Rather than focusing only on price direction, RL30Slope Z helps measure whether leadership is strengthening, weakening, or persisting beneath the surface of price movement. Over time, our research suggested that this information could be used not only for individual trade setups, but also for understanding broader market structure and institutional participation dynamics.

Why We Began Developing These Frameworks

Many traders struggle with the same problems:

• Too many symbols to scan

• Difficulty adapting to changing market conditions

• Confusion between broad market weakness and selective leadership

• Applying the wrong tactics in the wrong environment

We began researching whether RL30Slope Z could help address the traders’ concerns. This eventually evolved into two complementary frameworks now used inside OBUG:

• OTF (OBUG Tactical Framework)

• ORPA (OBUG RL30Slope Z Participation Analyzer)

While these tools are related, they serve very different purposes for traders.

OTF: Understanding the Market Environment

The OBUG Tactical Framework (OTF) was developed to help traders evaluate the overall tactical trading environment. Using market participation, leadership quality, macro alignment, and rotation analysis, OTF attempts to answer a critical question: “What type of trading behavior is statistically favored RIGHT NOW?”

For example:

• Are market conditions favoring trend-following or mean reversion?

• Is leadership broadening or narrowing?

• Is market participation improving or deteriorating?

• Are conditions supportive of longer holding periods or shorter tactical trades?

OTF uses RL30Slope Z to assess:

• Breadth participation

• Leadership participation

• Macro support

• Defensive rotation

• Trend quality

The goal is not to predict the future, but rather to help traders adapt their tactical posture to the current market environment. In many cases, traders struggle not because their systems are poor, but because they are applying the wrong tactics to the wrong market regime.

ORPA: Identifying Leadership Inside the Market

As OTF evolved, another important question emerged: “If leadership is narrow, which symbols are actually driving that leadership?”

This led to the development of ORPA (OBUG RL30Slope Z Participation Analyzer).

ORPA uses recurring RL30Slope Z participation analysis over rolling windows to identify symbols and themes showing repeated acceleration and persistence.

Rather than acting as a buy/sell signal generator, ORPA is designed as a market intelligence and leadership-detection framework.

It helps identify:

• Symbols showing recurring participation

• Strengthening market themes

• Emerging institutional sponsorship

• Persistent leadership clusters

• Areas where participation is accelerating or deteriorating

In many ways, ORPA attempts to identify the “next Ohtani” symbols — names quietly gaining repeated sponsorship before they become obvious market favorites.

Importantly, ORPA is not forecasting earnings or predicting macroeconomic outcomes.

Instead, it measures observable participation behavior inside the market itself.

How OTF and ORPA Work Together

One of the simplest ways to understand the relationship is:

Suppose OTF indicates:

• weak breadth

• fragile participation

• shorter holding periods favored

At the same time, ORPA may reveal recurring participation in:

• cybersecurity

• aerospace & defense

• infrastructure

Instead of scanning hundreds of random symbols, traders can focus their attention on areas showing persistent sponsorship despite weaker overall market conditions.

Another way to think about it: OTF helps traders understand: “HOW should I trade this market?” ORPA helps traders understand: “WHAT symbols and themes deserve my focus?”

Together, they help traders become more selective and context-aware rather than randomly scanning for setups.

Why This Matters for Traders

Most traders focus almost entirely on: entries, exits, and indicators

However, our ongoing OBUG research suggests that market context and participation quality materially influence trading-system durability.

Two traders can use the exact same setup and experience dramatically different outcomes depending on:

• market breadth

• leadership quality

• volatility regime

• participation persistence

• macro alignment

OTF and ORPA were developed to help traders become: more selective, more adaptive, and more context-aware rather than simply scanning randomly for setups.

Final Thoughts

Markets are increasingly driven by: concentration, institutional flows, thematic rotation, volatility regimes, and macro crosscurrents

We believe traders who understand market structure and participation quality may gain a significant advantage over those relying solely on isolated indicators or headlines.

