Using RL30Slope Z to analyze relative trend behavior and monitor potential market rotation

Market Behavior and Rotation

Markets often exhibit periods where leadership shifts:

• Across sectors

• Across asset classes

• Between risk-on and defensive areas

These shifts are commonly referred to as rotation.

While price reflects the outcome of this process, traders often look for ways to better understand:

• Which areas are strengthening

• Which areas are weakening

• How leadership is evolving over time

Limitations of Price Alone

Price is the most direct measure available, but it has limitations in how clearly it expresses underlying behavior.

It does not explicitly or consistently isolate:

• Whether trend strength is increasing or decreasing

• How current movement compares to its recent history

• Whether different instruments are behaving similarly on a comparable basis

Two assets may both be rising, but:

• One may be accelerating

• The other may be flattening

While these differences can be inferred from price, distinguishing them reliably requires additional structure or transformation.

Using the 30-Period Regression Line (RL30)

The 30-period regression line (RL30) provides a way to estimate underlying trend:

• Reduces short-term noise

• Represents the average directional movement over a defined window

• Provides a smoother view than raw price

RL30 can be thought of as: An estimate of the central trend of price over the last 30 periods

Why the Slope Matters

The slope of the regression line adds another layer of information.

It reflects how the trend is evolving:

• A rising slope indicates strengthening upward trend behavior

• A falling slope indicates weakening or downward trend behavior

This makes slope a useful measure of trend acceleration or deceleration, rather than just direction.

The Challenge: Comparability Across Instruments

Raw slope values are difficult to compare across instruments.

Different markets exhibit:

• Different volatility levels

• Different typical slope magnitudes

• Different structural characteristics

As a result:

• A “large” slope in one instrument may be normal in another

• Direct comparison can be misleading

Normalizing with a Z-Score

To address this, slope can be standardized using a Z-score:

Z = (Current Slope − Average Slope) / Standard Deviation of Slope

This expresses the current slope relative to its own historical distribution.

The result is:

• A dimensionless measure

• Comparable across instruments

Interpreting RL30Slope Z

Once normalized, values can be interpreted consistently:

• Positive values indicate above-average upward trend behavior

• Negative values indicate below-average or downward trend behavior

• Values further from zero indicate more unusual or extreme conditions

This allows traders to evaluate: How strong or weak current trend behavior is relative to normal

Application: Monitoring Relative Strength and Rotation

Because RL30Slope Z places different instruments on a comparable scale, it can be used to:

• Compare relative trend behavior across sectors or assets

• Identify areas where strength is improving or deteriorating

• Monitor changes in leadership over time

In practice, this can help highlight:

• Emerging areas of strength

• Areas losing momentum

• Potential rotation between groups

It is important to note: RL30Slope Z does not directly measure capital flows. The value of RL30Slope Z comes from how it is applied within a structured process.

Practical Use in a Trading Process

Within a structured process, RL30Slope Z can support several practical functions:

1. Ranking and prioritization
It provides a consistent way to compare instruments and identify where relative strength is improving or deteriorating. This helps:

• Highlight sectors that are strengthening

• Identify symbols aligned with stronger trend behavior

In practice, this can improve trade selection quality and support more deliberate portfolio construction.

2. Regime and context assessment
By observing how groups of instruments behave together, RL30Slope Z can help distinguish:

• Broad participation versus concentrated leadership

• Strength expansion versus deterioration

This context can inform:

• Position sizing decisions

• Overall risk exposure

3. Monitoring rotation and transitions
Changes in RL30Slope Z over time can help track shifts in relative strength across sectors or assets. This may highlight:

• Emerging areas of strength

• Areas losing momentum

• Potential transitions in leadership

While not predictive on its own, it can improve awareness of where attention should be focused.

What This Framework Provides

RL30Slope Z offers:

• A consistent method for comparing different instruments

• A way to quantify trend acceleration

• A structured approach to observing relative changes over time

It does not replace price, but adds context to it.

Final Thought

Markets are complex, and no single indicator fully explains their behavior.

However, improving how we measure and compare trend behavior can lead to more consistent analysis. RL30Slope Z provides a standardized framework for evaluating relative trend strength and monitoring potential rotation across markets.

About OBUG

Inside the Owl Bundle User Group (OBUG), these concepts are applied within a broader research and testing process, including:

• Systematic evaluation of indicators

• Backtesting across multiple conditions

• Integration into structured trading frameworks

Join us as we study the markets!

This material is provided for educational and research purposes only. Results are based on historical backtesting and do not represent actual trading performance. Past performance is not indicative of future results. This is not investment advice or a recommendation to buy or sell any security.

• If Market and Sector conditions align, trades should perform better.

This idea sits at the heart of what Dr Ken Long calls the Logic Chain framework:

• Market → Sector → System → Symbol

But rather than accepting this trading narrative, we decided to test it.

So we designed a structured research project using Dr. Ken Long’s Swing Systems and ran extensive back tests inside EdgeRater.

The Research Question

We wanted to answer a simple but important question:

• Does Logic Chain improve the performance of Swing Systems?

More specifically:

• Does it increase profitability?

• Does it reduce drawdowns?

• Does it improve risk-adjusted returns?

To answer that, we tested several of Dr Ken Long’s Swing systems strategies including: MPRC, ORL, CH, 5DD, TS, and WO.

We evaluated each system under three structural conditions:

1. No Logic Chain — Trade signals whenever the system triggers.

2. Sector Alignment Only — Trade signals only when the sector aligns with the system triggers.

3. Market + Sector Alignment — Trade signals only when both market and sector conditions align with the system triggers.

The First Surprise

The data showed something unexpected.

• Logic Chain does not create the Swing System edge.

• The Swing Systems themselves already contain the edge.

Instead, Logic Chain performs a different function:

• It determines where the edge should be deployed.

That distinction turned out to be extremely important.

What Actually Improved

At first glance the results looked modest.

Some systems showed small performance improvements. Others changed very little.

But when we examined the results more deeply, something important emerged.

We looked at losing streaks. That’s when the real insight appeared.

Losing Streak Compression

When we compared the backtests, we saw a dramatic difference in the worst losing streaks.

For example, the MPRC system showed the following maximum losing streaks:

Other systems showed similar behavior.

The Logic Chain framework dramatically reduced clusters of losing trades.

Why Losing Streaks Matter

In systematic trading, large drawdowns rarely come from a single trade.

They occur when losses cluster together.

A trading system may encounter a hostile market regime where its edge temporarily disappears. During those periods:

• Mean reversion systems keep buying falling markets

• Momentum systems chase exhausted trends

• Signals keep firing — but the environment is wrong

This produces loss clusters.

What Logic Chain does is filter many of those hostile environments.

Drawdown Compression

Once losing streak clusters shrink, something important happens:

• Drawdowns compress dramatically.

In our research we observed examples such as:

This represents a major improvement in risk efficiency.

The Key Insight

After studying the data, the conclusion became clear:

• Logic Chain is not a trading signal filter.

Instead, it functions as a portfolio construction framework.

Rather than eliminating trades, it helps traders prioritize where capital should be deployed first. .

How Traders Can Implement Logic Chain

Based on the research, the implementation is straightforward:

1) Run Swing System scans normally
Use your curated symbol list.

2) Identify Market and Sector alignment

3) Rank signals based on alignment strength

When capital is limited:

• First prioritize trades where Market + Sector align

• Next consider Sector-only alignment

• Finally consider system signals alone if capital allows

The position size depends on how strongly the different layers align. This approach preserves the edge of the trading systems while improving portfolio risk efficiency.

Why This Matters for Traders

Many traders try to solve drawdowns by adding:

• more indicators

• more filters

• more complicated strategies

But often the real solution lies in portfolio architecture.

Logic Chain works because it helps avoid regime-mismatch trades, which:

• compress losing streak clusters

• reduce drawdowns dramatically

• improve risk-adjusted returns

• allow larger position sizing

Final Takeaway

The Logic Chain research revealed a simple but powerful insight:

• Swing Systems generate the edge.

• Logic Chain determines where that edge should be deployed.

When those two layers work together, trading becomes significantly more stable.

