Ahan Analytics, LLC

10/12/2026

"A Slowly Changing Bearish Divergence – The Market Breadth"

(Link below)

* The stock market’s slowly changing bearish divergence showed early signs of improvement as equal-weight stocks rallied and small caps defended critical support.
* Market breadth remained under pressure, with AT50 at 28.9% and AT200 at 43.3%, despite encouraging signs of stabilization.
* The S&P 500 and NASDAQ maintained bullish technical positions near their highs, while the Russell 2000 and financial stocks contributed to a potential breadth recovery.
* Rising Treasury yields, persistent inflation pressures, and weakening consumer sentiment threatened the sustainability of the stock market’s advance.
* AI-related stocks delivered mixed performances as semiconductor weakness contrasted with new highs in Microsoft, Arista Networks, and Palantir.
* The short-term trading call remained neutral as improving technical signals failed to confirm an end to the primary market breadth downtrend.

$RSP $SPY $IWM $XLF $SMH $INTC $VRT $CAT $DE $CHRW $MSFT $ANET $PLTR $CSCO $ECG $VZ $SPCX $ACN $SBUX $CMG $ILMN $EWZ $SPX $COMPQ

10/09/2026

“I think that much better career advice is that coding is now so easy with AI assistance, it’s worth everyone learning to do. And this will affect the ability of everyone of us to tell the computer what we want and people that can do that will be much more powerful and much more effective.” Andrew Ng

10/06/2026

My latest on the U.S. housing market (link in comments):
"What Happened in the Housing Market – Increasing Pressure"

* Increasing pressure in the housing market pushed builder sentiment to a one-year low as more builders cut prices and increased their use of sales incentives.
* Homebuilder stocks weakened sharply, with ITB trading near the bottom of its three-year range and major builder Lennar falling near a four-year low.
* Single-family housing starts and new-home sales increased month-over-month in August off recent lows, but weak permits and declining existing-home sales kept the broader housing picture mixed.
* Mortgage rates rose to 7.28% in September while mortgage applications weakened, adding another affordability headwind for buyers and builders.

$ITB $XHB $LEN

10/05/2026

A while back I aligned myself with AI optimists like Box CEO Aaron Levie by creating the “PAIROS” framework for investing/trading counter to the AI Panic that created the “SaaSpocalypse.” Increasingly, I am experiencing Jevon's paradox with AI in my own work. AI makes existing work more efficient, but I also am more busy than ever because of all the ideas I can explore and then make happen. (I am also more antsy than ever because the bucket of ideas across domains just keeps overflowing.)

The latest podcast from Hidden Brain (one of my favorites now thanks to Michael Goldbach!) provides some golden quotes that perfectly reflect my current experience. These quotes are from University of Pennsylvania psychology professor Martin Seligman in “How to Make a Difference: Part 2” (toward the end):

“For me, these machines are enormous aids to human creativity, to my creativity….I wake up in the morning now, more excited than I’ve been in about 50 years to see what Claude and I can do together about hypotheses I had about human psychology that could not have been tested without the massive computation of a Claude and a ChatGPT.”

Before telling a story about his experience with AI, he provided this acknowledgement:

“The bottom-line is that the doomsayers have the press right now, and you can hear all the hypothetical awful things that might happen, and I think that’s part of responsible science, and we should worry about those things. My task as a positive psychologist is to ask about the upside: not what is the most catastrophic thing that might occur, but what are the best things. I am seeing some of those best things everyday in my life. They’ve convinced me that the upside of AI is enormous….I’ve never had a more creative partner than Claude.”

Life constantly presents us with conflicting truths that co-exist and regularly confound us. AI is the latest. Which truth keeps you occupied?

10/04/2026

In my partnership with AI, I like to take periodic pauses of reflection, especially after reaching a milestone or completing a significant body of work. After several recent "ah ha" moments and breakthroughs on my RAG (retrieval augmented generation) project, I asked ChatGPT to summarize the lessons we learned together. In case it helps someone else working on RAG, I post its accurate summary below followed by a little follow-up from me.

========================

The central lesson is: the fastest way to improve an AI knowledge system is often to ask the LLM to do less.

