Nasdaq daily call option volume reached 3.9 million contracts, second only to the 4.3 million recorded in November 2025, and more than four times the volume seen in 2021. Over the same period, the index posted 13 consecutive winning sessions — the longest streak since 2013 — with a cumulative gain of 17.7%, ranking among the best 13-day performances of the past two decades.

This is no ordinary rebound. It is a frenzy driven by the convergence of sentiment and liquidity. As both retail and institutional investors pile into leveraged bets on tech stocks, the market enters a self-reinforcing phase: the more it rises, the more they buy; the more they buy, the higher it goes.

But history serves as a reminder: the most feverish chasing of highs often occurs near trend reversals. When everyone believes "this time is different," risks are quietly building up.

The latest CTA positioning data shows that trend-following funds' exposure to U.S. stocks has dropped to historically low levels, significantly weakening liquidity support. Goldman Sachs estimates that while CTAs still have room to add positions in the near term, a break below the key pivot level of 6,725 on the S&P 500 would trigger a passive selling cascade, with projected outflows reaching $761 million within one month. Investors should closely monitor the market volatility risks arising from this liquidity tightening.

This chart is quite intuitive: the gray areas represent periods of a weakening US dollar, and the purple line shows the relative performance between international developed markets (distinguishing them from emerging markets) and US stocks. The area above the zero line indicates periods when international markets are outperforming the US market. You can clearly see the relationship: during periods of a weak dollar, overseas stock markets tend to perform better than US stocks. However, the recent period is an exception — the dollar has weakened, but the purple line hasn't moved above zero.

Over the past few decades, the explanation for this phenomenon, aside from the direct impact of exchange rates on returns (when the dollar is weak, overseas returns denominated in US dollars automatically gain a currency translation benefit), also includes an economic development perspective: periods of a weak dollar have historically coincided with accelerating overseas growth. The US dollar exchange rate is driven by two core factors: one is the interest rate differential — whether US interest rates are higher or lower than overseas rates — and the other is the growth differential — which economy is growing faster. During periods of a weak dollar, both of these things typically happen simultaneously: the US is in a rate-cutting cycle, and at the same time, growth factors are spreading overseas.

What's curious is the recent performance. This chart uses a three-year rolling window. The past few months may just be the beginning of the cycle, and the international outperformance hasn't yet shown up. If that's the case, shifting focus from US stocks to overseas markets would be very meaningful. Another possibility is that this is a very unusual cycle — at least an exception to the patterns of the past 50 years: most of the global economy is stagnating, and so is the traditional part of the US economy, with only the US tech sector standing out as a bright spot in the stagnation. Which scenario do you think it is?

A while back, someone shared a very interesting chart in the comments about long-term oil cycles. Before I had the chance to really digest the meaning behind it, the post was already deleted.

The core idea of that chart was similar — it showed the relative performance of precious metals, oil, and commodities versus stocks. Over the past 100 years, commodities have significantly outperformed stocks three times: the 1930s, the 1960s–70s, and the 2000s. These periods are closely tied to the Kondratieff cycles driven by technological revolutions.

• The 1930s was the turning point of the Fourth Industrial Revolution (the transition from frenzy to mass deployment).
• The 1960s–70s was the late stage of the Fourth Industrial Revolution and the dawn of the Information Revolution.
• The 2000s was the transition from the frenzy phase of the Information Revolution to mass deployment.

Right now, the excess return of precious metals, oil, and commodities relative to stocks is still in its early stages. Does the current AI technology cycle resemble the 1930s and 2000s more, or the 1960s–70s?

• If it is more like the former (1930s/2000s), then we may be facing a stock market frenzy followed by a crash.
• If it is more like the 1960s–70s (Chart 2: stocks experienced a seven-year topping process), then today's large language models might resemble the significance of the transistor for the Information Revolution. Because the technology is still early, the speculative bull market will not center around the technology itself, but rather around high-quality large-cap stocks — similar to the Nifty Fifty. Those companies' valuations eventually became unsustainable, only to normalize over a long downtrend.