Join the Owl Bundle User Group

If you enjoy research-driven trading and systematic market analysis, you will likely feel right at home inside OBUG. Each week we explore ideas exactly like this — testing them, challenging them, and refining them together. You can learn more about the Owl Bundle User Group at this link: OBUG

This material is provided for educational and research purposes only. Results are based on historical backtesting and do not represent actual trading performance. Past performance is not indicative of future results. This is not investment advice or a recommendation to buy or sell any security.

Rather than debate it philosophically, we decided to test it properly.

The Objective

Evaluate whether avoiding trades before or during earnings improves the risk-adjusted performance of the Critical States system within our curated stock portfolio.

Specifically:

• Does earnings exposure degrade expectancy?

• Does it increase drawdown fragility?

• Does it harm P&L or Monte Carlo stability?

• Is an earnings filter statistically required?

We ran EdgeRater backtesting & Monte-Carlo analysis across:

• 6-year window

• 11-year window

• 16-year window

This was not anecdotal analysis. It was systematic, data-driven testing.

Case 1: Avoid Trades ± X Days Around Earnings

If earnings were structurally harmful, removing them should materially improve performance.

It didn’t.

Across 11 and 16 years:

• Expectancy remained stable

• FE25 ( Final Equity at the 25th percentile - Monte Carlo analysis) showed no structural degradation

• Drawdowns remained comparable

• No durable long-term advantage emerged

Conclusion: Earnings exposure does not degrade expectancy or compromise long-term system integrity.

Case 2: Avoid Only Pre-Earnings Trades

We isolated entries occurring X days before earnings, while allowing trades after earnings.

What the data showed:

• Over 11- and 16-year windows, expectancy was flat to slightly lower than baseline.

• FE25 and drawdown metrics remained statistically similar.

• Trade count decreased without a compensating robustness gain.

Interpretation:

Blocking only pre-earnings trades does not enhance structural integrity.

It slightly alters trade distribution, but does not improve risk-adjusted performance.

In longer horizons, it marginally reduces edge density.

In short, it may change how the ride feels — but not the structural edge.

Case 3: Enter Only Before Earnings

This is where the data became interesting. When isolating entries 7–9 days before earnings: Across multiple timeframes:

• Expectancy improved vs baseline

• Win rate increased

• FE5 improved

• Drawdowns remained comparable

• Sample sizes were meaningful

Interpretation: There appears to be a volatility regime 1–2 weeks before earnings that slightly favors the Critical States profile.

Structural Insight

Critical States is a short-term volatility-aware system. Earnings events can create bigger price swings and occasional sharp gaps, but over 16 years of testing, there is no evidence that earnings exposure damages the system’s long-term performance.

Across 6-, 11-, and 16-year backtests of the Critical States template within our curated portfolio, we find no statistical reason to add an earnings avoidance filter. Expectancy, Monte Carlo robustness (FE25), and drawdown behavior remain stable with earnings included.

Important Execution Distinction

The conclusions above assume:

• Mechanical execution

• Taking every valid signal

• Consistent position sizing

Expectancy math only holds when the full distribution is sampled.

If a trader:

• Skips signals intermittently

• Sizes differently around earnings

• Avoids trades emotionally

Then the tested expectancy no longer strictly applies. In that case, avoiding earnings may be psychologically prudent — but that becomes a discretionary overlay, not a system requirement. This distinction matters.

Also, these conclusions apply to diversified portfolio implementation. Symbol-level earnings sensitivity may vary, and concentrated single-symbol execution warrants separate evaluation. Future research may segment results by market regime to assess earnings sensitivity during high-stress periods

Join us inside OBUG

Most traders adjust systems based on recent experience or isolated outcomes. We take a different approach. Inside OBUG, every idea is treated as a hypothesis to be tested — not assumed.

We focus on building a structured, evidence-based process: Systematic backtesting across multiple market cycles, Monte Carlo analysis to understand distribution and drawdown behavior, and forward testing to validate real-time execution If you’re interested in developing trading decisions grounded in data rather than reaction, you’re welcome to join us.. Join us at OBUG:

This material is provided for educational and research purposes only. Results are based on historical backtesting and do not represent actual trading performance. Past performance is not indicative of future results. This is not investment advice or a recommendation to buy or sell any security.

Rather than asking, “What stock should I trade?”, the Logic Chain asks: Under current conditions, where is capital flowing — and which trading edge is structurally aligned with that flow?

The Core Idea: The Logic Chain

The Logic Chain framework organizes trading decisions into a clear, hierarchical process:

Each layer has a specific and well-defined role:

• Market context provides participation guidance and strengthens sector selection

• Sector selection determines where capital is eligible to flow

• Systems define the trading edge and execution logic

• Symbols are traded only when all upstream conditions align

This structure enforces discipline and removes ambiguity. When conditions do not align across the chain, no trade is taken — by design.

Why Build a Logic Chain System?

Many trading strategies fail not because their entry rules are flawed, but because of structural weaknesses in how they are applied:

• Symbols are traded without regard to sector context

• Sectors are traded without regard to market conditions

• Systems are applied indiscriminately across environments

• Risk is evaluated trade-by-trade instead of at the portfolio level

The Logic Chain System addresses these weaknesses by separating responsibility across layers and ensuring that each decision is made at the correct level.

This approach is not about finding more trades.
It is about taking fewer, higher-quality trades with better risk control.

The Institutional Parallel: How Capital Is Actually Allocated

The Logic Chain framework mirrors how major institutions and professional asset managers allocate capital.

Institutions rarely begin with individual securities. Instead, they ask:

Where is capital allowed to flow under current conditions?

That decision is made top-down and revisited regularly—often weekly or monthly—based on observed market behavior, risk conditions, liquidity, and mandate constraints.

At a high level, the institutional allocation process looks like this:

• Market context determines overall participation and risk appetite

• Sector and asset-class rotation determines where incremental capital is eligible

• Strategy or style selection determines how capital should be deployed

• Instrument selection provides execution once all upstream conditions align

This reflects a Logic Chain in practice —even if it is not labeled as such.

Institutions are generally responding to observed conditions rather than forecasting outcomes, routing capital toward areas that meet predefined criteria and withholding capital when alignment is absent.

How We’re Building It

Long-Horizon System Backtesting

Each Swing System is evaluated using 16 years of historical data across a broad universe of liquid equities. The objective is to identify structural system–symbol compatibility across multiple market regimes. Each system ultimately results in a pre-defined, system-aligned symbol universe.

Sector Attribution (Not Optimization)

Symbols are tagged by sector to understand where each system naturally expresses edge. Sectors are treated as context and capital routing, not as curve-fitting tools.

Monte Carlo Validation

We use Monte Carlo analysis to examine:

• Distribution of outcomes

• Left-tail risk

• Drawdown behavior under adverse sequencing

This step shifts the focus from average performance to robustness under stress.

Below is a NotebookLM video overview of our Logic Chain Framework.

What Comes Next

The Logic Chain Trading System will be studied, tested, and refined inside the Owl Bundle User Group (OBUG) as an ongoing research and development effort, not as an already-finished or guaranteed trading system.

As with all AbleWayTech projects:

• Assumptions will be tested

• Results will be documented

• Weaknesses will be exposed—not hidden

There is no guarantee that this research will result in a deployable or superior trading system.

Some ideas may advance.
Some will be modified.
Some may be abandoned entirely.

That uncertainty is not a weakness—it is a core part of a disciplined learning and system-development process.

Our goal is not to promise certainty, but to work towards a repeatable, defensible trading system that stands up across market cycles.

Joining the Owl Bundle User Group (OBUG)

OBUG is where this kind of work happens.

Members don’t just see finished systems — they see how systems are built, why design decisions are made, and how risk is evaluated across time and regimes.

If you’re interested in:

• Developing a structured trading framework

• Understanding how market, sector, and system layers interact

• Learning through real research rather than hindsight examples

We invite you to join the Owl Bundle User Group (OBUG) and follow the Logic Chain System as it is developed.

— AbleWayTech

2/8/2026

In our last OBUG meeting, we noticed some unusual behavior in the XLF sector, specifically in the slope of the RL30Slope Z-scores, a standard measure we use for our weekly market report. 

Sector Rotation RL30 Slope Z-Scores:

The XLF’s Z-Score line is clearly lagging the other sectors and has dipped down to -2 standard deviations.  Let’s look at the charts to get a better idea of what is going on in XLF and how it could play out.

