“A trend persists until, self-similar at every scale, it does not; the turn is the market changing its mind about the future.”
About This Report
The ATS Classic Trend Benchmarking Portfolio is a systematic trend-following reference model. It runs a ten-strategy ensemble across sixty-eight futures markets spanning eight sectors, sized by a fixed-amount rule on prior-bar volatility, and serves as a clean benchmark for classic trend following. The figures in this report are produced by the ATS Classic Trend engine, a standalone Python implementation run on out-of-sample data using simple trend-following models. Results are computed as dollar profit and loss on a stated twenty-five million dollar capital base, with returns stated additively: the year-to-date figure is the sum of the monthly net returns, and drawdowns are measured on the same additive basis. The model carries no discretionary overlay; it is the rules, the markets, and the price data.
This is a high-volatility portfolio, and the figures should be read with that in mind. It is built to capture large, sustained trends rather than to smooth the path to them, so the return profile pairs strong compounding through trending years with sharp monthly declines and deep drawdowns when trends reverse. June’s -19.74% and the -42.83% peak-to-trough decline of 2024 to 2025 are characteristic of the strategy, not departures from it.
The report is published in the first trading days after each month-end as a fast, factual read on the prior month for classic trend followers. It documents the model’s performance, decomposes the result by sector and by market, and sets the numbers against the month’s market backdrop. The aim is a clean leading indicator of what the trend-following environment delivered, and what it may be signalling next.
Commentary
June was a regime transition, and the portfolio wore it. The month returned -19.74%, its sharpest since inception, as a single macro catalyst pulled market after market into synchronised selling across assets. A hotter-than-expected payrolls print early in the month lifted yields and the dollar, broke the precious metals lower, and set off a rotation that ran through the balance of June: the petroleum complex unwound the spring spike over three consecutive weeks, the metals deepened a pullback from their winter highs, and the long equity advance finally paused near record territory. June was less a market correction than a forest after the wind changes quarter: trees that had leaned the same way for months suddenly leaned the other, all at once. The result was a broad, one-directional move of the kind that closes a chapter of market leadership rather than extending it.
The drawdown is best read as the signature of that handover, not as a failure of the model. Trends that had carried the book for months, long the metals off their advance, long parts of the petroleum band into the spring, reversed together, and the model released risk into the turn as the stops came in. The exit is not a prediction of what comes next. The model never knows where the turning point sits; it knows only that persistence has gone, and it treats the stop as recognition that the old order no longer holds rather than as a forecast of the new one. This is the single distinction that separates the model from a static long-only book: where passive exposure absorbs the full reversal, the model lets risk go as trends break and rebuilds on the other side. The year-to-date figure steps back to +4.02%, and the portfolio now sits 23.19% below its April peak. That is a real drawdown, but not an unfamiliar one: the model absorbed a deeper decline of 42.83% between the March 2024 peak and the May 2025 trough and recovered to new highs by February 2026, so June is a shallower test than one the track has already passed.
The Month, Week by Week
The TTU Trend Barometer traced the transition in real time, firming from 43% to 55% across the month, not because markets rose but because they stopped diverging: a broad, synchronised decline is a stronger trend environment than a market rising in places and falling in others.
Performance Snapshot
June net return of -19.74% set against a 2026 that had built to +27.24% by the April peak. The month-by-month comparison with 2025 shows the year’s higher amplitude in both directions, a wider trending range that has paid on the upside and, in June, cost on the downside.
Cumulative Performance and Drawdown
Since the January 2020 inception the portfolio has compounded to +179.4% on the additive basis, reaching a peak of +202.6% in April 2026 before the June transition. The current drawdown is 23.19% from that April peak. It is not the deepest of the track: the drawdown panel below shows a larger decline of 42.83% between the March 2024 peak and the May 2025 trough, from which the portfolio recovered to new highs by February 2026. June is the sharpest single month of the track at -19.74%, but the equity line traces the familiar shape of a trend programme giving back part of an advance, having before rebuilt from further down.
Monthly Performance Since Inception
Key Statistics Since Inception
Computed from the monthly series since the January 2020 inception. CAGR annualises the cumulative return over the capital base; the MAR ratio is CAGR divided by the maximum drawdown; win rate, profit factor and skew are measured on monthly returns.
The profile is characteristic of classic trend following: a high compound return carried alongside a deep maximum drawdown and elevated volatility, a win rate near half, and a positive skew, the small-losses-and-occasional-large-gains shape the strategy is built to produce.
Trade Statistics Since Inception
This section reports the portfolio on a realised, closed-trade basis since inception, the lens by which classic trend followers assess a programme. It complements the equity-curve statistics above and is stated on the trading basis, net of commission. It does not reconcile to the additive equity curve, because open positions and mark-to-market timing differ; it is a distinct, realised view rather than a restatement.
Direction: long and short participation
Of 3,609 closed trades, 72.9% were long and 27.1% short. Long positions contributed 113.2% of net trade P&L and short positions -13.2%. This is the signature of a period in which synchronised upward moves across equities and commodities rewarded long exposure, while short positions paid the cost of participation and de-risking rather than generating profit.
