“The trend that breaks a portfolio in one month is often the one that pays it in the next; the reversal and the opportunity are the same event seen at two scales.”
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 -19.72% and July’s +10.72%, back to back, are characteristic of the strategy rather than departures from it, as is the -42.83% peak-to-trough decline of 2024 to 2025.
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
July answered the question June left open, and it answered it from the other side of the turn. The month returned +10.72%, the strongest since the January advance, as the petroleum complex broke out and ran. Heating Oil rose 17.05% over the month, Light Sweet Crude Oil 6.82% and RBOB Gasoline 7.23%, and Energy alone contributed +9.20% of the +10.67% trading result. June had been a regime transition in which established trends reversed together and the model released risk into the turn. July was the far side of that same handover: the exposure that had been let go in June was rebuilt in the direction the market had chosen, and the new trend paid before the month was out. The two months are not opposites. They are the two halves of one event, the second unreadable without the first.
The mechanism is worth stating plainly, because it is the whole argument for the approach. The model does not forecast reversals and did not forecast this one. It carried the old trends until they broke, took the loss that breaking imposed, and then re-established on whatever persisted next. A static long-only book has no equivalent second step: it absorbs the reversal and waits. The cost of the method is visible in June, the benefit in July, and neither figure means much read alone. Not everything went the model’s way. The equity uptrend that had held near records through the spring finally broke, the Dollar Denominated Nikkei 225 falling 11.23% over the month, and Equities detracted -1.77%. The year-to-date figure now stands at +14.7% and the portfolio sits 12.5% below its April peak, having recovered roughly half of June’s decline in a single month.
The Month, Week by Week
The TTU Trend Barometer traced a violent month, running 64%, 39%, 50%, 52% and 30% across the five weeks and finishing 34 points below where it began. The portfolio’s strongest month of the year was delivered against a trend environment that collapsed out of the Neutral band in the final week, a divergence that is the month’s most instructive feature.
Performance Snapshot
July net return of +10.72% against June’s -19.72%, the two sharpest consecutive moves the track has produced. The month-by-month comparison with 2025 shows how much wider 2026’s trending range has been in both directions, an amplitude that has now cost and paid inside a single quarter.
Cumulative Performance and Drawdown
Since the January 2020 inception the portfolio has compounded to +190.1% on the additive basis. The April 2026 peak of +202.6% has not been recovered, and the portfolio sits 12.5% below it, down from 23.2% at the end of June. The drawdown panel below shows this is a modest episode by the standards of the track: the -42.83% decline between the March 2024 peak and the May 2025 trough remains the deepest, and the portfolio recovered from it to new highs by February 2026. July is the second-largest single month of the track at +10.72%, behind only the outsized months of 2024, and the equity line has resumed the shape it has traced before, a sharp give-back followed by a rebuild from the other side.
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 just above half, and a positive skew, the small-losses-and-occasional-large-gains shape the strategy is built to produce. July lifted the compound return, the win rate and the profit factor without moving the maximum drawdown, which remains anchored to the 2024 to 2025 decline.
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 3,628 closed trades, 72.9% were long and 27.1% short. Long positions contributed 113.9% of net trade P&L and short positions -13.9%. Nineteen trades closed in July and they netted a small loss, none of them in Energy: the month’s result sits almost entirely in positions still open at month-end, which is precisely why this realised view does not reconcile to the equity curve.
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 2.52. 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 -0.33R, win rate 36.9%) funded by a small number of very large winners. The top 5% of trades account for 166% 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 +200.8% gross. This gross figure should not be confused with the headline net return. After the full cost stack, the portfolio returned +190.1% 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 +200.8% gross and not to the +190.1% 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 +200.8% 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 692.6R, and the Total column reproduces the portfolio’s realised figures on the frozen book of 3628 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
Five of the eight sectors contributed in July, and the result was concentrated rather than broad: Energy alone delivered +9.20% of the +10.67% trading total. The month and year-to-date contributions each sum to the portfolio result.
This is how trend following wins, and it is the same shape as how it loses. The return does not arrive evenly across sixty-eight markets; it arrives in a cluster, because trends cluster. In June a single change in leadership pulled market after market into synchronised selling across assets and the portfolio gave back 19.72%. In July one complex broke out and carried almost the whole result on its own. The concentration is not a flaw in the diversification, it is what diversification is for: the book holds enough uncorrelated exposure to still be present when one of them finally runs.
Energy has extended its lead as the year’s dominant engine, at +22.28% year-to-date after July’s +9.20%. The same complex that cost the book in June’s transition has now paid it twice over from the other direction, which is the clearest illustration in the track of why the model re-enters rather than waits. Against it, Softs remain the year’s weakest sector at -4.04% and the Metals have yet to recover the ground the June break took out of them.
