One of the most satisfying moments in strategy development is watching a backtest improve.

A small adjustment reduces drawdowns. A new filter smooths the equity curve. A parameter tweak increases returns. With each change, the strategy appears more refined and more intelligent.

The system starts to look less like an idea and more like a finished product.

At least on paper.

But this process carries a quiet risk. The more precisely a strategy fits historical data, the more fragile it often becomes when the future inevitably looks different from the past.

This is the hidden cost of overfitting.

When improvement is actually coincidence

Markets contain patterns, but they also contain a large amount of randomness. Backtesting makes it very easy to mistake the two.

When we adjust a parameter to improve past performance, we often assume we have captured something meaningful about the market. In reality, we may simply be aligning the strategy with a random fluctuation that happened to appear in the data.

Because that fluctuation already occurred, the backtest rewards the adjustment. The equity curve improves, the drawdown shrinks, and the system appears stronger.

But the future does not replay the past exactly. The small quirks that the system learned from the historical dataset rarely repeat in the same way.

What looked like insight was often just coincidence.

Why optimization feels so convincing

Optimization is powerful because it feels objective. You change a rule, and the data gives you an answer.

Returns go up. Volatility goes down. The system becomes easier to believe in.

The numbers seem to prove that the strategy has improved.

The problem is that the dataset we test against is fixed. Once we begin tailoring a strategy to it, we are slowly transforming that dataset into a blueprint for the system’s behavior.

The strategy begins to fit the past extremely well.

Sometimes a little too well.

The illusion of precision

Highly optimized strategies often give the impression of precision. Every parameter appears carefully tuned. Every rule seems justified by historical results.

The system feels engineered.

But markets are not stable environments. Volatility shifts. correlations change. Regimes evolve. A parameter that looked ideal in one period may be far less effective in another.

A system that depends on precise tuning is therefore vulnerable. When conditions drift away from the historical environment that shaped the model, performance begins to deteriorate.

The strategy has not necessarily stopped working.

It has simply lost the exact conditions it was optimized for.

Robustness is more valuable than perfection

A robust strategy behaves reasonably well across a wide range of environments. It does not rely on precise parameter values, and it does not collapse when conditions change slightly.

This often means accepting results that look less impressive in backtests.

Returns may be lower. Drawdowns may appear larger. The equity curve may look less smooth.

But robustness has a critical advantage: it survives outside the laboratory of historical data.

In live markets, durability matters more than perfection.

Simpler systems resist overfitting

One of the easiest ways to reduce the risk of overfitting is to limit complexity. Fewer parameters create fewer opportunities to accidentally fit noise.

Simple systems also make it easier to understand why a strategy works. That understanding becomes important when the inevitable drawdowns arrive.

If a system contains dozens of rules and parameters, diagnosing problems becomes difficult. Every component becomes a possible explanation for underperformance.

A simpler structure makes it easier to distinguish between normal variance and genuine structural issues.

The paradox of good research

The irony of strategy research is that improvement often becomes harmful when it goes too far.

The first few refinements usually remove obvious weaknesses. Beyond that point, additional changes often provide smaller and smaller benefits while increasing the risk of curve fitting.

What begins as thoughtful research gradually turns into an attempt to perfect the past.

And the past does not repeat perfectly.

Final thought

A good system does not need to explain every fluctuation in historical data.

It only needs to capture the broad behavior that tends to persist across changing environments. That behavior is rarely found by chasing the best-looking backtest.

More often, it emerges from restraint.

The goal is not to build a strategy that performs perfectly in the past.

The goal is to build one that remains believable in the future.

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Modern financial markets are fast, abstract, and emotionally demanding.

Prices change in milliseconds. News travels globally in seconds. Capital moves at a scale and speed that would have been unimaginable even a generation ago.

And yet the brain making trading decisions inside this system is not modern at all.

Homo sapiens have existed for roughly 300,000 years. For the overwhelming majority of that time, humans lived as hunter-gatherers in small tribes, navigating a world defined by immediate physical threats, scarce resources, and short-term survival decisions.

Our brains were shaped in that environment.

Financial markets have existed for only a few hundred years. Electronic markets for only a few decades.

Evolution hasn’t had time to catch up.

The Rustling in the Bushes

Imagine standing in tall grass 50,000 years ago and hearing something move nearby.

That rustling could be the wind.
Or it could be a predator.

If you ignore it and you’re wrong, you die.

If you overreact and you’re wrong, you burn a few calories.

Evolution strongly favors the overreaction.

As a result, our brains are wired to respond quickly and emotionally to perceived threats. The amygdala activates. Stress hormones rise. Attention narrows.

Now replace the rustling bushes with a sharp market decline.

A red screen triggers the same ancient circuitry. Your heart rate increases. You feel urgency. You want to act immediately.

But unlike a predator, a market drawdown does not require instant survival action. In fact, impulsive reactions are often what create permanent losses.

The survival instinct that kept our ancestors alive can work against us in a probabilistic financial system.

Feast When Food Is Available

Early humans also lived in a world of scarcity.

There was no grocery store. No food delivery. No guarantee of the next meal. When a hunt or scavenge was successful, the logical response was to eat — a lot. Store energy. Take advantage of abundance while it was available.

This behavior was adaptive. It increased survival odds.

In modern markets, it manifests as greed.

When a trade works quickly, the instinct is to press harder. Increase size. Double down. Extract as much as possible before the opportunity disappears.

The brain treats profits like scarce food.

But markets are not one-time hunts. They are ongoing systems governed by probabilities. Overexposure during favorable periods often leads to disproportionate damage when conditions shift.

Feast-and-famine thinking creates volatility in decision-making.

A Brain Built for Certainty

Our ancestors dealt with tangible problems:

• Is that animal dangerous?

• Is there food nearby?

• Is this person friend or foe?

These were binary, immediate questions.

Markets are different. They operate in shades of probability. There are no certainties — only distributions of outcomes. A trade can be well-structured and still lose money. A reckless decision can still pay off in the short term.

This ambiguity is deeply uncomfortable for the human brain.

Behavioral finance research consistently shows that humans:

• Feel losses more intensely than equivalent gains

• Overweight recent information

• Seek patterns in randomness

• Overestimate their predictive abilities

These are not flaws in character. They are cognitive shortcuts developed to help us survive.

But in trading, those shortcuts often distort judgment.

The Emotional Mismatch

Markets constantly trigger ancient emotional systems.

A rally stimulates reward circuitry and increases confidence.
A drawdown activates loss aversion and fear.
A string of wins creates overconfidence.
A string of losses creates paralysis.

Our nervous systems were designed for short bursts of stress, followed by resolution. Trading, however, exposes us to continuous uncertainty. The feedback loop never ends. Prices fluctuate all day, every day.

Expecting pure willpower to override 300,000 years of evolutionary wiring is unrealistic.

Emotion is not the exception in trading.

It is the default.

Why Systematic Trading Works

This is precisely why a systematic framework is powerful.

A rules-based system does not eliminate emotion, but it reduces its influence. Decisions about entry, exit, sizing, and allocation are defined in advance — ideally during calm conditions rather than in moments of stress.

Instead of reacting to every perceived threat, the system filters noise.
Instead of feasting impulsively during favorable conditions, it manages exposure deliberately.
Instead of improvising under pressure, it executes predefined rules.

The structure becomes a buffer between ancient instincts and modern complexity.

It acknowledges that we are not wired for probabilistic decision-making under constant emotional stimulus — and it designs around that limitation.

Trading Against Your Biology

Perhaps the most important realization for any trader is this:

Feeling fear does not mean you are weak.
Feeling greed does not mean you are reckless.

It means you are human.

The goal is not to eliminate emotion. That is impossible. The goal is to prevent emotion from dictating behavior.

A systematic process accepts that our brains evolved for survival on the savannah, not for managing risk in global capital markets. Instead of relying on discipline alone, it embeds discipline into the framework itself.

Over time, that structure becomes the edge.

The Real Advantage

As technology advances and information becomes more abundant, raw intelligence becomes less differentiated. Everyone has access to charts, data, and analysis.

What remains scarce is emotional stability.

Understanding that we are biologically misaligned with modern markets changes the objective. Success is no longer about outsmarting everyone else. It is about building a process strong enough to counteract our own instincts.

Markets are modern.

Our brains are ancient.

A systematic approach bridges that gap.

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When a strategy fails, traders often blame the logic. They revisit parameters, add filters, or question whether the edge was ever real.

Much less often do they ask a simpler question:

Was I trading it at a size that made discipline impossible?

Position sizing is usually framed as a risk management tool. It determines how much capital is at stake and how volatile returns will be. But its deeper role is psychological. Size determines whether a strategy can actually be followed.

A sound system traded too large will not survive its first serious drawdown.

Drawdowns are tolerable — until they aren’t

On paper, a 15% or 20% drawdown may look manageable. In backtests, it appears as a temporary dip in an otherwise upward equity curve.

In real life, that same drawdown can feel very different.

It may affect sleep. It may alter mood. It may introduce doubt where none existed before. Suddenly, the urge to override signals or reduce exposure becomes overwhelming.

The logic of the strategy hasn’t changed. The environment hasn’t necessarily changed. What has changed is the emotional weight of the numbers.

That weight is a function of size.

Oversizing turns variance into crisis

Every strategy with a positive expectancy still contains variance. Losing streaks occur. Regimes transition. Performance clusters.

At appropriate position sizes, this variance feels uncomfortable but manageable. It can be observed and evaluated calmly.

At excessive sizes, normal variance feels existential. Losses no longer register as statistical events; they register as threats.

When that happens, discipline collapses. Traders skip signals. They exit early. They “wait for confirmation.” The system becomes discretionary without being acknowledged as such.

The strategy didn’t fail. The sizing did.

Correct sizing feels too small

One of the paradoxes of trading is that appropriate position sizes often feel underwhelming during good periods.

When markets are cooperative and returns are strong, the temptation to increase size grows. The recent performance justifies it. Confidence expands alongside equity.

But increasing size based on recent success amplifies future emotional swings. When conditions inevitably change, the drawdown that follows will feel disproportionately painful.

Sizing should be determined by worst-case expectations, not recent gains.

Position size defines behavior under stress

A useful diagnostic is simple: imagine your strategy experiencing its historical maximum drawdown tomorrow.

If that scenario feels intolerable — if it would trigger panic or forced decision-making — then the size is too large.

This is not about minimizing returns. It is about preserving decision quality.

A system can only compound if it is followed consistently. Position sizing determines whether consistency is realistic.

Smaller size extends longevity

Trading is not a short-term performance contest. It is a long-term compounding exercise.

A slightly smaller position size that allows you to:

• stay aligned with signals

• tolerate drawdowns

• avoid emotional overrides

will often outperform a larger size that causes periodic behavioral breakdowns.

Longevity is an edge.

The psychological governor

In engineering, a governor regulates speed to prevent mechanical failure. It limits performance to preserve function.

Position sizing plays a similar role in trading. It constrains returns in strong periods to prevent collapse in weak ones.

Without that constraint, even robust systems can self-destruct under pressure.

Final thought

A strategy’s true risk is not its drawdown percentage. It is the point at which its trader abandons it.

Position sizing should be chosen not for maximum theoretical return, but for maximum survivability.

When size aligns with temperament, discipline becomes sustainable — and sustainable behavior is what allows compounding to work.

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Gold is in a drawdown.

Not a small one. Not a routine one.

A real one.

If you’re following the model right now, this is the part that tests you. Not the signals. Not the logic. The experience of sitting in it.

So instead of avoiding that reality, let’s put it into context.

What We’re Actually Experiencing

The current regime is Bearish, which means the system is positioned in GLD.

As of today:

• The trade is down ~3% from entry

• But more importantly, it is down ~14% from its intra-trade peak

That second number is the one that matters.

Because that’s what you feel.

The frustration doesn’t come from being down a few percent.
It comes from watching a trade work… and then give a large portion of it back.

That’s what this is.

Entry vs. Drawdown (Why This Feels Worse Than It Looks)

There are two ways to look at any trade:

1. Return from entry

2. Drawdown from the best point reached

Most people focus on the first.

But psychologically, the second is what matters.

You don’t experience your P&L from the entry price.
You experience it from the high watermark.

And right now, that high watermark is well above where we are.

That’s why this feels like a large loss — even though the trade itself is only modestly negative.

How This Compares to History

Since TQQQ began trading in 2010, there have been dozens of bearish regime trades where the system held gold.

When you isolate just those trades:

• The typical drawdown is around 4–5%

• Even the worse-than-average ones tend to stay under 8%

This one is sitting at roughly 14%.

That puts it in a very different category.

In fact:

This is the second worst bearish-trade drawdown in the full history of the model.

That matters.

Because it tells us something important:

This is not a normal pullback.
It is a tail event within the system.

The Only Worse Example

There is only one bearish trade that experienced a deeper drawdown.

• March 2011

• Max drawdown: ~15%

• Final result: +17%

That trade looked worse than this one at its low.

It felt worse.

And it still finished as a strong winner.

What That Means (And What It Doesn’t)

This is where it’s easy to jump to the wrong conclusion.

You could say:

• “This is one of the worst drawdowns ever”

• “Something must be broken”

But history doesn’t support that.