OTF helps traders understand HOW to trade the current market environment.

ORPA helps traders understand WHAT and WHERE to focus on within that environment.

We invite you to learn more about the Owl Bundle User Group (OBUG) and join us as we continue exploring these evolving frameworks together.

Lately I have been tracking the Dow Jones Index closely, and focusing on one setup that I have started to see occur with a good amount of frequency.  Narrowing down my focus to one setup, and even on one symbol, has consistently proved to my best way to move my own learning forward with the markets.  It becomes a very manageable process and ritual, and I get to know all the little nuances that make all the difference.

My area of focus has been the afternoon of the US session, coming out of the mid-day lunch hour.  Why this time period?  It works well for me, as my mornings are chock full of kids and pets and other wonderful chaos.  Plus, the lunchtime in New York is 9:30-ish out here on the West Coast, so it’s typically when I sit down at my desk, and am caffeinated enough to concentrate without any distractions.

The setup I want to talk about today is the Sideways Afternoon Breakout.  Dr. Ken Long would call this a ‘pinch’ or ‘Z3P’ breakout.  Others call it a sideways coil or sideways quiet channel.  A rose by any other name would smell as sweet, and this sweet setup is simply that price has generally been rotating in the morning session and come down to an equilibrium level.  The concept of contraction followed by range expansion of price has been around for nearly a century.

“Periods of dullness are followed by periods of activity.” -Charles Dow

The tell for this setup is that the price action will have converging trend lines that can be drawn on the recent highs and lows that show the market participants have temporarily have agreed on a price level.  But this agreement often doesn’t last long, and the beginning of the afternoon session can often be a nice directional move out of this equilibrium point.

Dr. Ken Long and many in the Owl Group of traders use a Bollinger Band function to identify the volatility compression.  I like to use a simple ADX indicator because it can measure the amount of overlap of consecutive price bars.  When the ADX hits a low reading, there is a confirmation of compression of price action.  

These are typically small bites type of trades, with a test of the intraday high or low a good place to look for price to test.  The example in Chart 1 ended up having a large follow-through, as price rallied all the way through yesterday’s high and closed near the high of the day. 

The second example in chart 2 shows a similar setup, but no follow through after the breakout.  Alas, no setup is infallible, but what’s interesting is to a take a quick look at the contrast in volume when the breakout occurred on these two examples. 

Chart 3 is an equivolume chart of the same price action as Chart 1.  Equivolume was developed by Richard Arms and combines price range and volume into a single visual element.  The width of the bar is proportional to the volume, so wide bars show that larger volume has come into play.  Large volumes can often mean higher timeframe players with large funds, and can lead to larger follow-through. 

The 1-minute equivolume chart pinpoints that at the moment of breakout from the sideways coil there was a massive influx of volume shown by the very wide bar.  This can be seen on the regular price chart as well, as the price bar had a very large range.  The equivolume chart just adds some confirmation. 

Contrast that with our second example, in Chart 4, the same price action as Chart 2, that did not follow through after price broke out of the narrow range, and there is no additional volume present.  No wide bar.  Nobody’s home.  No follow-through. The first example could give us more confidence and structure to hold the trade through the session, while the second example could serve as an alert to get ready and bail. 

If you’re interested in learning more about how we analyze markets and trade, check out Ablewaytech.com and stay tuned for our next course, Critical States and Kata Challenge, coming this Fall!

Happy Trading,

Griffin Cooper, MSTA, CFTe

• “Only a handful of stocks are driving the market.”

• “Breadth is weak beneath the surface.”

• “Leadership is narrowing.”

• “The rally is just MAG7.”

But how do you objectively measure whether market leadership is truly broadening or narrowing? And more importantly: How do you identify where institutional sponsorship is actually accelerating beneath the index level?

