The Nothing Ever Happens crowd is, once again, looking very smart right now. The playbook around geopolitical shocks held up perfectly: a selloff followed by a V-shaped recovery. And again… The bear case always looked so appealing at the bottom, so of course, most of us did not profit from the situation, and bears did not lock in profits at the bottom. Yep, I fell for it again.

The macro picture: nothing has changed (yet)

Let’s start with some macro fundamentals to better understand where we stand.

Growth: Resilient

Forward EPS for the S&P 500 did not flinch at all during the conflict. On the contrary, they are still ripping. Every single quarter of 2026 is expected to grow at double digits.

Sector wise, the re-rating higher is mainly coming from:

• Tech sector: In 3 months, the revenue growth has been revised from 20.2% to 27.4%. Oof! Profit margin expansion? Productivity gains? Layoffs? Probably a bit of all that. In any case, numbers are going up.

• Energy sector: From a depressing -3.1% contraction, analysts have revised up their estimate to +1.6%. This is the second highest jump in forward earnings across all sectors. And all it took was the Strait of Hormuz to be closed!

Are analysts now too optimistic? This is a legit question to ask now. The increase in estimates helped to push the forward P/E lower, and yes, stocks were looking pretty damn fair valued on an NTM basis in the midst of the Iran selloff. But now that we have retraced all the move (and more!), the bar has now been set quite high for the upcoming earnings season. Which means the forward guidance from the C-suites will be absolutely critical. A make-or-break moment with no clear-cut risk-reward.

The pessimists will point toward the soft Atlanta GDP nowcast:

Headline GDP is always noisy because of exports and imports and other shenanigans. Real PCE and Final Sales help us cut through the noise, and here again it is not looking so great. According to Yardeni, this is a weather effect from the extreme cold during last winter. I can personally vouch for that. I had the delightful experience to be in New York during the polar vortex. -15 degrees Celsius in the street. Yeah, you don’t really want to shop in that weather!

Redbook retail sales doesn’t put as much weight on leisure and other weather-dependent activities, and it is pointing toward a strong consumer.

The story remains the same. Inflation or not, the American consumer remains resilient because most of the consumption is coming from the upper K, and that’s what the stock market cares about, not how the average American consumer feels (sentiment is horrendous).

“I think the higher-end consumer, the premium consumer, is candidly immune or becoming more immune to the headlines and not delaying their investment in the experience economy, waiting to see what the next headline’s going to be on the margin.... I think as difficult as it is to see what’s going on with the conflict in the Middle East, I’m not sure that our premium customers are feeling affected by that.” – Delta CEO

“...net sales for the month [of March] came in at $28.41 billion, an increase of 11.3% from $25.51 billion last year. March had one less shopping day versus last year due to the calendar shift of Easter. This negatively impacted both total and comparable sales by approximately 1% and 1.5%. Reported comparable sales for the month were as follows: U.S., 8.7%; Canada, 10.7%; Other International, 11.9%; total company, 9.4%; digitally enabled 23.3%.” - Costco, Director of Financial Planning

Inflation: Still under control

Expectations might start to be unanchored over a one-year-ahead basis. The FRBNY survey is still showing a tame outlook, but the Consumer Sentiment Survey is spiking once again.

One-year outlook is not the most important horizon. The Fed will look through any short-term bump; however, they will not let the long-term expectations rise (3-years and 5-years). So far, these expectations are still anchored, and the Fed is in no rush to raise rates. Wait and see.

What about the inflation market? 5-Year breakeven inflation rates are notoriously correlated to the price of oil. The recent oil shock is still not feeding through the 5-Year Breakeven.

Kevin Muir, from the Macrotourist, wrote a very good post on the shorter-term inflation rates.

The MacroTourist

INFLATION BREAKEVENS HAVE NOT COLLAPSED!

Folks, I have seen a few bad takes out there when it comes to inflation breakevens, so I have written this post to help clear up what’s really happening in inflation markets…

Read more

3 days ago · 17 likes · 3 comments · Kevin Muir

While the point of the post was to debunk a popular post on X regarding collapsing inflation breakevens, the main takeaway from the 1-year and 2-year inflation swaps is that the market has repriced a bit higher the inflation expectations, but we are absolutely nowhere near the horrendous situation of 2021 and 2022.

The inflation market is also looking through the Iran conflict. A short-term bump in inflation is expected, but it will be transitory (yes, yes, yes, you’ve heard that before… But we will just have to trust the market on that one).

Naturally, the Fed Funds futures market is pricing the Fed to stay on hold for at least a year.

The bottom line for the macro picture is that nothing much has changed. Growth is still positive, forward EPS are climbing, the consumer is consuming, boomers are de-saving and throwing money at the economy, inflation is going higher but not for long, and the Fed is on hold.

And that’s exactly why the stock market is back to where it was before the war.

WELL AT LEAST THAT’S THE CONSENSUS. I AM NOT SAYING IT IS RIGHT OR WRONG, BUT IT IS WHAT IT IS, AND THE MARKET IS DISCOUNTING IT.

→ For the bulls, the closure of the Strait of Hormuz is the most beautiful wall of worry a market can climb.

→ For the bears, the closure of the Strait of Hormuz is the most beautiful slope of hope a market can slide.

In the next post (for paid subs), I will review the selloff from a pure sentiment and positioning perspective. And try to game where things are going with or without a happy ending to the conflict. If the macro picture has not changed, then one can also argue that the predominant themes moving the market before the conflict are still very much alive and worth a review. Finally, I will review the trades opened in the web app.

Q1 2026 is now behind us. Let’s review the trades I shared with you over those three months. Every one of them was logged in real time through the web app and sent as an instant email notification to paid followers. Each notified trade was executed live in my own account.

I’ll also share the full performance of my trading account. It may differ slightly from the shared trades because I occasionally take additional positions that I don’t notify. I only send out my highest-conviction ideas. As you’ll see, I would have been far better off sticking strictly to those. My live results are already solid, but they could have been exceptional. I’m still working on becoming a better trader by ruthlessly following my own process and eliminating those avoidable mistakes.

Becoming the best is a lifelong pursuit. My goal is to keep aiming higher, course-correct quickly, and stop repeating the same errors.

Enough preamble. Let’s dive into the results.

1. Q1-2026 official trades result

All the above trades have been closed during Q1 2026. If you are not yet comfortable with what the forecast and X-factor mean, you should definitely reread my guide about position sizing. This is by far the most important post I have written:

Risk-adjusted profit and loss, or RA PNL, is a metric that measures trading performance relative to the risk taken on each trade. Rather than looking solely at absolute monetary gains or losses, RA PNL expresses results in terms of multiples of the initial risk. This approach allows traders to evaluate how effectively they are managing their capital and whether their strategy truly has a positive expectancy over time.

At its core, RA PNL compares the actual profit or loss on a trade to the amount the trader was originally willing to risk, which is typically defined by the distance between the entry price and the stop-loss level. For instance, a result of +1.0 RA means the trade generated a profit exactly equal to the risked amount. A reading of +1.5 RA indicates the trader captured 1.5 times the initial risk, while a negative figure such as -0.5 RA shows that the position was closed at a loss equal to only half of the predetermined risk. This normalization makes it possible to compare trades across different instruments, position sizes, and timeframes on an equal footing.

To illustrate with an example from my own trading, consider one of my trades in the ES JUN18’26 contract. I entered a short position at 6632.75 with a stop-loss at 6771, establishing a risk of 138.25 points. The trade was later exited at 6466, producing a profit of 166.75 points. This translated to an RA PNL of +1.2, meaning I captured 1.2 times the amount I had initially risked.

For novice traders especially, tracking RA PNL offers several important advantages. It shifts the focus from raw dollar amounts or price points to the quality of risk management. By emphasizing how tightly losses are controlled and how well winners are allowed to develop, it provides clearer insight into the true edge of a trading approach. Over a series of trades, the average RA PNL becomes a powerful indicator of whether the strategy is profitable in risk-adjusted terms.

I’m excited to report that I have delivered a total +17.80 RA PnL. This is top-tier performance.

Key statistics:

• Total RA PNL: +17.80 ✅ (excellent)

• Trades: 21

• Win Rate: 66.7% (14 wins, 4 losses, 3 breakeven)

• Avg RA PNL per trade: +0.85R

• Avg Win: +1.40R

• Avg Loss: –0.45R ← this is the standout (I’ve been cutting losers very well)

• Avg holding time: 16.2 days (range 1–30 days) ← much shorter than I would have expected. I picture myself as a macro trader, but I’m in fact living in the short term end of the macro trader spectrum.

Longs (13 trades): +0.65R avg, 61.5% win rate
Shorts (8 trades): +1.18R avg, 75% win rate ← shorts were my edge this quarter

While these results are great, I still have some regrets on three trades, specifically on silver and KOSPI. I haven’t pushed these winners enough. When momentum is this strong, with very high retail participation, I should have kept riding these trades with a trailing stop loss. Instead, I took profits while the moves were strong. I didn’t wait for my trailing stop to be breached. This probably cost me close to 10 RA!

To put things in perspective, if you follow my sizing process for a portfolio with a 25% target volatility and an average of four positions, each trade risks around 0.78% of your trading account using a standard forecast of +/- 10 and an X-factor of 2. For Q1, with a total RA PnL of +17.80, this translates to a return of 17.80 x 0.78% = 13.88%. Not bad for a 25% annualized target volatility (!). Obviously, here we are only talking about absolute returns as of today. We are not looking at path-dependent variables like drawdown, realized volatility, Sortino ratio, etc. In the following section, my live trading account on Interactive Brokers provides these statistics.

The above screen is the sizing tool available at app.markethitchhiker.com

2. Q1-2026 live trading account result

As explained before, all the trades from the app have been traded live. I also traded a few extra unofficial trades, which were mostly flat to negative overall. The nasty drawdown in February was my own making: I f*cked around with some ill-timed Bitcoin positions and short-term options on SPX… Not a great look. Fortunately, I offset most of these bad trades during the Iran war blowout.

I eat what I hunt. This trading account is my only source of income. I’m all in on the Game. Full commitment or nothing.

Here are the results from Q1, Consolidate (blue line) is my trading account:

The good:

• A 9.64% return while my realized annualized volatility is 17% is very good. The equivalent quarterly volatility is 8.5%, so my Sharpe ratio is ~1.15. The IB report shows 2.00, which is probably annualized (?), not sure.

• Standard deviation is 1.08% while downside deviation is 0.66%. That’s great news. I am not running a short-convexity strategy. I thrive in volatility.

• Beta to the S&P 500 is only 0.23, and alpha is much higher at 0.40.

The bad:

• A 6.93% drawdown for a 9.64% return is below my expectations in terms of risk/reward profile. It happened because I fumbled a bit with my process and took some unnecessary trades. My biggest weakness is my tendency to overtrade. It kills returns.

Overall, this has been a good Q1, but it could have been an outstanding one. The February drawdown was my own making. Lesson learned: don’t overtrade and stick to your high-conviction trades.

3. Live trading account result since inception

Below is my cumulative performance since I started this Substack in December 2024. Trump has been a great source of volatility, and it matches my style perfectly. MAKE MACRO GREAT AGAIN!

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Looking at yesterday’s 1-day Z-scores for the large benchmark ETFs, one trend is clear: ex-US markets will benefit the most from the end of the Iran war. This is not a surprise, since the closure of the Strait of Hormuz has a direct impact on energy-importing nations, which are mostly in emerging markets. On the other side of the spectrum, the United States is the largest exporter of oil.

Naturally, once the strait is open, one should expect a resumption of the trend toward diversification away from the US stock market.

A few caveats:

• The Strait of Hormuz has never been formally closed. The increase in insurance premiums for VLCCs is the main factor. But once Iran agrees (willingly or not) to let any vessel cross the strait (not only friendly boats), the market should quickly price in a drop in insurance premiums.

• The market will initially rip higher once vessels are back to crossing the strait without any trouble. However, the market will be worried about an inflation wave caused by higher oil prices (most likely stuck above 80$), higher fertilizer prices, and disrupted supply chains. If the US dollar enters a new bullish phase on the back of rate hikes, you can kiss goodbye to your dear emerging-market stocks.