Instead, it shows something more uncomfortable:

Deep drawdowns can occur inside trades that ultimately work.

Not always.

But often enough that you can’t treat drawdown alone as a failure signal.

The Pattern Beneath the Surface

When you look at the worst bearish trades:

• Several experienced double-digit drawdowns

• Many of them still recovered and finished positive

• The path was messy, even when the outcome wasn’t

That’s the key distinction.

Outcome and path are not the same thing.

A trade can be “right” in the long run and still look very wrong in the middle.

What This Actually Tests

This isn’t a test of prediction.

It’s a test of process.

Because in moments like this, you don’t have new information.
You have the same system, the same rules, and worse-looking numbers.

And the temptation is always the same:

• override the system

• reduce exposure

• second guess the signal

Not because the rules changed.

Because the experience did.

System Stress vs. System Failure

Every systematic approach goes through periods like this.

The important distinction is:

• System stress → uncomfortable, statistically expected, historically observed

• System failure → structural breakdown, edge disappears, behavior changes permanently

Right now, this fits firmly in the first category.

It is extreme.

But it is not unprecedented.

And more importantly, it has precedent that resolved positively.

The Point of the System

The goal of a regime system is not to eliminate drawdowns.

It’s to make sure:

• you know when to be in

• you know what to hold

• and you don’t have to make decisions in the middle of uncertainty

Because the hardest trades are not the ones that go straight against you.

They’re the ones that work… then don’t… then maybe will again.

Final Thought

This is what a real drawdown looks like.

Not theoretical. Not backtested. Real.

It’s uncomfortable. It’s statistically rare. It tests conviction.

But it’s also part of the distribution.

And if you’re going to follow a system, you don’t get to choose only the easy parts of that distribution.

You get all of it.

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Every March, we celebrate luck.

Four-leaf clovers.
Pots of gold.
Fortunes discovered at the end of rainbows.

St. Patrick’s Day is built around the idea that success is something stumbled upon — something found unexpectedly, as if wealth and good fortune are simply waiting to be uncovered by the right person at the right moment.

Markets are often viewed the same way.

The Illusion of the Lucky Trade

When someone catches a big move — a breakout stock, a perfectly timed option, a surge in a trending theme — observers rarely ask about the process behind it. They assume the trader saw something special. Or worse, they assume the trader got lucky.

In the short term, it can be hard to tell the difference.

Markets are probabilistic. A reckless trade can win. A disciplined trade can lose. Outcomes and skill are not perfectly aligned over small samples. That ambiguity creates room for the myth of luck to thrive.

But over time, luck does not compound.

Process does.

Why Luck Is So Attractive

Luck is emotionally appealing because it requires no structure. It allows us to believe that a single bold decision can dramatically change our financial future. It suggests that somewhere out there is a “pot of gold” trade — the one that will make up for all prior mistakes.

That narrative is powerful. It is also dangerous.

Chasing luck encourages oversized risk. It encourages reacting to headlines. It encourages abandoning discipline when a tempting opportunity appears. The focus shifts from building a repeatable edge to chasing isolated wins.

The irony is that even when luck delivers a short-term gain, it often reinforces the wrong behavior.

Process Is Quiet

A systematic trading approach rarely looks dramatic. There are no heroic predictions. There are no sudden declarations that “this is the big one.” Instead, there is structure.

Rules are defined in advance.
Risk is sized deliberately.
Exposure shifts only when broader conditions change.

This kind of framework can feel underwhelming in the moment. It does not promise immediate transformation. It does not rely on perfect foresight.

What it does provide is consistency.

And consistency is what allows compounding to work.

The Difference Over Time

In a single week, luck and process can look identical.

In a single month, they may still look similar.

Over years, they diverge completely.

Luck produces erratic equity curves — bursts of growth followed by sharp reversals. Process produces something steadier. Not smooth, not perfect, but governed by rules rather than impulse.

A structured regime approach, for example, does not attempt to predict every twist in the market. It identifies broader environments — when risk is being rewarded and when it is being punished — and allocates accordingly. It accepts that not every move will be captured and that some drawdowns are inevitable.

That humility is intentional.

It replaces the search for lucky timing with a repeatable framework.

Skill Isn’t Obvious at First

One of the hardest parts of trading is accepting that good decisions do not always lead to immediate rewards. A disciplined trade can lose money. A reckless one can make it.

In those moments, luck appears superior.

But skill reveals itself in durability. It shows up in survival during difficult regimes. It shows up in the ability to follow the same process when recent results are disappointing. It shows up in the absence of catastrophic mistakes.

Luck may create stories.
Process builds track records.

The Real “Pot of Gold”

St. Patrick’s Day imagery suggests that wealth is discovered at the end of a journey — that if you chase the rainbow long enough, something extraordinary will be waiting.

Markets rarely work that way.

There is no single trade that solves everything. There is no hidden shortcut that eliminates risk. The “pot of gold” in trading is not a windfall. It is the accumulated result of disciplined behavior repeated hundreds of times.

It is risk managed carefully.
It is capital preserved during adverse conditions.
It is patience exercised when excitement is tempting.

Over time, those behaviors compound into something that may look like luck from the outside.

From the inside, it is simply process.

Choosing Process Over Luck

As information accelerates and commentary grows louder, the temptation to chase “the big one” will only increase. There will always be another headline, another prediction, another opportunity framed as rare and urgent.

Choosing a systematic approach is a decision to step out of that cycle.

It is an acknowledgment that long-term success is less about finding fortune and more about following structure. It is a commitment to discipline over drama.

Luck makes for a good holiday theme.

In markets, process is what endures.

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Most frustration in trading comes from asking the wrong question.

When traders look at performance, the first question they usually ask is simple: Did I make money?
The problem is that this question ignores the most important variable in markets — the environment.

Markets do not behave the same way all the time. They cycle through periods of expansion and contraction, clarity and confusion, momentum and stagnation. Strategies that thrive in one environment often struggle in another, and that struggle is not a flaw. It is a feature.

Understanding regimes doesn’t eliminate drawdowns, but it does something just as important: it makes them interpretable.

Why “one-size-fits-all” thinking breaks down

Many traders implicitly assume that a good strategy should work most of the time, regardless of conditions. When it doesn’t, they conclude something must be wrong.

This expectation is the source of enormous frustration.

Trends don’t persist forever. Volatility clusters. Correlations change. Liquidity expands and contracts. When the underlying environment shifts, the payoff structure of a strategy shifts with it.

A trend-following approach will struggle in choppy, sideways markets. Mean-reversion strategies will suffer during strong directional moves. Risk-on exposure will feel brilliant in expanding environments and reckless in defensive ones.

None of this is surprising — unless you’re ignoring regimes altogether.

Regimes explain when underperformance is correct

One of the most difficult ideas for traders to accept is that underperformance can be the right outcome.

If a system is designed to reduce risk during hostile environments, it will necessarily lag during sharp rebounds. If it is designed to preserve capital during contractions, it will feel overly cautious near turning points.

Without a regime framework, these periods feel like failure. With one, they feel expected.

The question shifts from “Why am I underperforming?” to “Is this the environment where this behavior makes sense?”

That shift alone reduces the urge to interfere dramatically.

Why regime transitions are the hardest periods psychologically

If clear regimes were the norm, trading would be much easier. The problem is that markets spend a meaningful amount of time in transition.

During these periods:

• signals flip back and forth

• trends fail quickly

• conviction evaporates

Performance often suffers not because the strategy is broken, but because the environment itself is unstable.

These are the moments where traders are most tempted to abandon structure in favor of intuition. Ironically, these are also the moments where structure matters most.

Regimes don’t predict — they contextualize

A common misconception is that regime-based systems are about forecasting. They’re not.

They don’t try to predict what the market will do. They respond to what the market is doing now, and adjust exposure accordingly.

This distinction matters. Prediction invites ego and overconfidence. Context invites humility.

When a system aligns exposure with prevailing conditions, the goal is not perfection. The goal is survival through unfavorable environments and participation during favorable ones.

Why this matters during drawdowns

Drawdowns feel very different when you can explain them.

A drawdown during a risk-off environment, while capital is being preserved, carries a different psychological weight than a drawdown that violates a system’s core logic. One is expected friction. The other is a warning.

Regime awareness gives you a way to tell the difference.

Instead of reacting emotionally to short-term results, you evaluate whether behavior still matches design. That evaluation is calmer, slower, and far more productive.

Trading the environment instead of your emotions

Most traders think they are reacting to markets. In reality, they are reacting to how markets make them feel.

Regime-based thinking creates distance between stimulus and response. It replaces emotional interpretation with contextual understanding.

That distance is where discipline lives.

Final thought

Markets are not broken when your strategy struggles. They are simply different.

When you stop demanding that a single approach perform equally well in every environment, drawdowns become less personal and patience becomes easier to maintain.

You’re no longer fighting the market.
You’re aligning with it.

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We are entering a period where intelligence is becoming abundant.

Artificial intelligence can now summarize earnings calls, scan thousands of headlines in seconds, generate trade ideas, simulate strategies, and produce confident explanations for almost any market move. The tools are impressive. They are accelerating quickly. And they are only going to get better.

But as intelligence becomes cheaper and faster, something else increases alongside it.

Noise.

The Coming Flood of “Hot Takes”

When information was expensive to produce, commentary was naturally limited. Publishing required friction. Analysis required time. Opinions had barriers.

AI removes much of that friction.

It is now trivial to generate:

• Instant market reactions

• Data-driven charts

• Confident explanations

• Highly persuasive narratives

The result will not be a shortage of insight. It will be a surplus of it.

Every market move will have dozens of interpretations. Every data release will produce hundreds of takes. Every technical level will have an AI-generated probability attached to it.

The challenge will no longer be access to information.

The challenge will be filtering it.

More Intelligence Doesn’t Automatically Create Clarity

There is a subtle assumption embedded in the AI conversation — that more intelligence equals better outcomes.

In markets, that isn’t necessarily true.

Markets are not solved by the fastest summary or the most eloquent explanation. They are probabilistic systems driven by human behavior, liquidity flows, and structural forces that don’t always respond cleanly to analysis.

In fact, more interpretation often leads to more overreaction.

When every pullback can be explained in real time, the temptation to act increases. When every rally has a narrative attached to it, conviction becomes easier — even if it’s misplaced.

Abundant analysis can create the illusion of control.

But control and understanding are not the same thing.

Signal Becomes Scarcer When Noise Becomes Cheaper

AI will make it easier than ever to sound informed.

It will also make it easier than ever to mistake commentary for edge.

As the volume of opinions increases, true signal becomes harder to identify. Not because signal disappears — but because it is buried beneath layers of interpretation.

In that environment, complexity can feel sophisticated. A strategy that references dozens of inputs and adapts dynamically to every new development can appear superior.

Yet complexity often introduces fragility.

The more variables involved, the more opportunities there are to react emotionally to short-term noise disguised as insight.

Why I Choose Simplicity

My market regime strategy is intentionally simple.

It does not attempt to interpret every headline.
It does not react to every economic release.
It does not require constant re-optimization.

It asks a narrower question:
Is the broader environment risk-seeking or risk-averse?

From there, it allocates accordingly.

That structure is not designed to win every week. It is designed to reduce decision fatigue and prevent overreaction. It focuses on process over prediction.

In a world where AI can generate thousands of signals per day, the ability to ignore most of them becomes an advantage.

Simplicity is not a rejection of technology. It is a filter.

Structure Over Stimulation

AI-driven markets will likely feel faster. Reactions will be quicker. Narratives will form instantly. Volatility may cluster around information events as algorithms and humans process the same data in real time.

The temptation will be to respond to everything.

A structured regime approach does the opposite. It acknowledges that most information does not require action. It waits for broader conditions to shift before adjusting exposure.

That patience is not passive. It is deliberate.

Structure creates distance between stimulus and response. In trading, that distance often determines survival.

The Real Edge May Be Emotional

As analytical tools improve, emotional discipline may matter more, not less.

When everyone has access to AI-enhanced research, the differentiator will not be who has the best chart or the fastest summary. It will be who can execute consistently without being pulled into every emerging narrative.

A simple, rules-based framework reduces the number of decisions that need to be made in the heat of the moment. It provides clarity when commentary becomes overwhelming.

In that sense, the edge is not superior prediction.

It is the ability to stay aligned with a predefined process while the noise grows louder.

The Long View in an Accelerated World

AI will reshape industries. It will reshape markets. It will reshape how information flows.

What it will not change is the underlying truth that markets reward disciplined behavior over time.

In an environment flooded with confident explanations and rapid-fire analysis, the most valuable skill may be restraint.

The discipline to follow a simple framework.
The patience to let probabilities play out.
The humility to admit that not every data point demands action.

As intelligence becomes abundant, clarity will become scarce.

And sometimes clarity begins by choosing less.

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Over the past several months, I’ve been experimenting with how to present the Market Regime model in the clearest, most useful way possible.

As the project has grown, it became obvious that the Daily Dashboard needed its own dedicated home.

So I’ve moved it.

Starting today:

• The Daily Dashboard posts will no longer appear directly in Substack.

• The live Current Regime page now lives on marketregimes.com.