R-multiple framework
Every trade is sized to a fixed initial risk, one R, so outcomes are directly comparable across all 68 markets. Average trade expectancy is +0.19R. The average winner returns +1.61R against an average loser of -0.64R, a win-to-loss ratio of 2.53. The largest winning trade reached +35.2R while the largest loss was contained to -3.0R. This asymmetry, small bounded losses against occasional very large gains, is the core of the approach.
Distribution and skew
The distribution of outcomes in R has a skew of +6.8, the hallmark of trend following: a majority of small, contained losses (median trade -0.32R, win rate 37.0%) funded by a small number of very large winners. The top 5% of trades account for 164% of total R; the remainder, in aggregate, net negative. This is the self-similar, fat-tailed payoff structure documented in Fractals of Finance research, and it is why discipline through the many small losses is the price of capturing the few outsized, regime-defining trends.
Holding period
The average trade is held 146 days and the median 86 days, consistent with a low-frequency, high-conviction programme. Holds range from same-day stop-outs to a single position carried for 1,376 days. Long trades are held slightly longer on average (151 days) than shorts (133 days), reflecting the longer duration of the dominant upward trends over the period.
System Analytics
The portfolio is built from seven independent trend systems, described here as System 1 through System 7. It does not trade one of each. On every market it runs a 10-model ensemble drawn from the seven systems, with each model’s parameters tuned to that market, so a system can appear more than once on a market, with different parameters, and need not appear on all of them. Across the 68 markets that is 680 models in total. This section groups those models by the system they come from and reports the seven resulting streams: how each contributes to the return, how each behaves in isolation, and how each moves in relation to the whole. The construction and parameters of the individual systems are proprietary and are not disclosed; what follows is the performance anatomy of the ensemble, not its recipe.
A note on basis: gross versus net
The cumulative figures in this section are stated gross, on the trading basis: net of commission, but before the portfolio-level overlays of slippage, roll execution and the collateral interest credit. On this basis the seven systems sum to +190.1% gross. This gross figure should not be confused with the headline net return. After the full cost stack, the portfolio returned +179.4% net, as reported in Key Statistics Since Inception. The difference of 10.7 points is a portfolio-level overlay and is not attributed to any single system, which is why the per-system curves reconcile to +190.1% gross and not to the +179.4% net headline.
How the ensemble works
Each of the seven systems expresses a different rule for defining and following a trend, and each is deployed several times over, with market-specific parameters, inside the per-market ensemble. Because their dollar results are additive on the fixed $25M base, the models, and the seven systems they roll up into, sum day by day to the portfolio equity curve: nothing is left unexplained, and no system is weighted by discretion. The benefit of combining them is not that any one is exceptional, but that they are imperfectly aligned. Across the period the average pairwise co-movement between systems was 0.58, and each system’s correlation to the whole ranged from 0.67 to 0.89. No single system is the portfolio.
That imperfect alignment is where the performance benefit sits. If the seven systems moved as one, their monthly volatilities would simply add, to 12.3% a month. In practice the portfolio’s monthly volatility was 9.9%, roughly four fifths of the summed standalone risk. The same return is delivered on a smoother path, which is what lifts the risk-adjusted result of the ensemble above most of its parts. Each system also captures a slightly different slice of every trend: some enter earlier, some hold longer, some carry more of the short side, so the ensemble stays exposed across more of each move, and through more kinds of regime, than any single rule could.
The convex, positively skewed payoff that defines classic trend following repeats at every level of the book. Within each system, and within each market inside each system, the same shape recurs: many small contained losses funding a few large winners, self-similar across scales in the sense documented in Fractals of Finance research. Diversifying across seven expressions of that one signature does not dilute it; it stabilises the path by which it compounds, and it keeps the programme in contact with trends that any single system would miss.
Per-system cumulative contribution since January 2020, additive on the $25M base, stated gross on the trading basis. The seven curves sum to the portfolio’s +190.1% gross result.
Per-system return characteristics
The table below reads each system on the equity-curve basis. Contribution is additive and sums to the portfolio; the CAGR, MAR ratio, maximum drawdown and correlation are properties of each system’s own curve.
Per-system R-multiple analysis
Every trade is sized to a fixed initial risk of one R, so outcomes are comparable across all systems and markets. The table applies the R-multiple framework of Trade Statistics Since Inception to each system as a separate column, with a total that reconciles to the portfolio. The Sum of R row is the reconciling line: the seven systems’ R contributions add to 698.6R, and the Total column reproduces the portfolio’s realised figures on the frozen book of 3609 closed trades.
None of these per-system differences is visible in the blended portfolio line, and that is precisely why the blend compounds more steadily than its parts.
This section is regenerated each month from the same frozen book of record and the append-only monthly loader. The per-system curves are decomposed from the engine’s daily mark-to-market and continue to sum to the portfolio, so the mark-to-market variance measured against the locked baseline each month applies to the systems and to the whole on one consistent basis. The System 1 to System 7 mapping is fixed and carried forward unchanged from month to month.
Sector Attribution
Seven of the eight sectors detracted in June, the losses led by the metals and the petroleum complex. The month and year-to-date contributions each sum to the portfolio result.