At OBUG, we recently explored a new participation study using Dr. Ken Long’s RL30Slope Z indicator combined with EdgeRater backtesting and market scanning tools. RL30Slope Z is the 30-period regression line slope Z-score, measuring trend strength relative to its own history. (See our prior RL30Slope Z article for a deeper explanation)

The goal was not to predict market tops or bottoms. Instead, the objective was to quantify:

• where leadership is clustering,

• which themes are accelerating,

• whether participation is broadening or concentrating,

• and whether market conditions favor offensive or defensive deployment.

Traditional Breadth Measures Have Limitations

Most market breadth indicators focus on price position:

• % Above 50DMA

• % Above 200DMA

• Advance / Decline

• New Highs / New Lows

These are useful, but they often treat weak trends, stagnant trends,and strongly accelerating trends as equivalent.

A stock barely above its 50DMA counts the same as a stock experiencing explosive institutional sponsorship. That is a major limitation. Traditional breadth often measures participation quantity, but not necessarily participation quality.

Why RL30Slope Z?

RL30Slope Z, developed by Dr. Ken Long, attempts to measure:

• trend quality,

• normalized trend strength,

• and acceleration relative to a symbol’s own history.

Adding the condition: “RL30Slope Z > 0 and Rising” filters for symbols whose trend structure is positive, and improving further.

In simple terms, we are not searching for what is simply strong, rather we are searching for what is improving fastest. One of the most interesting aspects of this framework is that it may help reveal early institutional footprints.

Institutions often accumulate positions gradually before headlines, before broad public recognition,and before obvious index-level confirmation. By scanning for improving trend quality, accelerating participation, and recurring thematic leadership, the framework attempts to identify where institutional sponsorship may already be emerging beneath the surface.

A Sports Analogy

Traditional breadth asks: “Who is currently ranked highest?”

The RL30Slope Z framework asks: “Which players are improving fastest and attracting increasing sponsorship?”

Imagine a tennis player currently ranked #50:

• rapidly improving,

• beating stronger opponents,

• climbing tournaments,

• attracting sponsors and coaches,

• building momentum.

That player may be far more important than:

• an aging top-10 player losing momentum.

That is the intuition behind RL30Slope Z > 0 and Rising. The framework attempts to identify accelerating leadership participation.

The Study

Using EdgeRater, the study scanned the S&P 500 daily for symbols where RL30Slope Z > 0 and Rising over the past 3 months.

Then, qualifying symbols were tracked over time, participation counts were aggregated, sectors and sub-industries were mapped, and recurring leadership clusters were analyzed.

Rather than treating SPY as a single object, the study attempted to look beneath the index.

What We Found

The results challenged several common media narratives. Leadership was broader than just MAG7. While mega-cap technology remained important, participation extended well beyond the handful of names.

Strong recurring participation emerged in:

• Industrials & Infrastructure

• AI Infrastructure & Cybersecurity

• Utilities & Defensive

• Financials & Insurance

• Select Energy & Commodity groups

This was not simply “NVDA carrying the market.” Instead, the study revealed thematic participation clusters beneath SPY.

Infrastructure Leadership Was Persistent

Repeated participation emerged in names tied to:

• electrification,

• power infrastructure,

• industrial automation,

• logistics,

• construction,

• and capital equipment.

These were not necessarily the largest index weights. But they repeatedly appeared in accelerating leadership state, such as PWR, ETN, URI, CAT, CMI, MLM, PH, GEV, etc.

AI Participation Was Broader Than Expected

The AI narrative extended well beyond semiconductors.

Recurring leadership participation appeared in:

• networking,

• cybersecurity,

• infrastructure software,

• hyperscaler supply chains,

• and data-center ecosystems.

Again the study was not simply identifying “stocks going up.” It was identifying accelerating participation quality such as PANW, CRWD, ANET, AVGO, PLTR, CDNS, etc.

Participation Structure Matters

One of the most important findings was this: Market structure and participation quality may matter more than index level alone.

