“All who follow trends may share a compass – but not all walk the same path.”
I. Introduction – Same Label, Different Games
Trend following is often discussed as if it is a single, unified strategy, a black box that simply “rides trends” and cuts losses short. From the outside, it is tempting to view all trend followers as interchangeable participants in the same game, differing only in skill or efficiency.
This is a mistake.
While many trend followers share a common ethos, such as rules-based execution, non-predictive models, and participation in sustained price moves – their strategies are anything but uniform. Objectives diverge. Constraints differ. Philosophies splinter. And so too must the systems they build.
Yet despite these differences, performance is still judged as if all trend followers are solving the same problem. They are stacked in league tables, ranked by Sharpe ratios, drawdowns, and calendar returns, with little consideration for the purpose behind each design.
The result is poor comparisons, misapplied metrics, and shallow rankings that confuse stylistic difference for inferiority. When one strategy appears “better,” it is often just better aligned to a specific objective, not universally superior.
This article breaks open the category.
We will explore the classic arguments that dominate trend system design, including diversification, volatility adjusting, correlation management, smoothness, and position sizing, and show why these choices only make sense in light of what the system is trying to do.
Because in trend following, there is no one-size-fits-all.
There is only fit-for-purpose.
II. When Trend Isn’t Trend – Not Everyone Follows the Same Philosophy
The phrase “cut losses short and let profits run” is often treated as the defining mantra of trend following. It sounds universal, even timeless. But in practice, many strategies that wear the trend follower badge do not adhere to this logic at all.
There is a growing family of momentum-based approaches that diverge significantly from the classic trend following playbook. This divergence is not a problem in itself. The issue arises when these strategies are compared, ranked, or benchmarked as if they are trying to do the same thing.
Two Roads: Absolute Momentum vs Cross-Asset Rotation
Consider the difference between an absolute momentum system and a cross-asset relative momentum strategy.
An absolute momentum model might enter a long position in crude oil when it breaks above its 100-day high and flip short when it breaks the 100-day low. It does not care what other assets are doing. It responds to directional behaviour in a single instrument and often uses trailing stops or breakout logic to capture trends and manage risk.
A cross-asset relative momentum strategy, by contrast, might rank equities, bonds, and commodities based on recent performance and allocate more capital to those that rank higher. These systems are typically long-only or partially hedged, with monthly or quarterly rebalancing. They do not cut losses in the traditional sense. Instead, they rotate out of lower-ranked assets. And they rarely let profits run. Exposure is managed by periodic reallocation, not by trailing stops or breakout persistence.
Both might be called “momentum” or “trend” strategies, but they operate under very different principles.
Trend Philosophy vs Momentum Mechanics
This is the heart of the philosophical rift.
Classic trend followers operate with non-predictive rules, letting positions run until the trend ends and exiting with predefined trailing logic. They often accept volatility, endure long drawdowns, and position themselves for rare but significant outliers. Their models are designed to embrace the unpredictable and stay in winning trades as long as possible.
Momentum allocators, on the other hand, often optimize for smoother returns. They blend momentum with other signals such as carry or volatility, rebalance on a fixed schedule, and avoid short exposure. While they may borrow elements of trend, like performance ranking or directional filters, they do not fully subscribe to the philosophy of “minimal interference, maximal exposure to asymmetric payoffs.”
This is not a critique. It is a clarification.
Not Wrong, Just Solving a Different Problem
Cross-asset momentum models are not broken. They are built for a different audience and a different purpose. They aim to:
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Reduce drawdowns
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Improve Sharpe
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Deliver smoother ride characteristics
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Fit into multi-asset portfolios with predictable exposures
But these goals necessarily shape their design. And in doing so, they move away from the defining principles of classical trend logic.
So when someone says they are a trend follower, it is worth asking:
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Do you cut losses with a rule, or rotate based on ranking?
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Do you let profits run open-ended, or do you cap exposure by schedule?
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Do you seek exposure to outliers, or do you suppress them for smoothness?
The answers to those questions do not just reveal tactics.
They reveal philosophy.
III. Objective-Driven Design – What Are You Solving For?
Every trend follower operates under a shared principle: respond to price, not prediction. But beyond that, the similarities often end.
To understand why different trend strategies look and behave so differently, you must start with a simple but powerful question:
What is the strategy designed to solve?
That one question determines everything, from universe selection to position sizing logic. It defines your tolerance for drawdowns, your willingness to embrace volatility, and your need for smoothness or asymmetry. Once you understand the objective, the design decisions that follow begin to make sense.