Long-Term Analysis of XLF

The XLF Monthly chart reached a new high in January of 56.52 and is pulling back slightly from that high this month.  A tentative uptrend line was formed following the nearly two-year sideways period from 2022 to 2023.  The consolidation took the shape of a triangle pattern that completed in December of 2023.  Price consequently made a dynamic up leg for the last two years.  The pattern completion was also confirmed with the 3/12 moving average buy signal a month before the pattern completed in November. 

We look fo a continuation of the bull channel, with a possible consolidation period of the last dynamic up leg.

A close below 29.59, a significant support level, would change our outlook.

We look for continuation of the dynamic up trend.

Medium-Term Analysis of XLF

After a large triple bottom pattern completed in December of 2023, the XLF weekly has been in an uptrend. However, the recent price action looks to be taking the shape of a possible wedge reversal, not uncommon in what looks like the late stage of this bull trend.  The durability of the trend is also called into question with the negative bearish divergence in the MACD lines.  However, price action comes first, and we go with the current trend. 

We look for a continuation of the present bull trend.  A measured move equal to the last leg, approximately 11 points, would project us up to the 63 level.  

A close below the 52 level would change our outlook and complete the wedge reversal and break an important support/resistance line.   This would project a minimum target of 42. 

 The XLF ETF is clearly in a critical state, and it will be fascinating to see how the current situation develops in the coming weeks.

Owl Bundle User Group (OBUG) Meeting 150 was built around a simple but powerful idea: markets move through regimes, and capital should only be deployed when the statistical odds justify it. This session wasn’t about predictions or hot takes. It focused on how skilled traders decide when to engage, when to stand down, and how to size risk intelligently.

OBUG trading studies are based on Dr. Ken Long’s trading process, with a strong emphasis on regime awareness, risk control, and capital discipline. To ensure rigor and transparency, all historical testing and analysis is conducted using EdgeRater, allowing us to evaluate long-horizon behavior, stress regimes, and portfolio-level interactions.

Below is a behind-the-scenes look at what was covered in Meeting 150—and why it matters for serious traders who want a repeatable process rather than a guessing game.

1. The Weekly Market Scan: Trading With the Wind, Not Against It

OBUG’s Weekly Market Scan isn’t a watchlist generator. It’s a decision framework.

Meeting 150 walked through how the scan answers one core question every week: Is this a week to press risk, reduce exposure, or stand down?

Using RL30Slope Z-scores, volatility alignment, and cross-asset rotation, the scan integrates:

• Macro stress and risk appetite

• Volatility regime (VIX, IV vs HV, RISKZ)

• Dollar and rate pressure (DXY, TNX)

• Global and cross-asset capital rotation

• U.S. sector and market-cap leadership

Net result: a clear bias for defensive, mean-reversion tactics during a risk-off environment—without panic, and without narrative guesswork.

This is what institutional traders mean by “trade with the wind.”

2. Critical States: Why “Not Trading” Is Often the Best Trade

One of the most important slides in Meeting 150 was also the least exciting visually: “No Green Signals Triggered.” And that’s the point.

Dr. Ken Long’s Critical States strategies are designed to withhold capital unless price location, volatility, and regime conditions align.

Most retail traders feel compelled to always be in the market.
Critical States is built on the opposite belief: Capital preservation is an active decision.

This explains why:

• Exposure is intentionally low during calm or suppressed regimes

• Drawdowns remain shallow even when SPY experiences large swings

• Performance looks “boring” until volatility and dislocation return

3. Monte Carlo Reality Check: Edge First, Sizing Second

Meeting 150 went deep into Monte Carlo analysis as risk validation.

Key findings:

• The edge is real and persistent

• Risk efficiency peaks at moderate sizing

• Drawdowns remain controlled below ~15% at realistic sizing levels

• Performance degradation beyond that point is smooth—not catastrophic

This is exactly what institutional risk committees look for.

4. Buy & Hold vs. Critical States: A False Comparison

One of the most misunderstood debates in trading education is: “Why not just buy SPY?”

Meeting 150 reframed this correctly.

Buy & Hold and Critical States are not competitors.
They solve different risk problems.

• Buy & Hold maximizes terminal wealth if you can at times tolerate sizeable drawdowns

• Critical States prioritizes capital protection, regime awareness, and psychological survivability.

The real insight: Critical States is a portfolio sleeve, not a benchmark replacement.

It creates unused risk capacity—which can later be allocated to non-overlapping systems, not forced into higher leverage.

5. From Research to Reality: The Incubation Process

OBUG is a systems research lab.

Meeting 150 clearly outlined the progression:

1. Long-horizon backtests (2010–present)

2. Stress regime validation (COVID, rate hikes, 2022 bear)

3. Portfolio-level integration

4. Paper incubation (live forward testing)

5. Capital-constraint simulations

Only after this process does real capital even enter the conversation—and that decision remains personal.

Final Thoughts

Meeting 150 made one thing clear: Trading success is about knowing when not to play. That discipline separates systematic traders from reactive ones. That mindset—rare, disciplined, and deeply professional.

If OBUG resonates with you, join OBUG. If you’re new to Dr. Ken Long’s trading process, consider starting with our Applied Swing Trading course and OBUG. We currently have a special promotion designed specifically for traders who want to build this foundation the right way.

When things go wrong, do my strategies fail together — or separately?

The Hidden Risk Most Traders Miss

It’s common to hear:

“Profits are additive, drawdowns are not.”

That statement is directionally correct — but incomplete. The real issue is timing. Drawdowns are not just about how much you lose. They are about when losses occur. If multiple systems experience losses during the same window of time, portfolio drawdowns deepen rapidly — even if each system is profitable on its own. This is the risk OBUG focuses on.

What We Mean by “Non-Overlapping Systems”

In OBUG, non-overlapping does not mean:

• Strategies never trade at the same time

• Losses never occur

• Drawdowns are eliminated

Instead, it means:

Drawdown-worsening events tend to occur at different times across strategies.

Losses still happen. But they don’t stack. That distinction is subtle — and critical.

Why This Matters More Than Entry Signals

Two portfolios can have:

• Similar CAGR

• Similar win rates

• Similar average trade metrics

Yet behave very differently under stress.

One portfolio experiences:

• Long recovery periods

• Compounding psychological pressure

• Capital constraints during drawdowns

The other:

• Drawdowns that are shorter and shallower

• Smoother equity progression

• Greater ability to stay engaged through regimes

The difference is not signal quality. It’s risk timing.

What We Study Inside OBUG

Inside OBUG, we take concepts like non-overlap and test them directly:

• Across multiple strategies

• Over long historical windows

• At the portfolio level — not trade level

We study when systems are active, when they lose, and when drawdowns worsen. This is not something you can see from a single equity curve or correlation number.

It requires:

• Specialized diagnostics

• Strategy-by-strategy alignment

• Event-based portfolio analysis

That work — including the data, tools, and interpretation — is what we are studying in OBUG.

LOOKING AHEAD IN 2026

As we move into 2026, OBUG will continue to deepen its research into market structure, regime behavior, and portfolio-level system design, building on Dr. Ken Long’s methodology and our ongoing EdgeRater backtest studies. We invite you to join the Owl Bundle User Group and be part of an exciting new year of focused study, collaboration, and disciplined market research.

To jump-start participation for those who have not already taken it, the Applied Swing Systems Trading Home Study provides a fast and practical way to get familiar with the core terminology, concepts, and framework used throughout our weekly OBUG sessions. Because portfolio-level insights develop over time and across market regimes, many members choose a multi-month commitment to OBUG to gain the full benefit of the research. Enrollment link: HERE

The Owl Bundle User Group (OBUG) is an educational research community hosted by AbleWayTech. All content is for educational purposes only and does not constitute investment advice.

OBUG is not a signal room or a trade-calling service. It is a research-driven study group dedicated to:

• Understanding why trading systems work

• Testing those ideas across full market cycles

• And evaluating results using EdgeRater’s institutional-grade backtesting tools

This last meeting of the year — OBUG Meeting #147 — was designed to step back from individual trades and review what the backtest data actually showed across regimes, volatility environments, and capital flows.

2025 in One Sentence: Structure Mattered More Than Direction

One of the strongest conclusions from this year’s research: Markets rewarded traders who aligned with structure — not those who chased direction.

