The Zero Beneath the Zero
What we found when we looked beneath the most quoted statistic in finance, and what it means for how we understand markets.
The most quoted statistic in quantitative finance is the autocorrelation of daily returns. It measures whether yesterday’s price move tells you anything about today’s. Across four decades and sixty-eight futures markets, the answer averages to zero. This number underwrites the efficient market hypothesis, the random walk, and most of modern portfolio theory. If returns carry no memory, the past is irrelevant. Structure does not exist. What happened yesterday cannot help you predict what happens next.
But an average can conceal as much as it reveals.
The existence of momentum and mean-reversion in financial returns is well established in the academic literature, from Lo and MacKinlay’s variance ratio tests in 1988 to Moskowitz, Ooi, and Pedersen’s time-series momentum study in 2012. What follows is not a claim to have discovered these phenomena. It is a claim to have found a unified measurement framework that connects them, and within that framework, to have produced specific empirical results that are new.
We tested this framework. Not by asking whether the average autocorrelation is zero, but by asking what the average conceals.
The answer is a permanently coupled system operating along a spectrum that no one had previously measured.
Why Markets Have Structure
The theoretical foundation for this series begins with a question that market microstructure research has already answered: what actually moves prices?
The work of Jean-Philippe Bouchaud and his collaborators at Capital Fund Management established something that should have rewritten the foundations of financial theory. The mechanism that moves price is not information. It is the physical act of trading itself. Every order placed, every position sized, every stop triggered imparts force to the price. This is not one force among many. It is the only force. Nothing moves price except a transaction.
Every other theory of price movement, whether based on fundamentals, sentiment, liquidity, or momentum, is ultimately a theory about why people trade. But the price itself only moves through the act. Consider two traders. A fundamental analyst concludes that a stock is undervalued and buys. A trend follower sees a breakout and buys. Their reasoning could not be more different, but their market impact is mechanistically identical: both place upward pressure on the price. The analyst’s impact is convergent, pushing price toward intrinsic value. The trend follower’s impact is divergent, pushing price further from equilibrium and inviting still more trend followers to act. Different decisions, same mechanism, very different consequences for market structure.
Bouchaud and his collaborators showed that this impact scales as the square root of order size, a sublinear relationship. Doubling your order does not double your impact. This is what allows markets to function without blowing up on every large trade. But sublinearity does not preclude outliers. It simply spaces the distance between them. When many agents act in the same direction simultaneously, each individually sublinear impact stacks. Collective action compresses the sublinear discount. The distance between extremes is wider than a linear system would produce, but the extremes themselves can be just as violent because they require coordinated behaviour rather than individual action to get there.
This series extends Bouchaud’s insight from microstructure to macro behaviour by asking a natural question: if agent impact is the mechanistic force for price action, what happens when you scale it across all participants simultaneously? It is a natural extension of his ideas, scaling up from the micro to the macro.
Every transaction applies directional force to the price. Some strategies combine opposing transactions whose forces largely cancel, but the individual impacts remain real. What matters for market structure is the aggregate balance across all participants at any given moment. When divergent impact dominates across that population, positive feedback builds and the market trends. When convergent impact dominates, negative feedback builds and the market oscillates. When the two forces roughly balance, the market sits in a transitional zone between the two states.
A brief note on taxonomy. Trend following and countertrend trading are both directional in nature. One trades with the prevailing move, the other trades against it, but both require a directional view and both apply directional force. Mean reversion is fundamentally different: it is range-bound, betting that price will return to a central value rather than continue in either direction. Countertrend is a family of trend, not a family of mean reversion. The distinction matters because these strategies exert different types of force on the price and interact differently with the feedback spectrum.
The transitional zone is often called noise, but that label misleads. It is not a third regime sitting between trend and mean reversion. It is a crossover point, a narrow boundary where dominance shifts from one force to the other. Both forces remain active throughout. Neither ever switches off.
Because agents operate across multiple instruments (a macro fund trades bonds, currencies, and equities simultaneously; a commodity trader operates across grains, metals, and energy) the feedback state is not confined to a single market. It propagates through one-to-many relationships. A single decision fans out into multiple market impacts, each of which feeds back into the agent’s next decision through their combined profit and loss. The system is inherently non-linear. One-to-many is a classic non-linear structure: the same input produces multiple outputs that interact recursively. Linear systems produce Gaussian outcomes. This system does not.