A market can continue rising while:

• participation narrows,

• sponsorship deteriorates,

• or leadership concentrates into fewer symbols.

Conversely, broadening participation often aligns with healthier trend conditions. This becomes highly relevant for swing traders, systematic traders, and portfolio deployment decisions. This may help traders better align aggressiveness, position selection, and deployment quality with evolving market conditions

Beyond Traditional Market Commentary

Much market commentary focuses on headlines, macro opinions, or static breadth metrics. This framework attempts to move toward evidence-based participation analysis.

The goal is to quantify sponsorship clustering, theme acceleration, participation persistence, concentration risk, and evolving market structure beneath SPY. The framework is not designed to predict exact market direction, but rather to quantify evolving participation structure beneath the index.

In many ways, the framework resembles tracking which athletes are accelerating through the rankings before they become obvious consensus leaders.

Final Thoughts

One of the most interesting aspects of this work is that it bridges discretionary market interpretation, systematic scanning, and institutional-style participation analysis.

The framework is still evolving, but early findings suggest that accelerating leadership participation may provide a far richer picture of market structure than traditional breadth measures alone.

At OBUG, we continue exploring:

• RL30Slope Z participation studies,

• Logic Chain Market → Sector → Symbol frameworks,

• systematic backtesting using EdgeRater,

• market structure analysis,

• and practical trader deployment models.

If this type of evidence-based market structure research interests you, consider joining us at OBUG as we continue developing and testing these frameworks in real time.

When You Can’t Take Every Trade

In backtesting, trading strategies are typically evaluated by taking every signal. This allows us to measure the strategy’s statistical edge—its average performance over many trades. In real trading, however, that’s often not possible.

• You may have multiple valid signals at the same time

• But capital for only 2–4 positions

So the question is no longer: “Does this strategy work?”

It becomes: “Which trades should I take first?”

The 5DD Strategy

This study focuses on 5DD (Five Days Down), one of the core Swing Systems Template developed by Dr. Ken Long and implemented in EdgeRater.

What is 5DD?

• A mean reversion strategy

• Triggered after five consecutive lower closes

• Designed to capture short-term rebounds after downside exhaustion

In simple terms: 5DD looks for stretched downside conditions where price is likely to revert toward the mean.

Where Logic Chain Comes In

This is where Dr. Ken Long’s Logic Chain framework becomes practical.

Logic Chain organizes decisions into: Market → Sector → Strategy → Symbol

Instead of treating all 5DD signals equally, we ask

• Is the market supportive (Risk-On vs Risk-Off)?

• Is the stock’s sector supportive?

Study Objective

We tested the following question:

When we can only take a few trades, does choosing 5DD signals that align with the market and sector make a difference in results?

Methodology:

We used EdgeRater’s batch testing framework to simulate multiple conditions:

• Universe: S&P 100 (broad, noisy environment)

• Strategy: 5DD

• Exit: 10-day hold

Capital Constraints

• 1 MAX → only 1 position

• 2 MAX

• 3 MAX

• 4 MAX

  This forces trades to compete for capital, just like in real trading.

Three Approaches Tested

1) Baseline

• Take signals as they appear

• No prioritization

2) Sector Alignment

• Look at trades where the stock’s sector is favorable

3) Logic Chain

• Look at trades where both market and sector are aligned

Understanding the Metrics

To compare results, we focus on two measures:

1) FE50 / MD50 - Reward to risk

• FE50 (Final Equity at 50th percentile) → Median final equity

• MD50 (Max Drawdown at 50th percentile) → Median drawdown

2) Expectancy — $ Per Trade

• Average profit/loss per trade

Market & Sector Alignment Using RL30Slope Z

To apply Logic Chain, we use: RL30Slope Z-score to measure tremd strength:

Applied to:

• Market (SPY)

• Sectors (XLK, XLY, XLP, etc.)

