Part 4 of 4 — the results. Part 1: every safety net failed silently · Part 2: the free stack · Part 3: publishing signals

The first three parts were about how my portfolio monitor works and how its safeguards failed. This one is the question everybody actually asks: do the AI buy signals make money?

The portfolio is up 13.8% on cost basis. Last post, three months ago, that number was 6%. I’ll keep the balances to myself — the ratios are the only part that carries information anyway, and a tool whose whole design premise is “don’t publish your position sizing” is a poor place to publish mine.

Now the four reasons that number tells you much less about the AI buy signals than it looks like it does.

Four caveats before you read anything into it

• It’s return on capital deployed, not a time-weighted return. I’ve been adding cash throughout, as the tool flagged gaps — that’s what it’s for. So a dollar that went in last week counts the same as one from February. You can’t line this up against “the S&P did X% over the same window” and call the difference alpha.
• The market was up too. Most of 13.8% is beta, not skill. I haven’t done the work to separate them, and I’m suspicious of anyone claiming they have on a six-month sample.
• One portfolio is not a backtest. N=1, no control, and I chose which AI buy signals to act on. The tool suggests; I still click the button.
• Six months is noise. Any strategy can look good over one up-trending half-year.

What I’d actually want to know about these AI buy signals — whether the STRONG BUY gate has predictive value — needs far more STRONG BUYs than it has produced, which is by design. Requiring all three models to agree made them rarer still, and that rule is why I eventually relaxed it: a gate this quiet can’t be evaluated. Ask me in two years.

The uncomfortable part: my best trade broke my own plan

44% of that gain came from a single position, MSFT. And MSFT was never in my target allocation.

It was on the watch list: a feature for tickers I’m interested in but haven’t committed to a target weight. On 21 June it came back a STRONG BUY at 88% confidence, Value A. I read the thesis, set a limit order where the card told me to, and it filled at $370.10. Off-plan, discretionary, exactly the kind of decision allocation targets exist to prevent.

Here’s the card, verbatim:

MSFT · STRONG BUY · Value A · 88%

(Watch) Exceptional multi-indicator bottom setup: trailing P/E of 22.6 is below the historical average of 23.4 (price-level signal), 52w position of 12% (price-level signal), price -15.9% below 200MA (price-level signal). RSI of 18.6 is deeply oversold, Stochastic %K of 6.6 is deeply oversold, and %B of 0.11 is at/below the lower Bollinger Band — all momentum signals confirming. Forward P/E of 19.6 below historical avg, 34% ROE, 23.4% EPS growth, low debt, analyst target of $561 (+48% upside). Death cross and bearish MACD are risk flags, but all other indicators overwhelmingly support a bottom-forming scenario. Historical conviction has been strengthening (42% → 88% → 70%). Risk: macro headwinds (high yields, strong USD) may extend the drawdown.

Momentum: bearish · RSI 18.6 · 50MA $412.98 (-8.1%) · 200MA $451.35 · death cross · MACD hist -5.027 · %B 0.11
Limit order: $370 — Set just below current price at ~$370, near the prior consolidation zone and round-number support; provides a small buffer given ATR-implied volatility
Bottom signal: RSI < 30, price below 200MA, death cross, 52w position < 30% — 4 of 4 bottom indicators active for a stock

RSI 18.6 on a mega-cap. I had to re-read that. A reading that deep on the largest companies in the world is rare — it’s what you get in a capitulation, which is precisely what the reasoning says. Two days later the same ticker printed 9.3, and that card is a story of its own.

The generous reading is that the feature did its job, and I’d go further: this is not a marginal call the gate waved through. Six independent signals — three price-level, three momentum — four of four bottom indicators, forward P/E below its own historical average, and the part I care most about: the risks are named, not buried. Death cross present. MACD bearish. Momentum bearish. Macro headwinds that may extend the drawdown. The model argued past those rather than omitting them, and left me a written thesis I could audit six weeks later.

That auditability is the real product. AI buy signals you can’t interrogate afterwards are just a number with a colour.

The historical-conviction line is the reasoning-persistence store paying off, too. Three features compounding: the watch list surfaced a ticker I didn’t own, the bottom-fishing model caught the extreme, and the 7-day history put the reading in context.

There’s also a fourth mechanism that was supposed to be involved and wasn’t. Both of these cards — the 88% I bought on and the 91% two days later — came from Claude alone. Gemini was quota-exhausted, so the cross-model agreement check never ran on either, and nothing in the email said so. The number I acted on was a single model’s opinion wearing the same badge a two-model consensus would have worn. That story is Part 1, and it’s the reason v1.9 exists.

I followed the one instruction it gave me. That was the mistake.

The card told me to set a limit at $370 — “just below current price at ~$370, near the prior consolidation zone and round-number support.” I did exactly that, at $370.10, and it filled.

Then MSFT came back a STRONG BUY the next day, and the day after that — 88%, 88%, 91% across 21, 22 and 23 June — trading lower each time. There was no window closing on me. The signal was still there three days later, and by then the same feature was naming a different price: $360, reasoning from the 90-day consolidation floor with the 10th percentile at ~$354. MSFT traded down to $349.20 shortly after.

Put those two limit prices side by side and the flaw is hard to unsee. The second one is anchored to a distribution — where the stock has actually spent the last 90 days. The first is anchored to the spot price: a small buffer below wherever MSFT happened to be trading that morning, dressed up in the vocabulary of support. Only one of them is a limit price in the sense I built the feature for. The other is “buy it now, but ten dollars less.”

