Market Regimes: What they are and why they matter

Regimens, what are they and why they matter?

Most traders, especially new ones, don’t understand trading regimens. This is actually normal. Even as a quant based trader with higher education in stats/sciences, I learned of Regimen trading later in my trading career, having successfully navigated trading without it; but insurmountably improving things when I discovered it.

What is a regimen you may ask? Is it what’s going on in North Korea? Or even the USA?
Chances are, most people may think regime is synonymous with something like fascism or some ultra political significance, but the truth is regime can mean a few things, and I think its important, before getting into the real details, to first understand the meaning of regime.

The Meriam-Webster dictionary defines regime as:

• regular pattern of occurrence or action (as of seasonal rainfall)
  
• the characteristic behavior or orderly procedure of a natural phenomenon or process
  
• mode of rule or management a government in power
  
• a form of government
  
• a government in power
  
• a period of rule
  

If you were to do a grad school ‘concept analysis’ on regime, you would get some interesting findings of regime. Essentially, all of these definitions have a significance/underlying overlap in meaning. The simplified meaning? I would say (without having done an actual concept analysis), a regime is a “pattern of behaviour / rules / government that forms repeating characteristics that can be measured and predicted against its previous characteristics”.

Still too complex? Let’s simplify with both political and scientific examples.

Political

In the current presidency in the U.S., the Republican party was swift to implement sweeping tariffs against international trade partners, blanketing entire continents in a matter of days with tariffs. These were then paused, resumed, paused, resumed, lowered, raised, lowered, raised, paused, resumed, revoked, resumed, lowered, raised, etc.

Under the current political regime, we can identify the behaviour of “tariff implementation”. From previous tariff implementation and revocation and adjustment, we have the characteristics of this regime. We can then use these characteristics to predict future outcomes under this regime, i.e. we would hypothesize “Tariffs will be paused within the coming 2 months”. We can say this because this is a characteristic of the current regime. In fact, the term TACO is a perfect example of repeating regime characteristics!

What about a scientific example?

Well we can draw on Meriam-Webster making reference to seasonal rainfall. In climatology, a "rainfall regime" refers to the characteristic pattern of precipitation over a region during the year—especially its timing, intensity, and variability across seasons. Identifying these regimes are pivotal to forecasting future meteorological and climatological events!

What about my field? Epidemiology and Biostatistics?

In Epi, we have multiple different regimes, such as:

Treatment Regime: A prescribed course of medical therapy, such as a drug regimen for tuberculosis or chemotherapy for cancer. It includes dosage, timing, and duration.

Vaccination Regime: A schedule of immunizations designed to prevent disease outbreaks—e.g., two-dose mRNA COVID-19 vaccine regime followed by boosters.

Control Regime: A set of public health policies or containment strategies—like quarantine protocols, mask mandates, or vector control in malaria-endemic areas.

Surveillance Regime: The systematic collection and analysis of health data to monitor disease trends—e.g., wastewater surveillance for poliovirus or syndromic surveillance for flu-like illness.

These all matter because these regimes dictate future characteristics/outcomes.
Great! Now that you have an idea of what a regime means, let’s talk about regimes in trading.
If you haven’t already guessed, there obviously exists “market regimes”. These are, more or less, defined as “a distinct period characterized by specific patterns in market behavior—such as trends, volatility, and macroeconomic conditions—that influence investment strategies and risk management.”

If you look back to our examples, you can begin to imagine why regimes matter. Remember, TACO! Previous behaviour dictates future characteristics. Once you understand the way or median in which some phenomena operates, you can use these characteristics to predict future characteristics.

If you wanted to dissect market regimes, it could get relatively involved and complex. For example, things such as:

1. Seasonality,
   
2. Momentum,
   
3. Mean Reversion,
   
4. Financial / economic stability
   
5. Geopolitical stability
   

These can all influence market regimes in their own way and can, in fact, be standalone market regimes. If you trade seasonality, you are trading “seasonal regimes”.

Momentum and Mean reversion are independent regimes of themselves (more on that shortly).
If you trade fundamentals, you will be trading economic and geopolitical regimes.

