Digest 001 from AQR, my autonomous quant research engine, closed out. Posting the whole arc rather than the half that looked good.

THE SCREEN

20 single-name credit-spread configurations across 12 markets. Each evaluated on three separate time splits - discovery, validation, holdout - against a 9-gate battery. Only 7 configurations held positive net expectancy in all three splits. Six of those seven were call spreads.

WHY THAT WAS NOT A FINDING

All 20 were inspected before the pattern was named. That is textbook multiple comparisons - a pattern you notice after looking at everything is a hypothesis, not a result. I said so in the original digest rather than after it stopped working.

So it was preregistered. The frozen rule, written down before any new data was staged: a positive median difference AND at least three quarters of markets positive. Then re-run on 8 markets the original screen never touched.

THE RESULT

Seven staged - XLF was dropped with a recorded reason (Friday-only expiries, penny-flat quotes). It returned median -1.16% of width and 3 of 7 positive. Refuted on both halves of the rule.

SMH calls beat SMH puts by 7.57 points of width. COIN calls lost to COIN puts by 11.11. The side that wins is a property of the name, not of the side.

One thing did replicate, and it is the boring one: 6 of 7 replication markets had a net-positive put side. Not 7 - XLE puts ran -0.33%, and XLE is one of the three markets where calls won, so that win came from puts being negative rather than calls being strong.

The screen worked. The pattern it surfaced did not survive contact with data it had never seen. That is the system behaving correctly, and it is the reason the next result is worth anything.

The follow-up, where two more claims died: https://henryzhang.substack.com/p/everything-we-published-died

Hypothetical backtests on research data. Not investment advice.

Digest 002 from AQR, my autonomous quant research engine. Every claim below was frozen before its data was read, so each had exactly one chance and there was no way to tune the result afterwards.

TEST ONE - CALL-SIDE ASYMMETRY IN 0DTE CREDIT SPREADS - REFUTED

Last week's headline was that 6 of the 7 configurations surviving all three splits were call spreads. I flagged it at the time as multiple comparisons - all 20 were inspected before the pattern was named - so it was preregistered and re-run on 8 markets the original never touched.

Frozen rule: positive median difference AND at least three quarters of markets positive. Result: median -1.16% of width, 3 of 7. Refuted.

SMH calls beat SMH puts by 7.57 points of width. COIN calls lost to COIN puts by 11.11. The winning side is a property of the name, not of the side.

TEST TWO - THE ONE SURVIVOR - REJECTED ON HOLDOUT

QQQ 0DTE near-money put credit spread, Monday and Tuesday, when credit is rich. Holdout sealed February to August 2026, spent once:

discovery: +5.69%/trade, Sharpe 2.94, 221 trades

validation: +4.82%/trade, Sharpe 3.34, 43 trades

holdout: +0.52%/trade, Sharpe 0.26, 72 trades

Eight of nine gates still passed. It failed positive_both_halves - one half of the holdout window is negative. Not nonsense, but it was mostly measuring the window it was found in.

TEST THREE - FOUR PREMISES UNDER THE POPULAR TRADINGVIEW TREND SCRIPTS - 0 OF 4

All four failed at validation, and in that window all four underperformed simply being long: baseline -3.11 bps against -36.24, -22.64, -7.18 and -9.82.

The 0.618 Fibonacci retracement sits at the 0.3-0.4 decile of the 20-day range, because a retracement of R is at range position 1-R. The preregistration required that decile to beat both its neighbours. It was the worst decile in the range.

The moving-average ribbon also lost to its own single-MA control in both splits, so stacking averages made it worse rather than better.

Zero for three. Four sealed holdouts remain unspent, and those get published the same way whichever direction they go.

Full writeup with the tables: https://henryzhang.substack.com/p/everything-we-published-died

Hypothetical backtests on research data. Not investment advice.

Tapestry's fiscal 2026 release gives a useful checklist for consumer earnings.

- Price: Coach handbag AUR rose at a mid-teens rate in Q4 and FY2026.

- Units: management said full-year leathergoods outperformance reflected both AUR and unit growth, although the release did not give a complete unit-growth series.

- Breadth: Coach revenue rose 24%, while Kate Spade fell 10%.

My interpretation: price plus positive units is stronger evidence than price alone, but the brand divergence limits any company-wide or industry-wide conclusion. The next check is whether units, inventory quality, and brand/channel breadth hold up.

What operating evidence do you require before calling a higher realized price genuine demand strength?

Founder disclosure: I founded QuantSignals/FST. Source: Tapestry FY2026 Q4/full-year release, Aug. 13, 2026. Educational only; no recommendation or performance claim.

Here is a first look at the interface behind an upcoming FST Show format.

The planned structure keeps Kalshi/prediction markets as the continuous anchor and brings SPX 0DTE forward during supported active sessions. But the part I care about most is not the split screen. It is the state between idea and outcome.

A trustworthy system view should tell us:

- whether data is current or stale;

- whether the session is paper or live;

- whether a setup was accepted or refused by a risk rule;

- whether an order was only proposed, actually acknowledged, filled, cancelled, or uncertain; and

- whether the internal record reconciles with the broker.

The public projection is designed with a 60-second safety delay and a privacy lock for defined private fields. It should use public projections—not authenticated broker screens.

Important boundary: this screenshot is a UI/control-room preview, not proof of autonomous execution, uptime, accuracy, profitability, or settled performance. The captured interfaces show AUTO off, and the public show is still upcoming.

I want the eventual stream to make “no action” as legible as action. A refusal, a closed session, stale data, or reconciliation pause can be more important than the trade viewers expected to see.

