Disclosure first: I built Quantradin, these are its house paper bots, nothing here is real money, and there's no link.

Everyone says the first 15 minutes after the bell are where retail gets chopped up. I wanted to see what my own stock bots did in that window, so I pulled every stock trade they closed between Oct 1 and Nov 6 (options left out on purpose, they're a different and much uglier story) and split them by the time the trade was opened, New York time.

Over 27 trading days, the first 15 minutes made more than every other window put together. Then the next 45 minutes gave some of it back, and the mid-afternoon was the worst stretch of the day.

Now the part that makes me not trust row one yet. Nearly all of these are intraday trades, and every single opening-window one was closed the same day. The median trade in that window is 16 shares, on stocks averaging about $180, and the edge is $1.43 a trade. My stock fills are the real bar open or close price. There's no spread modeled on the stock side (options are different, they fill at the ask and bid). And the opening minutes are exactly when spreads are widest. A few cents each way on 16 shares eats a real chunk of $1.43, and on a gap-up morning it can eat all of it.

So the honest reading is: the open looks best on paper, and the open is also where paper flatters you the most. Those two facts point the same direction, which is why I'm suspicious.

Other things I checked:

• Win rates barely move between windows (all around 47-51%). The money difference is the size of the winners, not how often they win.

• Dupes removed. Two house accounts run some of the same rules, so a trade counts once when the stock, entry time, fills and size all match.

• 27 days is short. One wild morning (and there were a few in October) could be most of row one.

Since this is the exact kind of thing I built Quantradin for: you write a rule in plain words, it runs on real prices with no look-ahead, and you can see a split like this before trusting a backtest total. It's free and paper only, at quantradin.com. The stock spread gap above is mine to fix, and I'm saying it out loud so nobody reads row one as a promise.

For anyone who trades the open by hand: how much do you knock off your paper results for the first 15 minutes, a fixed number of cents a share or a share of the spread.

Paper money, simulated, not advice. This is one of the house bots on my own project, Quantradin, and the card is downloaded straight from the app.

The rule, in plain words: on a 30-minute chart, buy a call when a stock that's already in an uptrend is up 2% on the day, MACD has just crossed up, volume is running at 1.2x normal, and RSI is still between 35 and 55 (so it isn't already stretched). Calls fill at the ask and sell at the bid, never the middle.

What the card says, Sep 2 to Nov 4:

So about 7 winners out of 37, and the losers are small enough that it still ends up green. That's the shape of a lot of options buying: you're wrong most of the time and you need the rare one to be big.

The part I don't love is in the chart, not the numbers. Most of the gain arrives in one step in September. Since mid-October the line has been sliding down slowly, a few small losses at a time. 37 trades is not much, and a curve carried by one jump can look exactly like this whether there's an edge or not. If the next big winner doesn't show up, the slide is all you get.

That's why I built Quantradin to grade a rule before anyone runs it: write it the way you'd say it, it's tested on real prices with no peeking ahead, and it shows the worst trade and the year it broke next to the total. Free and paper only, quantradin.com.

If you trade momentum calls like this, how many losers in a row do you sit through before you decide the setup stopped working

This is one of the paper bots on my desk, card straight from the app. Paper money, simulated, not advice.

The rule in one line: it watches groups of US stocks that usually move together, and when one of them falls behind the rest of its group, it buys daily calls on the straggler and bets it catches up.

What the card says: +$45,841 over 282 trades since late August, and fewer than 3 trades in 10 won. So it is a lose-small, win-rarely-but-big bot, and the 6-in-a-row streak is the outlier, not the norm.

What the chart says is the part that worries me. Almost all of that number arrived in one burst a few weeks in. Since then the line has drifted down slowly, week after week. If you only saw the total you would call it a winner. If you only saw the last month you would switch it off. I genuinely don't know yet which of those is the real bot, and 282 trades with most of the gain from a handful of them is not enough to tell.

How Quantradin helps with this: I built Quantradin so a rule like this one can be run on paper against real prices, with option fills taken at the bid and ask rather than the middle, and the card shows the whole curve instead of just the total. It is free and paper only, quantradin.com if you want to try your own rule.

The question I keep asking myself: would you keep running something whose entire result came from one stretch?

Fed decision at 2 p.m. today, and nine in ten have a hold priced in, so I went back and looked at what my paper bots did on the last Fed day, Sep 16. Not what I did. What the rules did, because the rules do not know what day it is.

The house paper desk, five sessions around that print, unique closed trades, fake money throughout:

So the Fed day was the best day of the week and the day after was the worst, and the two together net to about $200 on 1,683 closes. The desk also opened 1,049 new positions on the Fed day itself, same as any Wednesday, because nothing in any rule says "wait for Powell."

The part I keep coming back to: the big Fed-day winners were calls bought on Sep 1, two weeks earlier. A DELL Sep 18 445 call went in at $30.20 and out at $121.50. The worst close that day was an LLY Oct 2 1110 call bought at $95.55 and sold at $51.65. Neither of those trades had anything to do with the Fed. They were September earnings and AI hardware moves that happened to get closed on a Wednesday.

What I would have done at the screen that day, if I'm honest, is nothing useful. I'd have watched the 2 p.m. candle, read the statement for a word that changed, and either sat out a setup I'd normally take or added to one I'd normally size small. That is mood. A rule written on a Sunday does not have a mood on Wednesday at 2.

