AiPortfolios

Public record of 10 AI paper portfolios ($100k each).
Every decision timestamped & published before the outcome is known.
No backtests. No deleted trades.

[AiPortfolios @AIPortfolios186]

Leaderboard day 3:

Crypto 24/7 is leading by a wide margin followed by Digital Assets & Equities as it also holds a Crypto Position.

I'll prepare some explanation tweets for the next 2 days why a stock or asset moved that way.

[AiPortfolios @AIPortfolios186]

i‘m still recovering from surgery, I hope I will be able to bring you more updates tomorrow!

[AiPortfolios @AIPortfolios186]

In a short window we’ve seen crypto momentum, defensive rotation into names like $JNJ , and growth-stock pressure from yields all at once.

If you were designing a simple public experiment to test one idea from this week, what would the rule set look like?

[AiPortfolios @AIPortfolios186]

Same day, Picks & Shovels also bought $TER (Teradyne) as a ~4% paper position.
Teradyne supplies automated test equipment for semiconductors plus robotics systems. Testing complex AI chips (especially memory and advanced logic) is a necessary step before those chips can ship.

AI decision logic (from the published record):
It fits the mandate because test equipment is paid by the entire semiconductor ecosystem foundries, IDMs, OSATs regardless of which specific chip design ultimately wins market share.
Recent quarters showed very strong AI-related demand and sequential growth in the test business.

“Picks and shovels” is not just power and cooling. It also includes metrology, inspection, and testing — the quality-control layer that scales with wafer starts and chip complexity.
$TER gives the portfolio exposure to that layer. Like the rest of the book, it is constrained by position size, sector limits, and the no-mega-cap rule. Hypothetical paper trading only.

[AiPortfolios @AIPortfolios186]

Deep dive on $AMAT

Picks & Shovels (P16) just added $AMAT (Applied Materials) on Aug 19 as a ~5% paper position.
This is a classic “shovels” name: it sells wafer fabrication equipment, advanced packaging tools, and process equipment used by many chipmakers not the end chips themselves.

Why the AI selected it under the mandate:
The rule set only allows infrastructure and equipment providers that get paid by multiple participants in a growing industry (here: AI data-center buildout, leading-edge foundry-logic, DRAM/HBM).
$AMAT sits upstream of the hyperscalers and chip designers. Recent results showed record revenue and strong guidance tied to packaging and memory demand.

In a picks-and-shovels strategy you deliberately avoid betting on which AI model or chip wins. You buy the tools every contestant needs.
$AMAT has delivered strong fundamentals this cycle, yet the stock has been volatile relative to those results a reminder that even infrastructure names can trade on expectations and rates. Paper only. Short sample.

[AiPortfolios @AIPortfolios186]

Whether a name rose (ethereum:native , ethereum:0x514910771af9ca656af840dff83e8264ecf986ca , $JNJ ) or fell ($NVDA, $AVGO, $DAL) in this window, the common thread is that short-term price action is a mix of macro liquidity, positioning, sector rotation, and company-specific news.

The https://t.co/u61Q0opASc experiments force every decision into the open so we can study the process, not just the outcome.

Hypothetical. Short history. Process over prediction.

[AiPortfolios @AIPortfolios186]

What the short sample actually teaches

In just days we have seen:

• Violent crypto squeeze driven by positioning + macro
• Growth-stock pressure from yields
• Defensive rotation into healthcare
• Competition headlines hitting specific chip names

The educational value of these paper books is not the P&L itself. It is watching how different rule-sets (momentum, quality, mechanical 13F, consensus, volatility targeting) respond to the same market regime in public, with irreversible decisions and full audit trails.

[AiPortfolios @AIPortfolios186]

@Stealthct_Storm Yeah i get that. How are you? Im finally back from the OP

[AiPortfolios @AIPortfolios186]

Have you ever changed your own process after watching a transparent experiment (even a paper one) publish every trade and refusal?

What specifically made the difference the winners, the losers, or the rules that forced discipline?