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• Growth has been revised HIGHER:

• The inflation projection is revised HIGHER to 2.7% PCE in 2026, and 2.2% next year.

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Forward Profit Margins? Growing as well

Citi Economic Surprise Index? In positive territory

US Consumer? Doing fine

Q4-2025 GDP? Still mainly driven by Personal Consumption

Personal Consumption and Business Investment are near the 10-year average

Removing all the noise from net exports, inventories, and government spending, real final sales to private domestic purchasers still saw robust expansion.

The US economy is pushing higher with or without the AI spending.

According to Bridgewater, AI Capex is estimated to keep growing until 2028, adding up to 1% to GDP.

Job Market? Weekly ADP number is strong.

Withholding tax for 2026 is outpacing 2025. Reminder: this is the hardest data we have regarding the health of the job market. AI might be disrupting some industries, but on aggregate the US economy is still adding jobs.

Same picture for Initial Claims; the NSA time series is tracking past years’ impulses.

Inflation? Expectations have fully retraced the tariffs fear.

Truflation below 1%:

Inflation swaps and crude oil prices remain well behaved.

Wages? Still slowing down

Rent and shelter? Same

Let me summarize the macro picture:

• Growth is strong and potentially reaccelerating, sustained by both consumption and AI Capex spending.

• Forward margins and earnings are growing at a record pace.

• US job market is still at equilibrium with no sign of deterioration. AI could be impacting some industries; this is very well possible, but on aggregate we don’t see any worrisome slowdown.

• Inflation is tame, with rent and wage increases still slowing down. Crude oil at $66 a barrel will not move the needle, especially when a lot of the recent price appreciation is 100% geopolitical risk premium.

It all sounds very Goldilocks to me… yet the market has been throwing a fit for the past three months, with the S&P stubbornly flat while equity sectors are pricing a stagflation environment.

Energy: +22.30% over the past three months

Staples: +12.60%

Tech: -1.76%

Financials: -4.85%

This is a very risk-off mix, and it transpires through the S&P 500 skew, trading at a very high level:

In plain English, traders have been hoarding OTM puts fearing a total collapse in the market. Interestingly, this is happening while investors are very bullish:

Very bullish but also very hedged.

In terms of sector overweight, we are seeing both hedge funds and mutual funds overweight Industrials, Materials, Energy, and Health Care.

Industrials, Materials, and Energy make sense as they are expecting a “Boom.” They see the same macro data as us, and they extrapolate.

And by extrapolating, you end up trading momentum. US factor momentum is on an absolute tear; this is the best-performing factor YTD by a wide margin.

Momentum is currently correlated to four big themes:

• US retail traders’ favorite stocks (aka meme stocks), high vol, high growth, preferably negative earnings. This theme has not been in favor this year; it is not driving the momentum factor performance.

• AI and power infrastructure

• Defense

• Precious metals and miners

At the bottom correlation, we find AI losers.

A few charts to illustrate how stretched these themes are:

The “Boom” is here; it’s happening. And it also looks like we are near a blow-off top, typical of commodity boom/bust cycles. These moves are not sustainable.

Interestingly, this boom is happening at the same time as defensive names are ripping. Health Care, Staples, and Utilities are outperforming cyclical sectors like Consumer Discretionary and Financials

A “Boom” amid a risk-off move. Quite the unicorn we have there. It explains the very high dispersion we are seeing in the market.

I have been saying time and time again that the S&P 500 is simply a high-yield bond. An overlay of HYGH and SPY shows a perfect correlation; they are tied at the hip.

BKLN is a senior loan ETF; these are short-duration loans made to large private companies like X. It usually follows HYGH with lower volatility. The recent AI scare has been bleeding into the senior loan market. This is extremely rare to see such divergence between BKLN and HYGH. While I am still constructive regarding US equities, this divergence is giving me pause. A very bearish scenario would be to see a contagion from this AI scare into the high-yield credit world, and inevitably it would spill into US equities, dragging the entire market lower.

I am not yet worried; the Citrini doom post smelled like peak AI fear to me. And we are not seeing any more cuts being added in the SOFR market.

Summary:

• Macro is still supportive of a Goldilocks environment.

• Momentum is being pushed very far into big macro themes linked to AI Capex: energy, nuclear, memory, semis vs. SaaS, precious metals, miners, etc. This reminds me of a boom/bust cycle typical in commodities; we might be near the top of the cycle.

• This extreme in momentum paired with peak AI fear is also pushing risk-off trades, with staples and utilities outperforming consumer discretionary and financials.

• Senior loans are inflecting lower as the SaaSpocalypse is questioning the creditworthiness of some private companies…

• …but public high-yield debt is holding up well.

• Watch for any sign of spillover into high yield (HYGH ETF or IBOXX futures). Until then, the consolidation in the US stock market should resolve into a breakout (not a breakdown)…

• …But we need tech stocks to lead again, as the broadening-out theme will not carry the market long enough, especially if the crowded trades linked to the AI Capex receivers enter into a blow-off top phase.

• Finally, there are plenty of high-quality stocks currently trading at fair prices (Mastercard, American Express, IBM, MSFT, Uber, etc.). Consider buying them instead of buying SaaS stocks, which will probably stay in limbo for a long time. Do not confound multiple compression (or rerating) with indiscriminate selling. You want to buy what has been indiscriminately sold, but you stay away from an industry being rerated lower.

The Challenger data was particularly spooky, with scary headlines like LOWEST JANUARY HIRING ON RECORD.

The Financial Times has the below article on its front page.

But the reality might be more nuanced. If you take the time to read the Challenger report (I did!), you will find that close to 46,000 jobs are linked to Amazon.

This is not new information to you if you read the latest macro update where I dove into the labor market:

Indeed, I highlighted an article from Bloomberg stating the job cuts from Amazon. And, importantly, the cuts are a result of both a re-normalization of the pandemic-era hiring binge and efficiency gains thanks to AI.

Looking at the JOLTS data, we also had some spooky headlines based on the falling job openings. But again, this is not a new trend; they have been falling since 2022. The important numbers to track are hirings and layoffs. On that basis, hirings are still stable.

And the difference between hires and total separations is still at equilibrium. I am still not concerned about the health of the job market.

The most timely job data we have are the initial claims. Yes, we saw some kind of spike.

But we are closely following the seasonal pattern; there is absolutely no reason to be worried here.

Finally, the hard job data we can track is the US Treasury receipts for Withheld Income and Employment Taxes. Bill did the work for us, so I don’t even need to refresh my tables; the pace of collection is solid.

Based on this quick analysis, I am 100% fading this risk-off move based on the job data yesterday.

Of course, the market has been selling off for other reasons in the past week. The main narratives I am seeing:

• Mag7 capex will eat into buybacks, removing a key flow for the market.

• Warsh’s nomination is bad for risk assets.

• The SaaSpocalypse is a risk for the market.

• Bitcoin is an early sign of disappearing liquidity.

Historically, there is no evidence that decreasing YoY buyback notionals is negative for the S&P 500. I can count 10 different years where buybacks decreased, and only 3 years had negative S&P 500 yearly returns: 2001, 2002, and 2008. Post-dot-com bubble and GFC. We also forget that the 493 other companies of the S&P 500 are mostly estimated to increase buybacks.

Anna Wrong must be very short SPX, or very long bonds. She is really digging very hard into Warsh’s previous interviews.

But gladly we have more serious sources of information.

Warsh found the perfect excuse to keep rates low and please Donald: AI productivity boom. You might believe in it or not. It doesn’t really matter. What matters is that the Fed’s reaction function will remain dovish. And that is supportive of risk assets. I am 100% fading the Warsh narrative being bad for asset prices.

With AI capex still increasing and beating estimates, paired with a fiscal deficit running at 6% of GDP and bound to increase in H1-2026, I am quite confident that we will not see any recession this year.

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The puzzling aspect of the US economy at the moment is strong growth paired with rising unemployment, albeit still at a low rate of 4.4%. This has paved the way for a goldilocks regime where earnings are growing while inflation is slowing down, thanks to cyclical forces. But we are reaching a tipping point where the goldilocks regime could be at risk of transitioning to a reflation regime where growth and yields will rise together. Let’s now take a look at monetary and fiscal policy.

Monetary Policy

The last FOMC meeting delivered little in the way of forward guidance. As widely expected, Fed officials left interest rates unchanged. They remain firmly data-dependent and offered no bold or surprising statements. Interestingly, Chair Powell noted that the labor market now appears more stable. This aligns with the analysis outlined in Part I: the balance of risks is no longer skewed toward the employment mandate but has now shifted toward a more even balance between the dual mandates of maximum employment and price stability.

The market did not react much to the news, with a muted response in the S&P 500 and the SOFR futures market. Somehow, we are still pricing in two cuts by the end of the year.

But of course, this has little importance after the appointment of Kevin Warsh as the new Fed Chairman. The market initially sold off on the news, which first filtered through Polymarket odds. All risk-on assets sold hard, driven by the U.S. 30-year yield shooting higher. This knee-jerk reaction from the market stems from the perceived hawkishness of Warsh. This narrative is pushed by some well-followed economists like Anna W(r)ong:

I find it particularly laughable to base your judgment on what someone said more than 15 years ago. The market followed this narrative upon the initial news release by selling risk-on assets, but we saw quite some mean reversion happening thereafter.

In any case, the SOFR market barely moved, and it actually even started to reprice the odds of cuts by the end of the year slightly higher, confirming Warsh’s loyalty toward Trump.

Currently, the SOFR futures Dec 2026 contract is implying a terminal rate close to 3.15%. It is indeed a terminal rate, as the market is pricing a higher rate in 2027, with the December 2027 contract pricing 3.30%.

The chart below focuses solely on the number of cuts priced by the SOFR Dec 2026 contract. We have been pricing out cuts since last September, and the trend is still not broken. So far, I don’t see any evidence of Warsh having any impact on the rate/cut cycle. Until we have more clarity, anyone can speculate as much as they want, but the market is not giving any strong signal in the very front end of the yield curve.

As the SOFR futures were barely moving up, the yield curve was steepening, driven by a higher U.S. 30-year yield. This is not what usually happens during a risk-off move, when money flocks into the dollar and risk-on assets get sold off. In a typical risk-off move, the back end of the yield curve goes lower as growth and inflation expectations come down. Yesterday, we saw a steepening of the yield curve led by the back end; this is effectively an increase in the bond term premium. This validates the idea that the Warsh nomination is indeed another step toward the erosion of the independence of the Fed, and it invalidates the idea that Warsh is a hawk.

The Kobeissi Letter’s X account is really informative; it gives you a good idea of what the consensus is because it is mostly an AI slop account. This is a “negative alpha” account worth tracking. Remember Charles Munger’s mental model: “Invert, always invert.”

For what it’s worth, I believe Trump is 100% aware of who Kevin Warsh is, and he hand-picked him for his loyalty. There are strong ties between the Trump family and the Warsh family (especially on his wife’s side). Do not discount such close relationships in positions of power, especially during the Golden Age of Grift.

Here are a few X posts I agree with:

And please, stop—there is absolutely zero chance the Fed reduces its balance sheet. They will not manufacture a banking crisis just because Kevin Warsh decided to sound smart years ago. Let’s be serious. The real question is how Warsh will react during the next crisis. Is the Fed put much lower now? In the past, every—and I mean every—crisis has been fixed by balance sheet expansion and more financial engineering from the Fed and the US Treasury. This is not going to change anytime soon. However, I am open to the idea that the market will test the new Fed Chairman at the next FOMC or at the first sign of any market stress.

People were quick to link the Fed nomination news to the meltdown in precious metals. This is ridiculous. The speculation has been coming from the East, especially from Mainland China. Pardon my French, but I don’t think Mrs. Wang gives a flying fuck about who the new Fed Chairman is.

Markets, or more precisely market watchers, are always looking for a good narrative to explain large moves. It’s the proverbial “narratives always follow prices, not the other way around.” People are scratching their heads about the silver move, but it probably boils down to one simple thing: too much speculation with too much leverage.