But which is correct? Not all regimes can exist at the same time, correct?

Yes and no! Regimes can momentarily shift and flip into a different one. Take, for example, the U.S. implementation of Tariff’s at the beginning of 2025. The initial blanket tariffs caused a mean reversion regime fueled by financial/economic and geopolitical stability. We had 3 regimes working together for the result, which was ultimately a mean reversion. This quickly shifted from a mean reversion regime to a momentum based regime (more on this shortly).

So, yes, we can, theoretically, have more than one regime simultaneously. However, when it comes to markets, and this is where you are in luck, its actually pretty easy! Markets tend to be either:

1. Mean reverting; or
   
2. Momentum based.
   

And that’s really that. Those are the only 2 regimes you will ever truly need to pay attention to, which will give you a better edge at trading. Seasonality, financial and geopolitical stability will either augment mean reversion or momentum, but generally are not independent regimes in and of themselves.

In the end, markets either go up, down or sideways. It can be driven by broader contexts, but in the end the up/down/sideways is driven by a predominate regimen;

Down markets: usually mean reverting.

Up markets: usually momentum.

Sideways markets: usually mean reverting with occasional momentum deviations.

If you want to learn more about the evolution of the market, you can check out my post about how the market has evolved into its current regime here:

Now, let the real fun begin and let’s talk about how to correctly trade based on the current regime!

There are some steps, first one must:

1. Identify the current regime concretely.
   
2. Apply the correct strategies that are compatible with the current regime.
   
3. Understand the momentum, mean reversion paradox
   

I will walk you through how to do this step by step.

Identifying the Current Regime Concretely

The easiest way to identify the current regime is by using Hurst Exponent.
The Hurst exponent is a number between 0 and 1 that tells you how predictable a time series is—like stock prices or rainfall.

• If it's close to 0, the data is very random and tends to switch directions often.
  
• If it's around 0.5, the data behaves like a random walk—no clear trend.
  
• If it's close to 1, the data shows strong trends and tends to keep moving in the same direction.
  

So, it helps you measure persistence vs. randomness in patterns over time. The closer to 1 the more “persistent” the market is said to be. Persistence is basically the math equivalent of momentum. If a market is persistent, it will tend to trend with momentum.

The closer to 0 the more random the market is said to be. Randomness usually favours “mean reversion”

For simplicity, if you get a Hurst Exponent > 0.5, you are likely in a momentum regime. If < 0.5, you are likely in a mean reversion regime.

Let’s take a look at some examples using QuantNomad’s Hurt Exponent indicator (available here):



This is just before the crash in February 2025. We can see that up here, the Hurst Exponent was < 0.5, indicating a mean reversion preference. And indeed, the market ended up mean
reverting back to its quadratic mean (481) with the crash.

Then let’s see what happened:



After the crash, we can see that the Hurst Exponent was consistently > 0.5, indicating persistence in the market, i.e. trendy and momentum based.

Remember, as a rule of thumb, momentum markets generally faour upside and mean reverting tend to be downside favouring. If we narrow the regime to smaller timeframe regimes, you can see this phenomenon quite easily. Let’s look at SPY on a bearish day and bullish day against the Hurst Exponent:



We can see that on this bull trend day, Momentum and persistence reigned dominate. Hurst did not drop below 0.5, at least not for long, which indicated a persistent trend that was momentum driven.

Now a bearish day:



You can see on this bear trend day that Hurst stayed below 0.5 persistently, indicating mean reverting behaviour.

This also highlights how lower timeframes can have independent and day to day regimes, but its always important and critical to pay attention to the major regime a market is in on the larger timeframe.

Applying Correct Strategies

Depending on the regime, you MUST tailor your strategy to match the regime. If you are trading a mean reverting regime, oscillators like RSI and Stochastics aren’t going to work well. If you are trading a momentum regime with high persistence, mean reverting strategies like Bollinger Bands and Z-Score are not going to work.

As a rule of thumb, when Hurst is > 0.5, you want oscillator based strategies such as RSI, Stochastics, etc.