Across bull phases, rate-cut cycles, volatility shocks, and regime transitions, outcomes were driven less by directional forecasts and more by the market state at the time of entry. This insight guided every major study discussed during the year-end meeting:

• Weekly market scans

• Critical States backtests

• Swing Systems development

• Portfolio-level robustness analysis

How OBUG Studies Markets (and Why It’s Different)

OBUG does not rely on opinions, narratives, or one-off indicators.

Instead, markets are evaluated weekly using a repeatable, multi-layer framework rooted in Dr. Ken Long’s work:

Macro Regime & Volatility Context
• Stress vs stability
• Risk appetite
• Rates and currency pressure

Global & Cross-Asset Capital Rotation
• Where capital is flowing internationally
• Risk assets vs defensives
• Inflation and commodity behavior

U.S. Sector & Style Leadership
• Cyclicals vs defensives
• Large-cap vs small-cap
• Growth vs value

Institutional Positioning & Execution
• Allocation decisions already made
• Capital still in the process of deployment

At year-end, the structural read was clear: Risk-ON conditions persisted into late 2025, but leadership narrowed and acceleration slowed.

This is not a forecast. It is state awareness, grounded in data.

The Core Framework of 2025: Critical States

A central focus throughout the year — and the centerpiece of the final meeting — was the Critical States Template, derived directly from Dr. Ken Long’s methodology and implemented through EdgeRater scans and backtests.

Critical States reframes trading entirely:

• It does not predict outcomes

• It identifies conditions where certain outcomes become more probable

Rather than asking: “Is this bullish or bearish?” Critical States asks:

• Where is price constrained across short-, medium-, and long-term ranges?

• Is volatility normal or abnormal relative to history?

• Are pressures aligned across multiple time horizons?

When those constraints align, markets become fragile, unstable, and sensitive to flow — creating asymmetric reward-to-risk opportunities.

The Weather Analogy That Anchors the Framework

One concept revisited throughout 2025 — and emphasized again in the year-end meeting — was the weather analogy:

• Low pressure does not guarantee rain

• Volatility compression does not guarantee a breakout

But both increase probability.

Even more important: The same weather system produces different outcomes in different locations. Likewise, the same market state does not work on every symbol.

This is why OBUG’s backtests are:

• Run only on curated symbol universes

• Interpreted at the portfolio level

• Evaluated for regime robustness, not single-trade performance

What the EdgeRater Backtests Confirmed (2010–2025)

A major initiative in 2025 was running full-history and regime-slice backtests in EdgeRater, covering environments such as:

• 2017 low-volatility melt-up

• 2020 crash

• 2021–2022 rate-hike cycle

• 2022 bear market

• 2023–2024 AI bull market

• 2024–2025 rate-cut cycle

The data reinforced a critical principle from Dr. Ken Long’s work: No single system needs to work all the time. Instead, OBUG is deliberately building a portfolio of non-overlapping systems, each designed to activate under different structural states.

Several systems demonstrated strong cross-regime robustness, while others benefited from optional regime-based position sizing rather than rule changes. This is systematic research — not optimization theater.

Swing Systems and Critical States: Two Complementary Sleeves

A key clarification reinforced at year-end:

• Swing Systems define how to trade (patterns, execution, exits)

• Critical States define when conditions are favorable

They are parallel strategy sleeves, not substitutes.

This separation:

• Reduces overtrading

• Improves expectancy

• Encourages patience — one of the most valuable skills in 2025

The Real Takeaway from 2025

If there is one lesson OBUG members carry forward: Successful trading is less about predicting markets — and more about respecting when markets are structurally vulnerable.

By grounding its work in:

• Dr. Ken Long’s methodology

• EdgeRater-based backtesting

• Market structure and regime analysis

• Portfolio-level thinking

OBUG continues to train traders to think like risk managers and system architects, not signal chasers.

Looking Ahead to 2026

In the coming year, OBUG’s research focus will expand further into:

• Capital-constrained testing

• Correlation and drawdown alignment

• Multi-factor analysis

• Portfolio construction and allocation

JOIN US FOR AN EXCITING YEAR AHEAD

As we move into 2026, OBUG will continue to deepen its research into market structure, regime behavior, and portfolio-level system design, building on Dr. Ken Long’s methodology and our ongoing EdgeRater backtest studies. We invite you to join the Owl Bundle User Group and be part of an exciting new year of focused study, collaboration, and disciplined market research. Enrollment link: HERE

To jump-start your participation in OBUG, the Applied Swing Systems Trading Home Study course provides a fast and practical way to get familiar with the core terminology, concepts, and framework used throughout our weekly sessions.

The Owl Bundle User Group (OBUG) is an educational research community hosted by AbleWayTech. All content is for educational purposes only and does not constitute investment advice.

In our weekly meetings, we begin with one simple but important step:

1. We scan the market to understand the environment for the week ahead.

Before we talk strategy, before we run systems, before we look at backtests,
we first assess market health, volatility, sentiment, trend strength, and risk appetite.

Using Dr Ken Long’s tools—RL30Slope Z-scores, NDX indicators, volatility filters RISKZ, Market Classification regimes, and sector rotations—we build a real-time map of:

• Where money is flowing

• Which sectors are strengthening or weakening

• How volatility is evolving

• Whether the market is trending, chopping, or transitioning

• Which asset classes are under stress

This weekly scan places every strategy we run into context.

It ensures we don’t treat a mechanical system as if it exists in a vacuum. Instead, we understand exactly which environment we are walking into.

2. From Market Scans → Into Strategy Research

Once the environment is clear, we move into our latest OBUG’s study:

**Validating and refining a portfolio of non-correlated trading systems built on dr Ken Long’s Critical States Template.**

These include:

• Godzilla

• Fireworks

• FenceSitter

• Humpty Dumpty

• Catfish

Over the past several months, our focus has been disciplined and methodical:

Step 1 — Identify the right symbols

Using Multi-Factor Alignment (MFA), volatility filters, and structure analysis, we discovered which stocks “fit” each strategy. This step alone dramatically increased performance and stability.

Step 2 — Standardize the entry rules

We refined the setups to match the spirit of the Critical State, while enforcing mechanical clarity.

Step 3 — Establish simple, universal exit rules

We tested protective stops, trailing stops, fixed bars, and profit stops to find the most robust and durable exit structure.

Step 4 — Run full backtests from 2010–2024

This gave us a reliable statistical foundation across bull markets, bear markets, low and high volatility cycles.

3. This Week’s Milestone: Backtesting to the Present & Time-Slice Durability Testing

In our latest OBUG session,

We extended all Critical States systems from January 2010 all the way to the present,

and then ran time-slice tests across intervals:

• 3 Months

• 6 Months

• 1 Year

• 2 Years

• 3 Years

The goal is not simply to see “performance.”

The goal is to understand:

• When each strategy is strong

• When each strategy weakens

• Whether weakness is due to market regime or edge decay

• What conditions cause trouble

• How each strategy behaves during stress

• Which systems complement each other

This is the level of insight you need if you want durable, professional-grade trading systems—not just signals.

4. Why This Matters to Traders

When we complete this phase of research, we will have clarity on:

• Which systems are core holdings

• Which systems are regime dependent

• Which systems act as stabilizers

• Which systems should remain on the shelf

• How strategies behave across different volatility regimes

• How to combine them into a non-correlated portfolio

Most traders struggle because they rely on a single system. OBUG is addressing that problem by building:

**A multi-system, multi-regime, non-correlated strategy portfolio analyzing backtests across 15 years and multiple market conditions.**

This is exactly how professional trading desks operate.

5. Our End Goal: A Portfolio That Produces Positive, Risk-Adjusted Returns

Everything we do—market scans, strategy refinement, backtesting, symbol alignment, regime analysis—supports this one objective:

**Create a portfolio of strategies that complement each otherand deliver smoother equity curves with reduced drawdowns.**

This is the difference between trading reactively vs. trading systematically.

6. Consider Joining OBUG

We are actively studying and developing trading systems based on Dr Ken Long’s trading methodologies & techniques.