(Explanation of RL30Slope Z is available in an earlier article)

Results: 5DD on the S&P 100

Within this test framework, the following pattern was observed:

Baseline (No Alignment)

• FE50/MD50 ≈ 4.13

• Expectancy ≈ $55 per trade

Sector Alignment

• FE50/MD50 ≈ 9.35

• Expectancy ≈ $119 per trade

Logic Chain (Market + Sector)

• FE50/MD50 ≈ 11.85

• Expectancy ≈ $170 per trade

Results show: Trades in aligned Market–Sector environments showed stronger outcomes within this study.

What Actually Improved

Logic Chain does not change the 5DD signals, It changes: How trades are prioritized when capital is limited

Observed effects

• Higher average outcomes

• Higher win rates

• Higher expectancy

From Research to Practical Use

A practical interpretation of these findings is:

1. Generate all valid signals

2. Identify which trades occur in more favorable conditions

3. Use that information to:

   • Prioritize trades when capital is limited

Trades are not eliminated—only ranked differently

Extending Beyond 5DD

Inside OBUG, we tested Logic Chain across additional Dr. Ken Long Swing Systems in EdgeRater:

• ORL (Over-Reaction Long)

• CH (Channeling)

• WO (Washout)

• TS (Triple Screen)

• MPRC (Max Pain Range Compression)

Different strategies showed different sensitivities to market and sector conditions.

Why This Matters for Traders

Most traders focus on: Entries, Indicators, and Signal tweaks.

But in practice: Performance is often influenced by how capital is allocated across multiple opportunities

Inside OBUG

This article represents some of the work being done inside the Owl Bundle User Group (OBUG). If this type of structured, evidence-based research resonates with you, you may find value in joining us HERE.

Disclaimer

This article is provided for educational and informational purposes only.

The concepts, strategies, and results discussed are based on historical backtesting and analysis, and are intended to illustrate differences between approaches within a specific test framework. These results do not guarantee future performance.

The Default Reaction

When a trading strategy underperforms, the instinct is almost universal:

• “Maybe I should use a 20-day MA instead of 30…”

• “Maybe I need a better entry trigger…”

This thinking is logical—and necessary. It addresses a critical question: “Is the signal itself valid and robust?” But here’s the problem: It only addresses part of the overall process.

The Reality: Same Signal, Completely Different Outcomes

At the Owl Bundle User Group (OBUG), we study and apply Dr. Ken Long’s systematic trading methodology and use EdgeRater’s Multi-Factor Analysis to test strategies across large universes of symbols. What we consistently observe is a repeatable pattern: Signal effectiveness can vary significantly depending on the instrument.

For example:

• A mean reversion signal shows strong, consistent performance in:

  • ASML

  • MSFT

• The exact same signal shows weak or unstable performance in:

  • CPB

  • T

Nothing changed in the rules. Only the instrument changed.

The Core Insight Most Traders Miss

Trading rules identify opportunity — Symbol selection plays a major role in determining whether that opportunity has a consistent edge.

This is the difference between:

• A strategy that is statistically valid

• A strategy that is profitably deployable

What Most Traders Are Actually Missing

Most strategy development focuses on:

• Parameter optimization

• Walkforward testing

• Out-of-sample validation

These steps are essential—they validate that a signal is real. But they often under-emphasize the next step: Determining where that already-valid signal actually works best

This is where performance differences emerge—and where much of the edge is realized.

Two Complementary Approaches

1. Signal Validation

• Define rules

• Optimize parameters

• Validate across time

Purpose: Confirm the signal has a real edge

2. Deployment Optimization

• Apply the validated signal across many symbols

• Identify where it performs consistently

• Deploy capital selectively

Purpose: Capture the edge where it expresses most clearly

Why This Happens (Structural, Not Random)

This variation is often linked to structural characteristics of the instrument.

