Breakouts led the week while sweet spot setups stayed selective
Backtest patterns, sweet spot performance, and missed-trade analysis for August 17 to August 21.
THE EDGE THIS WEEK
Breakout volume stayed active, but clean follow-through was selective
This week’s research points to a market that produced plenty of scanner activity without delivering broad breakout efficiency. The key pattern was dispersion: a small cluster of symbols and themes converted at very high rates, while the broader setup pool struggled, especially in short-duration, lower-pressure conditions that usually form the system’s sweet spot.
The result was a split tape. Strong names rewarded trend alignment and persistence, but average setup quality weakened when measured across all 398 scanner signals. That combination matters because it suggests the edge was not in frequency, but in concentration.
BY THE NUMBERS
Weekly backtest and scanner profile
The broad backtest remains structurally healthy with a 64.2% average win rate across 2,610 trades and 189 symbols. But the live scanner tells a more cautious story. Only 68 of 398 setups converted into breakouts, while 156 failed outright, which is a low-efficiency environment for traders relying on raw setup count as a proxy for opportunity.
That disconnect between strong historical symbol-level performance and weak current scanner conversion is the central pattern of the week. It implies that selectivity mattered more than participation. Traders who narrowed focus to the highest-quality names likely outperformed those treating all signals equally. For active setup review, see live setups in the scanner.
SWEET SPOT REPORT
The usual setup profile underperformed
3-5 Bars
The sweet spot setup, defined by lower pressure and a compact 3 to 5 bar structure, posted a 37.5% win rate this week versus a 57.6% historical win rate across 276 trades. That is a meaningful drop and one of the clearest signs that normal pattern assumptions did not hold.
Research-wise, this matters because the sweet spot usually captures orderly continuation. When it underperforms by 20.1 percentage points relative to history, it suggests one of two things: either breakouts were occurring from more irregular structures, or the market was punishing early confirmation entries before trend resolution. In both cases, the lesson is the same. Pattern shape alone was not enough. Context and relative strength likely carried more explanatory power than compression quality this week.
When a historically reliable setup bucket weakens this sharply, the right response is not to abandon the model. It is to tighten selection and ask which secondary variables, sector strength, symbol persistence, or repeated retests, separated the winners from the rest.
SYMBOL SPOTLIGHT
Three symbols that reveal the week’s structure
NOW was one of the cleanest examples of concentrated edge. It finished with an 11 for 11 record and a 0.9993 average R, which is notable not just for perfection but for sample size. Among the 100% club, $NOW had enough repetition to suggest more than random variance. It likely benefited from persistent directional behavior where entries were repeatedly validated rather than whipsawed.
GDX offers a different kind of signal. Its 95.45% win rate across 22 trades is one of the strongest combinations of volume and consistency in the dataset. When an ETF posts that kind of result, it often indicates theme-level participation rather than isolated stock-specific strength. That tends to be more durable because the edge is distributed across a broader capital flow.
COST sits at the other extreme with a 16.67% win rate across 12 trades and a deeply negative average R. Weak outcomes like this are useful research inputs because they show where the model was repeatedly fighting tape character. $COST was not a one-off bad trade. It was a recurring mismatch between setup logic and actual price behavior. Similar caution applies to XRT and MA, where participation was present but follow-through was poor.
The broader takeaway is that symbol selection mattered more than average conditions. A trader anchored to a small set of repeat winners likely saw a very different week than one rotating across the full scanner list. For strategy observation and automation context, watch the bots in the Edge Lab.
WHAT THE BOTS MISSED
No execution leakage, but that is not the same as full opportunity capture
From a process standpoint, this is a clean result. No missed trades, no missed runners, and no measurable R left behind means the system executed according to design. That removes operational error from the week’s analysis and keeps the focus on setup quality.
Still, zero missed trades should not be misread as proof that conditions were strong. In fact, it sharpens the conclusion that the issue was not execution but environment. The bots did not fail to participate. They participated in a week where broad breakout conversion was weak. That distinction is important because it argues for improving filtering, not increasing aggressiveness.
SECTOR HEAT MAP
Breakouts clustered in growth and defensive demand pockets
Technology led with 13 breakouts, followed by consumer-related names with 11 and healthcare with 9. ETFs also produced 9 breakouts, which is an important confirmation signal because it suggests some moves were broad enough to register at the basket level, not just in single names.
Energy added 5 breakouts, while industrials and financials each posted 4. This distribution supports the idea that breakout opportunity existed, but it was not evenly spread. Leadership concentrated in a few areas, and those pockets likely offered better trend persistence than the market as a whole.
The duplicate consumer labels in the source data, one at 11 and another at 4, likely indicate either sub-group fragmentation or classification inconsistency. Even with that caveat, the directional message is clear. Traders were better served by focusing on the strongest concentrations rather than scanning all sectors equally.
When ETFs and sector leaders both show strength, the edge usually improves for names aligned with that theme. When lagging sectors still generate setups but not confirmation, false positives rise.
RESEARCH NOTE
The real edge this week was concentration, not confirmation
The most important research insight from this dataset is that setup abundance did not translate into tradable breadth. The scanner produced 398 setups, but only 17.1% became breakouts. At the same time, the best-performing symbols posted extremely high win rates with meaningful trade counts. That contrast suggests the edge was concentrated in a narrow set of names and themes rather than evenly distributed across the opportunity set.
For traders, this raises a useful question: should setup quality be judged less by standalone pattern criteria and more by whether a symbol already belongs to a confirmed leadership cluster? If the answer is yes, then the next layer of research should test whether repeated strength within top-performing symbols or sectors materially improves conversion rates over the base scanner. This week’s data strongly suggests it would.