Attribution Highlights
July’s contributors clustered in the petroleum complex: Heating Oil at +4.58%, ICE Brent Crude at +1.60% and NYMEX RBOB Gasoline at +1.55%, with Light Sweet Crude Oil close behind. The month’s detractors sat almost entirely in the equity indices as that uptrend finally broke, the Dollar Denominated Nikkei 225 at -1.06%, the E-mini NASDAQ 100 at -1.01% and the E-mini Russell 2000 at -0.46%.
Across 2026 the same names now lead. Heating Oil is the year’s largest contributor at +10.97%, joined by RBOB Gasoline at +4.69% and Light Sweet Crude Oil at +4.28%, the petroleum complex having paid on the short side through the first half and on the long side since. The year’s detractors remain the precious metals, Gold at -2.65% with Palladium and Platinum both at -2.07%, which have not recovered from the June break.
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. The petroleum complex has separated from the field in 2026 and did so twice, once short and once long, while the equity indices that compounded steadily from 2020 have flattened since June.
Trend Spotlights
Heating Oil is the month’s defining trend and its largest single contributor. A move that began as the petroleum complex turned up in the third week ran 17.05% over the month, and unlike crude it held its gain into the close, the one contract in the sector still rising in the final week.
Light Sweet Crude Oil shows the breakout and the give-back in one frame. Crude ran hard through the middle of the month, added a second week of double-digit gains, then surrendered a large part of it in the final week as the barometer collapsed. It still finished 6.82% higher and contributed +1.06%, the trend intact but visibly thinner than it was a fortnight earlier.
The Dollar Denominated Nikkei 225 was July’s clearest reversal. The multi-month equity uptrend that had merely paused in June broke properly this month, falling 11.23% and taking -1.06% out of the portfolio as the model de-risked out of a position it had carried since the spring.
Sector Rundown
The Trend Environment
The barometer ran 64%, 39%, 50%, 52% and 30% over the five weeks of July, opening in favourable territory and closing 10 points below the Neutral floor, its weakest reading of the year. The mechanism matters more than the level. The collapse in the final week did not come from markets going quiet; it came from established moves changing direction, crude surrendering a fortnight of gains, the dollar turning against the whole basket and all eight grains contracts falling together after four had advanced. Trend strength measures the persistence of moves, in either direction, so a board can reverse hard and read cold while the portfolio still books a strong month from positions taken earlier in the sequence. That is exactly what happened, and it is the self-similar point the Fractals of Finance research keeps returning to: the same reversal signature appears at the weekly scale, the monthly scale and the multi-year scale, and what separates a cost from a gain is only where in the sequence the exposure was established.
The Lesson of the Month
July is a reminder that a trend follower is paid for what it does after the loss, not for avoiding it. June’s -19.72% and July’s +10.72% came from the same market event, one complex reversing and then running the other way. A model that had refused to re-enter after being stopped out would have taken all of the first month and none of the second. Surviving the turn is the entry ticket; re-establishing on the far side is where the return actually comes from.
Monthly Wrap
July recovered roughly half of June’s decline in a single month. The petroleum complex broke out and the model, having released risk into June’s reversal, rebuilt into the new direction and was carried by it for a +10.72% month, Energy contributing +9.20%. Against that, the equity uptrend that had held near records since 2024 finally broke, costing -1.77%, and the barometer collapsed to 30% in the final week as crude, the dollar and the grains all turned at once. The year-to-date figure stands at +14.7% and the since-inception track at +190.1%, with the portfolio 12.5% below its April peak.
Looking Ahead
The barometer at 30% and falling rapidly describes a board on which a great many established moves have just reversed, and the model enters August with a book that was largely rebuilt in the past six weeks. Two questions follow. Whether the petroleum breakout survives the give-back it took in the final week, and whether the equity break is the start of a broader unwind or a deeper pause. The model will not answer either in advance. It will hold what persists and release what does not, and a cold barometer says only that the next set of trends has not established itself yet, not that it will not.
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, Complex Adaptive Markets, Carved by Impossibility and The Aussie Turtles Trend Following Guide.
Want to explore why structure exists at all?
Carved by Impossibility: What Remains When Everything Else Is Eliminated
The book explores the architecture of constraint, emergence, and reality itself, and what it means for how we understand markets, life, and the universe.
Available now on Amazon in paperback, hardcover, and Kindle.
Want the theoretical foundation for why trend following works?
The Fractals of Finance: Determinism, Adaptation and the Geometry of Markets bridges complexity science with practical trading implementation. With a foreword by Jerry Parker, original Turtle Trader.
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.