In managing our book, we run trend strategies across multiple asset classes and at different speeds, with exposure ranging from slower multi-day systems to faster intraday signals.
Regardless of model specifications, we keep observing the same pattern: small implementation details can produce surprisingly large differences in realized performance.
As we like to say… dispersion is in the details.
This theme has come up across several recent articles, from position sizing approaches to reverse-engineering the rules of a legendary portfolio manager in the CTA industry.
You can find our previous articles at the links below.
Today we test something new.
We take a simple intraday trend signal and study four distinct exit policies, treating this experiment as a practical exercise to reason about how a single design choice should (or should not) be made.
Study Setup
All tests are performed on the iShares Semiconductor ETF (SOXX), an asset class that has been firmly in play for several weeks, having staged a historically remarkable run-up through new all-time highs.
For all tests, we use a signal based on ATR band breakouts, something we already presented in a piece earlier this year.
The core idea is rather simple: at the start of each session, we compute upper and lower ATR bands anchored to the session opening price. When price breaks outside those bands while simultaneously establishing a new session high or low, we enter a position in the direction of the breakout, betting that such directional impulse will persist.
Conceptually, the framework attempts to isolate moments when a meaningful directional imbalance is affecting intraday price formation, and then position in anticipation of that imbalance persisting throughout the trading session.
Trade sizing depends on recent market volatility: the higher the recent volatility, the smaller the position size, and vice versa.
Keeping the entry scheme and sizing unchanged, we test four exit variations:
Session Open
Session Midline
VWAP (Volume Weighted Average Price)
PSAR (Parabolic Stop and Reverse)
The following chart provides a graphical illustration of how these exit mechanisms behave during a representative trading session.
Session Open
The most straightforward of the four. Its logic is directly connected to the signal itself: because the ATR bands are anchored to the session opening price, that level becomes the natural reference point for our imbalance measure.
In other words, if the market retraces all the way back to the session open, we consider the measured imbalance as invalidated: the buy or sell pressure that drove the initial move is no longer present, and we exit the position.
Session Midline
We define the session midline as the midpoint between the session’s cumulative maximum and minimum prices at any given point during the day.
If we enter a long position on a new session high or low, it appears reasonable to maintain exposure as long as price action remains closer to directional continuation than reversal.
VWAP (Volume Weighted Average Price)
The Volume Weighted Average Price (VWAP) is an indicator we have studied in depth in a prior research paper. We believe it serves as an elegant exit solution for intraday trend strategies because it defines a natural level of balance and agreement between buyers and sellers.
If the market retraces back to VWAP after a breakout, we interpret this as a sign that the directional imbalance has been absorbed and the earlier directional conviction is no longer supported.
PSAR (Parabolic Stop and Reverse)
The Parabolic SAR is a classic trailing stop mechanism originally presented by J. Welles Wilder in 1978. What sets it apart from the other three exits is its acceleration mechanic: the stop not only trails price, but tightens progressively as the trade extends in our favor, becoming stricter the more the trade moves in our favor.
The stop level updates each bar according to the following rule:
For the first trade of each session, PSAR is initialized at the session open. For subsequent trades within the same session, PSAR is not reset: instead, it picks up from the last stop level reached at the previous exit.
The Results
All four exit policies deliver positive results across the full backtest period on SOXX: Sharpe ratios range from 0.92 to 1.16, CAGRs from 16.2% to 18.3%, and maximum drawdowns are clustered in the -20% to -24% range. Every configuration remains profitable, although we observe some dispersion in performance.
The VWAP exit leads on both Sharpe (1.16) and CAGR (18.3%), with the Midline close behind (Sharpe 1.12, CAGR 17.8%). PSAR and Session Open trail somewhat, at Sharpe ratios of 1.02 and 0.92 respectively.
At this point one might be tempted to simply select the highest-Sharpe exit type and be done with it. Before drawing that conclusion, however, it is worth looking more closely at the trade statistics.
Once we move beyond portfolio-level metrics, the picture becomes considerably more nuanced.
The Session Open exit generates the fewest trades (2,406) and the highest average return per trade at 6.8 bps. On the surface that is appealing, but it comes with by far the worst average intra-trade drawdown at nearly -89 bps, meaning those returns require sitting through significantly deeper adverse moves.
The PSAR exit tends to offer less severe intra-trade drawdowns of (-73 bps) while maintaining a competitive average return of 6.3 bps. The Midline and VWAP exits compress drawdowns further (-65.5 and -64 bps respectively), at the cost of somewhat lower average trade returns (6.2 and 6.1 bps).
This is a pattern we observe consistently in our research: optimizing for one metric tends to come at the expense of another. Once evaluation criteria widen beyond a single metric, what “best” actually means becomes less clear, and every specification carries trade-offs.
The instability becomes even more apparent when looking at annual Sharpe ratio rankings, where we see that there is no consistent winner from one year to the next.
The same strategy that excels in one regime can look poor in the next: rankings rotate, and judging form their average values, no configuration has persistently dominated across the full testing sample.
What to Do Now?
From first principles, if we select one exit policy and discard the others, we are implicitly making a bet that this particular approach is meaningfully superior to the alternatives.
That would be justified if we had very strong confidence that one specific exit type is genuinely and durably better, but the evidence simply does not support that view: different metrics might favor different configurations, and rankings tend to rotate year by year.
Interestingly, the best course of action we have found in these situations is to avoid the choice entirely, or, in other words, choosing not to choose.
This is possible through a technique that is fairly well known in quantitative trading but, in our opinion, not appreciated or discussed nearly enough: ensembling.
Instead of being 100% exposed to a single specification, the more robust approach is to spread exposure across all specifications that, upon logical scrutiny, appear both intuitive and structurally sound. In our experiment, since we have no strong basis to favor one exit policy over the others, we should allocate a portion of the risk budget to each implementation. This eliminates the risk of selecting the worst configuration and removes the implicit assumption that we can reliably foresee which specification will perform best going forward.
Notably, ensembling does not require the individual strategies to be uncorrelated or dramatically different from one another. It simply reflects the fact that our uncertainty about which specification is best is real, and that uncertainty should be expressed in how we allocate rather than suppressed by an overconfident single-point choice.
Conclusion
We find ensembling to be both a robust and underrated approach, one that strongly resonates with the spirit of Occam’s Razor.
Occam’s Razor, in its most practical interpretation for system design, is about parsimony: the simpler a framework is, the fewer assumptions it rests on, and the fewer potential points of rupture it carries.
Every extra assumption we introduce is a place where something can break in the future, either because that assumption eventually stops holding or because it creates a temptation to tinker when performance disappoints. Fewer moving parts means fewer decisions to revisit and fewer levers to pull under pressure.
Ensembling across logically sound specifications is, in this sense, a form of simplicity. Instead of introducing the strong assumption that configuration X is best, we remove that assumption entirely and replace a fragile single-point choice with a robust distributed one. The result is a framework with fewer decisions to second-guess over time, and a design that is inherently more stable across changing market regimes.
Remember… markets rarely reward unnecessary complexity.
If you found this article useful, feel free to leave a comment and reach out via direct message or email at info@concretumgroup.com for any questions.
Disclaimer
This publication is provided by Concretum Group for informational, educational, and research purposes only. It does not constitute investment, financial, legal, or tax advice, nor a recommendation to buy or sell any security, instrument, strategy, or investment product. All investments involve risk, including possible loss of principal. Past performance, backtested performance, and historical analysis are not reliable indicators of future results. Readers should conduct their own research and consult qualified professionals before making investment decisions.
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