Different symbols respond differently due to:

1. Institutional Flow

• Large-cap growth → sharp, reflexive moves

• Defensive stocks → slower, muted reactions

2. Volatility Elasticity

• High-beta names → strong mean reversion response

• Low-volatility names → weaker response

3. Liquidity Depth

• Deep markets → cleaner, more reliable behavior

• Thin markets → noise and instability

The Hidden Truth About Edge

Most traders assume: “Edge comes from the strategy.”

A more accurate framing is: Edge comes from the interaction between the strategy, the instrument, and how it is deployed.

When testing across large universes, results are averaged and strong performers are diluted by weaker ones. In many cases, a subset of symbols may contribute disproportionately to overall performance.

A More Effective Workflow

Instead of asking: “How do I improve the strategy?”

Ask: “Where does this strategy perform most robustly?”

Step 1: Validate the Signal

• Confirm robustness across time

• Avoid overfitting

Step 2: Test Broadly

• Apply the same rules across many symbols

• Let the data reveal differences

Step 3: Identify Consistency

Focus on symbols with:

• Stable expectancy

• Controlled drawdowns

• Repeatable behavior

Step 4: Build a Curated Universe

• This becomes your tradable list

• Strategy and instrument are now aligned

A Simple Analogy

Think of your strategy as a fishing technique:

• Signal-focused → refining the rod and bait

• Deployment-focused → finding the lake where the fish actually are

Same method, completely different outcome.

Net-Net

In some cases, your strategy may not be broken — your universe may be misaligned.

• Parameter optimization validates the signal

• Symbol selection determines where it works

• Deployment determines results

Inside OBUG

At the Owl Bundle User Group (OBUG), this is the type of work we are actively studying and testing.

Using EdgeRater’s backtest and Multi-Factor Analysis, we explore this process by:

• Identifying robust trading signals

• Testing them across broad universes of symbols

• Isolating where those signals show consistent, repeatable edge

• Building curated symbol lists aligned with Dr Ken Long’s strategies, including:

  • Critical States Systems

  • Swing Systems

At OBUG, we approach this as an ongoing research and development process. If that aligns with your interests — Join us inside OBUG

In our prior articles:

1. RL30Slope Z: A Structured Approach to Analyzing Market Rotation and Relative Strength

2. Using RL30Slope Z to Evaluate Breadth and Leadership

…we introduced a simple but powerful idea:

RL30Slope Z is not just a descriptive tool — it can become a decision engine. RL30Slope Z is derived from the work of Dr. Ken Long, who pioneered the use of regression lines and slope-based Zscore indicators to quantify market behavior across multiple timeframes.

In this third installment, we move from analysis → application using EdgeRater for systematic backtesting and validation.

Specifically, we test:

Does using equal-weight sector breadth (RSPT) improve trading performance versus cap-weighted XLK when used inside the Logic Chain?

The Core Hypothesis

Traditional sector ETFs like XLK are cap-weighted. This means a handful of mega-cap names (e.g., AAPL, MSFT, NVDA) dominate the signal. Equal-weight ETFs like RSPT distribute influence more evenly across all constituents. So the question becomes:

Does RL30Slope Z applied to RSPT provide a cleaner, more robust sector signal than XLK?

Experimental Design

We tested this inside a fully systematic framework:

Strategy:

• 5DD (Five Days Down) — from Dr. Ken Long

• Mean reversion system designed to capture short-term exhaustion

Platform:

• EdgeRater (used for all backtesting and Monte Carlo analysis)

Logic Chain Structure:

• Market → Sector → System → Symbol

• Sector filter based on:

  • RL30Slope Z > -1

  • Slope rising

Test Comparison:

• Baseline: SPY + XLK

• Alternative: SPY + RSPT

Key Principle:

Only ONE variable changed → sector definition (XLK vs RSPT)

Important Context on the Results

This study was conducted on a curated list of symbols aligned to the 5DD strategy, not a broad or index-wide universe.

Therefore, the findings are specific to this curated universe and this strategy implementation — they should not be generalized to all symbols or all market conditions without further testing.