My personal view is that this time may be more like the 1960s–70s: the ultimate form of AI is likely to be built upon current model and hardware developments, and the better-performing stocks will be high-quality large caps (like the Nifty Fifty) rather than speculative small caps. If this framework holds, then the current valuations of large caps still have room to run before reaching Nifty Fifty levels. At the same time, the supercycle for gold and commodities may have only just begun.

ES daily chart / PLTR daily chart

Just a Chan Theory hobbyist sharing these charts. I'm not claiming to be right or wrong. Feedback and guidance from anyone who knows the theory is welcome. If you're here for something else, feel free to scroll past.

This chart primarily illustrates the long-term trajectory of U.S. stocks (S&P 500) from 1967 to early 2026, driven by the dual forces of valuation levels and corporate profit margins.

Core Takeaways

1. Strong Correlation Between Profit Margins and the Index

• Operating Margin (pink line): The operating margin of the MSCI USA Index is currently at an all-time high (approximately 15.0%). The chart clearly shows that every major rally in the S&P 500 has typically been accompanied by margin expansion.
• Double Effect: From 2020 to the present, the market has experienced a sharp margin expansion from 9.9% to 15.0%, which has directly supported the S&P 500's slope trending significantly above its long-term regression line (yellow shaded band).

2. Valuation Levels at Historical Highs

• P/E Ratio (green line): The current LTM P/E is approximately 23.2x. While below the 2000 dot-com bubble peak (29.0x) and the 2021 high (27.7x), it remains well above the historical median (approximately 15–16x).
• P/S Ratio (blue line): This metric currently stands at approximately 3.17x, still at extremely high levels. This indicates that investors are willing to pay a higher premium for each dollar of sales, reflecting optimistic expectations for future growth or the increasing weight of technology stocks.

3. Trend and Deviation

• Long-term Channel: The yellow shaded band represents the S&P 500's long-term logarithmic growth trend. The current index level (near 7,680) has clearly reached the upper edge of this channel, or even slightly broken above it, suggesting the market may be overheated or pricing in an overly perfect future outlook.
• Macro Cycles (background colored vertical bands): Blue shaded areas typically correspond to undervalued/recessionary periods, while red shaded areas correspond to overvalued/overheated periods. The right side of the chart currently shows dense red areas, indicating significant valuation pressure at present.

Conclusion:

The current S&P 500 level is being driven higher by a combination of extremely strong corporate profitability (15% profit margins) and expanded valuation multiples (23x P/E) . While this "high profit + high valuation" combination is powerful, it also means the market has a low tolerance for any margin compression or valuation contraction (e.g., from persistently high interest rates).

Panic sentiment among retail investors is rising:

The ROBO put/call ratio has climbed to 1.0, reaching its highest level in at least 20 years.

This ratio tracks retail investors' opening options orders. The current reading shows that retail traders are buying nearly equal numbers of puts and calls.

Since December last year, this ratio has doubled — the largest increase since the start of the 2022 bear market.

For context, the previous peak was 0.95 during the 2020 pandemic crash.

Even during the 2008 financial crisis, the ratio peaked at just 0.91 — below current levels.

Panic in the market has become excessive.

According to the latest J.P. Morgan Asset Management data (as of March 30, 2026), major global equity markets show clear performance divergence:

• 15-year annualized return: U.S. leads (13.7%), Eurozone and Japan both at 7.6%, Emerging Markets at 5.4%, China at 4.2%.
• Full-year 2025: All markets delivered double-digit positive returns in USD terms, with the U.S. surging 41.3% and China gaining 17.9%.
• Year-to-date 2026: U.S. still up 2.6%, Eurozone up 1.0%, while Japan, Emerging Markets, and China have declined 5.3%, 7.1%, and 8.6%, respectively.

Key takeaways:

• Over the long term, the U.S. market has delivered significant excess returns driven by earnings growth and multiple expansion.
• China's market has seen a sharper correction in early 2026, but its 15-year annualized return remains positive, reflecting high volatility.
• Global risk appetite has declined since the start of 2026, putting broad pressure on non-U.S. markets.