Below are four archetypes that capture the most common strategic intents within the trend-following universe.
1. The Outlier Hunter
The Outlier Hunter is built to maximize asymmetry. These systems are designed to endure long periods of frustration in order to capture rare, explosive moves. The mindset is not about steady returns but about being present when the market breaks.
Key characteristics:
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Volatility Treatment: No volatility targetting
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Sizing Logic: Equal risk per market based on ATR or volatility normalization
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Market Universe: Very wide to maximize optionality
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Exit Logic: Trailing stops only, with no profit targets
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Turnover Profile: Low turnover, high tolerance for drawdowns
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Position Construction: Small, uniform bet sizes
Design implications:
This strategy sacrifices short-term smoothness for long-term convexity. Its strength lies in rare but dominant outlier moves. Vol-adjusting or reducing exposure during volatility expansions would dilute the payoff.
2. The Volatility Targetter
The Volatility Targetter is focused on delivering stable, risk-adjusted returns. These strategies often optimize for Sharpe ratio, smoother equity curves, and tighter drawdowns. They cater to allocators who value consistency over convexity.
Key characteristics:
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Dynamic position sizing based on volatility levels
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Smaller market universe with deep liquidity
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Frequent rebalancing
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Correlation management to limit overlap
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Tight stop-loss logic or adaptive exits
Design implications:
By controlling volatility, these systems improve the ride. But they often cut exposure right when breakout volatility begins, meaning they may miss the biggest legs of major trends. Their goal is efficiency, not extremity.
3. The Crisis Hedge
The Crisis Hedge is engineered to deliver during market dislocations. These systems may bleed during calm periods but are designed to respond explosively when the traditional risk complex (equities, credit, FX) enters a volatility regime shift.
Key characteristics:
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Focus on equity indices, bonds, FX, and volatility products
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Convex position sizing that increases during signal strength
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May include long-volatility overlays
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Often short-biased or directionally reactive
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Built to exploit regime transitions, not just trends
Design implications:
Crisis Hedge strategies accept that they will underperform during bull markets or low-volatility periods. Their edge lies in rare payoffs when traditional diversification fails. Judging them by Sharpe during calm years misses the point entirely.
4. The Core Diversifier
The Core Diversifier aims to provide uncorrelated, long-term return streams to a broader multi-asset portfolio. These systems tend to sit in the middle of the spectrum, balancing responsiveness with consistency. They are the generalists of the trend world.
Key characteristics:
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Moderate volatility targetting
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Balanced asset class representation
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Blended timeframes
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Careful correlation management
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Seeks persistent, repeatable edge
Design implications:
This approach prioritizes portfolio fit. The goal is not to outperform in isolation but to complement other return streams. The design leans toward robustness, balance, and dependability over extremes.
The Core Insight
Each of these archetypes is valid, but only within the context of its objective. What seems like a poor design choice for one may be essential for another.
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An Outlier Hunter should not smooth returns.
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A Volatility Targetter should not chase convexity.
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A Crisis Hedge should not fear occasional flat periods.
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A Core Diversifier should not swing for the fences.
Understanding the objective clarifies the tradeoffs. Without that lens, it is easy to misjudge what you are seeing.
IV. The Classic Arguments in Design – Applied Differently
When trend followers debate design, the same topics always surface: how much to diversify, whether to volatility target, how to treat correlation, what level of smoothness to aim for, and how to size positions dynamically.
These are the building blocks of system architecture. But without understanding the underlying objective, these debates quickly turn circular. What is a “bad” design choice for one trend follower might be essential for another.
Let’s revisit each of these classic design levers, this time through the lens of objective-driven logic.
1. Diversification – Wide or Focused?
The conventional view is that more markets equal better diversification, which leads to smoother returns. But that assumes smoothness is the goal.
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The Outlier Hunter benefits from a very wide universe. Every additional market increases the chance of catching a rare, runaway trend. Even liquid but niche contracts are valuable optionality.
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The Volatility Targetter may prefer a smaller, highly liquid universe, where turnover and transaction costs are better managed.
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The Crisis Hedge often concentrates in key macro assets. Adding exotic markets might dilute the intended response during regime shifts.
Key point: Diversification is not about quantity. It is about whether breadth aligns with edge.
2. Correlation Management – Reinforcement or Redundancy?
Correlated positions are often treated as inefficient. The logic goes that if multiple positions move together, they should be reduced or removed. But correlation is not always a liability.