• Access is free — you just need to create a login.

Why the change?

Substack is fantastic for distribution, Notes, and long-form research.

But the Regime model is something people check repeatedly — like a tool. It deserves its own structured environment.

The new Current Regime page now includes a visual regime strength meter and a cleaner, more focused layout designed for quick daily reference:

At the same time, I’ve upgraded the Track Record page on the site. It now includes a fully updated time-weighted equity curve, structured performance summaries, and improved transparency around historical results. My goal is to make the process as clear and auditable as possible — no hidden smoothing, no selective windows.

Substack remains where I’ll publish:

• Research articles

• Commentary

• Notes

• Strategic updates

The live regime and dashboard now live on the site.

If you’ve been following along daily, nothing is going away — it’s just organized better.

You can access the Current Regime page here:

Current Regime

As always, the goal is clarity, discipline, and transparency.

Onward.

— Kevin

When traders experience a drawdown, the instinctive response is rarely to simplify. It’s to add.

Add a filter.
Add a confirmation.
Add one more condition to “avoid this next time.”

This instinct feels rational. It feels like learning. In reality, it’s often the beginning of the end.

Complexity feels like control

Complex systems are seductive because they give the illusion of precision. Every additional rule feels like risk reduction. Every extra parameter feels like foresight.

If a strategy struggles in certain environments, complexity offers a comforting narrative: the issue isn’t the approach — it just needs refinement.

But markets are not stationary problems. They change, adapt, and shift in ways that no set of finely tuned rules can fully anticipate.

The more precise a system becomes, the more fragile it often is.

Optimization works best on the past

Most complexity enters a system during backtesting. A parameter is adjusted to improve returns. A rule is added to smooth drawdowns. Performance improves — historically.

The problem is that optimization is exceptionally good at fitting noise.

A system that is tuned too closely to past conditions often struggles when those conditions inevitably change. What looked like intelligence was really coincidence. What looked like robustness was sensitivity.

This is why highly optimized systems tend to fail quietly. Not in dramatic blow-ups, but in prolonged underperformance that slowly erodes confidence.

Robustness is different from precision

Robust systems are not designed to be perfect. They are designed to be durable.

They work reasonably well across:

• different market environments

• different volatility regimes

• different cycles

They accept that no single configuration is optimal everywhere. Instead of chasing peak performance, they prioritize consistency of behavior.

This tradeoff matters more than most traders realize.

A system that performs slightly worse on paper but behaves predictably in real time is far more likely to be followed than one that looks exceptional in backtests but feels unstable in practice.

Simplicity protects behavior

One of the most underappreciated benefits of simplicity is psychological.

Simple systems are easier to:

• understand

• explain

• trust

• stick with

When drawdowns occur — and they will — a simple system allows the trader to say, “This is still behaving as designed.”

Complex systems invite second-guessing. Every component becomes a potential suspect. Every loss triggers a new round of doubt.

The more moving parts a system has, the harder it is to know why it’s struggling — and the easier it is to abandon it prematurely.

Complexity increases intervention risk

Ironically, complex systems often lead to more discretionary interference, not less.

When results deviate from expectations, the temptation to tweak becomes overwhelming. A parameter is adjusted. A rule is temporarily ignored. A signal is overridden “just this once.”

Over time, the system stops being a system at all.

Simple systems reduce this risk by limiting the number of decisions that can be questioned. Fewer levers means fewer opportunities for emotion to sneak in.

Robust systems accept imperfection

A robust system doesn’t aim to win every environment. It aims to survive all of them.

That means:

• underperforming at times

• lagging during sharp reversals

• feeling slow when markets are euphoric

These are not design flaws. They are the cost of durability.

The paradox is that strategies which look less impressive over short windows are often the ones that compound best over long ones — precisely because they can be followed consistently.

Final thought

Markets punish fragility more than they reward brilliance.

A system doesn’t need to be clever to succeed. It needs to be understandable, repeatable, and resilient to change.

When you prioritize robustness over optimization, you’re no longer trying to outsmart the market. You’re trying to stay aligned with it long enough for compounding to do its work.

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Most people struggle with trading because they expect progress to be visible far too quickly. A trade is placed, a setup is followed, and if the outcome isn’t immediately positive, doubt sets in. The system is questioned, the rules are tweaked, or the approach is abandoned entirely.

Systematic trading doesn’t work on that timescale. Like physical training, the most important progress happens quietly at first, well before it shows up in obvious results.

The Work Comes Before the Results

When someone starts working out, the early phase rarely looks impressive. You show up consistently, follow a program, and leave feeling tired but unchanged. For a long time, there is no clear visual payoff. The mirror doesn’t cooperate, and progress feels theoretical rather than real.

Trading systems behave the same way. Early on, the account balance may move sideways, and sometimes backward, even while the process is improving. What’s actually being built in this phase is discipline — the ability to execute the same actions repeatedly without overreacting to short-term outcomes.

That foundation matters. Without it, any gains that do appear tend to be fragile. With it, later growth has something solid to rest on.

One Trade Is Just One Rep

No single workout makes someone strong, and no single trade determines success or failure. Strength is built through repeated exposure to the same movements over long periods of time. Miss a rep here or there and it doesn’t matter. What matters is that the overall pattern remains intact.

Systematic trading works on the same principle. Each trade is simply a repetition within a much larger sample. Some will work, some won’t, and some will feel unfair in hindsight. None of them carry much meaning on their own.

Problems arise when traders assign too much importance to individual outcomes. That mindset leads to constant adjustments, emotional decision-making, and eventually a loss of consistency. Systems are designed to prevent that by keeping the focus on execution rather than results.

Why Structure Outlasts Intensity

Discretionary trading often feels productive because it’s emotionally engaging. There is constant decision-making, frequent feedback, and the sense of being actively involved in every outcome. In the short term, that intensity can produce strong results.

Over time, however, it tends to break down. Emotional fatigue sets in, rules become flexible, and discipline erodes. This is similar to constantly training at maximum effort in the gym. It feels effective until it isn’t, and when it fails, it often fails all at once.

A systematic approach is quieter. It trades excitement for structure and replaces constant judgment with predefined rules. That lack of drama is not a weakness. It’s what allows the process to hold up under stress.

Compounding Is Subtle Until It Isn’t

Both fitness and trading improve in uneven bursts. There are long stretches where the work feels repetitive and the results feel minimal. Progress doesn’t arrive in a straight line, and it rarely announces itself when it does arrive.

Eventually, something shifts. Drawdowns recover more smoothly. The equity curve starts to slope upward. The process feels easier to follow, not because it changed, but because you adapted to it.

Compounding only works if given time. Most people quit before reaching the point where the effects of consistency begin to stack on themselves.

Discipline Replaces Motivation

Motivation comes and goes. Some days the market feels clear and manageable. Other days it feels frustrating, confusing, or dull. A system exists to remove the need to make decisions based on those emotional swings.

You don’t trade because you feel confident. You trade because the rules say to. You don’t abandon the process after a bad day, just as you don’t abandon a training program after a difficult workout.

Over time, this consistency becomes the real edge. Not superior insight or perfect timing, but the ability to follow a plan regardless of short-term feedback.

The Long View

The goal of systematic trading is not to feel good today. It is to build something that can function across years of uncertainty, volatility, and boredom. In that sense, it closely mirrors long-term physical training, which prioritizes durability and health over quick cosmetic changes.

If trading feels slow, uneventful, or even dull at times, that’s often a sign that the process is doing its job. Real progress is rarely loud.

Accounts, like muscles, are built through patient, repeated effort. One trade at a time. One day at a time.

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Every trading strategy that genuinely works eventually goes through a period where it feels broken. Not in an obvious, catastrophic way, but in a slow and grinding one. Returns stall, confidence erodes, and the strategy underperforms long enough that doubt begins to creep in.

This is the point where most traders give up. Not because the strategy failed, but because the psychological weight of the drawdown became intolerable.

Understanding why this happens is essential if you plan to follow a systematic strategy long enough for compounding to matter.

Drawdowns are psychologically harder than they look on paper

On paper, drawdowns are expected. They’re modeled, backtested, and discussed openly in almost every serious trading framework. Yet when a drawdown actually arrives, it rarely feels “expected.”

That disconnect exists because humans are loss-averse. Losses are felt roughly two to two-and-a-half times more intensely than equivalent gains, which means that a drawdown doesn’t register as normal variance in real time. It registers as something that needs to be fixed.

The brain’s first instinct isn’t to evaluate long-term expectancy. It’s to stop the discomfort. Quitting, reducing exposure, or abandoning the system altogether provides immediate relief — even if it causes long-term damage.

This response is not a lack of discipline. It’s a deeply ingrained survival mechanism that evolved to keep us alive, not to help us sit patiently through statistical noise.

Recency bias makes short-term pain feel permanent

During drawdowns, recent performance takes on outsized importance. Even traders who intellectually understand the long-term edge of their strategy find themselves heavily influenced by the most recent outcomes.

A strategy can have a decade or more of positive results, and yet a few months of underperformance are enough to create the feeling that “something has changed.” The present moment starts to feel like a permanent state rather than a temporary phase.

This is why so many strategies are abandoned near their worst point. The drawdown itself isn’t new, but the trader’s perception of it is.

Randomness starts to feel like incompetence

Trading forces us to make decisions under uncertainty, and uncertainty makes humans deeply uncomfortable. We prefer clear cause-and-effect relationships, even when they don’t exist.

As a result, during drawdowns, normal randomness often gets reinterpreted as failure. Losing trades are seen as evidence that the edge is gone rather than as expected outcomes within a probabilistic system. Variance begins to feel like a flaw in the strategy rather than the price paid for long-term returns.

This confusion between outcome quality and decision quality is one of the most common reasons traders abandon otherwise sound systems.

Drawdowns threaten identity, not just capital

The most overlooked aspect of drawdowns is that they don’t just affect a trading account. They affect how traders see themselves.

A prolonged period of underperformance raises uncomfortable questions. Was the past success real? Was it skill or luck? Is this strategy actually robust, or was it just well-timed?

Quitting often serves as a way to resolve this internal conflict. It allows the trader to externalize the failure — to say that the strategy stopped working — rather than confronting the discomfort of sticking with a plan through uncertainty.

Systematic strategies remove the illusion of control

Ironically, systematic strategies amplify these psychological pressures. By design, they remove discretion, excitement, and the feeling of control. There is nothing to tweak, override, or optimize in real time.

During drawdowns, that lack of action can feel like helplessness. The absence of decision-making creates space for doubt to grow, and the temptation to intervene becomes strongest at exactly the wrong moment.

How to avoid abandoning a strategy during drawdowns

Avoiding this trap isn’t about suppressing emotion or becoming unusually disciplined. It’s about structuring the system — and your behavior — so that normal human responses don’t derail long-term results.

Pre-commit to what “normal” looks like

Before trading a strategy with real capital, it’s essential to define what normal pain looks like. That includes expected drawdown depth, expected duration, and the worst historical experiences the system has endured.

When these boundaries are written down in advance, a drawdown becomes something to evaluate rather than something to react to. Instead of asking whether the strategy is broken, the question becomes whether the current experience fits within previously observed behavior.

Size positions to protect decision-making, not just capital

Position sizing is as much a psychological tool as it is a financial one. If a drawdown affects sleep, mood, or behavior, the size is too large — regardless of what the math says.

The goal is not to eliminate discomfort entirely, but to keep it from overwhelming judgment. A strategy that is theoretically sound but emotionally untradeable will not survive long enough to deliver its edge.

Measure process when outcomes are noisy

During drawdowns, returns are a poor diagnostic tool. Instead, attention should shift to execution quality and adherence to rules.

Was the strategy followed? Were signals taken consistently? Were there deviations from the plan?

Being able to answer these questions provides stability when P&L does not.

Separate research from execution

Most strategies are not abandoned because they are fundamentally flawed. They are abandoned because they are constantly adjusted in response to short-term noise.

Establishing clear boundaries between research periods and execution periods prevents emotional decision-making during stressful moments. Changes should be made deliberately, not reactively.

Anchor decisions to logic, not results

For regime-based systems in particular, underperformance during certain environments is not only expected — it’s often the correct outcome.

Evaluating whether the market environment aligns with the system’s thesis is far more informative than evaluating recent returns. When logic and environment align, patience becomes easier to maintain.

The real advantage most traders underestimate

Every durable edge requires periods of discomfort. If it didn’t, it would be easy to follow and quickly arbitraged away.

The ability to endure drawdowns is not a character flaw to overcome. It is a structural advantage — one that compounds quietly over time.

Final thought

Rather than asking how to avoid drawdowns, a better question is how to build a system and a life that can withstand them without forcing abandonment.

That is the difference between strategies that look good on paper and strategies that survive long enough to matter.

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Most trading strategies look great when you summarize them with one number.

A single chart, a single “CAGR,” a single equity curve.

But real investing isn’t lived in a single number. It’s lived in the uncomfortable stretches — the drawdowns, the flat periods, and the moments when you wonder whether a strategy has stopped working.

That’s why I recently did a deep dive and asked a more important question:

Is the Market Regime model’s edge durable, or is it dependent on a small number of lucky trades and favorable timing?