The coupling is permanent because its source is permanent. Diversification is a foundational principle of investing. As long as investors diversify, they will apply impact across multiple instruments simultaneously, and the one-to-many relationship guarantees coupling. What changes is the phase. Sometimes the coupling is convergent across markets, sometimes divergent, sometimes mixed. The wiring between markets is structural and will persist as long as financial markets exist. Only the signal running through it oscillates.
The feedback operates asymmetrically between long and short positions, and this asymmetry has a mechanistic explanation. Margin calls and forced liquidation only happen to losing positions, creating asymmetric forced selling during drawdowns. Leverage limits differ for longs and shorts. Short squeezes carry theoretically unlimited loss potential while long losses are bounded at zero. Redemption pressure is one-directional: investors pull money during losses, not gains. Stop losses cluster differently in falling versus rising markets. These are structural features of how agents are forced to act under stress, not abstract statistical properties. The slight asymmetry in tail fatness documented in this series, left tails marginally fatter than right, is a direct consequence.
The rise of passive investing has intensified these dynamics. Passive funds are structurally long-only. Inflows arrive gradually as regular contributions drip in, but outflows during crises are concentrated as redemptions cluster. This amplifies the asymmetry between slow convergent buying and fast divergent selling. Passive investing also removes price-sensitive agents from the market. A passive fund buys regardless of valuation, which means fewer convergent participants exist to dampen divergent moves. And because passive investors are diversified by definition, they are a major contributor to the permanent coupling: when passive money flows out during a crisis, it flows out of everything simultaneously, amplifying the cross-market synchronisation of divergent feedback.
The book describes this architecture in full. Phase 1 of this research programme demonstrated that the feedback structure exists, that markets carry persistent memory in their magnitude dynamics even when directional memory averages to zero. The full-sample zero is not evidence of randomness. It is the weighted average of two opposing forces that cancel when you compress four decades into a single number. Phase 2, this series, extends the framework to explain why impact scales the way it does: how feedback waxes and wanes between spectral extremes, how the structural state of the market determines whether a given strategy profits or loses, and why there is no room for randomness in this interpretation. When we track autocorrelation across time, the trace traverses the spectrum continuously and rarely settles in the transitional zone. The engine is persistent. It is always running. It is always somewhere on the spectrum.
The Spectrum
When you compute the autocorrelation not over the full sample but over rolling two-year windows, the picture transforms. The trace oscillates between positive values, where moves tend to persist, and negative values, where they reverse. These are not random fluctuations. They are the signatures of two opposing forces that operate in every market, simultaneously, at all times.
The first is positive feedback. When it dominates, moves follow through. A price that rose yesterday is slightly more likely to rise again today. Trends develop and sustain. This is the trending state.
The second is negative feedback. When it dominates, moves cancel. A price that rose yesterday is slightly more likely to fall back today. Prices cover a lot of ground but go nowhere. This is the mean-reverting state.
What changes across time is which force dominates. The data reveals not a binary switch between two states but a continuous spectrum. At one end, positive feedback dominates and markets trend. At the other, negative feedback dominates and markets oscillate. Between them lies the transitional zone where the two forces roughly balance.
This zone is commonly called noise, but that label misleads. Noise implies randomness, the absence of structure. The dispersion data shows otherwise: markets are just as tightly coordinated in the balanced zone as they are at either extreme. The coupling does not pause there. What pauses is the net directional signal, because the two forces are roughly cancelling at the system level. The black space on the heatmap is thin not because calm periods are rare but because the balanced zone is genuinely narrow. It is a crossover point, not a third regime. The overwhelming majority of market time is spent in clearly coloured territory, either blue or red, with dominance firmly established in one direction or the other.
The full-sample zero sits at the centre of this spectrum, and it is the most misleading number in finance. It is not the actual state of any market at any time. It is what you get when you compress four decades of oscillation into a single average, when two opposing forces cancel each other out across time. It is not evidence of randomness. It is evidence that the average conceals the machine beneath it.
Permanent Coupling
The most important finding in this series is not the spectrum itself. It is that all sixty-eight markets travel along it together.
Think of it as gravity rather than a switch. The coupling does not decree that every market must be in the same state simultaneously. At any given date, the heatmap column is never a single colour. Individual markets retain their own character. What the coupling does is bias the distribution: when the system leans toward trend, more markets sit at the positive-feedback end than chance alone would produce. When it leans toward oscillation, more sit at the negative-feedback end. The gravitational pull is always present. The degree of dissent is always small.