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For Crisis Hedge or Outlier strategies, rising correlation may signal emergent regime change. These clusters of reinforcement are often where trends accelerate.
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For Volatility Targetters and Core Diversifiers, correlation drag reduces risk-adjusted return. Managing it becomes essential to control volatility and maintain consistent exposure.
Key point: Correlation is either a problem to neutralize or a signal to embrace, depending on your goal.
3. Smoothness – Comfort or Constraint?
Smoothness is often used as a proxy for quality. Investors gravitate toward strategies with clean equity curves and modest drawdowns. But smoothness often comes at the cost of dampening edge.
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Volatility Targetters deliberately design for smoother PnL to meet client expectations and portfolio mandates.
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Outlier Hunters accept lumpy returns because they are optimizing for asymmetry, not ride quality.
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Crisis Hedges may show long periods of flat performance, punctuated by explosive returns.
Key point: If your objective is convexity or tail exposure, smoothness is not your friend. It may actively suppress what makes the system work.
4. Volatility Adjusting – Equal Risk or Equal Opportunity?
Volatility targetting is one of the most polarizing choices in trend design. It aims to equalize the contribution of each position to overall risk. But it can also dampen the most important trades.
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Volatility Targetters depend on it. Their mandate is risk balancing, and volatility becomes a proxy for confidence.
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Outlier Hunters often avoid volatility targetting. They do not want to reduce position size when volatility rises, especially during breakouts.
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Crisis strategies may adaptively increase exposure as volatility rises, amplifying rather than dampening the move.
Key point: Vol targetting protects against path risk, but may also reduce payoff magnitude. The tradeoff depends entirely on whether your system needs stability or surge.
5. Dynamic Position Sizing – Reactive or Restrained?
Position sizing logic reveals how a system thinks about risk and opportunity. Some systems adapt constantly. Others hold fixed sizing to avoid injecting noise.
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Volatility Targetters size dynamically to maintain stable portfolio risk.
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Crisis Hedges may scale into positions during volatility expansion, treating it as signal rather than threat.
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Outlier Hunters often use static sizing and accept variability, relying on rare wins to drive returns.
Key point: Dynamic sizing is not universally better. It must reflect your philosophy of risk and your method of edge capture.
The Real Question Behind Every Debate
Whenever someone argues that a design feature is “wrong,” the immediate follow-up question should be:
Wrong for what objective?
Without clarity on what the system is trying to achieve, all design debates become disconnected from purpose. It is not about the features themselves. It is about the logic that ties them together.
V. The Failure of Universal Metrics
If strategy objectives differ, and system designs diverge to reflect those objectives, then it follows that no single performance metric can fairly compare all trend followers.
Yet this is exactly what happens.
Every month, trend strategies are ranked by Sharpe ratios, MARs, max drawdowns, and calendar returns. These metrics dominate league tables and investor dashboards. They provide a veneer of objectivity. But in reality, they reward certain styles and penalize others, not because of superior process, but because of mismatched purpose.
Let’s take a closer look at the flaws in the most common metrics.
Sharpe Ratio – Smoothness Over Substance
Sharpe rewards consistency. It favors strategies with low volatility and low path variance. This makes it perfect for Volatility Targetters, whose systems are engineered for stable return streams.
But for Outlier Hunters or Crisis Hedges, the Sharpe ratio is a poor fit. These systems may endure long flat periods followed by nonlinear payoffs. Their returns are often clustered and irregular, which penalizes them under Sharpe logic, even if their long-term outcome is exceptional.
Key point: A low Sharpe does not mean a system lacks edge. It may simply reflect a different edge that lives in the tails.
MAR Ratio – Punishing the Patient
The MAR ratio (CAGR divided by max drawdown) is commonly used to assess risk-adjusted performance. But this measure penalizes strategies with deep or protracted drawdowns, even if those drawdowns are necessary to stay exposed to extreme trends.
Outlier strategies and crisis models may experience long underwater periods as the price of admission for their upside. Reducing drawdown at all costs would mean reducing exposure to their core edge.
Key point: Drawdown aversion can lead to over-engineering. Not all systems are designed to avoid pain. Some are built to withstand it.
Skew and Kurtosis – Rarely Interpreted Correctly
Positive skew and fat tails are often seen as hallmarks of trend systems. But they are inconsistently captured and poorly interpreted.
A system with frequent small losses and rare large wins may show positive skew but poor Sharpe. Another system with more symmetrical outcomes may score higher on risk-adjusted metrics despite lacking outlier exposure.