One indicator that I would recommend in momentum based regimes is my own, Momentum Probability Oscillator indicator (available here). This indicator operationalizes probability/sentiment through momentum metrics instead of mean reversion metrics. Let’s take a look at some examples:



In this example on the hourly timeframe for SPY, you can see that momentum is lost (signified by the oscillator falling below the yellow line) indicating that the likely outcome will be selling, this is shown by the pink arrows.



In this next example, we can see where momentum is reclaimed and the bias shifts to upside.
Because this indicator quantifies momentum probabilistically, it does well in momentum based, persistent regimes to identify strong trends and pullback of trends.

In reality, you can use any oscillator in a momentum based, persistent regime, but obviously I am biased to my own creations.

What about a mean reverting regimen?

If we are in a mean reverting regime, your best indicators to use are Bollinger Bands or, my favourite, the Z-Score probability indicator (by yours truly) available here.

Let’s use $NYSE:IRDM as our mean reverting example



In this image, the red arrow marks the transition to a mean reverting regime. So what do we use here? Well let’s take a look at the Z-Score probability indicator:



The red lines mark the transition to a mean reversion based regime. At the time of this transition, IRDM was oversold based on the Z-Score probability. We can see it in fact rallied back up to a z-score of 0 (mean reversion) before rejecting back down from the 0.

This is incredibly powerful, as the Hurst Exponent tells you that you can trust a reversion back to a mean!

Let’s try a smaller, intraday example, going back to SPY:



This day, SPY looked pretty bullish; however, the Hurst Exponent was consistently below 0.5 indicating mean reversion.

If we applied the Z-Score probability indicator:



I flipped the indicator to use Candles so you can more easily see the mean reversion behaviour. SPY goes to either extremes and always mean reverts back to 0, at times even consolidating in the mean reversion range.

And Bollinger bands:



If we look at a momentum driven day:



We can see that there is a skew or bias to one side of the average. The z-score is all over the map with no real expansion within the average range and infrequent and sporadic reversions that come more from extensive consolidation rather than actual mean reversion.

The indicator isn’t unusable in momentum based trading, but its not ideal. If we flip this same chart to the momentum probability oscillator we can see a stark difference in utility:



You can see the trend is using the full range of the oscillator and there is clear bounces at lower range and rejections at higher range with frequent “mean reversion” of the oscillator momentum based mean.

Now finally, the last section:

The Momentum Mean Reversion Paradox

This is, obviously, a self made up term. However, this is a phenomenon that will happen in corrective environments, where a mean reversion is so substantial, it becomes augmented by momentum itself.

What does this mean? It means that, despite the market actually mean reverting, the Hurst exponent flips to > 0.5, as the market is “persistently bearish”.

We can see this if we flip back to our $NYSE:IRDM example:



Here, we can see despite IRDM selling, the Hurst Exponent is incredibly trendy, with a really high value of > 0.55. Yet, despite this, the ticker continues down. This is the hallmark of a correction.

This is incredibly important and I really would advise you to mark this down and remember this. You can actually tell that something is “correcting” using this exact approach. When Hurst > 0.5 and the trend is down, this is the hallmark of a TRUE correction. No speculation needed!

Statistics is the best, I’m telling you.

Let’s look at the SPY crash of 2025:



During the SPY crash of 2025, the Hurst flipped to > 0.5, with a max of 0.57 indicating a hugely persistent trend. This means that this was a strong correction for SPY, flipping from a Hurst of < 0.5 to a Hurst of > 0.5 with a strong downtrend.

Crashes tend to happen abruptly without such transitions. For example, if we look at the COVID crash:



Theoretically Hurst warned us in advance that SPY was entering mean reversion territory, but when it actually happened, it happened so fast, Hurst never truly converted from mean reversion to trending. It was just a jumbled mess. This is the hallmark of a crash.

Concluding Remarks

And now, my friends, you know all there is to know about how to identify market regimes! Understanding these concepts will put your eons ahead of the average trader and allow you to select the correct tools and actually understand what the market is doing and when its gearing up for some corrections/mean reversions.

This is a long post, I will leave it there, but I really hope you learned something from this and will take some of the key points away!

Thanks for reading and as always, safe trades!