Summary of Results (High-Level)

Observations from Backtesting:

• Both XLK and RSPT produced profitable and robust results

• RSPT showed:

  • Slight improvements in consistency

  • Reduced sensitivity to single-stock dominance

• Differences were incremental, not dramatic

What This Means

🔹 XLK (Cap-Weighted)

• Reflects where capital is concentrated

• Strong signal when leadership is narrow

• Can be distorted by a few mega-cap names

🔹 RSPT (Equal-Weight)

• Reflects true breadth participation

• More stable across rotations

• Better representation of “average stock behavior”

Key Insight

RSPT does not create a new edge — it refines the signal quality.

This aligns with what we consistently see in OBUG research:

• The strategy (5DD) generates the edge

• The Logic Chain improves trade selection and risk profile

Why the Improvement Is Not Dramatic

This is important — and often misunderstood.

The 5DD strategy:

• Is already a broad-market mean reversion system

• Works across many symbols

• Is less dependent on precise sector timing

So:

Changing XLK → RSPT improves signal quality, but not necessarily total return dramatically

Institutional Framing

Think of it this way:

• XLK answers:
  “Where is institutional capital flowing?”

• RSPT answers:
  “Is participation broad enough to support trades?”

Both are valid — they just measure different dimensions of the market.

Practical Takeaways for Traders

Use RL30Slope Z as a Sector Filter

• Focus on:

  • Z-score above threshold

  • Rising slope

Understand What You Are Measuring

• XLK → leadership concentration

• RSPT → participation breadth

Expect Incremental, Not Transformational Gains

• Logic Chain improves:

  • Trade selection

  • Drawdown control

  • Consistency

Avoid Overfitting

• Do not chase small differences between XLK and RSPT

• Focus on robustness across market regimes

Where This Leads Next

This study opens the door to a broader question:

Does equal-weight improve signal quality across other sectors?

Potential extensions include:

• RSPS (Staples) vs XLP

• RSPH (Healthcare) vs XLV

• RSPF (Financials) vs XLF

• RSPE (Energy) vs XLE

OBUG Net-Net

• RL30Slope Z remains a powerful tool for sector alignment

• RSPT provides a cleaner breadth signal than XLK

• Impact on 5DD is incremental, not dramatic

• Logic Chain enhances risk control and trade prioritization, not raw edge

• Further research on other RSPx sectors is warranted

Final Insight

“The edge is in the strategy.
The Logic Chain helps you trade it in the right environment.”

About This Research

All studies were conducted using:

• EdgeRater for systematic backtesting

• Long-term historical data across multiple market regimes

• OBUG research methodology focused on robustness, not optimization

This study is representative of the type of work we are doing inside OBUG.

We are not focused on isolated indicators or one-off ideas. Instead, we systematically test how market, sector, and symbol behavior interact within real trading systems.

Our goal is to build a robust Logic Chain of Swing Systems—where multiple strategies work together, deployed in the right environment, with consistency and discipline.

That distinction matters. Because trading performance often depends less on the index level and more on how many stocks are actually participating.

Linking to the Framework

In a prior article, RL30Slope Z: A Structured Approach to Analyzing Market Rotation and Relative Strength, we introduced RL30Slope Z as a way to standardize how trend behavior is measured across different instruments.

This work builds directly on the methodologies of Dr. Ken Long, where the goal is not prediction, but structured observation, measurement, and decision-making.

The objective is simple: Create a repeatable framework to evaluate relative strength and how it evolves over time.

1. The Hidden Risk in Market Strength

Broad indices can give a misleading picture.

To understand why, it helps to distinguish between cap-weighted and equal-weighted indices:

• In a cap-weighted index like SPY, larger companies have a greater influence on performance. A small number of large stocks can drive a significant portion of returns.

• In an equal-weighted index like RSP, each stock contributes equally, providing a clearer view of how the average stock is behaving.

This difference is important.