Our experience produced several broader lessons:

1. Retrieval quality matters more than prompt complexity. Clean titles, summaries, stable document IDs, and a dedicated index improved results more reliably than adding instructions.

2. Use deterministic logic where precision is possible. Exact verse parsing and indexed lookup should happen before the LLM is involved.

3. Make retrieval observable. A diagnostic mode that stops before generation exposed missing matches, irrelevant synonyms, title mismatches, and wasted context.

4. Optimize for sufficient evidence, not exhaustive recall. More retrieved documents can increase cost and distract the model without improving the answer.

5. Synonyms are not interchangeable meanings. For example, uncontrolled expansion can turn "identity" into loosely associated ideas such as "legacy", "destiny", and "talents".

6. Every additional prompt rule creates another possible interaction. A few clear principles are safer than a growing list of narrow guardrails.

7. Test the pipeline one stage at a time. Separating retrieval errors from generation errors prevents wasted effort and incorrect diagnoses.

The larger point: reliable RAG is primarily an information architecture and observability problem, with the LLM serving as the final synthesizer rather than the entire system.

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Number 6 above has been particularly critical. Too many times ChatGPT was all too happy to pour out volumes of rules that either overfit for a specific challenge and/or created endless rabbit holes. I would interrupt by 1) describing the unwanted behavior, 2) recommending what I thought of as a more generalized and broadly applicable principle, or 3) most recently, asking ChatGPT to review its recommendation for unintended consequences (yes, AI and unintended consequences is a hot topic these days).

ChatGPT will also freely throw in terms and phrases which could be interpreted too loosely and unpredictably. In these cases I would request more specificity or elaborate on the problem.

More yet to come....

10/04/2026

Weak Jobs Report Pulls the Stock Market From Oversold’s Edge – The Market Breadth
{link below}

* A weaker-than-expected September jobs report briefly pushed bond yields lower and helped pull market breadth back from oversold's edge, but the relief in yields faded by Friday's close.
* The S&P 500 confirmed 50DMA support and the NASDAQ reached a fresh all-time high, while the equal-weighted S&P 500 and small caps remained under technical pressure.
* AT50 rebounded from 23.2% to finish the week at 27.9%, while AT200 hit a new low for the year before Friday’s rebound, leaving the broader market’s health in a weak position.
* AI-related and semiconductor stocks produced several bullish breakouts, but long-duration Treasuries, financials, and other rate-sensitive areas continued to show significant weakness.

$SPX $IWM $TLT $XLF $XLI $USO $GLD.X $NVDA $CRDO $TER $AMAT $CAT $ACN $KMX $FICO $DASH $RBLX $JBL $CRM $RDDT $APP $CPRT $MGM $WHR $COMPQ $SPY

A weak September jobs report pulled market breadth back from oversold's edge, but rising yields and weak market breadth kept the rebound on uncertain footing.

09/21/2026

Fed Rate Hike Punishes Market Breadth While AI Stocks Rebound

(Access full article in comments)

* The Federal Reserve rate hike punished market breadth, pushing AT50 to a seven-month low while rate-sensitive stocks and small caps came under renewed pressure.
* The S&P 500 and NASDAQ recovered quickly from their post-Fed weakness, even as broader participation deteriorated and the Russell 2000 remained in a downtrend.
* AI-related stocks rebounded sharply after an early-week selloff, with AIS, Coherent, Intel, and other AI-linked names resisting the broader post-Fed weakness.
* Consumer-facing stocks, financials, and housing-related equities showed notable weakness as higher rates, rising oil prices, and tighter financial conditions added pressure.

$AIS $COHR $VRT $INTC $AMAT $HACK $XLF $GS $OWL $GLD $SLV$LEN $HOOD $PLAY $NFLX $SBUX $FRSH $SI $ATI $SE $DLTR $RRGB $SPX $SPY $COMPQ $IWM

09/16/2026

The Bimodal Data Team and the Rise of the Full-Stack Artisan

(see comment for link)

Tristan Handy is CEO of the company formed by the combination of dbt Labs and Fivetran, whose new name is pending. In a wide-ranging interview on DataCamp, Handy observed that “we are in the midst of one of the biggest shifts in the way that data teams operate” over the last 10 years. This claim caught my attention because of its implications for the Insights Supply Chain (ISC).