So, if we set aside the new Fed nomination and assume it won’t radically change the path forward, we can get back to analyzing where we are in the cuts cycle. As shown earlier, the market is now pricing close to two cuts for the year; this is roughly what you would expect the market to price toward the end of a cycle. There is no strong evidence to cut much further, nor strong evidence of hikes.

In their latest projection table, the Fed has been forecasting a stable unemployment rate but faster growth:

Yet, the Fed is still projecting a federal funds rate of 3.1% by 2027, while the SOFR market has been pricing 3.30%. This tells me that the Fed’s reaction function is still dovish. My reading is that the Fed is still concerned about the bottom half of the economy, a.k.a. the “lower-K.”

It doesn’t matter if the S&P 500 is at 7,000; it doesn’t matter if gold is at $5,000 (or was); it doesn’t matter if the DXY is below 100. It is not in their mandate to control asset prices (especially not to the upside).

The implication for the yield curve is rather obvious: if the Fed remains dovish around the end of the cutting cycle, naturally the steepener should keep working. However, it is at risk of switching from a bull steepener to a bear steepener given the acceleration in commodities and the Fed’s independence at risk.

With the outlook laid out regarding inflation in Part I of this memo, and with the end of the cutting cycle on the horizon, I strongly believe the back end of the yield curve (30-year yield) is at risk. The 2s10s, as shown in the chart below, is still trending up. After a period of consolidation from April to December 2025, we are finally breaking out higher.

The mythical term premium is also rising. A simple way of approximating it is using the spread between the 30-year and 10-year yields:

Notice how the term premium went on a tear around Liberation Day and never looked back. This is effectively the Trump premium; populism has a price.

The New York Fed offers a more robust measure of it using the ACM model:

The conclusion is similar: the term premium is inevitably rising. This is a secular trend that started with the (insane) fiscal impulse in 2020, and it makes a beautiful transition to the next section.

Fiscal Policy

Unless you have been living under a rock, you are well aware of the economic regime the US is living in: fiscal dominance. Eye-watering government debt levels, high cost of debt servicing, rising inflation, high nominal growth—all these are symptoms of the same disease.

2025 is now well behind us, so let’s review some macro hard data that frames this regime.

Despite Trump’s attempts to lower the deficit, DOGE did not make a single dent in federal expenditures last year. The US Treasury comfortably spent more in 2025 than it did in 2024. You’d be a fool to think 2026 will not follow the same path, and you’d be a very optimistic person to think we will not overshoot 2025 numbers.

The Bipartisan Policy Center’s website has a great tool for tracking the federal deficit. Despite the increase in expenditures, the nominal deficit is closely tracking that of previous years. Receipts are growing as fast as outlays.

The report below from the same webpage breaks down the outlays and receipts. Note that the financial year starts in October, so FY 2026 is actually the number for calendar Q4 2025.

Because of the tariffs ($69 billion), the deficit actually shrank a bit compared to last year at the same time. The market shrugged it off because it is forward-looking: Q1 2026 and Q2 2026 are expected to be very stimulative for the economy.

US consumers will be direct beneficiaries of this positive fiscal impulse; they are set for a strong tax refund season, with Morgan Stanley estimating that individual refunds will be about 20% higher year-over-year, boosting personal income and supporting consumption.

The deficit is mostly financed by short-term debt, a.k.a. U.S. Treasury bills. The amount of outstanding T-bills remains above 20% of the total outstanding debt.

Every two years, the U.S. Treasury needs to roll these T-bills in order to refill the Treasury’s checking accounts. But as the charts above suggest, there is no sign of remodeling the portfolio’s average maturity. Bills are still increasing in nominal size; the old ones are rolled while the Treasury also issues new ones because the financing needs are still growing.

On average, the US Treasury is paying an effective interest rate of 3.30% on an ever-growing amount of debt (chart above). Around $15 trillion of the debt is owned by the US domestic public (chart below); these interest payments fall directly to the bondholders and get reinvested into the economy through consumption. 3.30% of $15 trillion is $495 billion in interest paid to private investors; this is massive. As the Treasury rolls its debt pile, the effective interest rate climbs with it as long as the market interest rate is higher. The 2-year yield is sitting at 3.50%, just 20 bps above the marketable debt interest rate. The 10-year yield is at 4.30%. The Treasury is still incentivized to roll into T-bills instead of T-notes.

Large issuance of T-bills with an inability to decrease the weighted average interest rate and the inability to increase the weighted average maturity of the Treasury portfolio is typical of fiscal dominance (and emerging markets, sadly). Remember what Bessent said a year ago: “The market and the economy have just become hooked, and we’ve become addicted to this government spending, and there’s going to be a detox period.” I’m still waiting for that detox period because I don’t see it in the data. The fiscal dominance regime is still holding true today. In such a regime, nominal growth is artificially kept high by government spending, the probability of a recession remains very low.

Finally, consider the three charts below, each representing the debt burden for the federal government, corporations, and households. On one side, we have a ballooning debt-to-GDP ratio for the government, while on the other side, the balance sheets of the private sector and households are extremely deleveraged and healthy. The government is cash-poor, but the private sector and households are cash-rich. Is it a bug or a feature of the system where the largest cohort of the population is now retiring? I’ll let you decide. The next crisis will not come from a credit event like 2008; instead, the steam is being let out of the system through accelerating asset price appreciation, which ultimately puts strain on societal order and leads to the rise of populism. It’s a much different playbook than what we have seen in the past 80 years.

Liquidity

• US Liquidity: The domestic liquidity index (Fed BS - TGA) remains tight. The Fed’s balance sheet has bottomed, though, and the TGA can only go one way now: lower. This liquidity metric will improve with time.

• Global Liquidity: The global M2 expressed in US dollars is still shooting higher. All major central banks are steadily increasing their money supply, and the lower dollar is providing a tailwind for risk assets like gold and silver. However, Bitcoin is still decoupling from the liquidity index as it is caught in a cyclical correction and a lack of interest from speculators who are now overly focused on the boomer coins: gold and silver. Over the past few days, everyone is painfully learning that the inelasticity of supply with an elastic demand can cut both ways. With a lack of intrinsic value, price becomes extra sensitive to flows and sentiment, which can reverse in the blink of an eye. This is a cautionary tale on trading and investing in such assets.

  

Summary

The US economy is potentially at peak Goldilocks. The next major source of risk is a renewed rise in inflation and a transition toward an overheating economy. But we are not there yet.

Growth has been very strong and should remain resilient. My favorite forward-looking indicator is the S&P 500 EPS next-twelve-months (NTM) estimate. It continues to trend higher as US companies keep delivering earnings growth through a combination of rising sales and expanding margins. The sky seems to be the limit here.

Is this too optimistic? I don’t think so. Historically, one should be wary when analysts aggressively revise NTM EPS sharply upward in a short period. That has not been the case recently: the past quarter has shown a steady, gradual increase. 2026 EPS estimates remain largely flat, while 2027 estimates continue to move higher.

Historically, analysts have been relatively efficient at predicting these forward EPS; the direction is usually correct unless a recession is happening. This is not to be taken lightly. A recession could always happen, but I increasingly think that in an economy of high nominal growth pushed by fiscal dominance, the risk of a recession is extremely small. There is a big opportunity cost in sitting on the sidelines waiting for Godot.

This is consensus; I agree. Sentiment is (very) elevated, positioning is heavy, and valuations are stretched. But we are not living in normal times. A pullback will inevitably come along the way. Volatility is being suppressed by passive flows, retail traders’ mania buying every dip, and a president micromanaging the stock market. Volatility spikes will happen, whether from geopolitical events (Iran) or another technical blow-up in financial markets. All these are good reasons to own downside protection, but none of them are good reasons to stay uninvested. You will not survive this debasement world if you don’t adopt the debasement mindset and do not believe that this new economic regime is rewarding labor. No, not anymore. Another “K” for you:

The Fed’s reaction function will remain dovish because of this, independence from the White House or not. Forget all the nonsense we’ve heard in the last few days about Warsh, balance sheet reduction and hard money, etc. It’s noise.

All indicators are pointing toward a steeper yield curve, pushed by an increasing term premium and a potential flare-up in inflation. The main source of risk for equities will be a redux of 2022: rising volatility in the bond market. It will happen eventually, but until then, we keep partying in that room where the clocks have no hands.

The beneficiaries of this eventual regime transition from Goldilocks to Reflation are equities and commodities. The craziness in precious metals gives me pause, but I don’t think there is any need to overcomplicate it: the trend is not broken, past supercycles have seen such sharp corrections, and the secular forces will work their way through the waves of liquidations and inevitable drawdowns.

A bearish steepener will not be kind to bonds. I am currently trading TLT from the short side; only an AI productivity miracle could save the back end of the yield curve. While I am very optimistic about AI capabilities in the future, I doubt it will tame the incoming inflation dragon. AI is an angry energy beast, and its appetite is only growing with time.

Trade accordingly.

Share

Good luck,
~MH

Getting the macro picture correct is a crucial step toward getting the market right, but it is just the first step. Trading is the art of converting information inputs into returns. Macro is just one of many inputs, albeit a very important one.

I have written 33 updates in 2025, this is roughly an update every two weeks. I was initially aiming for weekly updates, but in retrospect this was too ambitious. More than that, weekly is probably an inadequate frequency for this exercise. Macro follows the ebb and flow of economic releases, geopolitical events, market developments, and the unpredictability of Trump tweets.

From now on, I will use a discretionary frequency to post macro updates. If the picture hasn’t changed, there is no need to write thousands of words about it. A simple reminder of my views in the chat or a very short post will be more than enough. However, if I see key changes in the economic trajectory or a shift in the balance of risks for macro assets, then I will write a detailed post.

This should be a better format. To make sure we always have an updated macro dashboard, I will host it on the web app. For new readers, please see the dashboard in the latest post here.

Your time is precious, and so is mine. Let’s dive right into today’s update, and because it is a lot to digest, this update is split in two different parts. Part II will be published tomorrow.

Growth

• S&P 500 Forward Operating Earnings: What does Gold and Forward EPS have in common? They go up every day. 2027 EPS are tagging 360, at 22x forward earnings that would be a SP500 at 7920 by end of year, a 13.5% return. Not so impressive for Gold who is already up 21% YTD.

  

  While a weakening US dollar reduces returns for foreign investors in the S&P 500, it simultaneously creates strong tailwinds for US big tech companies. Roughly half of their revenue comes from overseas, making them highly diversified global businesses with reliable, high-margin cash flows.

  There is a meaningful chance that investors will once again flock to the Magnificent 7 on this basis. In periods of uncertainty, long-only US equity investors have repeatedly gravitated toward these dominant names.

  But is the current broadening of the US stock market a lasting shift, or merely a fad? I believe it is the latter. Consider two possible scenarios:

  1. The market continues to broaden, with capital flowing from the Magnificent 7 and the broader S&P 500 into riskier small- and mid-cap stocks. Unlike the Mag 7, these smaller companies are not high-margin cash generators; they are cyclical businesses with structurally lower returns on equity. As valuations expand for small caps, multiples on large caps would compress. If this valuation rerating fails to attract fresh capital, it would likely be because earnings are deteriorating. After all, if large-cap earnings continue growing at the current pace (more than 20% for tech and over 13% for the S&P 500 overall), investors would quickly step in to buy these high-quality companies at more attractive valuations. Conversely, if large-cap earnings weaken, small- and mid-cap earnings would almost certainly decline even more sharply. In today’s economy—dominated by technological monopolies and economies of scale—any rotation out of large caps is unlikely to be smooth or sustainable. Broadening can only persist for a limited time; it cannot become a new secular trend. At best, it is a short-lived cyclical phenomenon.

  2. The recent small-cap outperformance fades, and mega-cap tech rebounds on the back of strong earnings growth. A weaker dollar would trigger upward revisions to earnings estimates for US multinationals. By year-end, market concentration would likely increase again, with the S&P 500 potentially trading at 23–24× forward earnings—implying an index level between 8,280 and 8,640.