• We’re refining system rules

• We’re validating symbol behavior

• We're stress-testing each strategy across time slices

• We’re preparing to integrate market regime models

• We’re moving toward portfolio construction and allocation

If you’ve wanted a structured path to consistent, data-driven trading please consider joining OBUG: LINK

11/21/2025

Long-Term Analysis of GLD

On the monthly chart, a four-year head and shoulders continuation pattern resolved at the beginning of 2024 that has resulted in a dynamic up move with just over a 100% gain in the last two years. The break of the neckline was strong enough that there was not the usual retracement to the neckline after the breakout.

Preceding the resolution of the head and shoulders continuation pattern, the 3 and 12 month exponential moving averages had a bullish crossover in late 2022, and was also confirmed buy a MACD signal line crossover in early 2023. 

A 50% retracement from the recent high would bring price back to the 275 level.  But with price continuing to make higher highs, the moving averages sloping up, and the MACD line showing positive momentum as well, we go with the trend.

We interpret the monthly chart as continuing to support a bullish long-term trend, while recognizing that this reflects historical chart behavior rather than a prediction or a recommendation.

A bearish moving average crossover, i.e. the 3-month crossing below the 12-month EMA, would change our view and forecast a consolidation period for the market to ‘digest’ the strong up move.

Medium-Term Analysis of GLD

On the weekly chart, a breakout in April of 2024 from a sideways range has produced a strong up trend as evidenced by the 10- and 40- week exponential moving averages sloping steeply upward.  Throughout the up move we have seen continued support from the 10-week exponential moving average and two continuation patterns in the forms of a bullish pennant late last year and an ascending triangle continuation pattern this Summer. 

Early October produced a large outside reversal bar, which could signal another consolidation after the last move up.  Recent price action shows a series of dojis as the market is undecided on its next move. 

GLD had been strongly underperforming compared to its sector, XME.  But the recent weakness in the metals has resulted in an upside trendline break in the Relative Strength line.

Going with the strong uptrend, a chart-based interpretation would be to monitor the 10-week EMA for potential support and observe whether any additional continuation patterns develop.

Another leg up in equal length to the last breakout would bring us up to a target at the 450 level.

Given the strength of the current trend, price would need to fall below the last consolidation level of 300 or signal a moving average crossover sell signal for our outlook to change. 

Short-Term Analysis of GLD

The recent weekly dojis are seen more clearly the daily chart.  The 20-period SMA has gone sideways, and price has gone from ‘walking the band’ for two months to a pullback below the support of the 20-day SMA.  Volatility has decreased as well as the Bollinger bands have narrowed.

Price is finding support in the last week at the 20-period SMA.  A breakout to the upside or a further move down to the bottom band are equally likely.  What is clear is that price is in a consolidation, or sideways, mode.  This is confirmed in the Commodity Channel Index that is sitting at the zero line, neither oversold or overbought. 

From a technical-analysis perspective, the higher timeframes suggest that an upside resolution may be more likely, but the daily chart remains neutral until price breaks the current range.

Another up leg, similar in distance to the previous move from August to October, would elicit a target of 450.  Although price is currently sideways until proven otherwise.  We would need to see consecutive closes out of the 360.12 to 388.18 range with price starting to ‘walk the band’ to confirm the possible start of a new trend. 

Strategy Example

One example of a trading strategy for a band trader could be to buy a new daily high if price pulls back to the bottom band.  Additional confirmation with a crossover from -100 to back above -100 in the Commodity Channel Index. 

Stop just below the recent low that touched the bottom band.  Take profit at the SMA or the top of the band.

For those looking to play the trend continuation, a hypothetical entry method could be initiated when price has posted consecutive closes above the recent swing high at 388.18 and an additional confirmation of the CCI crossing above +100.

Stop below 20-period SMA.  Take profits at the 403.30 swing high and eventual target of 450. 

This analysis is for educational purposes only and does not constitute investment advice or a recommendation to buy or sell any security. Trading involves risk of loss.

When gold (GLD) plunged 6 percent in a single day on October 21 2025, headlines called it “unexpected.” Inside OBUG – the Owl Bundle User Group, we weren’t forecasting the crash; we were studying whether the market had warned us.

Could those warnings have been visible earlier—in volatility, sentiment, or price structure—through Dr. Ken Long’s RL-stack framework (RL10, RL30, RL90, RL270)? That became the question at the heart of OBUG Meeting #138.

Could the clues have been visible earlier — in volatility, sentiment, or price structure — using Dr. Ken Long’s regression-line analytics? That question became the centerpiece of OBUG Meeting #138.

The Inquiry: Did the Market Whisper First?

Our goal was simple:

Identify early signals that hinted at stress before the October 21 sell-off.

To do that, we paired option-based volatility metrics with Dr. Ken Long’s Regression Line-stack (RL10, RL30, RL90, RL270) — a framework that converts noisy price data into a smooth, multi-time-frame map of trend alignment and energy.

• IV22 / HV22 Compare the 22 day implied vs. realized volatility — forward-looking fear vs. backward-looking calm

• Put/Call Volume Z-score Detect abnormal hedging or speculative demand — a Sentiment gauge

• RL-stack (10,30,90 270) Measure the alignment across the short term 10 period, medium term 30 period, intermediate term 90 period, and 270 period long-term regression lines

What We Found in the Data

Volatility Spoke First

By mid-October, GLD’s IV22 had surged to ≈ 30 % while HV22 hovered near 18 % — a 1.6× divergence. At the same time, the GLD Put/Call Volume Z-score climbed above +2 σ, showing heavy put buying and dealer hedging. These were the market’s first whispers of discomfort — option traders paying up for protection even as GLD prices pressed to new highs.

The RL-Stack Confirmed Later

In the following sessions, RL10 (short-term) began curling down while RL30 flattened. Soon RL90 and RL270 — the intermediate and strategic lenses — lost upward slope.
That sequential rollover is a hallmark of Dr. Ken Long’s “critical state” transition: a quiet, orderly up-trend shifting into disorder. By the time RL10 crossed below RL30, the volatility that had been building beneath the surface finally expressed itself — the 6 % gap-down completed the energy release that IV/HV had warned about.

How the Pieces Fit Together

This study confirmed a consistent timing hierarchy:

• Anticipation IV > HV + Put/Call Z risingRLs aligned upwardHidden stress building

• Transition IV still rising RL10 diverges from RL30 Momentum faltering

• Confirmation IV plateaus RL-stack compresses —> Critical State forms

• Expression HV finally surges RL10–30 cross downward —> Price collapse / volatility release

In Dr. Long’s language, volatility represents potential energy, while RL alignment and separation show kinetic release. The two frameworks complement each other — volatility tells when the spring is loaded, the RL-stack shows when it snaps.

Lessons for Traders

1. Volatility leads structure.
   Implied volatility expands before regression-line slopes roll over.

2. Structure leads price.
   RL10 → RL30 → RL90 → RL270 sequence provides visible confirmation of regime change.

3. Price is the final expression, not the first signal.
   By combining both layers, traders see the transition from quiet to violent well before the headlines.

Why OBUG Studies Matter

OBUG meetings aren’t forecasts — they’re laboratories of market behavior. Each session dissects real data using Dr. Ken Long’s analytical toolkit, building trader intuition about how volatility, structure, and psychology interact.

Members gain:

• Weekly multi-asset volatility studies (SPY, GLD, TLT, IWM, etc.)

• Live market-scan walkthroughs in EdgeRater

• Backtesting templates for the Critical State setups

• A global community focused on disciplined, evidence-based trading

Join OBUG to study the Market

If you’re ready to move beyond indicators and learn to read markets as adaptive systems, join the Owl Bundle User Group.

Enroll today at AbleWayTech.com/OBUG
Learn to detect the market’s whispers — before they become shouts.

Disclaimer

OBUG provides education, not investment advice. Trading involves risk; past performance does not guarantee future results.

10/3/2025

Long-Term Analysis of XLE

A dynamic, three-year bull move from 2020 to 2023 has been consolidating for the past two years.  Price has formed what looks like a rectangle pattern as it digests the last up leg.

A difficult pattern to forecast, but we go with the trend.

Price found support in April of this year at the bottom edge of the rectangle pattern at 74.79, and has since printed 4 up bars with higher highs and lows, showing bullishness. 

Although price has danced above and below the 12-month EMA during the consolidation, nevertheless it has recently crossed to the North, closing above the EMA in the last two months, another sign of recent bullishness. 