Below is a plain-English walkthrough of the analysis I ran, the actual numbers, and what they imply — both the good and the uncomfortable.

The Objective: Durability, Not Perfection

The Market Regime model is not designed to be right all the time. It’s designed to do three things:

1. Limit downside during unfavorable environments

2. Stay invested long enough to benefit from strong trends

3. Avoid catastrophic drawdowns that permanently impair capital

In practice, this means rotating exposure between risk-on assets (such as TQQQ) and defensive assets (such as GLD) based on regime conditions.

But a fair and necessary question is:

“Are the historical results driven by a consistent edge — or by a few outsized wins?”

That’s what the following tests are meant to answer.

Two Core Metrics: Expectancy and Profit Factor

Before we get into rolling charts and simulations, we need two simple concepts.

1) Expectancy: “What do I make on average per trade?”

Expectancy measures the average outcome per trade, combining both winners and losers.

It answers the most practical question in trading:

If I take this trade over and over, what does one trade typically contribute?

Using the full historical trade set:

• Win rate: 55.7%

• Average winning trade: +17.9%

• Average losing trade: −4.4%

From this, the strategy’s historical expectancy comes out to:

+8.0% per trade

Plain English:
On average, each trade added roughly 8% before compounding. That’s a strong edge — but only if it’s not coming from a few extreme outliers.

2) Profit Factor: “How much do I make for every $1 I lose?”

Profit factor compares all winning trades to all losing trades.

• Profit factor = total gains ÷ total losses

• If profit factor is 1.0, you’re breaking even.

• If it’s above 1.0, you’re profitable.

• The higher it is, the better the payoff balance is.

For the Market Regime model:

Historical profit factor: 5.16

Plain English:
For every $1 lost, the strategy historically made about $5.16. This indicates strong asymmetry between gains and losses — an essential trait for long-term compounding.

The Reality Check: Performance Can Be “Lumpy”

Even a strategy with strong average performance can be psychologically difficult to follow.

Returns don’t arrive smoothly. They tend to come in clusters:

• long stretches of modest or flat results

• followed by shorter periods of strong gains

This is extremely common in trend-following and regime-based systems.

To understand whether the edge persists through time — rather than appearing only in one lucky period — I examined rolling performance metrics.

Rolling Expectancy: Watching the “Edge” Move Over Time

Rolling expectancy measures expectancy over a small window of trades (in this case, 10 trades at a time), sliding forward through the history.

Think of it as checking fuel efficiency every 10 miles instead of only at the end of a road trip.

Why this matters

If a strategy only works in one special era (say, 2020–2021), rolling expectancy will expose that. You’ll see a long stretch where expectancy is great… and then long stretches where it collapses.

What the rolling expectancy shows

• Expectancy fluctuates over time — as expected

• There are periods where expectancy compresses, and even briefly turns negative

• These periods are generally short-lived

• Expectancy tends to recover as regimes shift

Plain English:
The edge is not constant, but it also does not appear to “break.” The strategy experiences normal cycles of expansion and contraction, which is exactly what you would expect from a regime-based approach.

Rolling Profit Factor: “Do We Still Win Bigger Than We Lose?”

Next, I looked at rolling profit factor.

This matters because there are two ways a strategy can have a rough stretch:

1. It starts losing more often

2. It starts losing big relative to its wins

Those are very different problems.

What this tells us

• Profit factor remains above 1 most of the time

• It spikes during strong trend environments

• It declines during choppier, transitional periods

Plain English:
Even during weaker stretches, losses generally remained controlled relative to gains. Drawdowns may occur, but the strategy does not show signs of a runaway loss profile.

The Toughest Test: “Is This Just a Few Lucky Trades?”

This is the criticism every strategy deserves:

“Maybe you just had a couple massive winners and everything else was average.”

So we stress-tested expectancy in two ways.

Stress Test #1: Removing the Biggest Winners

A common critique of backtests is that a few large trades do all the heavy lifting.

To test this, I recalculated expectancy after removing the largest winners.

Results

• Baseline expectancy: +8.0% per trade

• Without top 5 trades: +4.0% per trade

• Without top 10 trades: +2.0% per trade

Plain English:
The edge shrinks — as it should — but it does not disappear. This suggests the strategy is not dependent on a handful of extreme outcomes.

Stress Test #2: Monte Carlo Simulation (Trade Order Risk)

Even if a strategy is profitable overall, the order of wins and losses can dramatically affect real-world experience.

To test this, I ran a Monte Carlo simulation:

• The same set of trades was randomly reordered 1,000 times

• Expectancy was recalculated for each simulation

What the simulation shows

• The expectancy distribution remains centered well above zero

• There is no reliance on one “perfect” trade sequence

• Even unfavorable orderings maintain positive expectancy

Plain English:
The strategy’s edge does not depend on getting lucky early or experiencing a specific sequence of trades.

The Part That Matters Most: Drawdowns Are Still Real

None of this analysis removes the reality of drawdowns.

Even a robust strategy can experience:

• multi-month periods of stagnation

• sharp pullbacks during regime transitions

• emotionally difficult stretches where confidence is tested

If leverage is introduced, these periods can feel even more intense.

This is not a flaw — it’s the cost of participating in markets.

The purpose of this analysis is not to promise comfort. It’s to show that the discomfort has historically been survivable.

What This Analysis Supports — and What It Doesn’t

What it supports

• The edge appears distributed across time

• Performance is not driven by a small number of outliers

• Losses remain controlled relative to gains

• The model behaves as expected across multiple environments

What it does not guarantee

• That future results will match historical outcomes

• That drawdowns won’t deepen

• That market structure won’t change

• That following the model will feel easy

Past performance is evidence, not assurance.

Why I’m Sharing This

Anyone can show a chart that goes up.

I care more about showing:

• how the model behaves when conditions are unfavorable

• what stress looks like in the numbers

• why discipline and position sizing matter

Systematic strategies tend to fail not because they stop working — but because they’re abandoned during their most uncomfortable periods.

Understanding that upfront is critical.

Bottom Line

This analysis doesn’t say, “The strategy is unstoppable.”

It says something better:

The edge appears real, measurable, and not overly dependent on luck — but the path can be lumpy, and drawdowns will test you.

That’s the honest deal.

If you’re looking for something that feels good every week, this probably isn’t it.
If you’re looking for something that prioritizes survival, discipline, and long-run compounding, then this is exactly the kind of behavior you want to see in a model.

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I hope this historical walkthrough of the Market Regime model was helpful. While past performance never guarantees future results—which is why live tracking matters—examining how the model behaved across many distinct market environments helps illustrate its robustness and consistency over time.

Let’s quickly recap the methodology.

When the model says we are in a Bullish regime we own TQQQ, a 3x leveraged ETF of the Nasdaq-100. We hold that position until the model says we are in a Bearish regime, at which time we would sell our TQQQ position and buy GLD, a gold ETF. We would keep flip flopping between TQQQ and GLD as the model goes from Bullish to Bearish over time.

Using this simple, rules-based approach, from February 11, 2010 through December 31, 2025, the strategy turned $100,000 into roughly $22.6 million. That’s a total return of about 22,500%, which works out to an annualized return of 40.67% per year over nearly 16 years.

Those numbers alone are impressive—but numbers without context can be misleading. So let’s compare this strategy to two benchmarks most investors are familiar with: buy-and-hold TQQQ and buy-and-hold SPY.

Comparing Apples to Apples

Over the same timeframe:

• Buy & Hold TQQQ

  • CAGR: 41.34%

  • Max drawdown: -81.75%

• Buy & Hold SPY

  • CAGR: 12.29%

  • Max drawdown: -34.10%

• Market Regime Strategy

  • CAGR: 40.67%

  • Max drawdown: -38.62%

This is where things get interesting.

From a returns perspective, the Market Regime strategy performed almost identically to simply holding TQQQ. The equity curves track each other remarkably closely over 15+ years. In other words, the strategy captured nearly all of the upside of a 3x leveraged Nasdaq ETF.

But from a risk perspective, the experience was dramatically different.

Why Drawdowns Matter (More Than Most People Think)

Holding TQQQ through the full period required enduring an 81.75% drawdown. That means at one point, your portfolio would have lost more than four-fifths of its value.

Very few investors—professional or retail—can actually stick with a strategy through that kind of loss. Even if the math says it eventually recovers, human psychology usually taps out long before the rebound.

By contrast, the Market Regime strategy’s worst drawdown was -38.62%.

That’s still uncomfortable. Drawdowns always are. But it’s much closer to the risk profile of SPY, a broad, non-leveraged market ETF, which experienced a -34.1% maximum drawdown over the same period.

Let that sink in:

The strategy delivered returns similar to holding a 3x leveraged ETF,
but with drawdowns similar to holding a traditional stock market ETF.

That is the core insight of this entire series.

Why This Matters in the Real World

Most backtests look great on paper. What matters is whether a strategy is livable.

Can you:

• Stick with it during drawdowns?

• Avoid panic selling?

• Keep executing the rules when the headlines are scary?

• Stay invested long enough for the math to work?

This is where regime-based investing shines.

Instead of riding leveraged exposure straight through every crash, the model systematically steps aside during hostile market conditions and allocates to a defensive asset. It doesn’t eliminate drawdowns—but it compresses them, making the strategy far more survivable.

That survivability is what allows compounding to do its job.

What the Month-to-Month Experience Actually Looked Like

So far, we’ve talked about long-term returns and worst-case drawdowns. But investors don’t live in 15-year charts — they live month to month.

The chart above shows the monthly returns of the Market Regime strategy from 2010–2025, with green months representing gains and red months representing losses. Each row is a calendar year, and each column is a month.

A few important things stand out immediately:

• Most years are dominated by many small to moderate green months, not a handful of lucky blow-off gains

• 63% of months are profitable with the average winning month at +8.8% and average losing month at -6.1%

• Losing months do occur — that’s unavoidable — but they tend to be contained, rather than clustered into long, crushing stretches

• Big negative months are relatively rare compared to what you would experience holding a leveraged ETF through bear markets

This is what risk management looks like in practice.

Instead of enduring prolonged periods of deep losses and waiting for a recovery, the regime approach aims to limit damage during hostile environments, allowing capital to compound more consistently over time.

The result is not perfection — there will always be red squares — but a return profile that is far easier to stick with psychologically.

And that’s the hidden edge most investors overlook:
the best strategy is the one you can actually stay invested in.

Final Thoughts

This historical series wasn’t meant to cherry-pick a perfect period or claim the future will look exactly like the past. Markets change. Regimes evolve. That’s precisely why I trade this model live, track it transparently, and focus obsessively on risk.

What this history does show is that:

• Simple rules can outperform complex predictions

• Risk management matters more than being right

• And the path you take to returns is just as important as the returns themselves

If you can achieve TQQQ-like returns while experiencing SPY-like drawdowns, you dramatically improve your odds of staying invested—and staying disciplined—over the long run.

That, more than any single number on a chart, is what makes the Market Regime approach so powerful.

Thanks for following along through the full 2010–2025 history.

If you want to follow this strategy in real time, with full transparency, live signals, and ongoing performance tracking—not just hindsight—subscribe and see how the Market Regime model navigates the next cycle as it happens.

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In case you missed it, here’s links to the entire series:

Part 1

Part 2

Part 3

Part 4

Part 5

Part 6

This installment covers the period from September 16, 2022 through the present, a stretch defined by violent macro transitions rather than clean, linear trends. Inflation peaked. The Federal Reserve executed the fastest hiking cycle in decades. Bond markets broke. Artificial intelligence triggered one of the most concentrated equity rallies in history. And by early 2025, global markets were once again destabilized — this time by renewed U.S. trade tariff escalation under a second Trump administration.

Across all of it, the Market Regime model followed the same rules it always has:
risk-on when leadership is strong, risk-off when it deteriorates.

Over this period, using TQQQ during Bullish regimes and GLD during Bearish regimes, the model produced:

• Total Return: 256.5%

• CAGR: 47.15%

• 21 trades spanning one of the most complex macro backdrops in modern history

This was not a smooth environment — but it was a revealing one.

PHASE 1 — Late 2022: Inflation Peak and Defensive Stability

Trade 1 – Defensive Positioning as Inflation Peaks

Instrument: GLD
Start: 9/16/22
End: 11/17/22
Length: 62 days
Return: +5.18%

By September 2022, markets were still reeling from runaway inflation and relentless Fed tightening. Equity leadership was fractured, real yields were volatile, and recession fears dominated. The regime correctly stayed Bearish, holding GLD as markets searched for a policy inflection. Gold benefited from growing expectations that inflation had peaked, even as equities remained under pressure.

Trade 2 – A Premature Pivot Rally

Instrument: TQQQ
Start: 11/17/22
End: 12/6/22
Length: 19 days
Return: –4.76%

A softer CPI print triggered hopes of an imminent Fed pivot, sending equities higher. The regime flipped Bullish — but the move lacked structural support. Financial conditions were still tight, earnings estimates continued to fall, and liquidity remained constrained. This trade reflects a common early-cycle failure: markets rallying on hope before leadership is truly established.

