Why do I tell you this? I want to demonstrate how extreme the environment of trading is. I have experienced complete euphoria from trading, where at any moment, it feels as if the entire world were plotting in your favor, and conversely, experienced complete dysphoria, where it feels like not a single thing in the world can go your way.
The emotional reactions produced from trading are extreme, because the cognitive environment that is trading the financial markets with leverage, is extreme. And not just extreme, but also uniquely extreme and quite hostile to normal, human evolutionary behavior.
In fact, I’d dare to venture and say that a normal human being, with normal thought patterns and behaviors, might fail to successfully trade the financial markets. Not that they are unintelligent, or lazy, but rather the human brain was specifically designed, by evolution, to abhor the conditions the financial markets present.
And so, what conditions do the financial markets present?
Knightian uncertainty is not the same as risk. Risk is able to be quantified — I risk, and I will lose at most, 2% from this trade. Knightian uncertainty is genuinely unknowable: no model can account for it, and the destruction that it runs on the brain is largely responsible for the extremes of trading.
Think about it this way: out in the wilderness, there was no room for probabilities. There was only life and death. If you hear a rustle in the bush, you will likely survive more if you just assume there’s a predator there rather than if it was just the wind. Your brain is inherently not designed for uncertainty, as hundreds of thousands of years of evolution have equated uncertainty to a lesser chance of survival.
Recall Matthew McConaughey’s quote in Wolf of Wall Street:
“Nobody, and I don’t care if you’re Warren Buffett or Jimmy Buffet, nobody knows if a stock is gonna go up, down, sideways, or in fucking circles! Least of all stock brokers!”
Every trade you place is under an element of uncertainty. You can compute all the statistics you want, but you will never 100% definitively know if a trade will win or lose. That is simply a fact that the human brain abhors. If it were a physical entity, I am positive that the instinct would be to grab it by its neck and strangle it tightly.
The brain will equate the discomfort of a trade to a threat to your survival. There has been a false distinction between body and mind, but it is important to know, especially in trading, that no such distinction should exist. Because when your mind is uncomfortable and angry, what does that manifest into? Self destructive behavior:
These are all physical manifestations of a mind that is in deep discomfort. All people are going to be unprepared for this because there is no prior experience that can truly emulate how this feels in real time, with money on the line.
That is another thing that makes this pervasive uncertainty even more destructive: the use of leverage. From my trading today, a 0.35% move in the underlying NASDAQ represented a gain of 2.5% on my account. The same move downward would’ve represented an equal 2.5% loss.
When someone is able to take $10,000 and control, in theory, millions of dollars worth of notional exposure, gains and losses can accumulate rapidly. When losses accumulate, the brain must ease the pain of financial loss, which as empirically documented by Kahneman and Tversky, is twice as painful as an equivalent gain.
The operative idea between uncertainty and leverage is understanding that your brain does not process information. It does not process the numbers. It does not process the market you see in front of your eyes. It processes the emotional magnitude of whatever narrative you have sold yourself, which is likely a narrative of certainty, simply because that is the natural human tendency.
Between the emotional heaven and hell, it is virtually guaranteed that any trader’s brain will start reinforcing feedback loops for particular behaviors. Let’s put this into an easy to understand concept:
The probability of heads or tails on a coinflip is 50/50. That does not mean that each flip will alternate perfectly, as you might get streaks of 5 heads, for example. Statistically speaking, if you flip the coin long enough, you might see outlier streaks of 10 or 11 heads for example. The distribution of these streaks is random, meaning that if you start flipping the coin right now, you could easily encounter that outlier streak. As the number of coin flips reaches infinity, the distribution will even out to 50/50.
And how does this translate over into trading?
A trader who has a strategy with zero edge, let’s say a 50% win rate, where it is mathematically impossible for them to make money in the long run, might encounter a streak of six wins. This trader is feeling fantastic and they are on top of the world. No one can tell them no. The feedback is immediate from those six trades: the money is in his account and he is withdrawing it as I write this and is about to buy themselves some new clothes.
The feedback is also, unfortunately, delayed. This trader’s brain has now been conditioned to run the strategy because of the emotional feedback of this lucky fluke of six wins, and thus, the delayed feedback manifests itself as ruin over the long run. They will fail to produce a profit over the course of 200 or 300 trades because their brain has conditioned itself to associate this strategy with profits, despite the fact that the math shows otherwise.