A decisive above the important 100 level would signal a completion of the rectangle pattern, signaling a breakout with a close above highest high of 98.97 and the important psychological round number of 100.  In this case, we would look for a resolution of the pattern with a target of 124.

A decisive close below 74.79 would change our outlook, and signal instead a reversal pattern with further moves South.

Medium-Term Analysis of XLE

The rally off the low in April of 74.79 has triggered a zero-line crossover buy signal on the MACD histogram.  But the oscillator is always subordinate to the trend, which in this case is sideways.

The aforementioned rectangle pattern is traced out more clearly on weekly data, showing the sideways range XLE has been stuck in since April of 2023 between the 75 and 98 levels. 

We look for a little more sideways price action over the next one to two months pushing up toward the upper resistance level near 95.  As mentioned, the breakout close above 100 will resolve the rectangle pattern, with a resolution at a price target of 124.

Short-Term Analysis of XLE

We saw over a 20% drop in XLE in a matter of three days last April with a pair of red Marubozu candles and large gaps.  But the bullishness since the large drop has retraced almost all of the move down.  This up leg following the April low is traced out more clearly on daily data.

The MACD lines show a bearish divergence recently that was confirmed with a shooting star reversal candle, triggering a short-term reversal.  But we must look at it in a broader context, with price as always coming first.

We have a six-month confirmed trendline support with three touches. Note that the shooting star candle occurred near the top of the channel line and the resulting return move is now very close to the trend line.

Our current view is to stay with the support of the trendline, which will need to find support soon around the 88 level.

We look for price to find support in the next few days at the trend line, followed by an upside move to the top of the channel line near 94.

One example of what a Short-term trade strategy could look like:

 Buy at 88 (expected trend line support), stop just below 87 (recent low), take profit at 93/94.

  At that point, the trader/investor might wish to reduce risk (scale out or move up stop) and consequently play for the longer-term target of 124.

 This analysis is for educational purposes only and does not constitute investment advice or a recommendation to buy or sell any security. Trading involves risk of loss.

The motivation behind Godzilla is simple: find the movers—symbols most likely to make sharp, outsized moves. While most stocks churn quietly, Godzilla is designed to isolate those rare moments of imbalance when institutions are forced to act, creating powerful short-term opportunities.

What Is the Godzilla Strategy?

Godzilla is a Critical State filter, not a trend-following system. Its role is to detect temporary imbalances in market flows—precisely when a symbol is poised for a sudden, outsized move.

On EdgeRater’s Critical States Template, Godzilla highlights potential “movers”—symbols ready to explode in either direction. The filter evaluates a symbol’s price location relative to its 150-day, 30-day, and 10-day lookbacks (L150, L30, L10) and overlays volatility Z-scores (Z5 and Z1) to spot abnormal extremes.

The most compelling setup occurs when all three lookbacks are deeply depressed while volatility Z-scores show stress. This combination signals potential capitulation, often caused by institutional selling at lows. Historically, these conditions precede snapback moves as shorts cover and opportunistic buyers step in.

Finding Godzilla Candidates: Why Symbol Selection Matters

Godzilla isn’t a broad-market tool. Its edge is symbol-specific and depends heavily on liquidity and institutional participation.

Godzilla aligns best with symbols that have the following characteristics:

• High institutional flow: Symbols like ASML, MSFT, BKNG that react strongly to institutional buying and selling.

• Volatility elasticity: Stocks such as BABA and TMO that display sharp rebounds when stretched.

• Avoidance of illiquids/microcaps: These often lack the dynamics for Godzilla to work effectively.

At OBUG, we run EdgeRater’s Multifactor analysis on large, liquid universes (like CBOE Weekly Equities) and narrow to symbols that show consistent performance under Godzilla’s conditions before subjecting them to deeper backtesting.

Swing Trading with Godzilla: 5-Day holds

To confirm the “Find the Movers” narrative on a curated list of symbols, we ran a 15 year back test on the Godzilla rules - Long entry and hold for 5 days showing a 67% win rate, Avg P&L per trade of 1.77%, with a max drawdown of 13.5%. This approach produced smooth, stair-stepped equity curves, low drawdowns, and strong risk-adjusted returns. It aligns with the structural truth of Godzilla: snapbacks are short-lived.

Intraday Godzilla: Precision Is Key

Godzilla can also be applied intraday (long at next day’s open, exit at close). Our backtests revealed two key findings:

Full List Intraday = Noisy: Running Godzilla intraday across all symbols produced weak expectancy and profit factors.

Curated Sublist Intraday = Effective: Applying it only to high-persistence movers (e.g., AAPL, BABA, MSFT, TMO) yielded strong results with just 10.1% Max Drawdown.

This curated intraday sleeve complements the 5-day swing strategy, smoothing overall equity performance.

The Bottom Line for AbleWayTech Traders

The Godzilla strategy is built to find the movers—symbols primed for sharp, outsized moves.

✅ Symbol- and timeframe-specific — not universal.

✅ 5-day hold = best balance of return vs. risk, ideal core sleeve.

✅ Intraday Godzilla = effective only with carefully curated lists of persistent movers.

✅ Curated symbol lists must be refreshed monthly or quarterly to keep pace with evolving market conditions.

As always, all strategies discussed in OBUG and AbleWayTech are for educational purposes only. Trading involves risk, and past results do not guarantee future performance.

Want to Find More Movers?

Join the AbleWayTech Owl Bundle User Group (OBUG) and see how we combine Dr. Ken Long’s methodology with EdgeRater backtesting to identify high-probability trading setups like Godzilla—and refine them into actionable strategies.

👉 Learn more at AbleWayTech.com

9/12/2025

IYR Monthly Long-Term Chart:

IYR Long-term Analysis:

A dynamic selloff during the 2008 real estate crash, where price in IYR dropped nearly 78%, has been followed by a sixteen-year up channel on the long-term chart with a confirmed trend line at this stage. 

The selloff from the beginning of 2022 to the end of 2023 found support just shy of the 50% Fibonacci retracement level at 72.88.  The next leg up produced a lower reaction high in the middle of 2024 at 104.04, followed by a higher reaction low early this year at 81.53 This current consolidation looks to be taking the shape of a symmetrical triangle- a difficult pattern to use in forecasting, but more usually viewed as a continuation.

Our current view is to stay with the trend and the more likely upside triangle breakout.  Although another touch of the trend line support around 86 would still be in line with the forecast, a break will need to be achieved soon to produce a vigorous next move, particularly with triangle’s top line sitting at the important psychological round number of 100. 

A decisive two-day close below the last reaction low of 81.53 would change that view.

 IYR Weekly Medium-Term Chart:

IYR Medium-term Analysis:

The move off the October 2023 low of 72.88 resulted in a one-year uptrend that started a sideways consolidation period in early 2025.  The recent rally off the April low of 81.53 has led to a MACD crossover of both the signal line and zero line, a sign of bullishness and that the trend is picking up momentum.

Price comes first, and the current consolidation is taking the shape of an inverted head and shoulders pattern with two heads.  This can be viewed as a continuation pattern.  We look for a break of the neckline of the pattern in the next few weeks, with a pattern resolution of 120. 

Being an inverse pattern that will rely on buying pressure to resolve successfully, it is essential that the rally through the neckline be accompanied by a sharp burst of trading volume.

A failure to break the neckline in the next few weeks or a breakout and subsequent close below the neckline would change our outlook.

Strategy: 

If volume confirms the breakout with a sharp increase during the neckline break (and decrease during the return move), one could look to enter on the return move back to the neckline with a stop slightly below the neckline and a take profit of 120.

This analysis is for educational purposes only and does not constitute investment advice or a recommendation to buy or sell any security. Trading involves risk of loss.

9/4/2025

EWW Monthly Long-Term Chart:

EWW Long-term Analysis:

A double-top reversal at the end of the 2014 started a five-year downtrend that bottomed in March of 2020 at 25.03 The last five years on the long-term monthly chart show an up channel with a still tentative support line at this stage.

However, we go with the trend as the 2024 sell-off found support at the 50% Fibonacci retracement of the post 2020 bull market and a large bullish outside reversal bar with a significantly higher monthly close was achieved in April of this year. 

Also to note, the 3/13-month EMA combination still supports the long-term bull trend with a recent golden cross buy signal.