In practice:

• The index may appear strong

• While many individual stocks in the idex are flat or weakening

Understanding Concentration in Cap-Weighted Indices

A simplified way to think about cap-weighted indices is that they allocate more capital to larger companies. This does not mean each stock is held in equal amounts. Instead, the number of shares is adjusted so that larger companies represent a larger portion of the portfolio. This can lead to meaningful concentration.

For example:

• In XLK, a small number of large technology companies (such as Apple, Microsoft, and Nvidia) can account for a substantial portion of the ETF.

• In SPY, the largest companies collectively represent a significant share of index performance.

As a result:

• A small number of stocks can drive index-level returns

• While many other stocks may not be participating

In extreme cases, index performance can be largely explained by just a handful of stocks.

A Simple Framework to Evaluate Participation

To assess this, we compare:

Market Layer

• SPY (cap-weighted)

• RSP (equal-weighted)

Sector Layer (Technology Example)

• XLK (cap-weighted)

• RSPT (equal-weighted)

Why This Comparison Matters

Comparing cap-weighted and equal-weighted ETFs helps answer: Is strength broad — or concentrated? If cap-weighted is strong but equal-weighted is weak → strength is concentrated. If both are strong → participation is broad. This provides a more complete view of market behavior than price alone.

Bringing in RL30Slope Z

Using RL30Slope Z allows us to compare SPY, RSP, XLK, and RSPT on the same statistical scale. This makes it easier to evaluate:

• Relative strength

• Changes in leadership

• Emerging divergence

Implementation in EdgeRater

This framework becomes significantly more powerful when implemented in EdgeRater, where both scanning and backtesting can be performed.

Using EdgeRater, we can:

• Scan markets daily/weekly

• Track RL30Slope Z across:

  • SPY vs RSP

  • XLK vs RSPT

• Visualize:

  • Trend strength (Z-score)

  • Direction (rising/falling)

• Identify:

  • Rotation

  • Expansion or contraction in participation

Most importantly, EdgeRater enables you to test whether these observations actually improve trading results.

For example, within our Owl Bundle User Group (OBUG) we:

• Compare baseline strategy performance vs. performance with participation filters

• Evaluate how strategies behave in:

  • Broad participation environments

  • Concentrated leadership environments

• Measure impact on:

  • Expectancy

  • Drawdown

  • Trade consistency

This transforms the framework from a descriptive tool into a validated, data-driven process.

Interpreting the Combinations

Broad Participation (More Favorable Environment)

• SPY and RSP both show positive trend behavior

This suggests:

• Many stocks are participating

• Signals tend to be more consistent

Concentrated Strength (Caution)

• SPY strong, RSP weaker

This suggests:

• Leadership is narrow

• Fewer stocks are driving the move

In these conditions:

• Selectivity becomes more important

• Position sizing may need adjustment

Early Expansion (Potential Opportunity)

• RSP improving while SPY is flat

This may indicate:

• Strength building beneath the surface

• Early-stage rotation

Broad Weakness (Defensive Environment)

• Both SPY and RSP weakening

This suggests:

• Deterioration in participation

• Increased downside risk

What This Means for Trading Decisions

This framework is not a signal by itself. It is used for context and prioritization. In practice, it can support:

• Trade selection

• Position sizing

• Selectivity

How This Fits into OBUG

Inside the Owl Bundle User Group (OBUG), this type of analysis is part of a broader process:

• Evaluating market and sector context

• Monitoring changes in participation

• Aligning strategies with current conditions

• Use EdgeRater to backtest and validate ideas

The focus is not on prediction, but on improving decision-making through better measurement and process.

Final Thought

Market direction is only part of the story.

Participation and leadership often determine how well trading strategies perform.

Comparing cap-weighted and equal-weighted ETFs using RL30Slope Z provides a structured way to evaluate that participation.

Interested in Going Deeper?

If you find this type of structured, research-driven approach useful, you may find value in the work we do inside OBUG. Join us HERE.

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