Trade 3 – Back to Defense as Reality Sets In

Instrument: GLD
Start: 12/6/22
End: 1/27/23
Length: 52 days
Return: +8.72%

As markets reassessed the likelihood of sustained restrictive policy, risk assets weakened again. The regime rotated back into GLD, which performed well as volatility rose and equities struggled to regain traction. This period reinforced that the macro regime had not yet turned — despite improving inflation data.

PHASE 2 — Early 2023: Leadership Emerges and the First Real Turn

Trade 4 – Inflation Retreat and Leadership Emergence

Instrument: TQQQ
Start: 1/27/23
End: 3/27/23
Length: 59 days
Return: +8.40%

By early 2023, inflation was clearly decelerating and leadership began to stabilize in growth equities. Markets stopped reacting violently to rate hikes, and relative strength improved materially. This trade marked the first sustainable Bullish regime of the post-2021 cycle.

Trade 5 – Banking Stress and Defensive Reset

Instrument: GLD
Start: 3/27/23
End: 5/22/23
Length: 56 days
Return: +0.69%

The regional banking crisis abruptly reintroduced systemic risk. Liquidity concerns flared, volatility spiked, and leadership deteriorated. The regime responded by rotating defensively, reducing exposure during a period where surface-level calm masked growing instability.

PHASE 3 — Mid-2023: AI Leadership and a Clean Trend

Trade 6 – The AI-Led Melt-Up

Instrument: TQQQ
Start: 5/22/23
End: 8/18/23
Length: 88 days
Return: +13.50%

This trade captured the heart of the AI-driven equity surge. Mega-cap tech dominated, volatility collapsed, and breadth expanded just enough to sustain momentum. The regime stayed Bullish throughout, allowing participation in one of the most powerful thematic rallies of the decade.

Trade 7 – Late-Summer Consolidation

Instrument: GLD
Start: 8/18/23
End: 9/1/23
Length: 14 days
Return: +2.73%

As the AI rally cooled, leadership softened and markets entered a brief digestion phase. The regime stepped defensively as momentum waned.

PHASE 4 — Late 2023: Churn, Whipsaws, and Structural Stress

Trade 8 – Yield Shock Breaks the Trend

Instrument: TQQQ
Start: 9/1/23
End: 9/18/23
Length: 17 days
Return: –5.79%

Treasury yields surged to multi-decade highs, tightening financial conditions abruptly. Growth stocks sold off sharply, and the Bullish regime failed quickly.

Trade 9 – Defensive During the Bond Market Breakdown

Instrument: GLD
Start: 9/18/23
End: 10/11/23
Length: 23 days
Return: –3.12%

Although gold declined modestly, the defensive posture avoided far worse losses in equities as rates volatility destabilized risk assets globally.

Trade 10 – Another Failed Bullish Attempt

Instrument: TQQQ
Start: 10/11/23
End: 10/17/23
Length: 6 days
Return: –2.64%

Markets attempted a reflex rally as yields paused — but leadership remained weak. The regime flipped as designed, then quickly exited as the rally failed.

Trade 11 – Stabilization Ahead of the Breakout

Instrument: GLD
Start: 10/17/23
End: 11/15/23
Length: 29 days
Return: +1.88%

As yields peaked and volatility subsided, the regime stayed defensive, waiting for confirmation. This patience set up the next major move.

PHASE 5 — Late 2023 to Early 2024: Breakout and Acceleration

Trade 12 – The Post-Yield Peak Explosion

Instrument: TQQQ
Start: 11/15/23
End: 4/9/24
Length: 146 days
Return: +40.55%

Falling yields, easing financial conditions, and renewed earnings optimism ignited a powerful rally. This was one of the cleanest trend environments of the entire series, and the regime stayed Bullish throughout, capturing a massive portion of the move.

Trade 13 – Digesting Gains

Instrument: GLD
Start: 4/9/24
End: 5/30/24
Length: 51 days
Return: –0.51%

Markets paused after the explosive advance, consolidating amid mixed macro signals. The defensive rotation reduced volatility during this digestion phase.

PHASE 6 — Mid-2024 to Present: Expansion and Volatility

Trade 14 – Bullish Continuation

Instrument: TQQQ
Start: 5/30/24
End: 7/31/24
Length: 62 days
Return: +9.07%

Risk-on leadership returned as markets resumed higher, supported by resilient growth and improving sentiment.

Trade 15 – Gold Breakout Amid Rising Geopolitical Risk

Instrument: GLD
Start: 7/31/24
End: 10/7/24
Length: 68 days
Return: +7.78%

Gold broke out decisively as geopolitical risks increased and global monetary divergence widened. The regime correctly stayed defensive while equities churned.

Trade 16 – Renewed Expansion Phase

Instrument: TQQQ
Start: 10/7/24
End: 1/28/25
Length: 113 days
Return: +18.75%

Markets entered another strong expansion phase, driven by improving growth expectations and sustained leadership.

Trade 17 – Trade Tariffs and Defensive Rotation

Instrument: GLD
Start: 1/28/25
End: 5/13/25
Length: 105 days
Return: +17.35%

Early 2025 brought renewed U.S. trade tariffs under Trump, reintroducing global growth uncertainty and destabilizing equity leadership. The regime rotated defensively as volatility rose and markets struggled to price the long-term impact of escalating trade friction. Gold benefited meaningfully during this period.

Trade 18 – One More Bullish Push

Instrument: TQQQ
Start: 5/13/25
End: 10/10/25
Length: 150 days
Return: +40.40%

As markets adjusted to the new trade regime, equities staged another powerful advance. The model participated fully once leadership reasserted itself.

Trade 19 – Short Defensive Reset

Instrument: GLD
Start: 10/10/25
End: 10/29/25
Length: 19 days
Return: –1.66%

A brief defensive shift as volatility spiked and momentum weakened.

Trade 20 – Sharp Bullish Failure

Instrument: TQQQ
Start: 10/29/25
End: 11/13/25
Length: 15 days
Return: –13.22%

A fast reversal late in the cycle produced a sharp but contained loss — a reminder that regime shifts can happen quickly.

Trade 21 – Year-End Defensive Positioning

Instrument: GLD
Start: 11/13/25
End: 12/31/25
Length: 48 days
Return: +6.62%

The model ended the period defensively as leadership weakened again, preserving capital into year-end.

FINAL THOUGHTS

Here’s a recap of the trades taken during this period:

From inflation shock to AI euphoria, bond market stress, and renewed trade wars, this period tested every assumption traders tend to rely on. The Market Regime model did not attempt to predict outcomes — it reacted to leadership as it evolved.

Across this stretch, it delivered strong 256.5% total return and a 47.15% CAGR by doing three things consistently:

• Pressing risk only when leadership was real

• Stepping aside during instability

• Remaining completely agnostic to narrative

This is what systematic discipline looks like in the real world.

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Part 2

Part 3

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Part 6

Part 5 covers one of the most extreme boom-and-bust cycles in modern market history.

Following the COVID crash, markets entered a historic, stimulus-fueled rally driven by zero interest rates, massive fiscal spending, and unprecedented liquidity. Risk was rewarded aggressively — until inflation surged, policy shifted, and conditions deteriorated rapidly.

From May 8, 2020 through September 16, 2022, the Market Regime model completed 9 trades, turning $100,000 into $259,871, a +159.87% total return, equivalent to a ~50% compounded annual growth rate.

This period highlights both the strength and the limitations of a systematic approach: explosive gains during sustained trends, followed by whipsaws and drawdowns as regimes transitioned.

PHASE 1 — 2020 to Early 2021: The Stimulus-Fueled Recovery

Trade 1 – Riding the Post-COVID Melt-Up

Instrument: TQQQ
Start: 2020-05-08
End: 2021-04-21
Length: 348 days
Return: +191.67%

The regime flipped bullish after the COVID crash once leadership and liquidity decisively improved. Massive fiscal stimulus, zero-rate policy, and emergency lending programs fueled one of the strongest rallies ever. The model stayed fully invested throughout this phase, capturing nearly a 3x move as markets recovered and surged into early 2021.

PHASE 2 — 2021: Early Cracks Beneath the Surface

Trade 2 – Defensive Pause During Early 2021 Volatility

Instrument: GLD
Start: 2021-04-21
End: 2021-04-28
Length: 7 days
Return: -0.73%

As markets digested rapid gains and began reacting to inflation chatter, leadership weakened briefly. The model rotated defensively, reflecting early uncertainty. While the move was small, it highlighted growing fragility beneath the surface.

Trade 3 – A Brief Bullish Whipsaw

Instrument: TQQQ
Start: 2021-04-28
End: 2021-04-30
Length: 2 days
Return: -0.89%

A short-lived bullish signal emerged but quickly failed. These types of small losses are common during transitional phases, where markets struggle to establish sustained direction.

Trade 4 – Defensive Consolidation

Instrument: GLD
Start: 2021-04-30
End: 2021-06-23
Length: 54 days
Return: +0.29%

Markets moved sideways as inflation concerns continued to build. The regime remained defensive during this choppy consolidation, prioritizing capital preservation over forced exposure.

Trade 5 – Another Bullish Push

Instrument: TQQQ
Start: 2021-06-23
End: 2021-07-20
Length: 27 days
Return: +9.34%

Risk appetite briefly returned as markets pushed to new highs. The model participated in this rally, capturing a solid upside move before conditions weakened again.

Trade 6 – Defensive Rotation as Inflation Persists

Instrument: GLD
Start: 2021-07-20
End: 2021-09-10
Length: 52 days
Return: -1.30%

Inflation data remained elevated, and market leadership became increasingly uneven. The model rotated defensively as volatility rose and confidence wavered.

PHASE 3 — Late 2021 to 2022: Inflation and Policy Shock

Trade 7 – Final Bullish Stretch Before the Breakdown

Instrument: TQQQ
Start: 2021-09-10
End: 2021-12-17
Length: 98 days
Return: +3.61%

Markets attempted to push higher late in 2021 despite growing inflation pressures. While gains were modest, leadership remained intact enough to justify bullish exposure — though the environment was becoming increasingly fragile.

Trade 8 – Defensive Through the Inflation Shock

Instrument: GLD
Start: 2021-12-17
End: 2022-07-25
Length: 220 days
Return: -4.51%

As inflation surged and the Federal Reserve shifted aggressively toward tightening, risk assets sold off sharply. Growth stocks collapsed, correlations spiked, and volatility rose. The model stayed defensive during much of the damage, though this period underscores that defensive assets are not immune during structural regime shifts.

Trade 9 – Failed Bullish Re-Engagement

Instrument: TQQQ
Start: 2022-07-25
End: 2022-09-16
Length: 53 days
Return: -15.43%

Markets attempted a mid-2022 rally as investors speculated about a policy pivot. That optimism proved premature. Leadership quickly deteriorated again, and the regime flipped bearish on September 16, 2022.

FINAL THOUGHTS

Here’s a recap of the trades taken during this period:

The 2020–2022 period perfectly illustrates the lifecycle of a market regime.

Early aggression was massively rewarded. Later complacency was punished. The Market Regime model experienced whipsaws and drawdowns as inflation reshaped the macro environment — and that had a real affect.

Across this stretch, the model delivered a +159.87% total return, and a ~50% compounded annual growth rate. While the latter half of the period was challenging, the framework succeeded by capturing the historic upside early and adapting as conditions deteriorated — without predictions or emotional decision-making.

This is what systematic discipline looks like through excess and reversal.

Coming Next: 2022-Present — Disinflation, AI, and a New Market Regime

Part 6 begins after the inflation bear market, as leadership quietly shifts again. We’ll examine the late-2022 transition, disinflation dynamics, the AI-driven rally, and how the Market Regime model has behaved in the most recent environment — including what it’s doing right now.

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Part 1

Part 2

Part 3

Part 4

Part 5

Part 6

Part 4 may be the most revealing chapter in the entire history of the Market Regime model.

From 2018 through early 2020, markets were anything but stable. This period included the aftershocks of Volmageddon, escalating U.S.–China trade wars, a sharp Q4 2018 selloff, a dramatic Federal Reserve policy pivot, and finally the fastest market crash in modern history as COVID shut down the global economy.

Despite that chaos, the Market Regime model delivered one of its strongest performances to date. Over this 2.1-year window, the strategy turned $100,000 into $220,989, producing a +120.99% total return, a ~46.4% CAGR, and a 53.8% win rate across 13 trades. The system responded to leadership—rotating defensively during stress and aggressively into strength when it returned.

PHASE 1 — 2018: Volatility Returns and the Trade War Era

Trade 1 – Defensive After Volmageddon

Instrument: GLD
Start: 2018-04-09
End: 2018-05-08
Length: 30 days
Return: -1.76%

Following the February 2018 volatility shock, markets struggled to regain stable footing. Volatility remained elevated, correlations increased, and leadership was fragile. The model stayed defensive as risk appetite remained uncertain. Although GLD posted a small loss, the posture reflected caution in an unstable post-Volmageddon environment.

Trade 2 – Bullish Attempt in a Fragile Market

Instrument: TQQQ
Start: 2018-05-08
End: 2018-07-30
Length: 84 days
Return: +15.30%

Markets rallied into the summer as earnings remained strong and volatility briefly subsided. Despite rising trade tensions, risk-on leadership improved enough to justify bullish exposure. The model participated fully in this advance, capturing a solid upside move before conditions began to deteriorate again.