Handy describes a data team “bifurcated into two main buckets”: platform maintenance and data enablement. According to Handy, data practitioners (Handy’s terminology) in platform maintenance focus on the centralized infrastructure, including data pipelines, data governance, and data security. Data practitioners in data enablement focus on democratizing data access throughout the organization and partner with stakeholders to standardize business definitions into semantic models. The ISC describes this division of labor as “upstream specialization,” in which data engineering provides data services to analysts. In Handy’s bifurcation, an upstream platform-maintenance bucket supports a downstream data-enablement bucket. Together, these buckets form what I call the bimodal data team which accounts for contexts where data practitioners can switch between platform maintenance and data enablement.

At the same time, Handy rejects conventional role definitions centered on technical skills. Instead, Handy defines roles by how they create business value, insisting that “the best teams are focusing on full-stack data practitioners who can own projects end-to-end.” Although the DataCamp discussion did not explain how full-stack practitioners fit within the two buckets, Handy’s comments place a broad practitioner model alongside a bifurcated organizational model. The advent of AI and its application in data workflows and teams supports the emergence of the full-stack role. AI is blurring conventional distinctions between roles by augmenting the technical capabilities of data practitioners and expanding their capacity for cross-functional communication and collaboration.

Handy’s description of the full-stack data practitioner also evokes the notion of an artisan in the ISC, although John Thompson describes the artisan role as belonging to a data scientist. Viewed through the ISC, Handy’s full-stack practitioner becomes a full-stack artisan, extending the artisan concept beyond data scientists to a broader range of data practitioners. The full-stack data practitioner also extends Colin Zima’s conception of the data analyst as curator, communicator, and product thinker: a holistic, business-focused data analyst.

Handy’s full-stack practitioner, Thompson’s artisan, and Zima’s holistic analyst all combine broader practitioner capabilities with a direct focus on business value. AI puts this model within reach of more data practitioners. In the Insights Supply Chain, the bimodal data team divides the work; the full-stack artisan connects that work to business value.

09/14/2026

"Market Breadth’s Latest Warning As Oil, Inflation and Rates Rise"

* Market breadth delivered its latest warning as AT50 fell to 39.7%, its lowest level since April, while AT200 weakened toward its May low and the equal-weighted S&P 500 suffered a 50DMA breakdown.
* Rising oil prices intensified inflation concerns and higher bond yields, pressuring small caps, retailers, and other interest-rate-sensitive stocks.
* The S&P 500 and NASDAQ held critical 50-day moving-average support as strength in AI-related tech stocks offset broader market weakness.
* Consumer-facing stocks showed growing technical damage, while oil’s parabolic surge added pressure ahead of the Federal Reserve’s monetary-policy decision.
* The deterioration in market breadth prompted me to shift my short-term trading call from cautiously bullish to neutral.

(See link below in comments)

$SPY $COMPQ $IWM $USO $DASH $ETSY $CASY $SHOP $XRT $ABNB $AMD $INTC $QCOM

09/08/2026

Market Further Discounts eGain Despite Compelling 2030 Growth Plan
https://seekingalpha.com/article/4943781?gt=988b3bd50e9a59c9

* eGain Corporation (EGAN) is executing an ambitious AI transition, targeting nearly all customer ARR from AI by 2030 and trading at a significant discount.
* Despite a post-earnings collapse and a temporary decline in retention metrics, EGAN maintains a strong cash position, no debt, and aggressive share repurchases, supporting a $7.16/share near-term price target.
* eGain’s AI knowledge management leadership is validated by Gartner, with product innovation and quantifiable customer ROI underpinning long-term growth potential.
* I see the market as overly skeptical, with EGAN’s valuation derisked and the company well-positioned to benefit from enterprise AI adoption and operational leverage.

$EGAN

eGain (EGAN) targets AI-driven ARR by 2030; despite a selloff, cash strength and buybacks support upside.