  To me, the S&P 500 will either thrive on continued concentration or suffer from its unwind. That is the mechanical reality of today’s US equity market. If global decoupling is genuine and other developed economies are truly cutting ties with US tech giants, then concentration would indeed decline—but only after a significant de-rating in valuations. The only way this would not happen is if the US government and the Federal Reserve respond with aggressive stimulus on a scale comparable to extreme monetary experiments (Project Zimbabwe becoming a reality). Regardless of the path, the SP500 will end up much higher in the long term.

  

  On a more tactical note, the current earnings season is quite interesting in terms of expectations. As pictured below, the Q4 2025 forecasted EPS dipped last year during Liberation Day, but it has been gradually recovering. Usually, analysts tend to revise their estimates downward a few months before the actual earnings season. This time, it didn’t happen, and analysts have actually been increasing their forecasts. Some market watchers may think contrarily and point to optimistic earnings results.

  

  I am more inclined to the opposite interpretation: Liberation Day severely depressed the estimates, and analysts have been slowly revising up their earnings, but clearly not enough. It is reasonable to expect Q4-2025 earnings to be much higher; last year we had 18.3% growth realised, but now, one year later, we expect only 8.8%? And the AI trade is still booming? TSMC and AMSL earnings gave an early sign of the momentum still pushing earnings higher. (I wrote this part yesterday, in the meantime MSFT and META released earnings and are increasing capex on the back of higher revenues).

  

  With the dollar down and low earnings expectations, I would not be surprised to see the SPX nearing 7000, pushed by tech companies. My only concern is the heavy positioning, extreme bullish sentiment. But if you ignore that, we could potentially be looking at a higher SPX in a couple of weeks, unless geopolitics throw us a curved ball. In any case it is going to be volatile either on the upside or downside.
  

• US Economic Surprise: The CESI index is now firmly positive. The economy is indeed “running hot,” but nothing extremely out of line compared to expectations (the CESI index measures economic release surprises, not absolute values).

  

• GDP Nowcast: The headline number is running above 5% (that’s real GDP, not nominal!), but that number is overestimated by large net exports, stemming again from gold. The real headline number is around 3%, which is a solid figure. Once again, the Blue Chip consensus looks way too low.

  

Labor Market

I’m dedicating a full section to the job market in this post. The situation is nearly unique. As we’ve seen—and as everyone knows—the economy is growing quickly, and so is the stock market. Yet the job market has been stalling. It’s fair to say that these conditions are genuinely puzzling. Warren captured it perfectly in the post below:

As seen below, the unemployment rate is still rising; we triggered the Sahm Rule six months ago, but no recession is in sight. Actually, it looks more like a boom than anything else.

The unemployment rate can climb through several mechanisms:

• Layoffs (bad for the economy)

• Quits (not bad for the economy)

• Mismatch between requirements of open jobs and available skills in the labor force (awkward but not bad for the economy)

• Hiring freeze (can be either good or bad, depending on the cause)

• Technological displacement or automation (potentially disruptive but often positive long-term)

• New labor force entrants or re-entrants (neutral to positive for the economy)

What’s the data telling us? First, and that’s great news, we can rule out layoffs. The ratio of job losers as a percent of total unemployed remains very low:

Initial claims and Challenger-announced layoffs, both timely indicators, are at their lows. Challenger had some spikes that spooked everyone, but it doesn’t seem like the layoffs were for economic reasons.

Instead, what seems to be happening is a continued re-normalization from the pandemic-era hiring binge at large companies, along with a strong push toward automation. The following article from Bloomberg is from yesterday:

Bloomberg: Amazon to Cut 16,000 Corporate Positions to Trim Bureaucracy

An indicator I also like to follow is JOLTS hires minus separations. What we see here is a job market at equilibrium: despite a slightly rising unemployment rate, hires are outpacing layoffs.

The chart below represents what we call the jobs-workers gap. The big gap in 2022 created the hiring binge, but we are back to an equilibrium where each worker could theoretically find a job.

If these workers can’t find jobs, the remaining reason is a skill mismatch, as the US economy is rapidly evolving. Probably because of the sudden drops in the foreign-born labor force, many jobs were not filled while the labor force has been rising steadily. This supports the skills mismatch thesis.

This mismatch between labor skill demand and labor skill supply could be the reason why the “vibe” from US workers is still very depressed. The New York Fed’s consumer survey points to growing anxiety about the labor market. Perceived job security has worsened, and confidence in finding new employment has slumped to the lowest level since the survey began.

Once again, we have a big gap between feelings (soft data) and reality (hard data), or what everyone is now calling the K-shaped economy.

The most likely way to escape this K-shaped economy is through wage increases. Indeed, everyone enjoys a nice pay rise, don’t you? But what would trigger wages to accelerate?

Currently, wages are still decelerating and returning to the pre-pandemic upward trend. To resume a positive trend, the job market will need to tighten. There is a decent chance we will see this happening in the near future. The key indicator to follow is the ratio of vacancies to unemployment (often called the V/U ratio or vacancy-to-unemployment ratio). It measures labor market tightness, with a higher ratio indicating more job openings per jobseeker.

This ratio seems to be bottoming, which suggests wage growth is bottoming out too. And if wages are stabilizing at these lows, then one of the main drivers of inflation is doing the same. The other two big ones are energy and shelter. More details in the inflation section.

Bottom line: The unemployment rate has been rising for technical reasons, such as the hiring binge in 2021, a mismatch between the requirements of open jobs and the available skills in the labor force, and, to some degree, technical disruption from AI. None of these factors is negative for the economy or the stock market; in fact, they are very supportive.

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Inflation

• CPI: As of December 2025, the official year-over-year inflation rate stands at 2.7%, still above the Fed’s target. But you should get used to it by now; 3% is the new 2%.

• Inflation Expectations: Michigan Consumer inflation expectations are still renormalizing post-Liberation Day. It is interesting that the 5Y expectation has been in an uptrend since COVID. This is another symptom of the debasement trade/mindset.

  

  The Atlanta Fed Business Inflation Expectations Survey is now back to its pre-tariff level. Tariffs were not as inflationary as feared; their effect is similar to that of a tax as opposed to a subsidy. One is disinflationary, while the other is inflationary.

  

• Inflation Swaps: The 5-year and 5-year forward breakeven inflation rates are still in the same range, going basically nowhere.

  

  And 2-Year inflation swap is back to 2.55% after a steep decline.

  

The inflation picture is not concerning in the immediate future; both the backward-looking CPI and the forward-looking inflation expectations remain anchored. Inflation is structurally higher and now runs at a constant rate of around 3%, the new floor post-COVID and the fiscal bonanza.

The three broad vectors of inflation, namely wages, shelter, and energy, have been a tailwind for the cyclical disinflation. This is well identified by now and fully priced in by the market.

However, I am starting to get on edge. The structural forces are still here with us: fiscal deficit, debasement mindset, elevated asset prices pushing the wealth effect ever higher. The job market is stabilizing and might tighten from now on; the structural force at play is the lopsided demographic pyramid. As seen in the labour market section, the vacancies-to-unemployment ratio could shoot higher, leading to higher wages. And higher wages more often than not mean higher shelter prices.

Regarding energy, the current bull market in commodities is somewhat unusual, with very narrow participation from precious metals. It is natural to expect the bull market to broaden into other commodities, and in particular oil. Yes, there is still a lot of oil in storage and at sea, but that is cyclical; again, the structural forces point toward higher oil prices. US crude oil output is bound to decrease, Venezuela is just smoke and mirrors, and Iran poses an immediate geopolitical risk that is yet to be fully priced in.

The chart below shows a near-perfect correlation between metals performance and U.S. 5-year breakeven inflation.

Oil is also extremely correlated to the 5-year breakeven inflation rate. It will be the transmission vector to close the jaws of the above chart.

In conclusion, inflation is not an immediate risk, but the risk-reward has shifted tremendously: the cyclical forces behind the disinflation are getting exhausted while the secular inflationary forces are only increasing in strength, and the explosive rally in precious metals could spur the next inflation leg. In that environment, it is very difficult to be long bonds or any duration-sensitive assets. In the next post, we will link this to the Fed’s monetary policy and their reaction function, but there is a decent chance the Fed will be too late again, especially if they lose their independence. No bueno for TLT.

The economy is at risk of becoming too hot. This is not consensus yet, but market participants are seeing it coming: a BOOM.

“The U.S. economy has remained resilient. While labor markets have softened, conditions do not appear to be worsening. Meanwhile, consumers continue to spend, and businesses generally remain healthy. These conditions could persist for some time, particularly with ongoing fiscal stimulus, the benefits of deregulation and the Fed’s recent monetary policy.” – JPMorgan Chase ($JPM ) CEO Jamie Dimon

That’s it for today, see you tomorrow for Part II.

I woke up this morning and I was actually making money on the trade! I quickly proceeded to put my stop loss back to my entry. And sure enough, the Asia morning pump triggered it…

Lesson: Don’t waste your time trying to short an asset going parabolic with 200% realised volatility. It’s an absolute waste of time and energy.

But let’s not give up so easily. Yesterday was truly an epic day for silver. SLV volume EXPLODED:

It seems to be the largest volume ever for SLV. Oof x2!

The options market went bananas as well, with call OTM IV going to the moon, especially on the short end of the term structure.

And options volume... Oof x3!

We’ve already reached three “oof”s in just a couple of sentences, so you’re now fully aware that what we’ve seen is some kind of TOP for silver.

Is it THE top?

Or just A top?

Or the first top of a double top?

Who knows? Surely not me. But for now, let’s call it a ZZ TOP!

So, how do you trade a ZZ Top?

Spoiler alert: No, you don’t short silver; you go for the second-order trade.

Answer: You trade the relative value between miners, specifically long GDX and short SIL:

I don’t see the signs (yet) of a blow-off top in gold, and gold miners will just keep printing money in the next year. Silver miners, on the other hand, are already priced to perfection. If silver stalls at this price or finds a floor lower—say, at 60 bucks—there risk reward is not there on the long side. In essence, GDX is just a better option if the volatility in silver and gold decreases and we settle around these prices. Silver miners are very prone to underperformance in a flat market.

In terms of flows, SIL shows a 1-year net flow of +1.89B, but GDX is actually at -2.81B!

I will enter this long/short trade today. You can use the new feature Long/Short bet for sizing your trade in the web app:

A word of caution, though: the app will use the realised volatility of the historical ratio returns. These two ETFs have been correlated at ~95% in the past month. If the two assets are decorrelating from each other, the volatility of the ratio will be underestimated. The volatility of a long/short portfolio can be computed as below:

GDX and SIL vols are respectively around 40% and 50%.

To be conservative, I would use a coefficient of correlation of 50% (instead of the last measured 95%). This gives us a conservative volatility estimation:

You can override the volatility parameter for this long/short bet inside the web app. It will dramatically reduce the recommended weight. For this theoretical portfolio, it will decrease the weight from 54.86% to 17.74%. This kind of trick is what it takes to avoid blowing up on a long/short trade, or putting your stop loss way too close.

I will open this trade in the web app tonight.

Good luck!
~MH

In last week’s update, I highlighted the short positioning in the yen and explained the rationale behind a long position (long 6J = short USD/JPY). A trade was triggered at 0.006366 on the 6J March contract. It is trading right now at 0.0065245, a +2.5% return in one week. My sizing tool was telling me to allocate 95% of my portfolio toward this trade. Not bad for a week’s worth of (hard) work. You can find last week’s issue below; the paywall has been removed for free subscribers.

Let’s review the most interesting charts for this week, starting, of course, with the yen.

Japanese Yen: The CoT data is as of last Tuesday’s close. This was before all the MOF/Fed shenanigans of last Friday, so keep that in mind, positioning has most likely shifted by now. As of last Tuesday, however, the short positioning remained unchanged for the large spec crowd. We will have to wait until Friday night for the new data.

VIX: Large specs are still very short despite last Tuesday’s “risk-off” event. It’s interesting to see the VIX futures not resetting lower either. To me, it feels like we have some unfinished business here. TACO or no TACO, the positioning remains frothy.

SP500: This is also the case in ES futures: we can see an uptick in positioning for large specs as of Tuesday’s close. With both the VIX and ES crowded long, I am very cautious with my risk-on positions.

