Moderna basically cured cancer, so I used Grok Bot to create a trading strategy on it. It’s DESTROYING the market.

LinkedIn is drowning in AI trading apps.

This one audits your habits. Others launch backtests or generate signals. Some are useful. None of those demos answer the question I care about.

Would you give the AI real money and publish every fill?

I did.

Funded in February, first trade May 5, and up 26.58% in the 108 days since against SPY's 6.64%. A nineteen-name rotation of long-dated calls, built by describing it to an AI in plain English. I was writing about the same idea three years ago, using GPT-3. Every fill is public at that link.

$25,000 live account vs SPY · May 5 to August 21, 2026

Both indexed to 100 at the first fill. Portfolio series from the public shared-portfolio feed, SPY from the same price API. 108 days.

People call it dumb luck. So I am going to do it again, this time around the first positive Phase 3 result for a personalized mRNA cancer therapy.

The same platform Covid-19 accelerated. A new target: recurrence after melanoma surgery.

This time I am not going to hold the AI's hand. xAI shipped Grok Bot, an autonomous agent that gets its own cloud computer, signs into your services, and works for days without you. This is the test to see how good Grok Bot really is at developing trading strategies.

I pointed it at the Public Portfolio Challenge runbook, the same discipline that built the account above. Then I gave it the thesis.

The edge is simple. Moderna's innovation proves AI is not a fad. Which means two things: biotech is about to have its most explosive rally ever, and AI is not going to stop.

That was the whole brief. A few hours later it came back.

Before I show you what it built, you need to understand what Moderna actually did.

What actually happened, and why it is an AI result

On August 19, Moderna and Merck announced that their personalized cancer vaccine passed a Phase 3 trial. Every patient had melanoma a surgeon had already removed and was at high risk of it returning. Both arms got Keytruda; one arm also got a vaccine built from their own tumor. Did the cancer come back less often, and did it spread to distant organs less often? Yes to both. Nobody has released survival data, so we do not know yet whether people live longer. It is still the first Phase 3 win ever recorded for a personalized mRNA cancer therapy.

It is built one patient at a time. Sequence the tumor, compare it to healthy tissue, and you get hundreds of mutations unique to that person. Then the step that made me want to trade this: an AI model reads every one of them and keeps the 34 most likely to provoke an immune response. Everything on either side of that prediction is chemistry Moderna already knew how to do.

Moderna has said this on their own site since December 2023. Their program materials label the step plainly: "Up to 34 neoantigens. Automated algorithm integrated with workflow." And in the Phase 1 data, every immune response they measured came from a target the algorithm had predicted would work.

The hard part was never synthesizing the mRNA. It was choosing what to put in it, and an AI model did that. Biology just became a compute problem.

Which is exactly why I am still long AI

I know the prevailing wisdom on social media and in the mainstream media is that AI has run its course.

That is backwards, and Moderna is the proof.

An individualized neoantigen vaccine creates a fresh compute workload for every patient. Each dose requires tumor and healthy-tissue sequencing, variant calling, neoantigen ranking, and the scheduling of a bespoke manufacturing batch. Melanoma alone is roughly 100,000 new US cases a year, and intismeran trials are already running in lung, kidney and bladder.

That is the bridge to silicon: more personalized programs mean more recurring sequencing, model inference, storage and networking. It does not prove the revenue impact will be material to any chip company. That uncertainty is why semiconductors are a separate forward thesis and a separate book, rather than a result I smuggled into the Moderna readout.

Two theses, so I created two agents in Grok Bot.

How I used Grok Bot to create my AI and biotech strategies

Grok Bot is only as good as the tools you hand it. Left alone with a browser it will read Reddit and guess. I gave mine three surfaces into one engine: MCP for the entire toolbox, the Python SDK for anything that has to run a hundred times, and a browser it barely touched.

MCP is where all of the work happens. NexusTrade exposes 122 tools over it, and they are not only execution. Backtesting, walk-forward certification, options chains and deploys on one side. News search, SEC filings and web research on the other. Grok Bot never had to leave the connection to build either book. Connecting is one config block and an API key.

Live orders only ever stage. Nothing Grok Bot decides reaches the market without me clicking approve.

That includes the filings. Ten-Ks, 10-Qs, 8-Ks and the news wire, queried the same way as a backtest. It is why Personalis, which runs the tumor sequencing behind the Moderna program, is one of the twenty names the book trades. Tempus AI agreed to buy it on August 21 at a 28% premium, two days after the readout and three days after my measured window closed.

The Python SDK handles volume (pip install nexustrade). Studies enqueue and poll, so the Bot fires off a hundred backtests and collects them later.

The browser is pure redundancy. It can open NexusTrade and read the UI. Mine never needed to.

The rules it came back with

Here are the two original books, rule for rule. Not summaries, the literal conditions Grok Bot returned. The deployment architecture changed later, but these are the strategies that produced the sealed results.

The original books, rule for rule · every value read off the tested portfolios

BUY and SIZE are the pair that matters in both. A few hundred dollars of premium carries the exposure of several thousand dollars of stock, so each book runs a median of 15.44% deployed for biotech and 14.70% for compute and still moves like it is fully invested. That cuts both ways, and it is where the drawdowns come from.

I made Grok Bot prove it on data it was never allowed to see

Anyone can produce a backtest that looks incredible. Turn enough knobs against enough history and you will always find a setting that would have made a fortune, and then it dies the month you fund it. That is the whole problem with the genre.

So I made the tuning and the grading happen on different years. Grok Bot was allowed to adjust the rules on one stretch of market history, then graded on the year immediately after it, which it had never been shown. Then everything slid forward and repeated. Four times. Those four graded years are the four bars below, and no rule was ever tuned on the year it was graded on.

If you are quant-minded, you recognize this as walk-forward optimization.

Two rules on top of that. It had to deploy the settings that held up across all four graded years, not the settings that won the best one, because shipping your single best year is how you overfit while feeling rigorous. And the data stopped at April 14. No rule was tuned on a single day after that date, and I did not look at what happened next until the design was frozen.

The other place options backtests die is fills. Every fill is priced against real OPRA bid and ask, roughly 400 million quote rows per trading day. The engine does not fill at the midpoint. It fills at the midpoint plus half the spread against you, going in and coming out, and rejects contracts wider than the strategy's spread limit before the order is built. Minutes with no quote fall back to a measured spread table bucketed by moneyness and expiry, so a far out-of-the-money LEAP is charged about twice what an at-the-money one is.

Four graded years neither agent had seen · each book against its own benchmark

Eight graded years across the two books, every one positive, and seven of the eight beat their own benchmark. ARKG actually lost money in three of the four biotech years while the book made 26%, 25%, 56% and 21%. The semis book beat SMH in all four. No single lucky stretch dragging up an average, which is the failure mode that kills most strategies the day they go live.