The empirical proof is in the dispersion. Across the sixty-eight contracts, the cross-sectional spread of autocorrelation values measures approximately 0.071. That number is statistically identical whether the system is trending, oscillating, or sitting in the balanced zone between them. If markets were operating independently, dispersion would widen during the balanced state as each contract wandered its own path. It does not. The clustering is constant. What changes is where the cluster sits on the spectrum.
This is why markets do not couple during crises and decouple during calm. They are always coupled. During calm periods, the system is coupled in negative feedback: markets oscillate together, producing the quiet, range-bound behaviour of low-volatility environments. During shocks, the coupled system shifts toward the trending end: markets trend together, producing the coordinated directional moves that define crises and macro regime shifts. The coupling is not the crisis. It is the permanent architecture through which every phase expresses itself.
The source of that architecture is diversification. A macro fund trades bonds, currencies, and equities simultaneously. A commodity trader operates across grains, metals, and energy. Every diversified participant creates a one-to-many relationship between their decisions and the instruments they touch. Those one-to-many relationships are the wiring of the system. They will persist as long as diversification remains a principle of investing, which is to say, as long as financial markets exist. Only the phase of the coupling oscillates.
This reframes what diversification actually does. Correlation between asset classes does not spike during crises because previously independent markets suddenly become connected. They were never independent. What changes is the spectral position of the coupled system. During the noise zone, coupling produces offsetting returns and diversification appears to work. During coordinated trending, it produces co-directional moves and diversification appears to fail. Same wiring. Different signal running through it.
The Earthquake
The transition between spectral states follows a pattern that resembles geological earthquakes. The sequence has three phases.
First, the rupture. A shock arrives, driven by inflation, rate cycles, supply disruption, or policy failure. The coupled system shifts toward the trending end of the spectrum. Positive feedback floods through every market. Directional energy is released simultaneously across dozens of instruments.
Second, the aftershock zone. The initial force exhausts. The system transits through the noise zone. Both feedbacks are active and partially offsetting. Some markets are still trending while others begin to oscillate. The mixing zone empties as markets polarise toward end-member states.
Third, the settling. The system arrives at the mean-reverting end. Negative feedback dominates across the board. Markets oscillate in ranges, absorbing remaining energy through chop. This state can persist for years. The Greenspan calm of 2005 and the late-QE suppression of 2018 to 2020 are examples: periods where the vast majority of markets were simultaneously oscillating, the deepest negative-feedback coupling in the dataset.
The COVID transition is the cleanest earthquake in four decades. In early 2020, the system was at peak calm. By April, it had ruptured to trend dominance in two months.
Structural Alignment
Once you see that markets operate along a spectrum of positive and negative feedback, a deeper question presents itself. Every trading strategy is, at its foundation, either divergent or convergent. There is no third type.
A divergent strategy, such as trend-following, breakout trading, or momentum, pushes prices further from their starting point. It bets that moves will continue. A convergent strategy, such as value investing, mean-reversion trading, or contrarian positioning, pushes prices back. It bets that moves will reverse. Note that countertrend trading, despite trading against the prevailing direction, remains directional in nature. It is a member of the trend family, not the mean-reversion family, because it requires a trend to exist in order to trade against it and applies directional force in doing so.
This creates a structural alignment between strategy design and market state. When positive feedback dominates and markets trend, divergent strategies profit by design. They are part of the process creating the structure they are capturing. When negative feedback dominates and markets oscillate, convergent strategies have their natural advantage.
The transitional zone is hostile to both. No strategy can be simultaneously divergent and convergent. When the opposing forces are balanced, every strategy is structurally misaligned with half of the market’s behaviour.
The practical consequence is that the question practitioners should be asking is not the one they currently ask. The standard question is: what is the correlation between strategy X and asset Y? This is a surface measurement that changes through time for reasons the questioner cannot explain. The structural question is: what is the alignment between this strategy’s design and the market’s current spectral position? Correlation is the consequence. Structural coupling is the cause.
Crisis Alpha, Reframed
The managed futures industry has documented crisis alpha for years: the observation that trend-following strategies deliver positive returns during the worst equity drawdowns. The negative correlation between trend and equity during bear markets, measured at negative 0.47 across four decades, is one of the most commercially important findings in portfolio construction.