This is how trend following loses. It rarely gives back a little everywhere; it loses in clusters, because trends themselves cluster. When leadership changes across several sectors at once, positions that behave independently in normal conditions briefly move as one, and the diversification that carries the strategy thins at the very moment it is most needed. The portfolio is spread across sixty-eight markets, but it is not spread across a single change in market leadership, and June was one of those changes.
Energy remains the year’s dominant engine at +13.07% year-to-date despite June’s -4.18%: the petroleum breakdown that cost the book in the transition month is the same trend that short exposure has ridden lower through 2026. That inversion, a paying annual trend that stung in the month it accelerated, is the leadership change in miniature.
Attribution Highlights
June’s detractors clustered in the reversing trends: Gold at -4.05% and High-Grade Primary Aluminium at -2.67% as the metals broke, with ICE Brent Crude at -1.59% as the petroleum reversal caught long positioning. The month’s positive contributions were modest and sat in the equity indices that held near records, the Dollar Denominated Nikkei 225, Lead and the E-mini Russell 2000 the largest.
Across 2026 the picture inverts. The year’s largest contributors are the petroleum shorts, Heating Oil at +6.40%, Light Sweet Crude Oil at +3.22% and RBOB Gasoline at +3.14%, joined by the Nikkei from the equity advance. The year’s detractors are the precious metals, Gold, Platinum and Palladium, that gave back in June the ground they had built.
Market-Contribution Review
The since-inception contribution paths show the same self-similar structure at the portfolio scale: a handful of durable trends carry the result, and the leaders rotate by regime. Energy leads 2026, the equity indices have compounded steadily since 2020, and the metals that led earlier in the year are the ones that reversed in June.
Trend Spotlights
Gold is the clearest reversal on the board. An uptrend that had run through the winter and firmed again into late May broke lower on the payrolls print, the single largest detractor of the month as a firmer rate path and a stronger dollar raised the cost of holding a non-yielding metal.
Light Sweet Crude Oil is the year’s defining trend. The spring spike has fully retraced over three consecutive down weeks in June, a persistent decline that short exposure has carried lower and that stands as the portfolio’s strongest engine in 2026.
The Dollar Denominated Nikkei 225 was June’s notable performer. The equity uptrend paused from records late in the month but held its multi-month structure, the lone sector to contribute as the rest of the board rolled over.
Sector Rundown
The Trend Environment
The barometer firmed from 43% to 55% over June, closing the month at the favourable boundary where Neutral gives way to a strong environment. The mechanism matters more than the level: the reading rose because a broad, synchronised decline restored the directional alignment that had fractured in late May, not because conditions turned benign. Strong trends are not the same thing as rising prices. A market falling together is often a healthier environment for trend following than one drifting upward without agreement, because it is the persistence and breadth of a move, in either direction, that the strategy is built to capture. Rising trend strength on the downside, sitting alongside a portfolio drawdown, is the characteristic reading of an inflection in leadership, and it points to a firmer trending environment ahead than the one the year has offered so far.
The Lesson of the Month
June is a reminder that trend following is not paid for predicting the turn. It is paid for surviving it. Every major trend begins by looking like a false move, and every major reversal begins by looking like the continuation of the old one. The discipline is not in knowing which is which in advance; it is in responding correctly once the market has decided.
Monthly Wrap
June marked a handover in market leadership rather than an interruption of it. A single macro catalyst pulled the metals, the petroleum complex, the grains and the currencies into synchronised selling, the equity advance paused near records, and the model de-risked into the turn for a -19.74% month and a 23.19% drawdown from the April peak. The year-to-date figure holds at +4.02%, and the since-inception track stands at +179.4%, still above where it began the year and well within the amplitude the strategy has shown before.
Looking Ahead
The barometer at the favourable boundary and a risk-off tilt across energy and the metals leave the clearer opportunity on the short side of the complexes that broke down, with the equity uptrend the open question: whether June’s pause is a consolidation within the multi-month advance or the start of a broader unwind. The model does not forecast that resolution; it will carry the trends that persist and release the ones that break, and a firmer trending environment is the more constructive backdrop for that process than the choppier one that preceded it.
About these figures
All figures are generated by the ATS Classic Trend engine from back-adjusted continuous futures data across the sixty-eight-market universe. Returns are stated on a fully-loaded, pre-fee basis: net of estimated commission, a conservative slippage allowance and roll-execution costs on every trade and contract roll, and inclusive of collateral interest earned on the un-margined capital at the US 90-day Treasury bill rate. They are dollar profit and loss on a twenty-five million dollar capital base, additively. Results do not include performance or management fees, which would reduce the returns shown. Contribution figures are additive and sum to the portfolio result at the sector and market level. This report is a benchmark study and is not investment advice or an offer of any product.
Richard Brennan writes on systematic trading, complex adaptive markets, and the philosophical foundations of trend following at atstradingsolutions.com. His books include The Fractals of Finance and Complex Adaptive Markets. The forthcoming Carved by Impossibility completes the trilogy.
Want the theoretical foundation for why trend following works?
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.