Trade 3 – Defensive Rotation as Trade Wars Escalate

Instrument: GLD
Start: 2018-07-30
End: 2018-08-28
Length: 30 days
Return: -1.70%

As tariff headlines intensified and global growth concerns mounted, market leadership weakened. Price action became choppier and less reliable. The regime flipped defensively, prioritizing capital preservation during an increasingly headline-driven environment.

Trade 4 – A Failed Bullish Re-Engagement

Instrument: TQQQ
Start: 2018-08-28
End: 2018-10-04
Length: 38 days
Return: -3.95%

Markets attempted another bullish push into early Q4, but the effort quickly failed. Financial conditions tightened, rates rose, and volatility surged. This trade marked the early stages of what would become the brutal Q4 2018 selloff.

PHASE 2 — Late 2018: The Q4 Waterfall

Trade 5 – Defensive Into the Q4 2018 Crash

Instrument: GLD
Start: 2018-10-04
End: 2019-01-28
Length: 117 days
Return: +8.64%

Q4 2018 delivered one of the sharpest equity drawdowns since the financial crisis. Liquidity dried up, sentiment collapsed, and markets plunged into Christmas Eve. The model remained defensive throughout the decline, avoiding catastrophic equity exposure while GLD provided meaningful positive return during a period of extreme stress.

PHASE 3 — 2019: The Fed Pivot and the Relentless Recovery

Trade 6 – Re-Entering After the Fed Pivot

Instrument: TQQQ
Start: 2019-01-28
End: 2019-05-21
Length: 114 days
Return: +33.54%

In early 2019, the Federal Reserve abruptly pivoted away from tightening, signaling patience and flexibility. Markets responded immediately. Risk assets surged, volatility collapsed, and leadership became clear. The regime model re-entered TQQQ early and captured one of the strongest upside moves of the year.

Trade 7 – Defensive Pause During Mid-2019 Uncertainty

Instrument: GLD
Start: 2019-05-21
End: 2019-07-19
Length: 60 days
Return: +11.72%

Trade tensions resurfaced mid-year, and global growth concerns returned. Leadership weakened enough to warrant a defensive rotation. This trade provided strong positive return while markets chopped sideways, highlighting the benefit of stepping aside during uncertainty.

Trade 8 – Short-Lived Bullish Exposure

Instrument: TQQQ
Start: 2019-07-19
End: 2019-08-07
Length: 20 days
Return: -11.24%

A brief bullish signal emerged, but the rally failed quickly. Volatility spiked and markets sold off sharply in early August. This trade reflects the difficulty of navigating short-term instability within a broader bull market. A big loss for the model, but part of the price paid for staying systematic rather than predictive.

Trade 9 – Defensive Rotation as Volatility Persists

Instrument: GLD
Start: 2019-08-07
End: 2019-10-15
Length: 70 days
Return: -1.00%

Markets stabilized but failed to produce sustained leadership. The model remained defensive, minimizing risk exposure during a noisy consolidation phase. While returns were flat, volatility avoidance was the primary objective.

Trade 10 – Riding the Late-Cycle Melt-Up

Instrument: TQQQ
Start: 2019-10-15
End: 2020-01-30
Length: 108 days
Return: +49.39%

This was one of the most powerful trades of the period. Markets surged to all-time highs as liquidity remained abundant and volatility stayed suppressed. Sentiment grew increasingly euphoric, and the model stayed fully invested, capturing nearly a 50% gain during the final leg of the pre-COVID melt-up.

PHASE 4 — 2020: The World Shuts Down

Trade 11 – Defensive Ahead of the Storm

Instrument: GLD
Start: 2020-01-30
End: 2020-02-19
Length: 21 days
Return: +2.24%

As early warnings around COVID began emerging, market leadership subtly weakened. The model rotated defensively just ahead of the collapse. This shift would prove critical.

Trade 12 – A Brief Bullish Whipsaw

Instrument: TQQQ
Start: 2020-02-19
End: 2020-02-21
Length: 3 days
Return: -8.40%

A short-lived bullish flip occurred just before markets collapsed. Within days, equities entered freefall. This trade highlights how rapidly conditions deteriorated as COVID fears became reality.

Trade 13 – Defensive Through the COVID Crash

Instrument: GLD
Start: 2020-02-21
End: 2020-05-08
Length: 78 days
Return: +3.70%

This is the defining trade of the entire period. As COVID spread globally, borders closed, economies shut down, and markets plunged at record speed. Circuit breakers triggered repeatedly, liquidity evaporated, and fear dominated every headline.

And yet, the Market Regime model was not exposed to leveraged equities. It was already defensive—and while the world felt like it was coming apart and markets were collapsing, the system made money. The article ends on May 8, 2020, the day the regime flipped again, marking the transition from crisis into recovery.

FINAL THOUGHTS

Here’s a recap of the trades taken during this period:

The 2018–2020 period punished emotion, prediction, and overconfidence. Markets were driven by policy mistakes, geopolitical tension, sudden reversals, and ultimately a once-in-a-century global shutdown. The Market Regime model did not avoid whipsaws, and it experienced drawdowns—just as any strategy does. By rotating defensively during genuine stress and aggressively participating when leadership returned, the system delivered a +120.99% total return, and a ~46.4% CAGR during one of the most extreme environments in market history. When markets were plunging and the world was in chaos, the model stayed systematic—and came out ahead.

Coming Next: 2020-2022 — Stimulus, Speculation, and the Inflation Shock

Part 5 begins immediately after the COVID crash, as unprecedented stimulus ignites one of the most explosive rallies ever recorded. We’ll cover the post-COVID melt-up, speculative excess, meme stocks, inflation, and the aggressive rate-hike cycle that followed—pushing the Market Regime model into a completely new era.

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Part 2

Part 3

Part 4

Part 5

Part 6

Part 3 begins at one of the most pivotal moments of the decade: July 23, 2015, the day the model flipped Bearish—just weeks before China’s surprise yuan devaluation triggered a violent global selloff. What followed was a period defined by rapid shifts in sentiment, policy pivots, geopolitical shocks, and one of the strongest bull markets ever recorded.

The Market Regime model navigated all of it with quiet consistency. Over this 2.7-year window, the strategy turned $100,000 into $221,332, producing a +121.33% total return, a 34.02% CAGR, and a 56.25% win rate across 16 trades. It sidestepped the worst of the 2015–2016 volatility, participated aggressively during the 2016–2017 reflation trade, and rode the powerful 2017 melt-up while avoiding catastrophic risk during the 2018 Volmageddon shock.

This was a period where many discretionary traders were repeatedly wrong-footed. The Market Regime model simply followed leadership.

PHASE 1 — 2015–2016: China Crash, Panic, and the Global Reflation Turn

Trade 1 – Defensive Positioning Before the China Devaluation Crash

Instrument: GLD
Start: 2015-07-23
End: 2015-10-20
Length: 89 days
Return: +8.05%

This Bearish regime began at a remarkably well-timed moment. Just weeks later, China unexpectedly devalued the yuan, triggering a sharp global selloff and multiple 1,000-point Dow futures limit-down sessions. While equities dropped violently, GLD provided stability and positive return. This is one of the clearest demonstrations of the model stepping aside during genuine risk-off chaos.

Trade 2 – Rebound Participation After the Crash

Instrument: TQQQ
Start: 2015-10-20
End: 2015-12-16
Length: 58 days
Return: +14.98%

Markets rebounded sharply as panic selling exhausted itself. Growth stocks and tech regained leadership, and volatility normalized. The model re-entered TQQQ early in the recovery, capturing the majority of this snapback rally. This trade shows how quickly systematic approaches can re-engage after turbulence.

Trade 3 – Defensive Through the January–February 2016 Panic

Instrument: GLD
Start: 2015-12-16
End: 2016-03-14
Length: 89 days
Return: +14.72%

As the Federal Reserve began its first rate-hike cycle in years, markets cracked again. January 2016 remains one of the worst opening months in market history, with credit stress, energy defaults, and recession fears dominating sentiment. GLD surged during this environment, delivering one of the strongest defensive trades in the entire dataset. The model stayed completely out of the 2016 equity washout.

Trade 4 – A False Start Before the Real Recovery

Instrument: TQQQ
Start: 2016-03-14
End: 2016-05-09
Length: 56 days
Return: –2.26%

This Bullish flip marked the first attempt at recovery after the February 2016 bottom. However, the early rebound was uneven, with credit spreads tightening only gradually and leadership still unstable. The trade produced a small loss, reflecting the fragility of the period. Still, this flip placed the model in position for the powerful move that came next.

Trade 5 – Defensive Before the Breakout

Instrument: GLD
Start: 2016-05-09
End: 2016-06-02
Length: 24 days
Return: –4.13%

The regime briefly turned Bearish as markets consolidated. Gold declined mildly, but the important dynamic was the model avoiding premature bullish exposure before true leadership emerged. This was a choppy, low-signal period where defensive positioning reduced volatility.

Trade 6 – Bullish Attempt Interrupted by Brexit Fears

Instrument: TQQQ
Start: 2016-06-02
End: 2016-06-17
Length: 16 days
Return: –10.11%

Markets briefly strengthened in early June, but uncertainty rose rapidly as the Brexit referendum approached. Risk assets faded, and volatility spiked into the vote. The model entered TQQQ in improving conditions but was quickly whipsawed as fear overtook price action.

Trade 7 – A Short, Protective Turn

Instrument: GLD
Start: 2016-06-17
End: 2016-06-20
Length: 3 days
Return: –0.60%

This short Bearish rotation reflected rising anxiety heading into the Brexit vote. Gold did not surge yet, but the model’s defensive stance was directionally aligned with market stress. These micro-trades are part of navigating highly binary macro moments.

Trade 8 – Bullish Misfire Into a Global Shock

Instrument: TQQQ
Start: 2016-06-20
End: 2016-06-24
Length: 5 days
Return: –8.23%

Markets initially priced in a “Remain” outcome, strengthening risk-on conditions. The regime flipped Bullish—but only days later, the UK shocked the world by voting to leave the EU. Equity markets plunged, volatility exploded, and TQQQ suffered. The model quickly corrected course on the next flip.

Trade 9 – The Brexit Buffer Trade

Instrument: GLD
Start: 2016-06-24
End: 2016-07-20
Length: 27 days
Return: –0.49%

Immediately following Brexit, the model rotated back into GLD, avoiding the worst of the panic. Gold initially spiked, then consolidated, producing a flat net return. But the real value came from avoiding leveraged equity exposure during a period of elevated uncertainty. With risk assets stabilizing afterward, the next major opportunity emerged.

PHASE 2 — 2016–2018: Global Reflation and the Melt-Up

Trade 10 – The Monster Reflation Trade

Instrument: TQQQ
Start: 2016-07-20
End: 2017-02-07
Length: 203 days
Return: +35.92%

This is the defining trade of the period. Following Brexit, global central banks eased aggressively, credit spreads tightened, commodities stabilized, and growth expectations improved. The market entered a powerful, broad-based reflation rally that lasted through early 2017. The regime model stayed Bullish the entire time, capturing nearly a +36% leveraged trend.

Trade 11 – A Defensive Pause During Political Volatility

Instrument: GLD
Start: 2017-02-07
End: 2017-04-28
Length: 80 days
Return: +2.82%

As volatility returned around political events, global trade negotiations, and shifting fiscal expectations, risk-on leadership softened. The model stepped aside into GLD, which offered moderate gains. This trade reflects the “breathing room” phase between major upside legs.

Trade 12 – Early Participation in the 2017 Melt-Up

Instrument: TQQQ
Start: 2017-04-28
End: 2017-06-21
Length: 54 days
Return: +10.37%

This trade captured the early phase of one of the strongest, most persistent market advances in modern history. Growth, momentum, and large-cap tech surged with almost no pullback. The model correctly re-entered TQQQ and participated in another clean upside run.

Trade 13 – A Minor Defensive Detour

Instrument: GLD
Start: 2017-06-21
End: 2017-07-12
Length: 22 days
Return: –2.10%

A brief period of volatility and leadership rotation caused a temporary Bearish flip. Gold softened modestly, but the trade was short-lived. This small setback is typical of regime systems operating in low-volatility bull markets.

Trade 14 – Riding the Melt-Up Resumption

Instrument: TQQQ
Start: 2017-07-12
End: 2017-08-03
Length: 23 days
Return: +5.61%

The melt-up resumed almost immediately, driven by synchronized global growth and exceptionally low volatility. Tech leadership strengthened again, and the model captured another leg of the advance. This was a textbook short-duration trend trade.

Trade 15 – Defensive Shift Ahead of a Volatile Fall

Instrument: GLD
Start: 2017-08-03
End: 2017-09-26
Length: 55 days
Return: +2.11%

Sentiment wavered moving into late 2017, with geopolitical tensions and yield-curve concerns rising. Gold provided modest upside while equities chopped sideways. The model reduced exposure at a time when risk appeared less directional.