Slot machines work to exploit this same feedback loop psychology: bright lights and loud cha-ching noises help reinforce the behavior of hitting spin, despite the fact that someone may conceptually understand that the house always wins.
And so what is the implication for uncertainty, leverage, and destructive feedback loops? The failure rate is not an issue of knowledge, but rather traders being dropped into a cognitive environment that they could not have possibly known existed. They will not know of the destruction it runs on human judgement.
Virtually most of the available trading education is filled with “how-to’s.”
It is, at best, pattern recognition with a specific narrative attached to it. Now, before I develop further on this point, I am not saying you cannot go without some form of “how-to” because you need some form of technical analysis, but the TA provided by current trading education is not evidence based at all and cherry picked to produce a marketable narrative.
A broker that is explaining the MACD will equate the 9MA crossing above the 21MA as a bullish signal and will cherry pick examples of this happening and leave out every example on the chart where the crossover does not work.
The RSI is notorious because it will equate trending conditions as consistently overbought or oversold and the narrative attached is that the dominant move should be faded, despite the fact that a market in extreme trending conditions will likely persist with the same trending conditions.
A hammer candlestick pattern looks like such a good tool to time every top and bottom, until you go back to charts and realize that it occurs more frequently than you imagine and is not predictive of price at all.
There is a characteristic of the human brain called apophenia. It is the tendency for you to find patterns where no patterns exist. Again, a product of evolution. The education market has done a great disservice to traders because it reinforces aggressive pattern finding with virtually no statistical rigor attached to it, but rather an emotional and certainty filled narrative that does not do well in the markets. Have you ever been told to not marry a narrative? It is good advice. Your brain and my brain will do its best to form some type of story around the chart it is looking at. It will write a story for the losses and the wins you experience. Do not listen to it.
Well meaning brokers with a wholehearted interest in client success still pedal these flimsy “how-to’s” because the education itself is structurally flawed. They are not being purposefully dishonest. The issue is that many of the patterns and technical setups that are pedaled online and in books lack statistical validation that makes it distinguishable from randomness in any finite sample.
What would the alternative look like? It starts with a simple question: How do I know whether or not this pattern that I measured is a real pattern against noise? Not how do I identify this pattern, but how do I test if this pattern has any predictive value?
Before I write this section, I must preface that there is no feasible way that anyone can rid themselves of the uncertainty of the markets. It is its premier feature. Quantitative analysis only serves as a way to make the gap between knowing and not knowing a little more narrow. It is not a cut that is sutured fully.
The goal with any form of quantitative analysis (QA) or evidence based technical analysis (EBTA) is answering one question: does this approach have a positive expected value over a large number of repetitions?
Forget this trade. Forget the next trade. The mind should shift over to a longer horizon. Will this strategy produce a profit over the course of a year? Day by day as we bob on with our lives, it’s hard to see that the current is actually inching us ever so slightly closer to one direction. When you lose today, your brain will immediately and erroneously conclude that you have regressed backwards. When you win today, the brain will think of how far it has gotten. The short term horizon is dominated by noise and endless bobbing. QA shifts things towards the long term: how likely is it that this strategy will produce a profit over the course of this year?
In all of my strategies, I employ a statistical method called binomial significance testing. It is not exotic mathematics and a high school math student can do it. It simply answers one question: given the sample of trades that I have and the observed win-rate, what is the probability that this result happens by pure chance? Pure chance, in this scenario, being a 50% win rate strategy. Let me run you through an example:
Take a sample of 700 trades with a 54% win-rate: 378 wins and 322 losses. The question is how often a breakeven strategy at a 50% win-rate produces a result this extreme by pure randomness. The answer comes from how far 378 sits from the expected 350 wins of a coin-flip strategy, measured in units of standard deviation. In this case, the observed measurement of 378 wins sits 2.12 standard deviations above baseline. That distance corresponds to a probability of 1.7%. (p = 0.017). If the strategy had no real edge, this result would occur by luck once in every sixty times. This strategy is worth committing capital to.
There’s many more formulas I employ, which I discuss more on my website, that play a larger role in further validating a pattern or strategy.
This one particular formula is the entry for whether or not a particular pattern or strategy should have a single dollar committed to it. If one cannot statistically prove that such a pattern can be exploited with results better than chance, it should have no money dedicated to it.
Such testing never makes it to the education space, simply because it’s easier to sell a pattern and a narrative rather than a whole book discussing these formulas that are imperative and foundational to profitable trading.