We look for continuation of a broad bull channel.  A decisive setback, with a close below 46.41, would change that view.

EWW Weekly Medium-Term Chart:

EWW Medium-term Analysis:

A negative divergence in the first half of 2024 ended in dynamic move down, ultimately breaking the trend line and continuing lower to 46.41 in December of last year.  A head should and shoulders pattern triggered in April, coinciding with a MACD zero-line cross buy signal, that was able to reverse the medium-term downtrend, also confirmed with a bullish divergence in the MACD. 

The consequent rally off 46.41 sees the weekly MACD now overbought.  But price action comes first.  Current consolidation takes the shape of an ascending triangle with a recent breakout and close above the flat line.

We go with the trend and look for a triangle resolution target of 67.25, and an eventual test of the high of 71.12.

A close below 57.28 would change our outlook and signal further consolidation and unwinding of the MACD, with a further 61.8% pullback to prior resistance levels at 53.

Strategy: One possible strategy example is watching for a breakout above the high of the weekly bar, with risk management below the recent swing low (around 60.44) and potential profit-taking areas in the 67.25 and 70–71 zones.

This analysis is for educational purposes only and does not constitute investment advice or a recommendation to buy or sell any security. Trading involves risk of loss.

Why it matters:

In our weekly Owl Bundle User Group (OBUG) sessions we review the current market conditions and backtest trading strategies to identify and validate edges. OBUG is where we take Dr. Ken Long’s trading indicators, stress-test them in EdgeRater, and uncover insights for active traders. Here’s a preview from this week’s meeting:

1. Macro Regime & Volatility Context

• RISKZ at −0.26σ → Mild risk-OFF tilt; short-term VIX sits above the long-term baseline.

• TNX (12.7) & DXY (36) → Near the lower end of their 63-day ranges — easing macro headwinds, but vulnerable to reversal.

• Volatility mix: Realized vol (HV22/HV63) remains low; IV22 modestly above realized; Put/Call Z-score +1.40 indicates a calm tape but rising hedging demand.

Net takeaway: Conditions favor selective positioning — defensive sectors and safe-haven assets lead.

2. Global & Cross-Asset Rotation

• Regional ETFs: Japan (EWJ) strongest; EFA and ILF improving; Europe, Pacific ex-Japan, EM lagging.

• Cross-Asset Leadership: Utilities, Treasuries, Gold rising; credit (HYG) and crypto (BITO) weakening.

• Commodities: Agricultural commodities (DBA) lead; Gold/Silver improving; Platinum and broad commodities lag; Oil flat.

• Currencies: USD strong (UUP +1.87%); major & EM FX rebounding but still negative slopes.

3. U.S. Sectors & Structural Trends

• SPY: Long-term uptrend intact (RL90Z rising), RL30Z flattening, RL10Z curling upward.

• Leaders: Metals & Mining, Utilities.

• Weakeners: Financials, Energy, Materials, Industrials, Healthcare.

• Tactical Bias: Defensive, narrow leadership; SPY may drift higher if RL10 momentum builds — but broad participation is lacking.

4. Strategy Backtest – PSAR(RL10) with ADX/DMI Filter

Why we tested it:
The PSAR can flip on shallow pullbacks, creating false entries/exits in non-trending markets. Adding ADX/DMI seeks to filter out noise.

Key findings:

• DI+ > DI− filter ensures PSAR flips align with a valid directional bias.

• ADX > threshold exit override prevents premature exits during strong trends.

• Result: Higher profit factor and expectancy where directional runs are common and ADX stays elevated.

Bottom line:
ADX/DMI transforms PSAR(RL10) from a reactive trigger into a regime-aware trend rider — powerful for ETFs and FX, less so for single stocks without extra conditions. Understanding the edges will enable us to transform these rules into a robust trading systems.

5. Looking Ahead – Future OBUG Studies

1. Turning Point Detection: Multi-day trend change identification to adjust strategies in real time.

2. Template Expansion: Backtesting additional EdgeRater templates to complement current studies.

🎥 meeting insights

• NotebookLM Video & Audio: Market Scan + PSAR Backtest Breakdown.

•   (NotebookLM Video links )

  • Market Scan:   VIDEO LINK   &    Market Scan Audio

  • Backtest PSAR(RL10) w/ADX+DMI filter  VIDEO 

    (Unconstrained symbol-specific backtests for edge discovery)

• EdgeRater Special Series: 6-part video series on Dr. Ken Long’s trading techniques and systematic approach: View Ken Long’s Series on EdgeRater →

• Full Zoom Recording: Complete meeting replay with Q&A to OBUG members

🚀 Join the OBUG Community

OBUG isn’t just a meeting — it’s a live study group for traders. Each week we:

• Scan the markets using Dr. Ken Long’s proprietary indicators.

• Backtest and refine trading systems in EdgeRater.

• Share findings you can integrate into your own strategies.

If you’re ready to get ongoing market context, backtest insights, and member-only resources, join us here: More Info or Join OBUG Now →

Macro Regime and Volatility Context

We began our assessment by interpreting macro stress and risk sentiment indicators:

• RiskZ (VIX-based) fell to +0.83 and is declining, signaling fading bullish appetite.

• NDX63 readings from TNX and DXY show weakening macro support for equities.

• Volatility compression is evident: IV22 (21%) is rising while HV22 (12%) is flat, suggesting a "coiled spring" condition.

• The Put/Call Volume Ratio Z = +1.4, indicating cautious hedging ahead of potential macro events.

This setup reflects a neutral-to-defensive posture, consistent with volatility-based reversion or breakout potential.

Cross-Asset and Global Capital Rotation

Using RL30Slope Z-scores across major ETFs:

• Commodities (PPLT, SLV, USO) show rising slope Z-scores > +1.0.

• Real Assets (GLD, VNQ) and Long-duration Treasuries (TLT) are gaining slope, consistent with defensive flows.

• UUP (Dollar) shows a Z-score of -0.50 and falling; foreign currencies (FXE, FXA, CEW) are rising.

Implication: Institutions are rotating into hard assets, safety, and non-U.S. exposures—a classic signal of late-cycle or regime shift conditions.

U.S. Sectors and Structural Trends

We analyzed SPY and sector RL30Slope Z:

• SPY shows decaying slope across all timeframes.

• Only XME (Metals) and XLE (Energy) are trending up; all other sectors are flat or declining.

• XLV (Healthcare) and XLP (Staples) are turning up, reflecting early defensive positioning.

This confirms a stealth rotation out of growth and into capital-preserving sectors.

Conclusion: Regime Awareness Drives Tactical Precision

The June 2025 OBUG Market Scan identifies a subtle but decisive macro and sectoral transition. Price-based complacency masks underlying slope deterioration, which precedes volatility re-expansion or reversion trades.

By integrating tools like RL30Slope Z-scores, RiskZ, and cross-asset rotation logic, traders can:

• Time entries aligned with volatility regime.

• Avoid directional traps during slope flattening.

• Anticipate institutional capital shifts before price confirmation.

Join the Owl Bundle User Group (OBUG)

If you're ready to:

• Learn more about Dr Ken Long’s macro-aware technical analysis indicators

• Build a diversified portfolio of non-correlated systems

• Learn how traders apply regime filters and slope logic

Then it’s time to join us at AbleWayTech.com and become part of the OBUG community.

Discover the Edge-Driven Process Behind Trading System Development Using Dr. Ken Long’s Methodologies

Welcome to the Owl Bundle User Group (OBUG) — a weekly study session where members explore Dr. Ken Long’s trading frameworks through structured backtesting studies using EdgeRater. Each session is designed to deepen our understanding of market structure, volatility regimes, and system design — all in a collaborative learning environment.

What We Covered in OBUG Meeting #117 (June 1, 2025)

At our latest session, we tackled what most traders miss: the intersection of macro structure, volatility regimes, and tactical setups. :

Market Scan Highlights — The Calm Before the Storm

We analyzed SPY’s RL30Slope Z-score, VIX/MOVE behavior, and volatility premium structures:

• SPY’s momentum is stalling — a tactical pullback or volatility breakout is likely.

• VIX and MOVE rising together: historic signal of hidden macro stress.

• DXY stabilizing from oversold: often precedes cross-asset rotation risk.

Study Insight: While markets appear calm, our data suggests underlying pressures may be building. These are conditions worth studying for future model refinement.