That cost about four percentage points on the position: 33.7% instead of the 37.5% a $360 fill would have returned. Trivial in absolute terms, and the most instructive line in this whole series — because I did everything right by the process. I read the reasoning, I agreed with it, I placed the order at the number it gave me. There was no 11pm override to blame.

There is one tell, though, and it’s mine. The card said $370 and I set $370.10 — ten cents up, to be sure of the fill. I wanted the position more than I wanted the price, and I expressed that in the only digit the tool had left me.

If you build AI buy signals for yourself, the lesson isn’t “trust the limit price” or “don’t.” It’s that a suggested number gets exactly as much credibility as the thing it’s anchored to, and a model will narrate a spot-price offset in the same confident register it uses for a real support level. I couldn’t have caught that from one card. It took three of them across three days, which is the argument for persisting the reasoning rather than just acting on the number at the bottom of it.

The honest reading of six months of AI buy signals

• A good thesis and a good outcome are different things. Everything in that card was true on 21 June, and it would still have been true if MSFT had kept falling. “Buy the capitulation” works until the death cross the model waved past turns out to have meant something. I got the outcome; I can’t claim the process is validated by it.
• N=1. If this trade had gone the other way, the same feature and the same reasoning would have produced the opposite anecdote — and I probably wouldn’t be writing a section about it. That asymmetry is worth naming, because it’s how every tool’s success story gets written.
• The split is unflattering to the plan. That single off-plan position returned 33.7%. The other fourteen — only nine of which are the actual target allocation — returned the remaining 56% of the gains on a much larger base, about 9.4%. My least disciplined decision beat my whole system by 24 points, which is either a lesson about signals or a lesson about small samples, and I genuinely don’t know which.
• 44% of gains in one unplanned position is a concentration risk, not a result. I’d flag it red in anyone else’s portfolio.

So I’m recording it, not celebrating it — one lucky trade is not evidence about AI buy signals in general. Ask me again after the tenth watch-list buy.

The bug this exposed

There’s a design gap underneath the anecdote, and it’s more useful than the anecdote.

Buying a watch-list ticker gives it nowhere to go. I took MSFT off watching, but never gave it a target allocation — so it became what the codebase calls a held-only ticker: held, no target, not watched. Its allocation gap is permanently negative against an implied 0% target, which means:

• the daily brief rendered it as HOLD/WAIT noise (“MSFT is N% overweight vs. a 0% target”), and
• the weekly rebalancing report handed it a standing TRIM, every week, forever.

My best-performing position was being told to sell itself on a schedule, because I bought it through a path with no final step. The watch list can surface a ticker and hand me a signal, but there is no promote to portfolio — no moment where a watch-list buy becomes a target allocation with a real percentage.

I’d written a design for half of it back in June, before MSFT gave me a reason to care — the standing TRIM was already visible on other holdings, just not on one I felt anything about. It shipped in v1.10: held-only tickers route to a separate untrackedItems array, so they stop generating buy and trim recommendations while still counting toward portfolio value, beta, dividends and ETF overlap. The weekly report now lists them neutrally — value and current %, no action verb.

The nice side effect is token cost. Every consumer that feeds the AI iterates the tracked-items array, so removing them from it took them out of the prompt entirely. Four tickers I was paying to have opinions generated about, opinions I could never act on.

That still doesn’t fix the real gap. Silencing the TRIM is not the same as deciding what MSFT’s target weight should be, and that decision is mine, not the tool’s. The most a tool can honestly do is stop pretending a deliberate off-plan holding is a rebalancing error — and then leave the question with me.

One more thing I only noticed while implementing it. Two other holdings, MU and MRVL, sit in both currentHoldings and watching, because I bought them off watch-list signals and never cleaned up. That combination accidentally did the right thing all along: watched tickers were already excluded from the allocation table, so those two never got the standing TRIM the others did. I’d been running the fix by hand, on two tickers, without knowing it.

Where that leaves the experiment

Six months in, the honest summary is that I can’t tell you whether AI buy signals beat buying the index, and neither can anyone else with one portfolio and one up-trending half-year. What I can tell you is what changed about using it: I read the briefs again. Before the frozen-data guard, a Telegram buzz meant “maybe something happened,” and I’d started ignoring them.

That’s the real failure mode for a tool like this — not being wrong, but being noisy enough that you stop reading it. Fixing the noise was worth more than any individual recommendation — AI buy signals you’ve stopped opening are worth exactly nothing.

Still looking for alpha testers

Richfolio is open source and free to run: github.com/furic/richfolio, docs at furic.github.io/richfolio. Fork it, add your allocations to a config file, set a Gemini key, and GitHub Actions does the rest.

Most useful to me right now:

• Non-US portfolios, especially LSE/JSE tickers — the sub-unit currency guard in Part 1 came out of exactly one such bug, and I doubt it’s the last
• Larger universes (20+ tickers) — both truncation bugs this cycle came from a big universe, and mine isn’t big
• Anyone running two AI providers — Gemini + Mistral is now free on both sides, so this costs nothing to try. I’d like to know how often two models actually disagree on portfolios that aren’t mine, and how far apart they land when they do — a one-rung disagreement and a flat contradiction now mean different things to the consensus rule, and I’ve only ever seen my own sample

Open an issue on the repo or DM me.

Nothing here is financial advice. It’s a description of a tool I built for myself and the mistakes I made using it.