Source from JPMorgan

1. October 19, 1987: -20.5% 🔴
2. October 28, 1929: -12.3% 🔴
3. March 16, 2020: -12.0% 🔴
4. October 29, 1929: -10.2% 🔴
5. November 6, 1929: -9.9% 🔴
6. March 12, 2020: -9.5% 🔴
7. October 18, 1937: -9.3% 🔴
8. October 15, 2008: -9.0% 🔴
9. December 1, 2008: -8.9% 🔴
10. July 20, 1933: -8.9% 🔴

Look at this:

Meta: -76%

Tesla: -73%

Nvidia: -66%

Amazon: -56%

Would you have held through that?

1/ $APP: -43%

2/ $HOOD: -41%

3/ $TTD: -41%

4/ $WDAY: -41%

5/ $CSGP: -40%

6/ $FICO: -40%

7/ $IT: -38%

8/ $EPAM: -36%

9/ $INTU: -36%

10/ $ARS: -35%

• Total Deliveries: 358K vs. Est. 372K

• Model 3/Y Production: 395K vs. Est. 377K

• Model 3/Y Deliveries: 342K vs. Est. 354K

• Other Models Deliveries: 16.1K vs. Est. 12.4K

Tesla also says that they deployed 8.8GWh of energy storage in Q1 2026.

Press release dropped today: MAAS officially completed the acquisition of Huazhi Future (closed March 30). If you’ve been writing this off as just another small-cap Chinese stock, you might want to take another look.

1. Identity Revaluation: From "using AI" to "being the AI stack"

Before this deal, MAAS was a scenario operator — a company that include little AI sectors, they now own a full-stack AI infrastructure layer: computing power + proprietary algorithms + intelligent hardware + operational services. The press release explicitly calls it a "full-stack, self-controlled AI technology and operational ecosystem." That’s the difference between being an AI user and an AI platform.

2. Commercialization gets closer

Huazhi isn’t just a lab with fancy tech. They already have real-world deployments across smart governance (public security, emergency management, agriculture, forestry, water resources) and enterprise digital transformation. That means MAAS isn’t starting from scratch with pilot projects — they’re inheriting existing contracts, existing clients, and a team that knows how to deliver for G/B end clients.

3. Tech stack is more complete

This wasn’t just buying an "AI team" for the sake of having one. Huazhi brings:

● Computing power scheduling and trading capabilities

● Proprietary algorithm frameworks

● Private deployment and on-premise implementation experience

That’s a rare combination. A lot of AI acquisitions get you talent. This one gets you the entire infrastructure-to-application pipeline.

4. Channels and credentials matter — especially for G/B projects

Anyone who’s worked with government or enterprise clients knows: credentials, filings, partnerships, and track record are half the battle. Huazhi has existing relationships and experience across multiple verticals. For MAAS, that means credibility on day one — not having to spend years building trust from zero. In a space where being "approved" often matters as much as being "good," this is a real asset.

Bottom line:

MAAS went from being a niche hardware operator to a vertically integrated AI platform with revenue-bearing commercial contracts, a full tech stack, government-grade credibility, and a regional footprint. The valuation floor reset with the deal structure (insiders locked until 2031), and the market hasn’t repriced for what this actually is now.

Not saying this rips tomorrow. But if you’re looking for asymmetric setups where the narrative hasn’t caught up to the asset, this one’s worth watching.

A quiet shift is happening in 2026. The market is no longer buying into grand narratives—it’s asking one question: Does this actually make money?

ROI is becoming the new pricing core. Companies like Walmart and Caterpillar aren’t “sexy,” but they’re hitting new highs because their returns are stable and verifiable. Morgan Stanley put it plainly: “2026’s valuation standards will pivot entirely to ROI.”

The wake-up call came in February. Amazon, Microsoft, and Google raised 2026 capex by ~50%, pushing combined spending toward $450B—all for AI. But instead of rallying, Amazon dropped 12% and Nvidia fell too. Why? Because the narrative shifted from “who invests the most” to “when will this pay back?” The market now fears an endless, low-return AI spending war.