[AiPortfolios @AIPortfolios186]

$LINK outperformed many other cryptos in the short window even as the whole sector rose.

Relative strength (an asset beating its peers) is one of the factors the AI decision records referenced.

In both discretionary ranking and pure momentum experiments, that signal showed up clearly.

[AiPortfolios @AIPortfolios186]

Mechanical 13F copy strategies (like tracking Berkshire’s latest filing with its 45-day lag) vs quality compounder mandates that sit in cash waiting for better prices.

Which approach do you find more interesting to study in real time, and why?

[AiPortfolios @AIPortfolios186]

$IBIT as Bitcoin proxy

In the hybrid Digital Assets book, $IBIT (Bitcoin ETF) delivered solid paper gains alongside spot crypto.
It moved with the same liquidity + regulatory + risk-on wave that lifted bitcoin:native toward $70k.

Useful observation: regulated ETFs can transmit crypto beta into traditional portfolios with different operational characteristics than holding the coins directly.

[AiPortfolios @AIPortfolios186]

Macro thread that moved both crypto and equities

The same Treasury buyback announcement that helped lower yields and fuel the ethereum:native / bitcoin:native squeeze also influenced equity rotation.

Lower yields → support for risk assets (crypto)
But earlier yield spikes → pressure on high-multiple tech.

One macro event, two different effects depending on asset class and duration.

The AI portfolios simply held whatever their rules allowed some caught the crypto wave, others the defensive rotation, others the chip weakness.

[AiPortfolios @AIPortfolios186]

Cash as the quiet winner

Several books (Long-Term Compounders ~68% cash, Digital Assets ~78% cash) kept large cash buffers.
In a week of sharp crypto upside and growth-stock pressure, that cash reduced drawdown risk and preserved optionality.

Educational core: Cash is an active decision, not residual. Volatility targeting and quality mandates both use it deliberately.

[AiPortfolios @AIPortfolios186]

Lesson the platform is stress-testing in public: positive expectancy must survive both the winners and the friction of the losers. Short sample, extreme risk, fully transparent.

[AiPortfolios @AIPortfolios186]

Important educational contrast inside P09:
Open positions ($ETH, $LINK, $MORPHO, $ZEC, etc.) show strong paper marks.
All six closed round-trips so far are losses.

[AiPortfolios @AIPortfolios186]

This is classic high-vol trading reality. Momentum entries can work while the trend is live, but realizing gains (or cutting losers) under retail costs and continuous markets is hard. Turnover caps further limit the ability to chase new names.

[AiPortfolios @AIPortfolios186]

$DAL showed one of the weaker paper performances in the 13F tracker (~–6.5% since entry).

Airlines remain sensitive to fuel costs, consumer spending signals, and any macro caution. Even with solid operational updates earlier, short-term price action can diverge from longer-term thesis.

Mechanical copy simply holds the disclosed weight no discretionary exit.

[AiPortfolios @AIPortfolios186]

Why $AVGO and storage names struggled

In the Billionaire Consensus book, $AVGO and blockstack:native were among the larger paper losers this window.
$AVGO specifically reacted to news that Marvell expanded its custom AI-chip work with Google raising questions about share of spend from a key customer.

Broader chip/storage complex also felt higher yields and profit-taking after strong prior runs.

Educational point: Even “AI infrastructure” names are not immune to competition headlines + rising discount rates. Consensus ownership does not protect against short-term re-rating.

[AiPortfolios @AIPortfolios186]

$CVX and $OXY both showed modest paper gains in the 13F tracker.

Energy has been supported by firmer oil prices and the broader commodity backdrop this year. Integrated majors and producers can act as partial hedges when growth stocks wobble.

Mechanical strategies that inherit these positions simply ride the sector exposure.

[AiPortfolios @AIPortfolios186]

If you had to choose one constraint for an experimental trading system, which would you pick and why?

A) Hard turnover caps
B) Volatility-targeted position sizing
C) Mandatory pre-set stops
D) High cash buffer by design