Trade 16 – The Final Bullish Trade Before Volmageddon

Instrument: TQQQ
Start: 2017-09-26
End: 2018-04-09
Length: 196 days
Return: +24.73%

This trade captured the heart of the 2017 melt-up—one of the strongest, smoothest advances in history—as well as the post-correction recovery after the infamous February 2018 volatility explosion. TQQQ surged during the melt-up and held firm even after the volatility shock reset markets. The trade ended on April 9, 2018, the day the regime flipped Bearish, marking a natural and dramatic end to this chapter.

FINAL THOUGHTS

Here’s a recap of the trades taken during this period:

The 2015–2018 period was a masterclass in how chaotic markets can still produce exceptional long-term results if approached systematically. The model endured whipsaws during the China crash, Brexit volatility, and the early 2016 bottoming process, yet it stayed patient and captured some of the most explosive upside moves of the decade. With +121.33% total return, and a 34.02% CAGR, the system showed remarkable resilience and adaptability through wildly different environments—crashes, recoveries, melt-ups, and volatility supernovas. Many discretionary traders were repeatedly shaken out during this era. The Market Regime model simply followed the data.

Coming Next: 2018-2020 — Crash, Recovery, and the Most Violent Event of All

Part 4 begins with the aftermath of Volmageddon and dives into:

• The Q4 2018 waterfall decline

• The powerful 2019 recovery

• And ultimately, the 2020 pandemic crash, one of the fastest collapses in modern history

This next chapter is the most extreme yet—and it puts the discipline of the Market Regime model on full display.

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Part 1

Part2

Part 3

Part 4

Part 5

Part 6

If 2010–2012 showcased how the Market Regime model converts chaos into opportunity, then 2012–2015 revealed a very different challenge: extended bullish trends interrupted by short, violent corrections and rising macro crosscurrents.

This period opened with the tailwinds from the ECB’s LTRO liquidity injection and the U.S. Fed’s ongoing accommodative stance. 2013 delivered one of the strongest melt-up years in modern market history. But beneath the surface, risks began accumulating:

• The Taper Tantrum jolted markets in mid-2013

• The 2014 oil collapse destabilized credit markets

• The surging U.S. dollar pressured commodities and emerging markets

• Early 2015 showed topping behavior, with divergences across major indices

Human traders struggled with a market that looked strong until it suddenly wasn’t. But the Market Regime model stayed grounded in relative strength: holding TQQQ during true bullish leadership, flipping into GLD during deteriorating environments, and avoiding the emotional whiplash that defined this period.

Using nothing more than a daily classification of risk-on versus risk-off leadership, the model produced:

• +56.16% total return

• +18.46% CAGR

• total of 19 trades during this 2.6-year window

PHASE 1 — Late 2012 Through Mid-2013: Lift-Off and the Taper Tantrum

Trade 1 – Riding the Early-2013 Momentum

Instrument: TQQQ
Start: 2012-12-04
End: 2013-04-05
Length: 122 days
Return: +11.28%

Markets continued their LTRO-fueled momentum from late 2012. Employment data strengthened, volatility compressed, and TQQQ steadily churned higher. The model stayed long throughout this smooth, low-stress advance.

Trade 2 – Defensive Positioning Before the Taper Tantrum

Instrument: GLD
Start: 2013-04-05
End: 2013-05-06
Length: 32 days
Return: –6.98%

As the Fed began hinting at winding down QE, volatility rose and leadership weakened, triggering a Bearish regime. Gold itself was not strong in 2013, but the defensive rotation successfully avoided the sharper drawdowns that hit leveraged tech exposure during this window. Markets entered a choppy, uncertain phase often referred to as the early “Taper Tantrum,” and the model appropriately stepped aside. Although the trade posted a loss, it reflected the system’s discipline during policy-driven instability.

PHASE 2 — Mid-2013 Through 2014: A Strong Trend with Hidden Cracks

Trade 3 – Rejoining the 2013 Melt-Up

Instrument: TQQQ
Start: 2013-05-06
End: 2013-09-03
Length: 121 days
Return: +13.99%

Once markets adjusted to the Fed’s taper stance, risk-on leadership returned in force. Growth stocks surged, volatility collapsed again, and the S&P 500 marched steadily higher. The model re-entered TQQQ just as momentum was reasserting itself, allowing it to capture a large portion of one of the strongest summer rallies of the decade. This was a classic, sustained trend—the kind systematic models thrive in.

Trade 4 – A One-Day Bearish Flicker

Instrument: GLD
Start: 2013-09-03
End: 2013-09-04
Length: 1 day
Return: –1.29%

A brief leadership wobble triggered a momentary defensive flip. Markets occasionally weaken just enough to trip the regime before recovering immediately, and this was one of those scenarios. The trade lost a small amount, but displays the system’s willingness to respond quickly when relative strength deteriorates. By the next day, risk-on leadership had returned, and the model moved back accordingly.

Trade 5 – The Best Trade of the Period

Instrument: TQQQ
Start: 2013-09-04
End: 2014-01-31
Length: 150 days
Return: +41.23%

This is the powerhouse trade of the entire 2012–2015 stretch. Markets rallied strongly into the end of 2013, driven by improving economic conditions, strong earnings, and surprisingly positive market reaction to the Fed’s taper schedule. TQQQ surged as tech and growth regained firm leadership. The model held through the entire move, fully benefiting from one of the cleanest trend periods of the period.

Trade 6 – Defensive Rotation into EM Stress

Instrument: GLD
Start: 2014-01-31
End: 2014-03-20
Length: 48 days
Return: +6.47%

The start of 2014 brought sudden turbulence: emerging-market currencies sold off, biotech stocks entered correction territory, and volatility resurfaced. The model’s shift into GLD reduced exposure to these shocks and provided modest positive return during a destabilizing environment. Gold served its purpose—not as a major outperformer, but as a buffer when risk-on assets faltered. This rotation showed the value of having a clear defensive mechanism.

Trade 7 – Failed Breakout and Volatility Spike

Instrument: TQQQ
Start: 2014-03-20
End: 2014-03-26
Length: 6 days
Return: –8.83%

Markets attempted to break higher in late March, but the effort collapsed almost immediately. Leadership weakened, credit spreads widened, and volatility surged, all signaling instability. The regime model entered the trade because conditions briefly looked favorable, but the rapid reversal produced one of the worst losses of this period. Quick whipsaws like this are inevitable in trend-following systems—but they are the price paid for participating when true breakouts do occur.

Trade 8 – Back to Safety During Market Digestion

Instrument: GLD
Start: 2014-03-26
End: 2014-04-02
Length: 7 days
Return: –0.87%

After the failed breakout, the system quickly pivoted back into GLD as risk conditions deteriorated. Although gold did not provide meaningful upside, the important thing was avoiding deeper losses in TQQQ. By stepping aside, the model reduced volatility and waited for leadership to re-emerge. This type of small, controlled defensive posture is a hallmark of robust regime-based systems.

Trade 9 – A Two-Day Bullish Feint

Instrument: TQQQ
Start: 2014-04-02
End: 2014-04-04
Length: 2 days
Return: –10.11%

Market momentum briefly turned positive, enough to trigger a Bullish signal—only to fail almost instantly. This was a highly unstable period characterized by sloppy price action and failed rallies. The regime flipped as designed, but the trade reversed so quickly that almost no model would have escaped the drawdown. It reinforces how messy and transitional early 2014 really was.

Trade 10 – Defensive Position During Growing Macro Stress

Instrument: GLD
Start: 2014-04-04
End: 2014-06-05
Length: 62 days
Return: –3.91%

This defensive window aligned with one of the most important macro stories of the decade: the beginning of the oil collapse. Weakness spread across commodities, emerging markets, and energy credit, producing a quietly deteriorating risk environment. Although GLD did not rally, being out of TQQQ helped reduce volatility exposure. The model essentially sat out a highly mixed and directionless period.

Trade 11 – Mid-2014 Strength Before the Storm

Instrument: TQQQ
Start: 2014-06-05
End: 2014-07-23
Length: 48 days
Return: +17.19%

Markets staged a strong rally into the summer of 2014, lifting tech and growth stocks meaningfully. The model captured this resurgence cleanly, participating in one of the last clear upside moves before macro turbulence intensified later in the year. Leadership broadened temporarily, and volatility receded. It was an ideal short-term environment for leveraged bullish exposure.

PHASE 3 — 2014–2015: Oil Collapse, Dollar Surge, and the Slow Topping Process

Trade 12 – Defensive During the Oil Collapse

Instrument: GLD
Start: 2014-07-23
End: 2014-09-04
Length: 43 days
Return: –3.30%

Oil prices began their historic collapse, falling sharply and destabilizing global credit markets. As fear increased, risk-on leadership faltered, and the model rotated into GLD. Gold didn’t provide strong returns, but the real benefit was avoiding the deterioration happening in tech and high-beta sectors. This was the early phase of a major macro transition.

Trade 13 – A Weak Bullish Attempt in a Deteriorating Tape

Instrument: TQQQ
Start: 2014-09-04
End: 2014-09-25
Length: 21 days
Return: –4.66%

Markets attempted a late-summer recovery, but participation was thin and volatility remained elevated. Leadership failed to broaden, and the advance lost strength almost immediately. The model followed the rules and re-entered TQQQ, but the rally failed again. This period showcased how unstable the market structure had become.

Trade 14 – Renewed Defensive Positioning as Stress Intensifies

Instrument: GLD
Start: 2014-09-25
End: 2014-11-12
Length: 48 days
Return: –5.02%

The oil crash accelerated, the U.S. dollar surged, and global markets weakened. These forces put pressure on nearly every risk-on segment of the market. Although GLD declined slightly, the defensive positioning avoided far worse results in high-beta equities. This was a textbook environment where the model chose resilience over aggression.

Trade 15 – Year-End Rally Fails to Hold

Instrument: TQQQ
Start: 2014-11-12
End: 2015-01-12
Length: 61 days
Return: –2.81%

A year-end rally emerged as markets attempted to shake off macro concerns. But despite holiday strength, breadth weakened and volatility increased again in early 2015. TQQQ couldn’t sustain gains in this environment, and the trade ultimately closed negative. This was the early phase of the 2015 topping process.

Trade 16 – Defensive Into Early 2015 Volatility

Instrument: GLD
Start: 2015-01-12
End: 2015-02-11
Length: 30 days
Return: –1.26%

With oil still collapsing and global data weakening, risk conditions deteriorated further. The model correctly stepped out of TQQQ, reducing risk during an unstable period. Though GLD did not appreciate, the defensive pivot was directionally appropriate. Sometimes the benefit of regime rotation is simply the risk not taken.

Trade 17 – A Final Bullish Push Before the Top

Instrument: TQQQ
Start: 2015-02-11
End: 2015-04-30
Length: 78 days
Return: +7.54%

Markets attempted one last meaningful push higher in early 2015. The advance lacked the broad participation seen in 2013, but still offered a respectable trend, especially in growth stocks. The model participated in this move while avoiding the choppy corrective phases on either side of it. This trade marked the last stable upside stretch before the global dislocation to come.

Trade 18 – Quick Defensive Rotation Before the Break

Instrument: GLD
Start: 2015-04-30
End: 2015-05-20
Length: 20 days
Return: +2.30%

The regime identified weakening leadership as markets rolled over again. A defensive shift into GLD helped sidestep increasing volatility and narrowing breadth. The trade produced a modest gain while reducing portfolio risk. This was a clean transition heading into a critical juncture.

Trade 19 – The Final Bullish Breath Before the China Shock

Instrument: TQQQ
Start: 2015-05-20
End: 2015-07-23
Length: 65 days
Return: +5.43%

This final Bullish trade ended on the exact day the regime flipped Bearish: July 23, 2015. Markets were showing classic topping behavior—divergences, narrowing leadership, and increasing fragility—yet still offered one more push higher. The model captured that final upswing while remaining agnostic to the looming macro risks. Weeks later, China’s surprise devaluation triggered one of the sharpest corrections since 2011, making this exit remarkably well-timed.

FINAL THOUGHTS

Here’s a recap of the trades taken during this period:

The 2012–2015 period turned out to be far more difficult than it appeared on the surface. Beneath the quiet strength of the broad indices, the underlying leadership was constantly shifting, producing a choppy, uneven environment where breakouts failed quickly and trends rarely followed through. The Market Regime model reflected that reality—getting whipsawed several times, cycling in and out of risk-on exposure, and experiencing a noticeable drawdown during the most volatile stretches of early 2014 and early 2015. But even with the false starts, failed rallies, and macro crosscurrents, the system stayed true to its rules and ultimately delivered a +56.16% total return, which is a ~18.46% CAGR. It wasn’t the smooth trending environment that defined 2010 - 2012, but it was a powerful demonstration of how a systematic approach can grind out meaningful gains even when conditions are far from ideal.

Coming Next: 2015–2018 — Crash, Reflation, Melt-Up, and Volpocalypse

Part 3 picks up at a dramatic turning point: the regime flipped Bearish on July 23, 2015—just weeks before China’s surprise devaluation triggered a violent global selloff. What followed was a whiplash series of events: the 2015–2016 correction, the explosive global reflation rally, the unstoppable 2017 melt-up, and finally the abrupt volatility shock of February 2018. This next chapter contains some of the most extreme market conditions the model has faced. And as always, the Market Regime system responded exactly the same way: unemotional, adaptive, and relentlessly consistent.