Nasdaq-100: At least NQ futures are showing light positioning, this is one point for the bulls here…

Russell 2000: …But small (unprofitable) caps are now crowded longs. The broadening-out narrative is still going strong. The big question is whether the market can march higher without Tech leadership. Historically, it cannot. So we better have strong earnings from the Mag-7; otherwise, we are cooked.

Let’s dive a bit deeper here and go beyond the CoT data.

Systematic Investors:

Looking at BofA’s latest snapshot, the positioning is really not pretty here. Because realised volatility has been so low, CTAs are selling in both an up market and a down market:

GS has a slightly different picture, but the conclusion is the same: Estimated global equity positioning of systematic macro is… very high.

Discretionary Investors:

Reminder: I wrote a guide about this crowd of degens. Read it if you haven’t yet:

The retail crowd did not disappoint last Tuesday, thanks to the TACO:

CNBC:

Tuesday was the third biggest single day for retail trader buying in a year, JPMorgan said. Flows of dollars into stocks from individual investors hit $12.9 billion this week — nearly double the weekly average over the past 12 months and close to the level of buying seen in the wake of Trump unveiling higher tariffs last April, the bank found.

Sustained buying momentum in the new year has pushed retail activity to new records on a rolling monthly basis, Jain added.

And this is not just Tuesday, retail traders pressed the buy button during the entire month of January for size!

Same analysis from Citadel:

And don’t get me started on Instit money…

And our beloved implied correlation is back to the lows:

In terms of implied volatility, ATM implied vol reset a bit, but it doesn’t look like an all-clear signal yet: skew ticked up on Friday, and the term structure is not resetting lower. As I said earlier, we have some unfinished business on the downside for SPX.

Regarding Sentiment, Yardeni always have the right wording:

Sentiment. So we have nothing to fear but nothing to fear (other than Greenland and other geopolitical risks). The Bull/Bear ratios suggest that too much good news is boosting bullishness, which may be bearish from a contrarian perspective (chart). Then again, we might have to raise our 20% subjective odds of a meltup/meltdown scenario. Stay tuned.

Given all this information, I am again quite bearish on US equities. After taking profit in the pre-market last Tuesday on the short NQ, I am very tempted to reopen a short position. But first things first: it is about time to close that awful trade on the quantum computing theme. I was hoping for a rebound in animal spirits, with momentum pushing these names higher in January. Alas… I picked the absolute worst thematic basket! The momentum factor decided to leave this theme but stick to uranium, space, neo-cloud providers, and other unprofitable tech baskets. This is comical, honestly…

Anyway, we are exiting the trade at a tiny loss and we move on. The process was good, I just got unlucky. Good process with bad outcome is something I can live with.

Momentum factor looks extended too, another bear argument…

One last chart for this post, the CoT on Euro:

You gotta love this: large spec where degrossing at the bottom. Classic.

I write weekly on positioning, covering all asset classes, this is usually a paid post. Become a paid subscriber if you wish to access these posts weekly. You also get access to the web app, where you have access to a lot of great trading tools to better understand what moves the market.

I’ve just rolled out a major new feature: the Volatility Lab. This powerful addition lets you to dive deep into implied volatility surfaces, scan for options market opportunities, and customize your analysis. Let’s break it down into its two main views: IV Time Series and Quadrant.

View: IV Time Series

This view allows you to load any US-listed stock, ETF, or US index ticker and explore advanced charts of the implied volatility surface. Here’s an example with NFLX (Netflix):

The default layout displays three key charts:

• ATMF Implied Volatility (30 days and 6 months): This represents the IV for at-the-money-forward (ATMF) options with a delta of 50%.

• Term Structure Short Term: A measure of the term structure’s steepness. A high positive value signals backwardation—where short-term IV exceeds medium-term IV—often indicating market stress or an anticipated event.

• Skew 30 days: This gauges how expensive one-month puts are relative to one-month calls. Higher values suggest traders are seeking downside protection through put buying.

I’ve included several built-in layouts curated by myself as excellent starting points for analyzing stocks or ETFs. For instance, the “Vol Premium” layout highlights the gap between realized and implied volatility:

For earnings plays, there’s a specialized view showing how upcoming earnings announcements influence IV levels.

Take your analysis further with the Build Custom Spread feature, which lets you add, subtract, divide, or multiply any metrics. In this example, I’ve created a custom spread to chart total daily options notional: total options volume × closing price (unadjusted).

The tool boasts an extensive library of metrics, hover over the info tooltip for detailed explanations on each one.

Keep in mind that options liquidity varies by stock. The “Data Quality” metric provides a confidence score for IV calculations. While ATMF IV (e.g., 30 days) remains reliable even at lower quality levels, metrics involving out-of-the-money (OTM) options may require caution. See this example with RDW, where liquidity dropped in December, making the Skew 30d (yellow line in the bottom panel) notably noisy:

View: Quadrant

Shift to scanning mode with the Quadrant view, ideal for spotting opportunities across the options market. It’s structured into three sections.

Layout Template & Parameters: Select a pre-built template or design your own using dual axes with normalized IV metrics. In the “RR Overview” example, the Y-axis plots IV 30d, while the X-axis shows Risk Reversal 30d (the IV difference between 25-delta calls and puts). Both are normalized with a one year lookback percentile rank. Higher Risk Reversal values indicate bullish sentiment (opposite of skew metric seen earlier).

The quadrant plots up to 200 points based on your filters.

Interact easily: Zoom in by drawing a rectangle with your mouse (left-click and drag) or double-click a quadrant to focus on it. Below, I’ve zoomed into the upper-right quadrant, revealing stocks and ETFs where traders are bullish and willing to pay up for upside IV. As of the latest close, SLV (Silver ETF) leads, followed by AGQ (2x leveraged Silver ETF), USO (Oil ETF), and NFLX (a potential pre-earnings dip buy).

The Extreme Corners section highlights the most outlier points in each quadrant. Convenient when browsing the web app from your iphone where the quadrant chart becomes inconvenient.

Click any ticker in the chart or table to open a quick stock overview, complete with a link to jump straight to IV Time Series for deeper dives.

Iv’ve also added a new widget to the homepage, showcasing the four most extreme tickers per quadrant for at-a-glance insights:

Key Note: IV data updates daily at 12:00 AM Eastern Time. Always verify the latest timestamp in the widget or at the top of the Volatility Lab page.

Misc updates

Beyond Volatility Lab, I’ve made these enhancements:

• Sizing tool: It is now possible to save bets as a portfolio. Each user can create up to 10 portfolios.

  

• Sizing tool: A negative forecast was interfering with the Div Multiplier, leaving it at a conservative value. This is now fixed. For example, you can use a negative forecast for hedging part of the portfolio.

  

• Sizing tool: You can now have a long/short position as a bet. Just enter two tickers: one for the long position and one for the short.

• User Session: updated from 24hours to 7days.

I hope these updates improve your trading experience! If you spot any bugs or have feedback, message me directly.

Japanese Yen: I officially opened a trade this morning, the setup is looking too good to pass up now. Large speculators are now net short, the first time since January 2025.

Back then, the yen was pushing higher on the narrative of rising inflation and potential rate hikes. Speculators became extremely bullish and drove the yen back to 140 against the US dollar.

Flash forward to today: the narrative has completely flipped, and everyone is aboard the Takaichi trade.

• 76% approval rating for the Iron Lady…

• …emboldening her to call for a snap election…

• …which will consolidate her power and unlock a $135 billion stimulus package plus further reforms.

Nothing stops this Japanese train…

This narrative pushed the yen back to 159 against the USD. The market is sending a signal to the BOJ that rates are still too low and that they are falling behind. They can either intervene directly in the FX market or simply do their job and… hike. But the market is pricing a 0% chance of a hike in January and only a 25% chance in March.

My feeling is that we have a lot of lazy long in Nikkei and USD/JPY. And that the Takaichi trade is now fully priced-in, at least in USD/JPY.

From Bofa regarding Nikkei CTA positoniong:

"With equity realized volatility now at depressed levels, volatility‑sensitive strategies may have pushed aggregate systematic equity positioning to its highest level in roughly five years.

Looking ahead, the risk profile is asymmetric: downside moves could prompt material positioning unwinds, while flat to higher equity markets would likely result in more gradual adjustments. Based on our models, CTA unwinds would begin following declines of roughly 3.5% in the EURO STOXX 50, and 6% in the Nikkei."

For me, this is the cherry on top. The trade has been officially logged in the web app, and you should have received an email among the other two trades (closing the gold trade and opening the bitcoin trade).

Swiss Franc: Positioning is favoring a long here. This is a great way to play for a risk-off move.

Bitcoin: I bought the dip this morning, it looked like a quick liquidation event from all the recent bulls. While positioning is not completely clean in my opinion, it still provided a good risk-reward setup to finally enter this trade.

Russell 2000: Large speculators are now long. The rotation trade is really unfolding before our eyes. No call for action yet.

MSCI EAFE: Similar to the rotation/broadening-out trade, we are seeing increasing positioning in ex-US indices.

Gold: Finally, we are seeing some buying from the large spec community.

Silver: The same goes for silver, with small speculators now over their skis. Large specs still have a lot of buying to do before the COT data becomes crowded long.

Copper: Interestingly, we are seeing some selling in copper. It was the most crowded trade and turned out to be one of the most disappointing ones in terms of performance relative to gold, silver, and platinum.

Crude Oil: Still the same story, very light positioning, but all of Twitter and Substack is all over it. They got burned badly last week. I’m keeping an eye out for a buying opportunity.

Natural Gas: Very large short positioning… Nat Gas futures opened up 13% this morning, maybe the beginning of a short squeeze. I could potentially buy shallow dips in the coming days for a very tactical play.

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The CPI number was released before the US open. Overall, this is what we could call a “friendly” print for risk assets: Core CPI came in below expectations.

One would expect US equities to close higher on the day following such news. Yet SPY closed 20 bps l…

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Yesterday’s report included the following highlights:

So first of all, all you degens are trying to pick a bottom in oil. 99% of my Substack feed are people picking a bottom. And now it’s showing up in the oil volatility surface, with calls getting bid. Congrats!

But this post isn’t about oil, it’s about US equities. Yesterday I opened a tactical short on Nasdaq futures right after the initial post-open pump. I strongly suspect the systematic 0DTE put sellers pumped the market into unchanged. A spotgamma subscription will give you the answer, but honestly I don’t really care about the why. What matters is that implied vol is grinding higher despite the intraday recovery in the spot.

The CBOE report highlights the steepening in put skew and convexity, and we can see it clearly in the VIX decomposition tool:

A parallel shift simply means the vol surface went up across all strikes. This is surprising, as it shouldn’t happen on an unchanged day.

Downside convexity at +0.42 means traders have bought more downside OTM puts, while upside convexity at -0.21 means traders have sold upside OTM calls. This is a reversal from what we saw last week and what I highlighted in yesterday’s positioning note. Re-read it:

The regression tool in the web app is also a convenient way to check vol dynamics. Pretty much all parameters (VIX, vol-of-vol, term structure, and skew) overshot yesterday relative to the spot move:

This dynamic is occurring while the SPX sits basically at all-time highs. We saw the same pattern last year too: in a speculative bull market, traders tend to hedge their downside as the market reaches all-time highs.

The cost of hedging remains cheap here, and we’re seeing a positive impulse across the entire vol surface. This is what a local bottom in IV looks like.

Mandy Xu is also highlighting that COR1M has fallen to a new low. Single-stock volatility is being pushed higher amid the broadening narrative and the upcoming earnings season. We discussed this dynamic yesterday and pointed toward a possible short squeeze on the short-volatility trade at the index level.

While US equities managed to close flattish, they severely underperformed the rest of the world. A quick look at the Mover screen in the web app gives you a clear picture of what’s happening: bully your central bank and watch capital rotate away from your financial markets.

And that move up in the SPX was driven mostly by momentum in small- and mid-caps. I won’t complain, my quantum computing bag got pumped a bit:

And finally, the cross-asset screen: a good day for the globalists and metals! The more Trump opens his mouth, the more they win!

The market has been sending clear signals since the beginning of the year. Some new secular trends are unfolding right before our eyes, and yesterday’s price action was a preview of them.