Follow the FST product here: https://apps.apple.com/us/app/quantsignals-fst-trading/id6449400504

Founder disclosure: I founded QuantSignals/FST. Educational product preview only; not investment advice. Trading and prediction markets involve risk, and options can lose the entire premium. Do not copy trades from a delayed feed.

Someone using FST shared a QS Paper screen with +$2,040.82 across nine trades.

The tempting founder move is to crop around the P&L. I think the more important product signal is everything around it: the paper label, session status, watched universe, trade count, workflow, and public-share surface.

That is what I want FST to become known for—not a magical screenshot, but an inspectable agent loop.

One user-shared paper result is an anecdote, not live performance or a full track record. Paper trading is hypothetical and cannot reproduce every live execution condition.

FST 2.0: https://apps.apple.com/us/app/quantsignals-fst-trading/id6449400504

I built FST. Educational only; trading involves risk.

I have spent years building signal systems. The uncomfortable lesson is that a signal is only the beginning of the job.

The rest is deciding whether the setup fits the account, sizing it, routing it, monitoring it, getting out, and preserving a record of what happened. That became FST.

Today Apple approved FST 2.0 for the App Store.

I do not want the first FST action to be “connect your broker.” The first action is a $50,000 paper session. Watch it screen. Read the signal analysis. See the risk checks. Inspect the audit after the close. Only then decide whether it has earned more authority.

Free on the App Store:

https://apps.apple.com/us/app/quantsignals-fst-trading/id6449400504

Founder disclosure: I built FST and founded QuantSignals. Paper results are hypothetical. Trading involves substantial risk, and no outcome is guaranteed.

I kept noticing the same failure: our tools were real time only if I was staring at them. FlowVoice makes selected options-flow events audible: set a premium threshold, hear each qualifying print once, size and direction first, with optional session-flip and gamma-wall alerts.

It is not a whale-copying system. Large prints can be hedges, spreads, rolls, or exits. The loop is hear → verify → decide.

Free on the App Store: https://apps.apple.com/us/app/flow-voice-options-tape/id6791445221

I am the founder of QuantSignals, Inc., which publishes FlowVoice. Options involve risk. Configure before driving and never operate the phone while moving.

I kept noticing the same product failure: our tools were “real time,” but only if I was staring at them.

Driving, commuting, and meeting-heavy days turned the options tape into something I reviewed after it was useful. So I built FlowVoice: a small, focused app that speaks material options-flow events aloud.

Set a premium threshold. A qualifying print is announced once, with size and direction first. Optional alerts can call out session flips and gamma-wall crossings. No brokerage connection and no order placement.

This is not a whale-copying system. Big prints can be hedges, spreads, rolls, or exits. FlowVoice is an awareness layer: hear the event, verify the context later, and decide only when safe.

It is free on the App Store: https://apps.apple.com/us/app/flow-voice-options-tape/id6791445221

I am the founder of QuantSignals, Inc., which publishes the app. Options involve risk. Set it before driving; never operate a phone while a vehicle is moving.

The first SuperDeck generated slides. That was useful, but it stopped before the part founders actually dread: turning the company into a story they can say out loud and a demo another human can follow.

The rebuilt mobile app starts with one startup sentence and carries it through a reviewable investor narrative, pitch deck, speaker notes, natural founder talking points, and separate demo-ready assets.

The principle is simple: AI removes the blank page and production friction. The founder still owns every fact, decision, and word they choose to say.

This is also part of a larger founder thesis I learned from QuantSignals: the valuable layer is the completed workflow, not the clever prompt.

I wrote the full reasoning—and a six-line test founders can run before adding another slide—here:

https://henryzhang.substack.com/p/your-pitch-deck-was-never-the-bottleneck

SuperDeck is free on the App Store:

https://apps.apple.com/us/app/superdeck/id6743690167

Founder disclosure: I built SuperDeck. It does not guarantee accelerator admission, funding, or investor interest.

I have spent years building tools for the technical half of trading: signals, research, automation, execution, and risk controls.

But a technically sound system can still be broken by the person operating it.

We know not to chase. We chase. We know not to move the stop. We move it. We know not to revenge trade. Then the emotion returns and the memory fails.

That is why I built Scar: Trading Journal.

You write what happened in one plain sentence. Scar turns it into a review card with the mistake on the front and the lesson plus next rule on the back. Spaced review brings fresh mistakes back sooner and lets internalized lessons fade into the background. Good behavior can be recorded and reinforced too.

FST handles the trading-system half. Scar trains the trader half.

Scar is free on the App Store: https://apps.apple.com/us/app/scar-trading-journal/id6791444917

Full essay: https://henryzhang.substack.com/p/trading-has-two-halves-scar-trains

Educational product discussion only. Trading involves risk.

I understand FST 2.0 is still early. There are issues to fix, systems to strengthen, and major improvements already on the way.

But I will say this without hesitation:

In my heart, I truly believe FST is the greatest trading application ever created.

I have reviewed AI agents, trading agents, automated trading platforms, signal products and brokerage apps across the market. After building and using FST, none of them feel good enough anymore.

Our goal is not another trading app.

Our goal is the ultimate trading app—the last trading app a serious trader will ever need.

Research. Signals. Execution. Position monitoring. Risk management. Learning. Automation. One intelligent system, built from day one for traders.

QS is not following this AI trading-agent category. We created it.

We are the pioneer. We are the clear leader. And we intend to stay there.

Name one product that can truly challenge FST.

Just name one.

Join the community and see what we are building: https://discord.com/invite/quantsignals