But the standard explanation is incomplete. It describes what trend does during crashes. It does not explain why the opportunity exists, what it connects to, or why the same mechanism also produces positive performance during sustained bull phases.
The spectral coupling framework provides the mechanism. During equity bear markets, the coupled system shifts toward the trending end of the spectrum. Equities fall persistently. Bonds rally. Commodities react to the macro shock. Currencies reprice. The positive-feedback state floods through dozens of markets simultaneously. Trend-following, a divergent strategy by design, is structurally aligned with the system’s spectral position. It captures the directional energy being released across the full market universe, not just the short equity leg.
This means crisis alpha is not a special property of crises. It is one expression of permanent coupling. The same feedback mechanism that produces protective returns during equity crashes also produces additive returns during bull markets when the system is in the trending state. The direction is different but the mechanistic source of the opportunity is identical: divergent feedback dominates, and a divergent strategy captures it.
The only hostile environment for trend-following is coupled mean-reversion, where the system is oscillating together and every directional signal fails simultaneously. This was not a failure of trend-following. It was structural misalignment between a divergent strategy and a system in its most extreme negative-feedback state.
Buy and Hold, Reframed
The coupled spectrum tells a story that extends well beyond trend-following. Read it from the perspective of a buy and hold equity investor and a different picture emerges.
Buy and hold is structurally long only. It benefits during one spectral state: coupled trending with equities rising. In every other state it is either stagnant or exposed. During coupled mean reversion it drifts, going nowhere while absorbing volatility. During coupled trending with equities falling it experiences the full force of correlated losses across the portfolio, with no offsetting mechanism and no structural response.
This is not bad luck. It is architecture. The permanent coupling ensures that when the crisis arrives it arrives everywhere simultaneously. The diversification that appeared to work during the oscillatory calm was never switched off. It was simply running in a phase that produced offsetting returns. When the spectral position shifts to coordinated trend, that same coupling produces co-directional losses. The investor who held a diversified equity portfolio believing they were protected discovers that the coupling was always present. Only its expression changed.
The duration of trending states makes this more serious than the standard crisis alpha narrative suggests. The GFC sustained directional moves for over a year. The 2022 inflation shock ran across bonds, equities and currencies simultaneously for months. A buy and hold investor has no mechanism to benefit from or protect against that duration. The longer the trending state persists, the deeper the exposure compounds without relief.
Trend-following has a structural response to every spectral state. It earns during trending states regardless of direction, struggles modestly during mean reversion, and is maximally aligned with the system during the worst equity crises. The protection scales with the severity and duration of the trending state. The worse and longer the crisis, the more the asymmetric protection compounds. This is the mechanical consequence of a divergent strategy operating in a permanently coupled system that is releasing directional energy across dozens of markets simultaneously.
The question for any investor building a portfolio for the long run is not whether crises will arrive. The coupled spectrum guarantees they will, and that they will be felt everywhere at once. The question is how long they last, and whether the portfolio has a structural mechanism to respond. Buy and hold does not. A trend allocation does.
The Paradox
The engine that produces this structure has not decayed. After four decades of exponential growth in systematic trading capital, the oscillation amplitude is statistically unchanged. The universe mean rose from 0.0491 in the first decade to 0.0558 in the most recent. Six of eight asset classes show higher amplitude in the most recent decade than the first. The adaptive markets hypothesis predicted convergence. The data shows none.
But strategy returns have declined. A 200-day moving average trend strategy delivered a MAR ratio, return divided by maximum drawdown, of 1.63 in the first decade. It has fallen to 0.19 in the most recent. The engine persists. The simplest harvest does not.
This is not a contradiction. Four forces explain it. The dominant force is regime: quantitative easing and zero interest rates suppressed the variance ratio across the entire universe, starving trend strategies of directional raw material. During the 2021 to 2022 inflation build, every trend strategy in the test recovered to pre-QE quality. The second force is structural: central bank backstops and passive fund flows have severely impaired short-side standalone alpha under symmetric calibration. The long side dipped with the regime and recovered. The short side appears to have collapsed, but investigation reveals that bull and bear markets operate at different frequencies, the escalator and the elevator, and a symmetric lookback is structurally misaligned with the faster downside dynamics. Asymmetric calibration recovers measurable improvement, particularly in equities, though standalone short-side alpha remains negligible post-2020. The short side’s value is portfolio construction, not standalone edge. The third force is the horizon gradient: within any regime, shorter lookback horizons deliver less edge than longer ones, likely due to crowding at the most visible signal frequencies. The fourth force is the frequency mismatch itself: independent calibration for long and short signals improves portfolio-level performance across most asset classes.