Good luck!

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Paid subscribers will receive positioning updates on other asset classes tomorrow.

If you are new to CoT data, please read the first part of this post:

S&P 500 E-mini Futures: Small speculators are getting very long here. It suggests limited upside over the next few months. Large speculators are still net short, but they bought back a significant amount of their shorts toward the end of last year. It could be some end-of-year window dressing; I’m not drawing any firm conclusions here.

VIX futures: Large speculator positioning can give us a rough idea of how crowded the carry trade has become. With VIX futures in contango and embedding a significant carry premium, this trade is getting fairly crowded. The front-month contract might have found a temporary bottom around 16.00.

Nomura’s McElligott made the below comment regarding this large short positioning:
ASSET MANAGER positioning / 1m change in VIX futures , [has seen] a major outlier “Short” in futures ( assuming on account of “Rolldown” - type trades, i.e. Long UX3 vs Short UX1) who have just PUKED VIX futures to the tune of ~$51mm of Vega sold / 3%ile change over 1m period, and with the absolute level at -$0.5B, just 1%ile (fwiw, Leveraged has also sold ~$27mm Vega over the same past 1m period)

McElligott again:
We’ve actually seen some customer buyers of Vol recently here and away, yet it hasn’t made much of a dent in the HILARIOUS Skew flattening in SPX Options ….due to the more notable stuff in Options demand coming in bits-and-pieces of UPSIDE being [BOUGHT] in both SPX (e.g. buyer of SPX Feb expiry 7200 Calls, 2700 times yday) and Nasdaq QQQ... while Puts / Downside went kinda friendless EOY when folks monetized / de-grossed.…so Skew absolutely CRUNCHED while Call Skew jacks as a function of both a touch of Upside demand
and Downside bleed-out.

So we have this pretty interesting setup where small speculators are very long ES futures and large specs are very short VIX futures—especially on the front end, where speculators have piled into the carry trade. At the same time, we are seeing skew on SPX flatten a lot, meaning speculators have been buying OTM calls and selling OTM puts. This is also confirmed by the put/call ratio, which ticked lower last Friday.

Tactically, the risk/reward looks poor for SPX. This is a nice setup to either sell call spreads or, more aggressively, buy puts or VIX calls.

While the setup for a short is tactically appealing, we can push the analysis further by looking at other positioning data as well as sentiment and market internals like breadth and correlation. This can give us another clue about how attractive the risk/reward is and how large we want to size any position.

DB’s latest update regarding discretionary and systematic positioning signals rather mild overall exposure. Systematic investors are definitely long, but not over their skis. Discretionary investors remain skeptical of this rally, just as they have been throughout 2025 since Liberation Day.

If you’re unsure about discretionary investors and what they are, I wrote this educational post that will answer a lot of your questions:

BofA also kindly provides an update on systematic equity flows, with projected buying and selling for CTAs. As expected, when positioning is already rather long and the market is at all-time highs, the selling profile is highly asymmetric: very little buying in an up market, and a lot of selling in a down market. Hot tip: this is not priced in the SPX skew.

On the discretionary side, Goldman Sachs has the following numbers regarding their long/short hedge fund clients:

This is getting pretty interesting. Gross exposure remains very high, as it has been for a while now. But net exposure has also been climbing, now reaching the 83rd percentile over the past year while sitting only at the 65th percentile over the past five years. This tells me that fundamental hedge fund managers are slowly getting longer and longer, though there is still some room left for them to add exposure.

The big theme playing out right now is the broadening of the bull market and the rotation out of the “Mag7” and into the “Impressive 493,” as coined by Dr. Yardeni.

Because in markets, nothing lives in a vacuum, we can link this rotation, the high gross exposure among L/S funds, and the SPX implied volatility picture. Indeed, the hallmark of a rotation is high implied volatility on individual stocks (VIXEQ, see below), low implied volatility at the index level (VIX), and falling implied correlation (COR1M). If you’ve read The Hitchhiker’s Guide to Positioning: Part I, then you know what that means: Dispersion (DSPX) is going up!

This setup is very supportive of the market and usually makes it grind higher. Speculators are busy selling losers and buying winners, buying volatility on single names while limiting their carry cost by selling volatility on the index.

This is a virtuous cycle that pushes the market higher—until it isn’t. The logical effect of this trade is crowding into shorting SPX implied volatility, and therefore the VIX. And now we have come full circle, back to the very beginning of this post where we highlighted the short crowding on VIX futures.

The last missing piece is sentiment. Unfortunately, this is not something we can measure accurately, but BofA and GS have their own composite indices, which are better than nothing:

BofA’s sentiment indicator is significantly more stretched than Goldman Sachs’s. The truth is probably somewhere in between, but it’s no secret anymore that everyone is bullish on equities in 2026.

Two last CoT charts in equity land before jumping to the conclusion.

FMFA Future: This is the futures contract tracking the MSCI EAFE (Europe, Australasia, Far East, Japan) Index. It is designed to measure the equity market performance of developed markets outside the U.S. and Canada.

The outperformance of the rest of the world is no secret anymore. The index has been trending up and to the right. It was one of the best trades of 2025, along with gold.

Inevitably, outperformance attracts new capital, and the CoT data shows increasing long positioning. But zooming out, we are still very far from historical highs.

So yeah, the broadening out is not only a rotation within US stocks; it is also a capital rotation from the US to outside the US. And the CoT data is confirming it.

In my opinion, this is not something you want to fade. On the contrary, this could be a new super trend, and you’d be wise to buy the dips instead of selling the rips on this future.

Am I calling for the chart below to break out to the upside? Hell yeah!

To conclude this section on equities, I believe there is a decent chance of a pullback in SPX on the order of 5%. This is a tactical view, not a call for a crash or anything dramatic, just your average drawdown in a bull market.

This correction could come from the dispersion crowd needing to cover their VIX short positions if the broadening trade starts to fail. I’m not entering a trade yet. I’d like to see VIX grind higher slowly—that would pressure anyone short vega to begin covering gradually. A tick up in implied correlation would be the other clue that it’s happening. Stay tuned; it could play out very soon.

For a longer-term view, I actually do believe in the broadening out within the US stock market, but I also believe in capital rotation out of the US and into other developed markets. The long/short FMFA1!/FNG1! spread is a great expression of this trade because it allows you to capitalize on both the broadening (short large-cap tech) and the capital rotation (long FMFA). It fits very well in a global macro portfolio as an uncorrelated long-term position, and I will be adding to this trade on dips.

Crude Oil: This has to be one of the most interesting charts right now. Large speculators’ positioning is at a record low; the last time we saw this level was back in 2010. That light positioning is not entirely new, it has persisted for the past three months. Sentiment is also heavily one-sided. It is well established that the Trump administration wants to keep a lid on oil prices and that we are facing an “oil glut”. The latest developments in Venezuela, while not having an immediate significant impact on the oil market, further nourish the bearish thesis.

Is it time to pull the trigger on the oil trade?

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Since there are quite a few new subscribers here, and because I have procrastinated on part II of the Hitchhiker’s Guide to Positioning, I will start with a reminder of how the CoT works.

The CoT covers all futures traded on the COMEX. My TradingView watchlist is based on the universe of futures below:

Luckily, TradingView has a built-in library for accessing CoT data (and it’s free!). I modified their script to make it my own, reusing Jason Shapiro’s color coding (the contrarian OG, check him out at crowdedmarketreport.com). My default template looks like this:

The data in CoT reports is collected as of Tuesday and released on Friday. It provides a weekly data point expressed in number of contracts. TradingView simply holds the same value from Tuesday until the following Monday.

The CoT report categorizes participants into three main groups:

• Commercials (or Hedgers): These are businesses that use futures markets primarily to hedge price risks associated with their underlying commodities or financial instruments. They are often producers or processors of the commodity and are considered to have an informational advantage due to their closeness to market fundamentals.

• Large Speculators (or Non-Commercials): This group includes professional traders such as hedge funds, commodity pool operators or family offices. Their main objective is to profit from price movements, and they typically focus on longer-term trends.

• Small Speculators (or Non-Reportables): These are smaller retail traders whose positions do not meet the reporting thresholds set by the Commodity Futures Trading Commission (CFTC). They also speculate on price movements.

In equity markets, such as E-mini S&P 500 (ES) futures, the “Commercials” category consists of institutions that use futures contracts for hedging rather than speculation. Unlike in commodity markets, where commercials are producers or processors, participants in equity futures are financial firms hedging risks in their stock portfolios.

For equity futures, the Commercials category includes:

• Banks and large financial institutions: Proprietary trading desks at investment banks may use ES futures to hedge exposure to the broader equity market or to offset over-the-counter (OTC) derivative products like swaps.

• Pension funds and mutual funds: These institutional investors hold very large stock portfolios and use ES futures to hedge against potential declines in the overall market, protecting the value of their holdings.

• Insurance companies: As large institutional investors, insurance companies hold extensive equity portfolios and use futures to manage market risk.

• Index arbitrage desks: These desks exploit the difference between the price of index futures and the value of the underlying stocks. They are classified as commercial hedgers because their futures trading is directly tied to cash-market activities.

Although hedge funds are major players in the futures market, they are typically classified as Large Speculators (Non-Commercials) in the CoT report because their primary objective is to profit from market movements rather than to hedge a core business activity. This can be an issue for the data quality as they hedge other positions using futures.

Open Interest (OI) refers to the total number of outstanding futures contracts that have not yet been settled or closed out, across all expiration months. OI increases when new buyers and new sellers enter the market and create new positions. It decreases when existing positions are closed out (e.g., through offsetting trades). Flat OI indicates that the number of new positions opened roughly equals the number of positions closed.

The table below summarizes these mechanics:

Keep in mind that Open Interest does not measure the balance between buyers and sellers. Instead, it reflects whether trades are creating new contracts (attracting fresh capital/participants) or closing existing ones (participants exiting). Flat OI typically suggests a roughly balanced flow—new entrants replacing those who are leaving—rather than net expansion or contraction of positions.

Once we understand how net open interest (OI) changes, we can interpret it in the context of price action and volume.

Some trading books provide blueprints for how markets should behave based on trends and OI changes. In practice, however, each market has its own dynamics and must be studied in light of its specific microstructure. You cannot interpret open interest and positioning the same way across Bitcoin futures and S&P 500 E-mini futures. They feature very different players and futures curve dynamics.

For example, Bitcoin is still a relatively new asset, so its forward curve can swing wildly, attracting both directional speculators and basis carry traders. One group takes liquidity to establish directional bets, while the other provides liquidity to collect funding rates. Both affect open interest identically, as they are two sides of the same coin. This dynamic does not apply to S&P 500 futures, an extremely mature market with far more participants pursuing widely varied objectives. Commodity markets represent yet another distinct world where physical delivery add another layer of complexity.

This is a long way of saying that you should be cautious about generalizing positioning across markets. Nevertheless, a few key insights hold true regardless of the underlying asset:

• Large Speculators, as defined by the CoT, are the primary drivers of OI changes because they act as liquidity takers. They enter when speculating on a new trend, while their counterparts—commercial traders—are liquidity providers who are content to lock in favorable prices for hedging future cash flows. By definition, Large Specs are trend followers, while commercials are mean reverters.

• Rising OI during an uptrend is bullish; rising OI during a downtrend is bearish. New speculators are entering aggressively, buying (or selling) into the bull (or bear) move and sustaining momentum.

• Rising OI also signals that “paper hands” or fast money is piling in. These momentum players typically use stop losses, so as OI grows, the cluster of potential stop orders below current price increases. The distribution of future outcomes gradually shifts to negative skew: the most likely path is trend continuation, but any setback risks triggering forced selling that exacerbates downside moves.

• Trees don’t grow to the sky, and neither does open interest. At extreme highs, OI tends to correct, either through gentle price consolidation or sharp resets. COMEX copper futures offer many examples of nasty resets; the August flash crash is a recent one. Such events clear out weak hands and pave the way for the next leg.

• Spikes in OI near contract expirations often reflect rollovers rather than new trends. In contango or backwardated markets, hedgers and speculators roll positions to the next contract, temporarily inflating OI without directional intent. Ignoring this can produce false signals.