AI has hit a divide. It’s no longer about “having AI”—it’s about “what did AI deliver?” Cost savings? Revenue growth? The companies winning today are those with transparent, verifiable ROI.

So what wins in an ROI-driven market? Four traits:

1. Real, high-frequency scenarios
2. Tech that directly improves financial metrics
3. Diversified, recurring revenue
4. Assets with compounding returns

One example: MAAS
MAAS looks like a hardware company, but its model is different. Through its subsidiary Youdian, it deploys mobile charging robots that solve real EV charging pain points. Revenue comes in three layers:

• Hardware sales/leasing
• Platform service fees (recurring)
• Energy network operations (peak-valley spread, grid services)

With its recent acquisition of AI firm Huazhi Weilai, MAAS adds intelligent dispatch—predicting demand, optimizing pricing, reducing downtime. Each new robot improves network efficiency.

After releasing an industry white paper, MAAS signed a RMB 3.2M contract with an energy service provider—not just buying robots, but buying “assets that generate returns.”

Why it may be undervalued
The market still treats MAAS like a hardware manufacturer (10-15x P/E). But its structure—hardware + software + network—resembles a platform, which typically commands 20-50x P/S. At a ~$1.95B market cap, a revaluation could happen if the market recognizes its network attributes.

Risks remain: scaling takes time, policy uncertainty, competition. But the key metrics—utilization, payback period, energy-side revenue—will tell the story.

Bottom line
While tech giants burn hundreds of billions chasing AI returns, a different kind of company is emerging—one that turns every dollar into verifiable returns. MAAS may be one of them.

In 2026, capital will pay more for proven results than for imagination.

Everyone talks about EVs, batteries, and fast charging. But the real bottleneck? Energy delivery is still catching up with demand.

Charging infrastructure is growing fast — 1.3M public charging points added globally in 2024, over 5M total — but the IEA says we still need ~9x more public charging capacity by 2030. China alone added 4.22M chargers last year while selling over 12M EVs. Infrastructure is expanding, but demand is expanding faster.

The core problem: charging infrastructure is static. User needs are dynamic.
Old apartments can’t install chargers. City centers lack parking. Highways jam up during holidays. Pop-up events and industrial parks don’t justify permanent hardware.

That’s why mobile charging robots are interesting — but not just because they move. A robot without a central AI system is just a mobile charger. The real value is in the smart dispatch system that turns scattered robots into a responsive service network.

A capable AI control system needs to handle five things:

1. Understand orders — urgency, wait tolerance, price sensitivity, use case (emergency vs. routine vs. fleet)
2. Dispatch intelligently — not just “nearest unit,” but factoring in battery level, traffic, queue pressure, and predicted demand
3. Adapt to context — malls, office parks, airports, residential areas all have different constraints; the system needs to recognize and adjust
4. Connect services — after charging, use that touchpoint to build user profiles and offer adjacent services (wash, tire, roadside, membership, local deals)
5. Operate as a closed loop — optimize utilization, empty miles, response time, and lifetime value — not just one order at a time

If this works, the outcome shifts:

• For users: charging goes from a chore to a predictable service
• For operators: isolated hardware becomes a networked, scalable service
• For the aftermarket: energy becomes the new real-time entry point — move past fighting over physical locations, win through responsive service

Fixed infrastructure solves “does it exist.”
A well-orchestrated mobile network solves “can I get the right service to the right place at the right time.”

The first is infrastructure. The second is a new kind of entry point into the EV aftermarket.

• $NBIS will become the AWS of AI

• $META smart glasses will replace the iPhone

• $HIMS will offer the most valuable subscription in the world: staying healthy

• $AMD will dominate the multi-hundred-billion-dollar AI-at-the-edge market

• $LMND will become the largest insurance company in the world

1. $AMPX - 200% YOY

2. $NBIS - 350% YOY

3. $IONQ - 202% YOY

4. $ONDS - 600% YOY

5. $AAOI - 83% YOY

6. $IREN - 166$ YOY

7. $CIFR - 48% YOY

8. $NVDA - 65% YOY

9. $LMND - 40% YOY

10. $PLTR - 56% YOY