The edge is not dying. It has relocated. From short horizons to long ones. From symmetric to asymmetric calibration. From uniform signals to those calibrated to each market’s natural frequency. The 300-day Donchian delivers a Sharpe of 0.74 in the post-2020 period, compared to 0.17 for the 20-day. Longer horizons and asymmetrically calibrated models have become the dominant source of trend alpha.
The Investigation
This synopsis draws together the findings of a nine-part research series. Each episode asked one question and built on the answer from the last. Together they form a single, cumulative case. The episodes are summarised below.
Episode 1: Zero Doesn’t Mean Nothing
The near-zero full-sample autocorrelation is the number everyone trusts. We show it is a lie. Rolling two-year windows reveal that every market oscillates between periods of positive feedback, where moves persist, and negative feedback, where moves reverse. The zero is not the absence of signal. It is the exhaust of a machine whose two forces cancel when you average across time.
Episode 2: The Coupled Spectrum
Sixty-eight markets. One system. The heatmap reveals that markets do not oscillate independently. They are permanently coupled along the feedback spectrum. The coupling never switches off. And within each feedback state, direction carries structure. The zero that appears when you average across regimes conceals opposing directional memories. Directional memorylessness is not a property of price. It is a property of the average.
Episode 3: The Trend-Equity Paradox
Everyone knows trend-following provides crisis alpha. Nobody has explained why. The permanently coupled spectrum is the mechanism. The correlation between trend and equity is not constant. It flips sign and deepens with the severity of the drawdown. The spectral framework explains the flip.
Episode 4: Proving It Is Not Random
The variance ratio test provides independent confirmation. We decompose it by spectral state. Donchian breakout periods produce VR of 1.130. Flat periods produce VR of 0.282. The full-sample average sits between them, concealing the structure. The same average-conceals-truth principle, now confirmed through a completely different test.
Episode 5: The Structure Persists
The adaptive markets hypothesis predicts that the feedback structure should decay as systematic capital floods in. We test this across four decades. The oscillation amplitude is statistically unchanged. Thirty-four of fifty-five paired contracts show higher amplitude in the most recent decade than the first. State 2 is dead. The structure is not an anomaly being arbitraged away. It persists.
The two forces are not symmetric. Positive feedback is gentle and sustained. Negative feedback is intense and brief. The ratio between them varies by a factor of four across asset classes. Livestock trend for more than twice as long as they oscillate. Equities are nearly balanced. This ratio is the fingerprint: the same structure, operating everywhere, but calibrated differently in every asset class.
The feedback structure persists. Simple trend-following returns do not. This paradox is the most important finding in the series. The resolution: four forces explain the decline. Regime suppression from QE is cyclical and dominant. Short-side impairment is structural. Horizon crowding has relocated the edge from short lookbacks to long ones. The engine is intact. The simplest harvest has moved.
Episode 8: The Escalator and the Elevator
Bull trends are slow escalators. Bear trends are fast elevators. A symmetric lookback is structurally misaligned with the faster downside dynamics. Asymmetric calibration recovers measurable improvement. The short side’s value is portfolio construction, not standalone edge. The frequency mismatch is a direct consequence of the agent-population asymmetry established throughout the series.
Three zeros. Three tests. One spectral structure. State 1 died in Episode 4. State 2 died in Episode 5. State 3 stands. The feedback structure is a permanent feature of how markets process information through the behaviour of their participants. The zero is not the absence of signal. It is the exhaust of a system whose two forces cancel when you look from far enough away.
What This Means
If you manage money, allocate capital, or construct portfolios, here is what this series has established.
Markets are not random. The zero that anchors modern finance is the weighted average of two opposing forces, not evidence that neither exists.
Markets are permanently coupled. They do not couple during crises and decouple during calm. The coupling is always present, wired into the system by the act of diversification itself. What shifts is the spectral position of the coupled system, from trending to balanced to oscillating. The coupling will persist as long as financial markets exist.