• Extreme OI relative to historical norms signals vulnerability. When OI reaches multi-year highs, it frequently precedes volatility spikes or trend reversals as crowded trades unwind.

• Combining OI with volatility metrics strengthens trend confirmation. Rising OI in low-volatility environments suggests building tension and a potential breakout, whereas in high-volatility regimes it can amplify moves but also indicate froth.

Now that we’re all clear on the CoT data, let’s look at the actual positioning data and what caught my eye. Starting with everyone’s favorite...

Silver: Not a single day passes in the financial media without a mention of silver. The bull case is common knowledge and goes as follows (thank you, Grok):

Silver’s bull case in early 2026 is driven by its dual role as a precious and industrial metal, with surging demand from green energy (solar panels, EVs) and tech (AI, data centers) amid structural supply deficits. Institutional buying, including central banks, has fueled a multi-year rally, with 2025 gains exceeding 140% and potential for triple-digit prices if monetary risks persist. Its addition to the US critical minerals list underscores strategic importance, while cheaper valuation versus gold offers asymmetric upside as an inflation hedge. Analysts see further momentum from energy transitions and tech adoption, with targets up to $150/oz possible in bullish scenarios.

With all the cheerleading around silver, you’d assume everyone is uber-long. According to the CoT data, large speculators are actually selling into this parabolic rise! Small speculators are maintaining roughly the same exposure as always. This leaves commercial positioning rather long (relative to the past).

In addition, open interest net change remains quite flat. While the price action resembles a blow-off top, the positioning is actually very conservative. People are selling and taking profits into this move. Trying to pick a top here seems to offer very poor risk/reward. Fading the price will get you nowhere if market participants have already sold. I absolutely hate to say this after such a dramatic rise, but the risk/reward favors the bulls. However, I am not chasing silver here because I cannot bring myself to buy this “high,” and I am not shorting it either. Sometimes you just want to stay out of trouble.

Palladium: Contrary to silver, palladium is exhibiting what looks like crowded positioning. Large speculators have fully bought back their short positions, while small speculators have never been this long. Commercials are now net sellers.

One’s first reaction might be to think that palladium is a much better short than silver, based purely on positioning analysis. While this could make sense for a short-term trade, the long-term picture is more nuanced. Palladium is emerging from a nasty bear market; zooming out over the last 10 years, large speculators’ positioning is actually very light.

What seems to be happening right now is simply a repricing of palladium. On the back of the decline of ICE vehicles, palladium was left for dead in 2024, only to come back as part of the debasement trade.

Copper: Now that’s what a crowded long looks like! Large speculators are getting very long; last week saw a small downtick, but with copper above $6.0, I expect the next reading to be higher. Small speculators are rather long too. Open interest has been rising gradually after a brief reset around November. I am not shorting this market now, as I still expect one last parabolic move. But it could offer a very nice risk/reward on the short side very soon.

Grok’s bull thesis, or, what everyone knows:

The bull thesis for copper in 2026 hinges on surging demand from the global energy transition, electric vehicles, and AI data centers, outpacing constrained supply. Mining disruptions, declining ore grades, and potential U.S. tariffs exacerbate shortages, projecting a global deficit of ~330kmt. Economic recovery in China and lower interest rates further bolster prices. Analysts forecast averages of $12,000-$12,500/mt, with peaks up to $15,000/mt amid structural imbalances.

To be continued tomorrow. I will look at forex, crypto, and equity indices.

Sizing a trade is often either oversimplified or overcomplicated. You will frequently hear that risking 1% per trade is a good rule of thumb, but is it really? Other investors sit at the opposite end of the spectrum, finding comfort in highly complicated models that run large covariance matrices with backward looking optimisation, only to realise that during market crises, correlations become unstable and often shift to 100%. Risk management that is too simple leads to erratic day-to-day volatility in your portfolio. Conversely, a system that is too complicated gives the illusion of perfect control over risks when, in fact, you do not have it.

At the end of the day, managing risk in a portfolio comes down to answering one question: How much risk do you want to take, and how confident are you? So let’s start with the latter: confidence.

Forecasting

When entering a trade, you should assign a level of confidence to it. For example, you might be very bullish on a stock, or simply bullish. This represents your forecast for the asset’s performance—a discretionary input to your risk management system, with values ranging from -20 to +20.

The Golden Rule of Forecasting: Over time, your average absolute forecast must be 10. This means most trades are a standard +/-10.

Reserve a +/-20 forecast for "Perfect Storm" setups where technical, fundamental, and macroeconomic factors all align.

If you are new or lack a tangible track record, stick strictly to +/-10. Scaling down to +/-5 is also a valid way to test new signals without over-committing capital.

Volatility targeting

The most important input to your risk management system comes from answering the question: How much risk do you want to take? This is not an easy question to answer—after all, what is risk, and how do you even measure it? We will keep it simple for now: risk is best measured by the expected standard deviation of the portfolio’s returns, also known as its volatility.

Let’s assume you are running a portfolio of $1 million. After spending some time on WallStreetBets, you decide that your target volatility is 69%. Truly a great number for outperforming your neighbours by trading meme stocks. But can you cope with that volatility?

To put things in perspective, because volatility scales with the square root of time, 69% annual volatility means that on average your portfolio will move by +/-4.31% per day (69% / √252). You should expect that 3.33 days in a month your portfolio will be down by more than -4.31%, and 5 days a year it will be down by more than -8.62%.

I’m not sure about you, but personally, I can’t cope with that level of daily volatility in my portfolio. NGMI, right…

And the above figures are just based on daily normal uncorrelated returns. Introduce some autocorrelation between the returns—or what we call strong momentum—and you see where this can go: either a glorious run for the portfolio or a catastrophic rout sending it into a very deep drawdown. Big drawdowns are what kill returns: after losing 50%, you need a 100% return to break even. Good luck with that.

While everyone has a different risk appetite, we can still infer a recommended range of volatility targets. The only thing you need is your expected Sharpe Ratio (SR). Knowing your SR is no small feat, however—even with a solid backtest over many years, the SR remains a random variable following an unknown distribution. It can be positive on average, but inevitably some years will be negative. So I urge you to be conservative when selecting your expected Sharpe Ratio. Anything above 0.70 is too optimistic; yes, you can achieve a Sharpe >1.0 for years, but you are not immune to volatility, mean reversion, or the gravitational pull of a drawdown.

Below are the recommended volatility targets based on a half-Kelly criterion. While the full Kelly criterion identifies the mathematically optimal bet size to maximise long-term growth, it is notoriously aggressive and sensitive to errors in your assumptions. By using half-Kelly, we create a heuristic safety buffer that protects the portfolio against over-leveraging and the inevitability of a drawdown.

Note: If you are running a strategy with negative skew, such as harvesting equity volatility premium by selling options, you should divide the recommended volatility target by 2. Any volatility measured in a short convexity strategy is always underestimated. Don’t be stupid (looking at you, theta gang).

X-factor: average holding period

Traditionally, trading systems use simple money management: they allocate a certain percentage of trading capital to risk on each trade. The natural question, then, is how to link this capital at risk with volatility targeting at the portfolio level.

Consider Trader A, who risks 1% of his account on a daily intraday strategy. To simplify, assume one trade per day, a 50% win rate, and a 1:1 risk/reward ratio. On average, this trader risks 1% to make 1%. What is the annualized volatility of his portfolio? The answer is 15.87%. Volatility scales with the square root of time, so 1% daily volatility multiplied by the square root of 252 trading days yields 15.87%.

Now consider Trader B, who also risks 1% of his account but on a monthly swing trading strategy. Using the formula, the annualized portfolio volatility is 3.46% (1% × √12). As you can see, the expected portfolio volatility is much lower despite risking the same percentage per trade.

There is a direct relationship between the capital at risk per trade and the expected holding period of each trade. To achieve a desired annual target volatility at the portfolio level, you must account for the frequency and expected holding time of your strategy when determining the capital at risk per trade. In other words—contrary to common belief—the capital at risk per trade is not an input but an output of the risk management system.

You might now wonder how to know the expected holding time of a trade in advance. In the simple case of a buy-and-hold position, the trade remains open as long as technically possible. If you buy a stock on a cash account without margin, you can hold it indefinitely or until a corporate event occurs. At the opposite extreme, buying with leverage and a tight stop loss means the trade will likely last only a short time. The expected life of a trade depends directly on the distance to the stop loss.

From now on, I will assume we trade every position with a trailing stop loss. This creates trades with positive skew and a finite expected life. To scale the stop loss independently of the instrument’s volatility, we measure the distance to the stop loss in daily standard deviations—this is known as the X-factor.

Example:

1. SPY: Let’s assume the realized volatility on this ETF is 16%. The daily standard deviation is therefore 1% (16% / √252). If I place my stop loss 1% below my entry, that means I used an X-factor of 1. If I place it 3% below, the X-factor is 3, and so on.

2. NVDA: Volatility is 40%, so the daily standard deviation is 2.5%. A stop loss with an X-factor of 3 means the stop loss is 7.5% below my entry.

Unfortunately, when using a trailing stop loss, I don’t know of any straightforward mathematical formula to calculate the expected holding period based on the normalized distance to the stop loss. But fortunately, Monte Carlo simulations are perfect for this!

We can use geometric Brownian motion to simulate theoretical asset price paths. Below is an example of 100 simulated paths:

I ran a script with 50,000 simulated paths, and for different X-factor levels (1, 2, 3, 4, 6, and 8), I plotted the distribution of the holding periods. Using a trailing stop loss yields a log-normal distribution, which is to be expected: many trades get stopped out quite early, while a few winners remain open for long periods. This also illustrates the benefit of using a stop loss: it removes the left-tail risk of holding onto losers and lets winners run.

From the chart above, we can map an expected holding period to each X-factor stop loss level:

This table is a good rule of thumb but it should be taken with a grain of salt. First, the Monte Carlo simulations were generated with zero drift and constant volatility, which is a strong assumption. Second, the distribution of holding periods is log-normal, so the expected holding period falls roughly between the mode and the median of the distribution. When trading such a system, it might feel painful and uncomfortable because mode < median < mean: the most likely outcome is to get stopped out in a short period, while the average holding period is stretched much further due to outliers. The mental pain of getting stopped out is the price to pay for outsized returns in the long run.

Sizing one bet at a time

Now that we have defined volatility targeting, forecasts, and the X-factor, we can finally examine how to size a trade in practice.

Consider a swing trader on S&P 500 futures; let’s call him John. This trader exclusively trades the same asset and never holds more than one position at a time in his portfolio.

After backtesting his strategy—or simply running it with low capital—John decides to scale up and follow our risk management system. Historically, his strategy has averaged a Sharpe ratio of 0.90, but John is conservative, so he assumes he can deliver at least 0.6. According to the table introduced earlier, this leads him to a volatility target of 30%. Running the strategy on a $1 million trading account, he expects daily P&L volatility of +/- $18,750. He considers that an acceptable dollar amount given his risk appetite.

On average, John trades in and out of S&P 500 futures every week; that is his expected holding period. According to the X-factor table from the previous section, John should use an X-factor of 2.

John currently has no open position, but this morning he receives a buy signal. The signal is neither weak nor strong; he has average confidence in it, so his forecast for the trade is +10.

John checks the annualized realized volatility of S&P 500 returns over the last month and finds 14.50%.

Now John has all the inputs needed to compute the trade size:

He needs to buy $2,068,965 worth of S&P 500 futures, effectively introducing leverage of 206.97% into his $1 million trading account.

The stop loss calculation is straightforward:

John will need to place a stop loss 1.82% below his entry price.

At the account level, this 1.82% instrument stop loss means John is risking 1.82% × 206.97% = 3.77% of his capital, or $37,700. This might seem large, but it is the amount he must put at risk to achieve his 30% annual portfolio volatility target. No pain no gain!

There is a handy simple approximation formula to double-check our calculation. Assuming John risks 3.77% every week, we can compute the expected annualized volatility for this trading account as follows:

27.19% is close enough from our 30% volatility target.