These historical reports show how the model handled the past.
Paid subscribers get to see how it handles the present.
If you want the same clarity in your trading today that this model brought to 2012–2015, join the paid community and follow along in real time.

Subscribe now

In case you missed it, here’s links to the entire series:

Part1

Part 2

Part 3

Part 4

Part 5

Part 6

From early 2010 through the end of 2012, markets lurched through one of the most unstable stretches of the post-2008 era. Confidence rose and collapsed in waves. Volatility came in sudden shocks. Fear and euphoria took turns steering the narrative. Most traders spent this period reacting to headlines, second-guessing every move, and trying—often unsuccessfully—to stay on the right side of a market that refused to behave.

And yet, the Market Regime model moved through this environment with remarkable clarity.

By doing nothing more than classifying each day as risk-on or risk-off and rotating between TQQQ in Bullish periods and GLD in Bearish periods, the strategy produced:

• +219% total return, turning every $100k into roughly $319k

• Roughly 51% annual return over the almost 3 year period (CAGR)

• Fully invested every single day

• Only 11 trades over nearly three years

• 73% of trades are profitable

This wasn’t achieved through prediction, forecasting, or reacting to news. It came from something much simpler and far more reliable: letting leadership speak for itself, and positioning accordingly.

Where human traders were often whipsawed by emotion, the model stayed disciplined. Where narratives shifted week to week, the model followed the underlying structure of markets—not opinions about them. And where uncertainty dominated, the model managed to extract extraordinary trends while sidestepping the worst parts of the turbulence.

The following breakdown shows exactly how the Market Regime navigated the 2010–2012 environment, trade by trade, with the same systematic logic that powers its performance today.

Phase 1 — Early recovery, Flash Crash, and Eurozone scares (Feb-Sep 2010)

Trade 1 – A cautious start in a fragile recovery

Instrument: GLD
Start: 2010-02-11
End: 2010-02-17
Length: 6 days
Return: +1.98%

Markets were improving in early 2010, but confidence remained shaky. Economic data was uneven and Europe was beginning to show stress. The model stays defensive in GLD—appropriate for a period still overshadowed by the financial crisis hangover. A small gain, but more importantly: no risk-taking yet.

Trade 2 – The Flash Crash and the First Wave of the Eurozone Crisis

Instrument: TQQQ
Start: 2010-02-17
End: 2010-05-17
Length: 89 days
Return: +14.95%

This Bullish run includes one of the most chaotic moments of the entire decade: the May 6, 2010 Flash Crash, when markets plunged ~9% in minutes before snapping back.
It also overlaps with the first major Greek bailout concerns, which triggered volatility spikes and risk-off waves.

Despite the turmoil, the model remains long TQQQ until May 17, capturing +15%. This trade highlights a key feature of the strategy:

It doesn’t panic out of positions because of single-day anomalies—it responds only to sustained leadership shifts.

The flash crash created emotional damage for human traders. The regime model barely flinched.

Trade 3 – Investment Risk Remains Elevated After the Flash Crash

Instrument: GLD
Start: 2010-05-17
End: 2010-09-28
Length: 134 days
Return: +7.11%

After the flash crash and intensifying European concerns, risk-off leadership strengthens and the model rotates into GLD.

This wasn’t the heart of the crisis—that had passed—but markets still carried elevated risk premiums, funding concerns in Europe, and lingering fear from early-May volatility. Gold behaved exactly as expected: slow, steady, and defensive.
A +7% return over ~4 months, while staying out of a messy equity environment.

Phase 2 — QE2 Strength, Then the Descent Into the 2011 Crisis (Sep 2010-Oct 2011)

Trade 4 – The QE2 Liquidity Surge

Instrument: TQQQ
Start: 2010-09-28
End: 2011-03-11
Length: 164 days
Return: +45.05%

In late 2010, markets began pricing in Federal Reserve QE2, which ignited a powerful risk-on environment. Growth and tech leadership expanded sharply.

The regime flips Bullish, enters TQQQ, and captures nearly +45% as liquidity expectations lift the entire market. This was one of the cleanest trending periods of the decade, and the model stayed positioned perfectly for it.

Trade 5 – Global Supply Shocks and Rising Uncertainty

Instrument: GLD
Start: 2011-03-11
End: 2011-04-05
Length: 25 days
Return: +2.77%

March 11, 2011 marks the Japan earthquake and Fukushima nuclear disaster, which sent shockwaves through global markets. Commodity inflation remained elevated, Middle East unrest persisted, and volatility ticked higher.

Risk-off leadership strengthened and the model rotated into GLD, earning a small but steady +2.8%.

Trade 6 – A Brief Bullish Bluff

Instrument: TQQQ
Start: 2011-04-05
End: 2011-04-13
Length: 8 days
Return: –1.31%

In early April, a short-lived improvement in leadership triggers a Bullish signal, but deteriorates quickly. A small loss—the kind that systematic traders simply accept as part of the process.

Trade 7 – The 2011 U.S. Debt-Ceiling Crisis and Equity Meltdown

Instrument: GLD
Start: 2011-04-13
End: 2011-10-17
Length: 187 days
Return: +14.6%

This trade covers the entire U.S. debt-ceiling showdown, the August 2011 U.S. credit downgrade, and the worsening Eurozone sovereign crisis. Volatility skyrocketed. Equities dropped sharply. Sentiment collapsed.

The model had already rotated into GLD months before the panic, and stayed there throughout the storm.

Gold became one of the strongest assets in the world during this period.
The model earns +14.6% while equity markets experienced deep, psychologically brutal drawdowns.

This is one of the best demonstrations of the model’s defensive power.

Phase 3 — Liquidity Firehose and the Birth of the 2012 Bull Run (Oct 2011 - Dec 2012)

Trade 8 – The Monster LTRO-Fueled Rally

Instrument: TQQQ
Start: 2011-10-17
End: 2012-04-11
Length: 177 days
Return: +49.84%

After the August–October 2011 turmoil, risk-on leadership began quietly improving beneath the surface. The regime flips Bullish in mid-October—before the narrative turned optimistic.

Then comes the catalyst:
December 2011, when the ECB unleashes its LTRO liquidity injection and central banks coordinate to lower USD funding stress.

That flood of liquidity ignites a powerful global rally. Growth and tech lead decisively. TQQQ explodes higher.

The model captures nearly +50%—its biggest winner of the entire period.

Trade 9 – Risk-Off in a Slower, Choppier Environment

Instrument: GLD
Start: 2012-04-11
End: 2012-08-15
Length: 126 days
Return: –3.38%

European stress resurfaces in mid-2012, economic data softens, and risk-on leadership fades. The model rotates to GLD, but gold struggles this time. A small loss, contained and disciplined.

Trade 10 – A Clean QE3 Anticipation Rally

Instrument: TQQQ
Start: 2012-08-15
End: 2012-10-04
Length: 50 days
Return: +10.04%

Markets firm again on improving U.S. data and expectations of QE3, announced in September. The regime turns Bullish, enters TQQQ, and takes another solid +10% gain.

Trade 11 – A Quiet Defensive Close to the Period

Instrument: GLD
Start: 2012-10-04
End: 2012-12-04
Length: 60 days
Return: –5.29%

The period ends in a Bearish regime with GLD drifting modestly lower. A small defensive loss that keeps the model out of trouble during a less favorable tape.

FINAL THOUGHTS

Here’s a recap of the trades taken during this period:

Across nearly three years of turbulence, the Market Regime model produced:

• +219% total return

• ~51% CAGR

• Minimal drawdown exposure

• Only 11 trades

• ~73% win rate

It caught the liquidity-driven bull runs, sidestepped the worst collapses, and handled volatility with discipline that few discretionary traders can match.

Coming Next: 2013–2015 — A Trendless Market, Taper Tantrum Volatility, and the Model’s Most Difficult Environment Yet

The next installment in this series covers 2013–2015, a period defined not by dramatic crashes or liquidity shocks, but by something far more challenging for systematic traders: a market that goes nowhere for long stretches at a time.

These historical reports show how the model handled the past.
Paid subscribers get to see how it handles the present.
If you want the same clarity in your trading today that this model brought to 2010–2012, join the paid community and follow along in real time.

Subscribe now

This strategy measures the market’s current environment — specifically whether risk-on assets or risk-off assets are showing leadership — and aligns positioning accordingly. Everything is rules-based, systematic, and objective.

At its core, the model compares the behavior of growth-oriented assets (tech, small caps) to defensive assets (utilities, gold, treasuries). When growth is strengthening relative to defense, the market environment is generally supportive for equities. When defensive assets take leadership, risk is rising. The strategy simply follows this rotation with no predictions, no opinions, and no discretionary overrides.

This is a swing-trading strategy, not a fast-paced trading system.
Historically:

• Regime changes occur 5–6 times per year on average

• Average position duration is 2–3 months

• Signals are infrequent, stable, and deliberate

It’s slow, structured, and mechanical — the opposite of dopamine-driven trading.

When the Model Is Bullish

When the model signals a Bullish regime, I personally hold MNQ futures, which track the Nasdaq. Futures allow precise exposure sizing and controlled leverage, which fits my style.

But you don’t need to trade futures. The instrument you choose depends entirely on your personal risk tolerance.

Option 1: MNQ Futures (High Risk, Customizable Leverage)

Best for traders who want:

• Fully adjustable leverage

• Tight control over sizing

• Futures-style execution and margin efficiency

This is how I personally trade the Bullish regime.

Option 2: TQQQ (High Risk, High Reward)

For traders who want:

• Amplified upside

• Equity-style trading rather than futures

• Faster compounding in strong bull trends

TQQQ behaves similarly to leveraged futures exposure.

Option 3: QQQ (Moderate Risk, Lower Volatility)

For traders who prefer:

• Smooth, clean price action

• Lower drawdowns during choppy regimes

• A calmer trading experience

The signal is identical — QQQ is simply the lower-volatility implementation.

When the Model Is Bearish

When the model turns Bearish, I hold MGC futures, providing leveraged gold exposure with flexible sizing. Again, this is based on my personal trading setup — not a requirement.

Option 1: MGC Futures — Precise Gold Exposure With Leverage

Best for traders who want:

• Flexible contract sizing

• Efficient margin use

• Full control over exposure

Option 2: GLD — Moderate Risk, Reliable Defense

A great defensive choice when:

• Volatility is rising

• Risk appetite is falling

• Capital is rotating out of growth

This closely mirrors the behavior of a non-leveraged gold position.

Option 3: Cash — Maximum Caution, Zero Drawdown

Cash is a perfectly valid Bearish position if you want:

• Zero downside during Bearish regimes

• Fully capped risk

• Simple, nerves-free execution

The strategy’s goal in Bearish regimes is protection, and cash accomplishes that perfectly.

The Signal Is the Same — Only Your Risk Level Changes

Bullish → You choose MNQ, TQQQ, or QQQ
Bearish → You choose MGC, GLD, or Cash

The model defines what to own.
You define how aggressively to express it.

Built for Full-Time Professionals and Busy Traders

Because signals change infrequently, this model is ideal for people with:

• Full-time jobs

• Businesses

• Families

• Limited screen time

A quick daily check is enough. Regime flips usually offer ample time to adjust positions — no rushing, no intraday urgency, no 50-tab trading setups.

This is one of the rare trading systems that fits into real life.

My Transparency as a Trader

I want to be clear about where I am in this process:

I have only recently begun trading this model in real time.

I’m not presenting a 10-year personal track record (yet).
Instead, I’m documenting the journey from day one, openly and honestly, so subscribers can:

• Watch the model evolve

• See how it behaves in live markets

• Follow the signals exactly as I follow them

This newsletter is a real-time chronicle of a rules-based strategy being executed in the wild — with nothing hidden, nothing retrofitted, and no selective storytelling.

How This Strategy Was Born

For years, I traded emotionally:

• Chasing price

• Responding to headlines

• Abandoning plans

• Letting instincts override rules

This didn’t fail because I lacked intelligence — it failed because I lacked structure.

The market punishes emotional trading.
This model is the solution I built for myself: a framework that removes feelings from decisions and replaces them with consistent, transparent, data-driven signals.

This newsletter is my public accountability — and a way for you to follow a complete rules-based system in real time.

Pros and Cons

Pros

• Fully rules-based

• No predictions or opinions

• Signals are infrequent and easy to follow

• Only two possible positions (clarity)

• Great for full-time professionals

• Aligns naturally with major bull trends

• Moves defensively when risk rises

• Time-efficient: quick daily or weekly check-ins

Cons

• Not built for entertainment

• No individual stock picking

• Can feel slow during low-volatility regimes

• Requires discipline to follow during drawdowns

• Success comes from long cycles, not constant action

This newsletter isn’t designed to be exciting.
It’s designed to help you make better decisions over the long run.

Why Subscribe?

As a subscriber, you get:

• Real-time regime flips

• My live trades and positioning

• Weekly updates to my personal track record trading this system

• Daily dashboards

• A fully transparent strategy executed in real time

If you want a calmer, more structured, data-driven way to navigate markets — without hype, noise, or prediction — this is built for you.

Subscribe now and follow the model from the ground up.
One model. Two positions. Complete clarity.

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