Global Capital Rotation — Interpreting Flow Behavior

We mapped global flows using RL30Slope Z across ETFs:

• All regional ETFs (EFA, EEM, IEV, etc.) show declining slopes → synchronized deceleration.

• Bond ETFs (SHY, TLT) and safe-haven currencies (FXF, FXY) are starting to attract capital.

Interpretation: These shifts may reflect reduced institutional risk appetite — a useful signal for understanding cross-asset sentiment dynamics

Backtest Spotlight — Mapping Strategy Behavior zones

We conducted backtest heatmap studies using:

• RL30Slope Z-scores to measure trend acceleration/deceleration

• RISKZ indicators tied to VIX, GVZ, as potential regime filters

Observations:

• SPY + VIX RiskZ combo gave positive results when RiskZ was low and rising.

• GLD + GVZ RiskZ also showed strong edge

• RL30Slope Z works best for mean-reversion or momentum depending on Z value and holding period.

Strategy Insights: Combining macro regime filters (RiskZ) with tactical entry signals (RL30Slope Z) potentially yields strong systematic trading setups. Further research planned.

Swing Systems V5.6 — Tactical Edge for DOW30

We reviewed which DOW30 stocks aligned with Bullish/Volatile conditions using Swing systems like:

• 5DD (Five-Day Down)

• MPRC (Mean Price Reversion Channel)

• AutoFramer

• Channeling

System Insight: These Swing systems provide a framework for students to study trade behavior in different market environments — fostering rule-based thinking and robust system design

Why Join OBUG?

OBUG is a study group, not a signal service. We emphasize learning, not predicting, and focus on:

✅ Weekly informational sessions on Zoom
✅ Structured Swing & Intraday backtesting using EdgeRater and Thinkorswim
✅ Collaborative research on market behavior
✅ Explorations of Dr. Ken Long’s tactical indicators and trading templates

Ready to Elevate Your Trading?

Join a dedicated group of traders and students from Dr. Ken Long’s trading community who are committed to mastering the market with discipline, structured systems, and statistical edge. OBUG offers a supportive environment to test ideas, evaluate systems, and grow through shared learning.

Learn more and sign up at www.AbleWayTech.com

Using advanced tools like EdgeRater and Thinkorswim, we systematically analyzed, backtested, and stress-tested strategies across multiple timeframes, market regimes, and asset classes.

Here’s a look at what we explored over the past two months (March - April 2025)

Key Areas of Focus

1️⃣ PSAR-Based Strategy Enhancements

• Explored counting PSAR dots and PSAR Count Z-Scores to identify trend exhaustion.

• Parameter optimization of PSAR parameters (Acceleration Factor, Acceleration Limit, Regression Period) to optimize long entry and exit rules.

• Found significant improvements in strategy durability and trend capture by adjusting PSAR settings.

2️⃣ Critical State System Refinements

• Deepened our work on the Critical States Template ("Sniper Radar") to identify statistically abnormal price movements.

• Investigated Profit Stops, Protective Stops, and Adaptive Exits using volatility bands and NDX-based price locations.

• Developed systematic processes for building diversified portfolios using Critical States models.

3️⃣ Market Scan and Sector Rotation Insights

• Weekly Market Scans combining RiskZ plots, RL30Slope Z-Scores, Cross-Asset Rotations, and Breadth Metrics to guide trading biases.

• Added HYG (High Yield Bonds), DXY (Dollar Index), HV22/HV63 Volatility Studies, and Put/Call Ratio Z-Scores into market condition templates.

• Backtests confirmed that sectors like Materials (XLB), Technology (XLK), and Financials (XLF) act as leading indicators for SPY rallies.

4️⃣ Portfolio Diversification Studies

• Showed that combining low-correlated ETFs (e.g., SPY + EMB + XLP) produced higher returns and lower drawdowns than trading SPY alone.

• Reinforced the principle that "performance is additive across uncorrelated systems, but drawdowns are not."

5️⃣ Advanced Intraday Studies

• Integrated multi-timeframe confirmation techniques:

  • Example: 3-Day RL10 > Baby Dragon to filter daily PSAR Flip entries for higher performance.

• Tested Z3Pinch filters and RL30 trend filters on intraday NQ futures trading, improving win rates and expectancy.

Results at a Glance

• PSAR Flip Strategy: Increased win rates from ~55% to ~67% using advanced exit structures.

• Exits: Increased robustness with multi-level filters and adaptive exits.

• Sector Leaders for SPY Timing: Materials, Tech, and Financials identified as early movers.

• Volatility Filters: HV22 > HV63 crossovers validated as significant warning or trading triggers.

• Portfolio Construction: Diversified, non-correlated ETF portfolios outperformed concentrated SPY-only portfolios, delivering smoother, higher risk-adjusted returns.

Join us

If you're interested in understanding trading edges — and learning how to exploit them across markets and regimes — we invite you to join. Click here to learn more about OBUG at AbleWayTech. Learn, test, adapt — and trade smarter!

The Quest for Alpha, One Heatmap at a Time

At AbleWayTech, we believe great trading isn’t the result of luck — it’s built on data, structure, and edge awareness. That philosophy was front and center in our latest Owl Bundle User Group (OBUG) Meeting 111, where we revealed the results of our deep-dive study on regional ETF market behavior using 15-year backtests with EdgeRater.

This wasn’t just another indicator study — it was a multi-regime, multi-horizon quantitative analysis of global equity markets using the RL30Slope Z-score, an advanced price slope momentum indicator developed by Dr. Ken Long to quantify trend acceleration and regime shifts with statistical precision.

Study Objective: Where is the Edge?

The core question behind our research: Which market regimes offer the greatest edge for swing trading across regions? We applied this question to seven major ETFs representing global equity regions — including the U.S. as a reference benchmark:

• EFA – Developed Markets ex-US

• IEV – Europe

• EWJ – Japan

• EPP – Asia Pacific ex-Japan

• ILF – Latin America

• EEM – Emerging Markets

• SPY – U.S. (as a comparative baseline)

Each ETF was analyzed using:

• RL30Slope Z-scores at entry (from +3 to -3 sigmas)

• Holding periods: 3, 5, 10, and 15 days

• FE50 outcomes (Final Equity / Starting Equity at the 50th percentile)

Figure 1.0 below is an example of the Heatmap analysis for ILF the Latin America ETF:

• Z < -2.0 → Strong mean reversion signals; price often rebounds sharply

• Z = +0.5 to +1.5 → Continuation trades fueled by institutional momentum

• Z between -1.5 and 0.0 → Low-conviction zones; prone to sideways chop (red box)

We analyzed all 7 regional ETFs in this manner with results shown in Figure 2.

Key Takeaways: Where Alpha Lives

Z = -2.0 to -1.5 sigmas is the universal sweet spot

• This zone represents deeply oversold conditions followed by slope reversal — ideal for mean reversion swing trades with high expectancy.

Z = +1.5 is your momentum breakout zone

• Strong upward slopes + institutional follow-through = continuation setups that favor trend trades.

Trap Zones (-1 < Z < +1)

• Price lacks conviction. These are the “drift zones” — low expectancy, high noise. Great for tourist traders. Not for us.

Global Macro Implications

Certain ETFs behaved differently based on macro capital flows:

• EEM and EPP weren’t momentum-driven.

• These instruments react best to panic-buying reversals, not strength chasing.

This underscores a key insight:

“Not all instruments respond the same way to the same signals.”

Portfolio Optimization in Action

This Z-score matrix allows traders to:

• Avoid low-expectancy trade zones

• Allocate capital by regime and conviction

• Tailor holding periods to volatility conditions

• Use data-driven filters to deploy trades with greater precision

This is not just technical analysis — it’s edge-aware, regime-adaptive trading

Join the Owl Bundle User Group (OBUG)

If this level of rigor, insight, and collaboration excites you — don’t trade alone. Join the Owl Bundle User Group (OBUG) traders gather to build:

• Robust systems based on statistical backtests

• Backtested frameworks designed for risk-adjusted returns

• Non-correlated strategies for smoother portfolio equity curves

Join us today at www.ablewaytech.com/obug

Disclosures: All OBUG materials are for educational purposes only. This is not investment advice. See full disclaimer at the bottom of this page.