There’s an erroneous belief I’ve seen floating around the trading space that psychology, discipline, and fortitude are what’s needed for profitable trading. I agree to an extent. No one leaves out the fact that even if your mindset is fortified with obsidian, it wouldn’t matter if you were trading a strategy or pattern with zero detectable edge.
QA and EBTA shift the questions and narratives. If I can statistically prove that a strategy is unlikely to be profitable by pure randomness alone, how will I feel when I come across a losing streak? Sure, I will be in pain when I lose five or six trades in a row, but I will be much more confident going into the next trading day because I have a degree of certainty in my strategy. I am not operating on blind faith here. I do not have my hands bound to useless prayer that I will hopefully make money, rather I have put in the work to validate that it is likely I will make money over the longer term period and I shouldn’t worry about what this day, or what this week has brought me!
I must reiterate this — QA and EBTA do not make the psychological harshness of trading disappear. It does however, reframe the pain narrative that exists in your brain. When the pain narrative has been changed in your brain, going from, “this strategy doesn’t work,” to “this is just a losing streak in an otherwise profitable strategy,” you are far less likely to deviate and engage in self destructive behaviors.
Richard Dennis allegedly turned $400 into $200 million. He turned novice traders into millionaire traders with his Turtle trading system. He reportedly said that he could post his exact strategy on the front page of the New York Times and still people would fail to make money.
Dennis did not have a deep cynicism for the human race’s intelligence or discipline, but he understood that a profitable strategy means virtually nothing without the correct behavioral techniques needed. What do I mean by this?
Dennis understood that even a statistically validated strategy is still prone to the human tendency to override it in the moment. Recall the idea of feedback loops from earlier: if a perfectly validated strategy has produced five losses in a row, the brain does not register a binomial significance test, but it registers, and quite strongly, the pain of five losses in a row. Cognitively, the tendency to want to deviate from a perfectly sound strategy will be present.
In my own experience trading an account at $3M for an office, I saw a drawdown across several statistically validated strategies. In the span of two trading days, I had gone one win out of twelve trades. I was ripping my hair out and began feeling as if the size I was trading was somehow large enough to where someone, somewhere was targeting and fading my moves. There was, of course, nothing wrong with the strategy, and I was not trading a meaningful enough size to where any of my positions were being targeted. Even with experience and intellectual understanding that my strategies were statistically sound, the narrative feelings of explaining why something has lost will always persist.
I tell you this because these behavioral techniques; being able to take a loss, overriding your impulses, acting equanimous in the face of immense uncertainty and pain, are not things that are learned through a trading course or a broker. These are things that are learned through exposure therapy coupled with deep introspection.
The act of thinking doesn’t sell well, which is why there is virtually no trading book that will tell you that introspection is a necessary skill. Let’s go back to the ESMA number: 74–89% of people must’ve also thought they were the exception. Maybe most of those people took a course and learned about a new candlestick pattern and thought that come market open they were ready to conquer the markets and claim their riches. Again, no matter how good a strategy is, the most important question to ask is if you can tolerate and behave the same when the strategy is losing. It will always experience periods of losses. It is not a matter of ‘if’ but ‘when.’
You can still fail with a validated strategy. Knowing is not doing. Doing is far harder than knowing. Have you ever heard the quote: “Practice and theory are the same in theory, but not in practice.”?
I am not demo trading. I don’t want to backtest forever. I am a trader first, not a researcher. I want to head into the dog-eat-dog world of the markets and make money. Having a statistically validated framework can remove some of the ambiguity on how to actually operate a profitable system. But it does not remove the requirement for behavioral discipline. It does not teach you how to be equanimous when faced with a losing streak. It cannot teach you to sit with an inhuman amount of pain. No book, teacher, course, or article can. Only you can!
I am not a psychologist, but I can provide some guiding insights. Market information is neutral: candlesticks are visual representations of price on a time axis. There is no narrative attached to a candlestick, the narrative is made by the human brain. When a narrative fails to play out as the brain has predicted, the brain will experience the pain of financial loss as well as “intellectual loss.” You were wrong, and no one naturally likes being wrong. The operative idea to try to neutralize the pain of being wrong is by understanding that you don’t need to be right or wrong on this trade. You need to be right in the long run, of course, but on each individual trade, the result is largely unimportant.
Sit down with yourself and think about your character traits. How do you handle being wrong? How do you handle being confused? How do you handle sitting on your hands and doing nothing?