Key point: Metrics like skew can describe a return distribution, but they cannot tell you whether the distribution is intentional or a byproduct of system design.
Calendar Returns – Seduced by Short-Termism
Many allocators rely on annual or monthly returns as indicators of quality. But lumpy strategies rarely shine on calendar schedules. Outlier moves often span arbitrary reporting periods, and their impact may be distorted depending on when the measurement window opens or closes.
A system that delivers a 40% return over two years may look unimpressive on a rolling 12-month basis , but devastatingly effective across full market cycles.
Key point: Judging nonlinear systems by linear schedules creates false impressions. Long-term logic gets obscured by short-term framing.
The Deeper Problem: Metrics Enforce Conformity
When metrics dominate, they begin to shape behavior. Designers optimize for Sharpe instead of edge. They engineer smoother curves to attract flows. They shrink universes to reduce internal correlation.
This leads to homogenization, where systems begin to look and behave alike, not because that is best, but because it is rewarded.
Key point: Metrics are not neutral. They are prescriptive. They encourage a certain type of strategy at the expense of others.
What Matters Instead: Purpose and Process
Performance metrics should not be the starting point. They should be the byproduct of a process built to solve a specific problem.
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If your objective is to capture outliers, expect low Sharpe and deep drawdowns.
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If your mandate is to smooth portfolio volatility, expect high turnover and muted tails.
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If your aim is crisis convexity, prepare to underperform during quiet markets.
There is no metric that can unify these outcomes.
And that is exactly why they should not be compared side by side.
VI. The Only Test That Matters – Survivability and Long-Term Track Record
If metrics fail to fairly compare divergent trend strategies, and if each design is a reflection of distinct objectives, then what can we rely on to assess quality?
There is only one measure that cuts through it all.
Survivability.
A strategy that persists through market regimes, adapts without losing its core logic, and continues to function in real capital environments over time has passed the only test that matters. This is real-world validation, not simulation.
Track Record Reveals What Metrics Cannot
A long-term track record exposes more than numbers. It reveals:
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Robustness of process under stress
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Consistency of philosophy across market cycles
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Tolerance for volatility without intervention or style drift
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Discipline to stay the course even when underperformance tests conviction
It tells you that the strategy did not just work once.
It endured.
The Mirage of Optimized Systems
Backtests can be curated to look impressive. Metrics can be engineered through selection bias, curve fitting, or restrictive filters. But surviving ten or twenty years in live trading, across interest rate cycles, commodity booms, financial crises, and low-volatility regimes, is a different achievement entirely.
Many strategies are designed for backtests.
Few are designed to survive reality.
The Market is the Ultimate Adversary
Markets punish fragility. They expose assumptions, break optimizations, and test every element of your system design. A live, audited track record under capital constraint, slippage, execution latency, and psychological pressure is the final proving ground.
And that is what separates hypothetical edge from real, time-tested resilience.
Conviction Over Calibration
A long-term track record does not always shine on paper. The best-performing strategies over decades may have had extended drawdowns, embarrassing calendar years, and suboptimal Sharpe ratios. What they did have was a clear process, philosophical clarity, and the conviction to keep executing.
That is what true robustness looks like.
Not the best system on paper, but the one that is still standing.
VII. Conclusion – Fit for Purpose
Trend following is not one strategy.
It is a family of solutions, each designed to solve a different problem, in a different way, for a different kind of investor or portfolio need.
When we try to judge all trend followers by the same standard, whether it is Sharpe ratio, drawdown, or monthly performance, we ignore the most important question of all:
What is the strategy built to do?
The Outlier Hunter is not trying to smooth returns.
The Volatility Targetter is not trying to capture tail risk.
The Crisis Hedge does not care about calendar-year rankings.
The Core Diversifier does not chase extremes.
Each one is valid within its domain. Each one reflects a different view of risk, edge, and purpose. Design decisions like diversification level, volatility adjustment, correlation treatment, and position sizing are not arbitrary. They are expressions of intent.
So instead of asking who is best, we should ask:
Best for what purpose?
Under what conditions?
Over what time horizon?
And most of all:
Did the strategy survive? Did it deliver on its mandate through uncertainty, regime shifts, and real-world friction?
That is the test that matters.
Because in trend following, just as in all complex systems, there is no one-size-fits-all.
There is only fit-for-purpose.
Allocators and practitioners alike benefit when we evaluate strategies based on what they are built to do, not how they rank on a scoreboard designed for something else.