This sizing system may feel counterintuitive at first, as traditional approaches treat capital at risk per trade as a fixed input. Here, however, it emerges as an output derived from three key elements: your desired annual portfolio volatility, the confidence level (forecast) in each trade, and the normalized stop-loss distance (X-factor) tied to expected holding periods.

This method is superior because it forces you to confront the central question of how much risk you truly want to bear over the full year before placing any trade. By aligning position sizes with your long-term risk appetite rather than arbitrary per-trade rules, it delivers a more resilient, consistent, and psychologically sustainable approach to risk management.

This is not a "set and forget" system. If an asset’s volatility rises, its risk weight has increased. You must re-calculate and likely sell some exposure to maintain your target. It is best practice to re-calculate whenever there is a significant change in market regime or realized volatility.

So far, we have only addressed the case of a trader with one position at a time, which is not what most traders or investors do. In the next section, we will examine the sizing of multiple bets held simultaneously in a portfolio.

Diversification: optimal number of active bets

Realistically, most traders and investors hold multiple positions simultaneously across various instruments or asset classes.

Diversification offers significant benefits. As Harry Markowitz famously stated, “Diversification is the only free lunch in investing”. This holds true in general: adding uncorrelated bets reduces overall portfolio volatility compared to concentrating on a single position, which can improve the expected Sharpe ratio.

However, diversification is not foolproof. Warren Buffett has cautioned against excess: over-diversification can lead to diworsification. In his words, “Diversification is protection against ignorance. It makes little sense if you know what you are doing”. Including more bets introduces hidden risks. During market stress, correlations often spike, transforming apparently uncorrelated positions into one large correlated bet. By the time this becomes evident, it is usually too late, and the portfolio suffers a severe drawdown.

So, should we diversify or concentrate? Sorry, folks, we are going to need a little math here, nothing too advanced though.

To explore this question, let us examine the formula for the total variance of a portfolio:

with:

In our case, we use risk-adjusted bets: each bet contributes the same amount of risk to the portfolio. This leads to the following assumptions:

Then we can simplify the portfolio variance formula to:

Edit: on mobile, the LaTeX formula is missing a “+” sign, this should read as sigma^2/N + 2sigma^2/N^2*Sum{rho_i_j}

The first term, σ²/N, represents the “idiosyncratic” or diversifiable risk from individual assets, which becomes diluted as you add more assets. Variance scales linearly with the number of assets, while standard deviation (what we call portfolio volatility) scales with the square root. As you add more assets, the marginal diversification benefit decreases.

The second term incorporates the pairwise correlations (ρ between i and j) and reflects “systematic” or undiversifiable risk due to how assets move together.

The formula strikes a balance between isolation (low correlations spread risk) and synchronization (high correlations amplify it). As correlations rise, portfolio variance increases, causing the portfolio to behave more like a single asset exposed to broad market risks, typically macroeconomic or other systemic factors. If you did not intend to take those risks, you have effectively diworsified your portfolio. Conversely, low or negative correlations shrink the second term and can even make it negative if enough pairs are negatively correlated (similar to a hedge), thereby reducing overall portfolio risk and creating the possibility of undershooting your target volatility.

In an idealized world, you would hold only uncorrelated bets. If all correlations are zero, you achieve perfect diversification nirvana: risk drops with the square root of the number of positions. In reality, correlations are not fixed. Even traditionally “uncorrelated” assets like stocks and bonds can become highly correlated during events such as inflation spikes.

It is easy to underestimate correlation, especially across asset classes. Correlations are not normally distributed: most of the time they appear low, but in crises or regime shifts they can spike dramatically alongside volatility. This creates the potential for severe drawdowns that wipe out months of profits and undermine the careful risk management you have built.

My solution is to avoid assuming correlation stability and to recognize that recent historical correlations are often poor predictors of future ones. Instead, I use conservative correlation assumptions and I aim for an average correlation target across my bets. The below table should be used as a rule of thumb for estimating correlation across bets using their instrument asset class. It doesn’t require any calculation and provide a good enough framework for achieving most of the diversification benefits.

If we assume an average pairwise correlation​, meaning that the correlations differ across pairs but we approximate their effect by using a single average value, the portfolio variance formula simplifies significantly into:

Using this formula, and assuming a volatility of 20% for each bets, we can easily compute the expected portfolio volatility for different level of average correlations and number of bets:

Unsurprisingly, adding more bets when correlation is high (90%) does not provide any benefits. It keeps the portfolio volatility very close to 20% and adds significant systematic risk—this is diworsification. However, adding more uncorrelated bets lower the overall portfolio volatility, and of course, the lower the correlation, the greater the diversification benefits. In reality, it is quite difficult to find many purely uncorrelated bets, and you will eventually stumble upon only 3 to 5 purely uncorrelated bets when trading or investing in traditional publicly listed assets. My sweet spot, and what I try to average over time, is to find 4 to 5 bets while aiming for an average correlation of 50%. This gives me enough diversification benefits while keeping the number of bets manageable. That’s my interpretation of Stanley Druckenmiller’s adage: “Put all your eggs in one basket and then watch that basket very carefully.”

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Diversification multiplier

As we have seen in the previous section, adding low-correlated bets lowers the total portfolio variance. While this is excellent for achieving higher Sharpe ratios, it also means we are undershooting our volatility target. To address this issue, we need to increase the notional exposure of all our bets. This adjustment is known as the diversification multiplier. The multiplier is calculated by dividing the volatility target by the expected volatility of the portfolio. Using the previous table, we can compute the diversification multiplier for each pair of number of bets and average correlation.

We can now revisit the formula for computing the instrument notional for a given bet by incorporating the diversification multiplier.

I strongly suggest capping the diversification multiplier at 200%. Personally, anything above 130% makes me uncomfortable; that is my usual cap.

Putting it all together: sizing a portfolio of bets

Let’s review everything we have covered so far and walk through the entire sizing process using a hypothetical trader: Jane.

Jane is a global macro trader who draws on her experience in markets and macroeconomics to form views on various assets. She aims to capture major trends and ride them until exhaustion. She has consistently achieved a Sharpe ratio of 0.50 over the years and is managing an account value of $1mil. Based on our table mapping Sharpe Ratios to volatility targets, she chooses to run a 25% volatility target at the portfolio level.

Given the current macroeconomic environment, Jane believes copper is the next metal poised for a strong upward move. She is very bullish and assigns a forecast of +20. She also calculates the one-month realized volatility of copper at 30%.

Jane is also bullish on Brazilian equities, believing the bull run is just beginning. She assigns a forecast of +10 and measures a volatility of 25%.

She has been riding the gold trend for the past year, but now views the risk-reward as less attractive. She remains mildly bullish with a forecast of +5 and measures a volatility of 16%.

Finally, Jane is bullish on TLT, believing the bond market is overpricing strong US economic growth and underpricing the probability of a slowdown. She assigns a forecast of +10 and measures a volatility of 10%.

Using the recommended correlations by asset class, we can construct the following correlation matrix for Jane’s bets, yielding an average correlation of 37.5% ((50% + 100% + 0% + 50% + 25% + 0%) / 6).

To obtain the diversification multiplier, we reuse the expected volatility of the portfolio:

and we get:

Now, Jane can apply the sizing algorithm to each bet:

A few observations:

• The total notional exposure is $2,037,161. The portfolio operates with 103.71% leverage (total net exposure is 203.71%), meaning Jane needs a margin account and will borrow shares or use futures.

• Copper receives a very large weight of 57% due to the +20 forecast and the 25% target volatility. Do not assign a +20 forecast lightly; it will significantly increase portfolio risk.

• Keep in mind that we are dealing with risk-adjusted bets. If an instrument’s volatility rises, you must recompute the weights. For example, if copper becomes more volatile, Jane will need to recalculate its instrument weight, which will reduce it, requiring her to sell some exposure.

Web App’s sizing tool

Now that you have a good understanding of how the system works, I can finally introduce the web app’s sizing tool, available here:

https://app.markethitchhiker.com/

You will find the sizing tool on the Trades page, nested within the Sizing Tool view.

When creating a new bet, the app will prompt you to select the type of asset you would like to add:

The Dummy Asset option allows you to add a bet on a theoretical asset, where you can manually set the asset class and volatility. By adding four dummy bets, we can recreate Jane’s portfolio as computed in the previous section:

If you prefer to create bets on real assets, you can use other bet types, such as “ETF or Thematic Basket” or “Single Stock” (any ticker quoted in the US). I have recreated Jane’s portfolio below using real ETFs:

By using real assets, the app automatically calculates the actual volatility for each instrument with a conservative approach and computes the correlation matrix based on historical returns. The resulting weights differ slightly from those calculated with dummy assets, but they remain quite close. The portfolio metrics section also provides a handy overview of how well the portfolio is constructed.

The bets are saved automatically, so you do not need to recreate them the next time you log into the website.

The final bet type you can add is “Cash,” which is actually a very useful option. Imagine you have three strong investment ideas for 2026:

• A thematic basket: long offshore oil drillers

• A single stock: long Micron (MU)

• An ETF: long IBIT

Like me, however, you prefer to run an average of four bets with a 20% target volatility. This provides concentration while still capturing diversification benefits. If you currently lack strong conviction for a fourth idea, using only three bets in the sizing tool would make the portfolio too aggressive. Adding a fourth bet of the Cash type solves this: the weights will be computed assuming four total bets. Indeed, when calculating the instrument weight for a given bet, we divide by the total number of bets:

Once you gain conviction in a new investment or trade idea, you can remove the cash bet and add the new one.

In a similar way, if you get stopped out on a trade but lack another idea, simply replace the stopped-out bet with a cash bet. This keeps the weights of the other bets unchanged. While you will undershoot your portfolio target volatility, you will avoid taking on overly concentrated risk.

Having Cash bets also provides a vital psychological benefit: it frames "doing nothing" as an active, risk-managed choice. It prevents you from forcing a mediocre trade just to fill the portfolio, allowing you to wait patiently for your next high-conviction setup.

Summary

This guide introduces a systematic framework for position sizing that rejects arbitrary rules like risking a flat 1% per trade. Instead, it positions capital at risk as an output of a deliberate system driven by three key inputs: your discretionary forecast, annual portfolio volatility target, and normalized stop-loss distance (the X-factor).

To apply this effectively, focus on these core principles:

• Forecasting and Conviction: Assign trades a score from -20 to +20, with +/-10 as the default for standard confidence. Beginners should limit to +/-10 until building a proven track record; reserve +/-20 for rare “Perfect Storm” alignments.

• Volatility Targeting: Assess your risk tolerance by mapping a conservative expected Sharpe Ratio to an annual volatility goal (e.g., 25% for SR 0.5), creating a psychological and financial buffer via half-Kelly sizing.

• X-Factor Alignment: Normalize stop-loss distance in daily standard deviations to match expected holding periods. A lower X-factor shortens trades and alters risk profiles, fostering positive skew.

• Diversification and Scaling: Use a diversification multiplier to counter “diworsification” (over-diversifying into correlated risks) or volatility undershoots. Aim for the sweet spot: 4-5 risk-adjusted bets with ~50% average correlation for balanced benefits.

• Practical Implementation: Leverage the Market Hitchhiker web app to automate calculations for real assets, thematic baskets, or dummy/cash placeholders, ensuring consistent risk even during idea droughts.

This approach compels you to address total annual risk upfront, delivering consistent volatility and sustainable performance. Though frequent stops can test your resolve (embracing uncertainty over illusory certainty), it’s the essential trade-off for averting deep drawdowns and letting winners thrive.

To apply this framework, consider subscribing to The Hitchhiker’s Guide to the Market. The free tier offers regular updates, while paid subscribers gain full access to the web app at https://app.markethitchhiker.com/, including tools for position sizing, thematic baskets, and other trading features. Paid members also get access to my open trades, which apply the Forecast and X-factor system described here, giving you the key inputs to size them intelligently in your own portfolio.

As an example, here’s a screenshot of some recently closed trades using this approach:

Growth

• S&P 500 Forward Operating Earnings: The next-twelve-month EPS continues to climb toward 2026 levels, but revisions have stalled for the past three weeks.

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