I was, for the longest time, a person that was unable to look at their mistakes. After a big losing day, I could not open the chart and see where my trades were placed and see the losses I accrued. It absolutely disgusted me. This is not a trait that serves me or anyone well. You must see your mistakes and learn from them.
I trade on a 15 minute chart. I have one strategy that uses a 30 minute chart. For a long time, I was a person that could not sit on their hands and I impulsively would take trades on a one minute or two minute chart, trades that were not according to plan.
Naturally, the feeling of disgust will follow when you look at all your mistakes. This is the most powerful feeling you can experience, as disgust is one of the strongest catalysts for transformation. I can confidently speak on this topic as well:
For the longest time, I was a person that was disgusted by my own body. My ribcage was visible and I was so skinny that you could see the left side of my chest beat in tandem with my heart. I discovered the gym when I was around 16. I started taking care of my diet and I started to take pride in training with intensity because I was so disgusted with myself. Fast forward six years later, and I have built a body which I am satisfied with. My nutrition and training are intuition now. I can look at my body and be satisfied with it, not because people compliment me, but because I have achieved a level of self-love that my previous self never had. I trained like I was possessed, and force-fed myself meals. I recall one instance where I had a 1500 calorie dinner, then woke up several hours later for breakfast. I was still full from the dinner which had not fully digested and threw up my breakfast, which I remade without hesitation. Achieving these behavioral modifications and tolerating the discomfort of training intensely and eating beyond what my body wants to were manifestations of disgust that I had over what I used to look like.
I think about it this way: I am not as enamored by the beauty of a bouquet of roses, as I am enamored by avoiding getting pricked by its thorns.
Imagining my dreams and imagining what life would be like with them is cool — imagining what would happen if I did not change and I failed is terrifying. So terrifying that I would do anything to keep it from happening. If this meant that I had to sit on my hands and tolerate the boredom of staring at a 15 minute chart, then so be it.
There is a myth of fearlessness in trading. At least, I believe so. I don’t believe that as human beings we can ever truly rid ourselves of emotion. All we can do is to build up the courage through repeated exposure to act against our own fears. The pain will always be present, regardless of how well done a strategy’s framework is. The key is acting equanimously. That, I believe, is the great secret to trading. It is not so much ridding ourselves of our human emotions, but rather acting in a way where they are not distinguishable from our actions.
In relation to trading, much of human nature, our ingrained thoughts and behavior, is evil and counter-productive. Overcoming this through self-control is the real challenge. It has and will prove to be a non-linear process.
That is the great secret in trading — no matter how much statistical work you do, the pain and fear will never truly dissipate. It puts the trader in a tightrope between heaven and hell: on one side, there is the reality that happens where you give into your human nature and start lashing out on the markets, self destructively, and on the other side is an imaginative and idealistic world in which human emotions are able to be separated from a human brain.
Quantitative analysis cannot fully remove the balancing act, though it can dissipate some of the ambiguity. The rest of the balancing is up to the trader’s will.
Build a theoretical framework, run statistical tests, and most importantly, learn to sit with an enormous amount of discomfort. Without doing so, no amount of math can save you.
The strategy is the cart. The statistical framework to back it up is the horse. The driver is you. No movement occurs if one is off.
Lance Ladia · June 2026
Systematic trading focuses on a necessary, but insufficient question: does this edge work?
The right question should be why it works.
Of course, absolute certainty in a noisy and probabilistic system is impossible, but the difference between the two questions is what determines if an edge is real, if an edge will persist, and whether you can identify it will end before data tells you so.
A strategy that cannot explain itself is difficult to distinguish from a statistical accident.
During my experience working at a trading desk, I recall an experience where a new trader came in and was explaining a proprietary indicator he had developed that allegedly marked out support and resistance levels more accurately than any discretionary method.
I looked at the indicator myself, and there was a support or resistance level at every 10 point interval on the NASDAQ futures contract. He pointed out every instance where price barely touched a level and perfectly reversed. He seemed so adamant that there was a true edge to be found in his indicator and pushed me to further collaborate on it in order to develop a rule-based strategy for it.
What made the experience so memorable was the fact that I had an inside joke with one of my colleagues at the time — if you put a line anywhere on a price chart where price has previously traded before (i.e. not at an all-time high) it will eventually act as a perfect support or resistance level.