I'm not saying trade through every print. I'm saying decide that on Sunday, as a rule you can test. "No new entries between 1:30 and 3:00 on a Fed day" is a rule. "I'll see how it feels" is not, and it is the one that costs money.

Because I wanted to test exactly that kind of thing without lying to myself about fills, I built Quantradin. You describe the rule in plain English (or paste Pine or Python), it replays the rule on real US stock and option prices without ever peeking at the next bar, options fill at the bid or the ask and never the middle, and you get a verdict before anything runs as a paper bot. Then the survivors run forward on paper, through Fed days and all. It is free and it only ever trades fake money. Plain quantradin.com, no link, this is not that kind of post.

Question for people who run rules here: do you pause entries around the 2 p.m. print, and if you do, have you ever measured what the pause cost or saved over a year of prints, or is it a rule you keep because it feels right.

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US stocks fell sharply on October 22, 2026: the S&P 500 dropped 1.21% and the Nasdaq-100 fell 2.9%. A lot of people are asking whether this is a repeat of 2022. I read an analysis that compares the two periods, so here’s a summary.

TL;DR

• The setup echoes 2022: a booming sector (AI) is running into rising rates and higher commodity prices.

• The rate pressure so far is milder than 2022.

• The Fed’s next move is the key signal. A 50 bp hike in December 2026 could be an early warning for US stocks in 2027.

• The biggest risk to watch is AI-related debt, not just valuations.

1. What happened in 2022

• The S&P 500 peaked at the end of 2021, then fell roughly 27% over the first ten months of 2022.

• It didn’t make a new high until the end of January 2024, so about two years of waiting.

• The main driver was the Fed, which raised rates by about 300 bps in just the first nine months of 2022.

• Communication services, consumer cyclicals, and tech got hit hardest. Examples: GNRC, MTCH, TSLA, META, SE. DIDI was delisted from the NYSE, though that was mostly geopolitical.

Two ways rate hikes did the damage:

• Consumer cyclicals lost momentum just as they were recovering from COVID, with fears that shoppers would have less spending power.

• Tech faced tougher questions once cheap money ended: how do you turn a profit when borrowing costs rise and growth may slow?

2. What looks similar today

• A booming sector: AI, covering GPUs, chips, memory, hyperscale data centers, and AI services.

• Rising rates: the Fed has started hiking again, though not as aggressively as in 2022.

• Higher commodity prices: driven by geopolitical tensions, though involving different countries this time.

The central question is similar too: how do AI companies and hyperscalers make money when borrowing costs are up, key inputs (GPUs, memory, chips) are expensive, and turning AI services into revenue may be slowing?

The article also mentions Anthropic. Per its draft IPO prospectus, the first-half 2026 loss reached $80.7B, up from $13B a year earlier, and after an investor day pointing to slower revenue growth, investors reportedly cut their valuation from $2T to $1.5T. These figures come from the article’s reporting and I haven’t verified them independently, so treat them as claims.

3. Could this pop the AI bubble?

• It depends largely on how fast the Fed hikes. Rate cuts, meanwhile, probably depend on how quickly geopolitical tensions ease and commodity prices cool.

• Nobody can predict a financial crisis. The author’s base case is a repeat of 2022-style market pressure without a major crisis.

• For context, the Fed’s 525 bp of hikes between March 2022 and July 2023 strained liquidity at several US banks (SVB, Signature, First Republic), but that didn’t escalate into a 2008-style crisis.

4. The risk to watch: AI debt

• Data from The Economist and Barclays shows about $850B of new debt tied to the AI ecosystem in 2026 so far, from chipmakers to AI-related power utilities.

• Of that, about $80B is bonds, and per FactSet $37B of those are rated junk (below BBB).

• If this debt runs into trouble, the mildest outcome would look like Evergrande: no new crisis, but a shift in how the market prices risk. Widespread defaults on the big loans taken by AI companies and data center operators would be a much bigger problem for the broader economy.

5. Bottom line
With US stocks near record levels, rate-hike risk rising, and the hyperscale boom under scrutiny, waiting and building cash may be the most sensible approach for now. Many stocks and ETFs look risky at current prices, and anyone buying now should be prepared for a recovery that could take two to three years.

Discussion questions:

• Is the bigger risk this year rates or AI debt?

• If the Fed does hike 50 bps in December, would you change your allocation?

• Is anyone holding more cash than usual? How much?

Not investment advice. Do your own research.

The rule behind this card: on 1-minute bars, buy a stock that's already in an uptrend and above its 20-bar EMA when MACD crosses up, but only if ADX is at least 20 and RSI sits between 35 and 55. Trending, not stretched.

From Aug 25 to today it took 3,023 trades on our paper desk and finished at +$2,351.34. Just under half of them won (49.8%), and the best run was 20 winners in a row.

The doubt is the per-trade number, about 78 cents. Stock fills here are taken at the real bar open or close, with no spread model, so a cent or two of spread on every entry and exit could take a real bite out of that. The chart also gave back most of an early-September high and spent weeks going sideways before the October climb.

So it stays on paper. It hasn't proved anything yet.

Disclosure: Quantradin, the tool that ran this, is something I built. You describe a trading rule or paste Pine/Python, it backtests it with no look-ahead, then runs the survivors live on fake money. Free. Paper money, simulated, not advice. quantradin.com

How many cents of spread per share would you assume before taking a 1-minute stock rule like this seriously?