The mechanism is singular. Every theory of why markets move is ultimately a theory about why people trade. The price itself moves only through transactions. This mechanistic interpretation, extending the work of Bouchaud from microstructure to macro behaviour, explains why the statistical fingerprints we observe are not anomalies but inevitable consequences of how markets actually work.
Strategy performance is structural alignment. When a divergent strategy meets a trending market, or a convergent strategy meets an oscillating market, performance follows by design. The correlation between strategy and market is a consequence of this alignment, not an independent phenomenon.
Crisis alpha is one expression of a permanent mechanism. The same coupling that protects equity portfolios during crashes produces additive returns during bull markets and identifies the precise conditions under which trend-following will struggle. Buy and hold has no structural response to the trending state when equities are falling. The coupling that produces co-directional losses during crises is the same coupling that appeared to diversify during calm. The protection that trend-following offers scales with the severity and duration of the crisis, which is precisely when the investor needs it most.
The edge has relocated, not disappeared. The engine is intact. The macro environment, not competition, is the dominant driver of trend-following returns. The current post-QE environment of higher rates, fiscal dominance, and geopolitical fragmentation may provide more directional raw material than the lost decade suggested.
The full architecture of this framework, including the fractal geometry that connects feedback structure to fat tails and the practical implications for portfolio design, is developed in The Fractals of Finance: Determinism, Adaptation and the Geometry of Markets. This series provides the empirical evidence. The book provides the blueprint for building on it.
Why Fractals
The title of this research programme is The Fractals of Finance. Over nine episodes, we have documented a spectrum, a coupling, a fingerprint, and a paradox. But we have not yet answered the question that the title raises: what is a fractal, and what does it have to do with what we found?
A fractal is a pattern that repeats across scales. Zoom in on a coastline and you see the same roughness at every resolution. Zoom in on a fern and each branch mirrors the whole. The defining property is self-similarity: the part resembles the whole, and the whole is built from repetitions of the part.
The findings of this series are fractal in precisely this sense. The feedback spectrum documented across these episodes does not exist at a single timescale. It operates at every resolution simultaneously. The volatility clustering that Phase 1 measured in daily data appears identically in weekly and monthly data. The trending and mean-reverting states that Phase 2 mapped through rolling windows repeat across shorter and longer horizons. The permanent coupling holds whether you measure it over months or decades. The same structure, at every scale.
This is not coincidence. It is consequence. Fractal patterns arise when non-linear feedback operates across scales. A day trader reacting to this morning’s price move is the same mechanism as a pension fund reacting to last year’s performance. Their decisions fan out through the same one-to-many relationships. Their impacts stack through the same sublinear scaling. The feedback loop is identical. The timescale is different. The geometry repeats because the mechanism repeats.
This is what connects every finding in both series. Phase 1 proved the fingerprint: memory, persistence, and fat tails appearing universally. Phase 2 mapped the spectrum: the oscillation between trending and oscillating states, the permanent coupling, the asymmetric fingerprint of each asset class, the earthquake transitions, and the paradox of declining returns from a persistent engine. Both are expressions of the same fractal geometry, the same self-similar structure repeating across scales, produced by the same non-linear feedback operating through the only mechanism that moves price: the physical act of trading.
The data in these two series is the evidence. The fractal is the geometry that connects it. The book is the complete architecture of both.
The Fractals of Finance: Determinism, Adaptation and the Geometry of Markets tells the full story. It explains how feedback creates fractal structure. It explains why that structure produces fat tails, memory, and the spectrum we have measured. It explains what this means for systematic trading, for portfolio construction, and for understanding the nature of risk itself. It explains why simple rules outperform in complex environments, why trend following works as an expression of universal principles rather than a statistical anomaly, and why the geometry of markets reflects the geometry of every complex adaptive system in nature.
These two research series provide the empirical evidence. The book provides the blueprint for building on it.
This research series is drawn from The Fractals of Finance: Determinism, Adaptation and the Geometry of Markets
The book explores the full architecture of feedback, fat tails, and fractal structure in financial markets, and what it means for how we trade, invest, and understand risk.
Available now on Amazon in paperback, hardcover, and Kindle.
Want a practical field manual for trading trends and capturing outliers?
The Aussie Turtles Trend Following Guide: A Field Manual for Hunting Outliers adapts the timeless principles of the original Turtle traders into a systematic, rules-based approach for modern markets. Co-authored with Adam Havryliv.
Available now on Amazon in paperback, hardcover, and Kindle.