The point of this story is to demonstrate the idea of false-positives: if you test enough ideas, enough rules, and enough strategies, then solely by chance some of them will appear to have a statistically compelling p-value. At a 95% confidence level, testing 100 will, in theory, give you five false positives: strategies that have compelling quantitative evidence to support capital deployment, but are nothing more than statistical accidents that survived chance.
Quantitative researchers recognized this and implemented the Bonferroni correction. The issue is that it’s a patch on a wound that doesn’t solve the underlying issue. Bonferroni adjusts the threshold after the fact. The correct methodology would then seek to subvert any post-hoc adjustments: one pre-specified theory, one test, and measure what the data supports.
Pre-specification is a commitment to what you are testing before you see the data. Pre-specification only works if you have a plausible theory as to why an edge should exist before you look for it. Theory turns the arbitrary into honesty.
Pre-specification is not a statistical technique, but rather a qualitative discipline that requires a theory to be meaningful.
There is an inherent decay risk in publicly known and replicable trading signals. EMA crossovers, Bollinger Band touches, RSI levels, MACD Crossovers — these signals are documented, widely implemented, and algorithmically replicable.
If such an edge were to be found solely because a sufficient number of participants are reacting to the same signal, the edge is visible to participants who can exploit the predictability of it. This is the crowding problem in trading. The mechanism is not structural. It is behavioral, and behavioral edges are contingent on the continued presence of the specific behavior.
As such, the distinction must be made: obligation versus convention and discretion.
Options dealers hedging a short gamma position are not discretionarily choosing to create directional flow. It is a mechanical consequence of the market. It does not go away because price is touching an EMA level. It does not get arbitraged out because it cannot be predicted far enough in advance to front-run it at scale. It exists because of what the market is, not because what traders have agreed to do.
I have used options hedging as an example, but virtually any mechanism of market microstructure would fall into the realm of obligation, rather than convention and discretion.
The honest qualification is that some indicator strategies do work because the indicator happens to approximate a genuine structural variable. A moving average might coincidentally track volatility regime changes. There is potential predictive power, but the power comes from an underlying variable, not the indicator itself, so the honest response is to identify the underlying variable and measure it directly.
The question to ask of any signal is not whether it has worked historically. As previously discussed, enough trials and enough chances will produce false positives. It is whether there is a plausible reason it should continue to work, a question which is answered by a theory.
On the nature between obligation vs discretion, I would hypothesize that two types of edge decay would stem from these, differing in their predictability:
Strategy performance tracking is limited to quantitative methods, but a structural theory gives you a qualitative method for tracking performance. You are not waiting for the equity curve to tell you something changed. You are monitoring conditions that make the edge possible and can identify deterioration before it appears in returns.
One honest pushback on my idea here: Theory first development could just be sophisticated post-hoc pattern finding. Generating a plausible-sounding story constructed after observing a pattern is indistinguishable from a genuine theory.
Theory first pre-specification establishes a sequence. A theory written before data is collected is accountable in a way that post-hoc rationalization is not. Timestamp on development is the first line of evidence against this. Theory, rules, data collection, then examination.
Theory can also have an independent verifiability of the mechanism in question. A genuine structural claim makes predictions about market structure that can be verified through channels other than a chart or the strategy’s performance. Theory can be corroborated by independently observable market history.
Lastly, a genuine theory would be able to make predictions on performance based on new and untested conditions. What happens to dealer hedging when options open interest is low? When the VIX is elevated? In instruments with low options activity? A data-mined pattern cannot generate these predictions because it has no mechanism to reason from. A theory can, and the accuracy of those predictions is the strongest possible evidence that the mechanism is truly real and robust.
As the nature of all speculation, complete certainty is not achievable. What is achievable is a methodology that makes coincidence and chance increasingly less likely to attribute to performance as evidence accumulates.
The claim is not that theory-first strategies are always vastly profitable and have significant edges. They are, however, testable, always revisable, and always generating knowledge — especially when they fail. A data-mined strategy that stops working will only tell you that it stopped working.
Falsifiability is not a technique layered on top of strategy development. It is a natural consequence of having a theory that can be independently substantiated. If you cannot specify conditions where an edge should not work, you merely have a description.
Before testing any strategy, ask whether you can plausibly explain why it exists and why you are able to extract alpha from it. Who is on the other side? What obligation, incentive, or other reason do they have to be there? And under what conditions would this disappear.
Inability to answer those questions makes a strategy indistinguishable from a statistical accident regardless of what a backtest says.