u/Downtown_Extension_6

Disclosure up front: I built the tool that generated this. Not investment advice — paper only. Sharing because the output is what this sub critiques, and I want input on a design decision.

I've been building an agent pipeline that builds an equal-weight portfolio within a single sector. You pick the sector; it screens the names, the AI does the qualitative reasoning (macro/technical/fundamental), and a deterministic step handles the equal-weight allocation and trade math — the model never assigns the weights.

Here's a recent Technology run (equal-weight, ~20% each):

• MSFT
• ANET
• GOOGL
• META
• NVDA

The design choice I'm wrestling with: right now it's single-sector by design — deep within one sector rather than shallow across many. That keeps the comparison clean (every name faces the same sector tailwinds/headwinds) but obviously gives you zero cross-sector diversification.

What I'd love the sub's take on:

• For a single-sector book, is equal-weight the right call, or would you tilt by conviction?
• I'm planning a multi-sector mode next. Would you build it as equal-weight across sectors, cap-weighted by sector size, or risk-parity? Each has a very different risk profile.
• Single-sector concentration is the obvious weakness — how would you hedge it without abandoning the "go deep in one sector" premise?

Happy to explain how the pipeline reasons about any pick. (Tool link in a comment for anyone curious — not the point of the post.)

I built my agentic solution ProspectAI few months ago, just to learn how to implement multi-agentic solutions that resolve a complex workflow usually done by humans. This tool mimics how an investment firm works: a market analyst finds the stocks with better opinions in Reddit, then a parallel flow starts a fundamental and technical analysts, which output is used by draft strategist to create first portfolio. Then a critic agent challenges the strategist to improve the final result.

As I was learning thru iterations, I made this system better and more effective. Up to this point, I am using it as a basis for my own portfolio investments, and I had good results that outpaced my investment in tokens.

This is not a post for promotion, it is just to explain my experience by using this new technology. I am really surprised to see that across 99 long-buys recommended stocks, 55% are green and the average return is 3.1%, in a window range of less than 3 months. This is without “selling” the bad ones when the stop loss limit is reached, in such case the return would be double-digit.

I charge nothing for using my tool, this is just an individual project I did to complement my professional work, but I encourage you to use it, experiment it and provide any feedback.

I've been running a multi-agent pipeline that generates LONG-BUY equity signals, and I finally have enough of a track record to look at it honestly. The methodology turned out to be more interesting than the headline number.

The setup
- 66 LONG-BUY signals generated between Apr 23 and May 24, 2026
- Each measured from its own entry date to today's close (Jun 26) — every position has held 1+ month
- Equal-weight, paper-traded, public data. Positions still open: this is unrealized, mark-to-market.

Raw results
- Avg return: +4.75%
- Win rate (positive): 63.6% (42/66)
- Median: +5.15% · Best: MU +56% · Worst: PLTR −18%

Raw return means nothing without a benchmark — and that's where it got interesting.

Benchmarking done right
The naive way is to compare against the index from day one of the set. That's wrong: a signal opened on May 24 shouldn't be measured against an index window starting April 23. So I measured each signal against the benchmark over *its own* holding window and aggregated the 66 differentials.

vs SPY (cap-weighted)
- Avg alpha: +6.24pp · Beat rate: 65.2% (43/66)
- Over these exact windows SPY actually *fell* −1.49%, dragged by mega-cap tech.

vs RSP (equal-weight S&P 500)
- Avg alpha: +1.46pp · Beat rate: 57.6% (38/66)
- Over the same windows RSP *rose* +3.29%.

The honest read
The +6pp vs SPY looks great, but it's mostly **sector allocation, not stock selection** — the pipeline avoided the mega-cap tech that sank cap-weighted SPY. Against equal-weight (the fairer comparison, since the book is itself equal-weight), the edge shrinks to ~+1.5pp and a 58% beat rate. Real, but modest, and on a single regime.

Caveats I'm not hiding
- Unrealized / mark-to-market, positions still open — moves daily
- Single ~6-week regime
- Equal-weight, paper-traded, no costs/slippage
- Duplicate tickers counted as independent signals
- n=66 is small

Question for the room: for a long-only equal-weight signal set, would you benchmark against RSP, SPY, or a sector-neutral construction? The choice swings the conclusion from "strong" to "marginal," and I'd rather get the methodology right than flatter the result.

(The signals come from an AI pipeline I built — prospect-ai.moisesprat.dev. Happy to get into the architecture in the comments.)

Full signal list (sorted by alpha vs SPY, best → worst)
**Full signal list** (sorted by alpha vs SPY, best → worst)

*Prices marked-to-market Jun 26, 2026. Benchmarks measured over each signal's own holding window.*

I've been running a multi-agent pipeline that generates LONG-BUY equity signals, and I finally have enough of a track record to look at it honestly.

The methodology turned out to be more interesting than the headline number.

The setup
- 66 LONG-BUY signals generated between Apr 23 and May 24, 2026
- Each measured from its own entry date to today's close (Jun 26) — every position has held 1+ month
- Equal-weight, paper-traded, public data. Positions still open: this is unrealized, mark-to-market.

Raw results
- Avg return: +4.75%
- Win rate (positive): 63.6% (42/66)
- Median: +5.15% · Best: MU +56% · Worst: PLTR −18%

Raw return means nothing without a benchmark — and that's where it got interesting.

Benchmarking done right
The naive way is to compare against the index from day one of the set. That's wrong: a signal opened on May 24 shouldn't be measured against an index window starting April 23. So I measured each signal against the benchmark over *its own* holding window and aggregated the 66 differentials.

vs SPY (cap-weighted)
- Avg alpha: +6.24pp · Beat rate: 65.2% (43/66)
- Over these exact windows SPY actually \*fell\* −1.49%, dragged by mega-cap tech.

vs RSP (equal-weight S&P 500)
- Avg alpha: +1.46pp · Beat rate: 57.6% (38/66)
- Over the same windows RSP \*rose\* +3.29%.

The honest read
The +6pp vs SPY looks great, but it's mostly sector allocation, not stock selectionv— the pipeline avoided the mega-cap tech that sank cap-weighted SPY.

Against equal-weight (the fairer comparison, since the book is itself equal-weight), the edge shrinks to \~+1.5pp and a 58% beat rate. Real, but modest, and on a single regime.

Caveats worth mentioning
- Unrealized / mark-to-market, positions still open — moves daily
- Single \~6-week regime
- Equal-weight, paper-traded, no costs/slippage
- Duplicate tickers counted as independent signals
- n=66 is small

Question for the room: for a long-only equal-weight signal set, would you benchmark against RSP, SPY, or a sector-neutral construction? The choice swings the conclusion from "strong" to "marginal," and I'd rather get the methodology right than flatter the result.

(The signals come from an AI pipeline I built — prospect-ai.moisesprat.dev. Happy to get into the architecture in the comments.)

Full signal list (sorted by alpha vs SPY, best → worst)

# |Ticker |Buy date |Return |α vs SPY |α vs RSP
1 |MU |16-May |+56.3% |+57.7pp |+52.0pp
2 |MU |18-May |+56.3% |+57.6pp |+52.6pp
3 |SN |24-May |+29.5% |+31.7pp |+27.7pp
4 |GRC |24-May |+22.8% |+25.0pp |+21.0pp
5 |AMD |19-May |+23.9% |+24.5pp |+19.6pp
6 |AMD |17-May |+23.0% |+24.4pp |+18.7pp
7 |LLY |01-May |+25.4% |+24.2pp |+21.7pp
8 |WDC |08-May |+22.2% |+23.4pp |+19.2pp
9 |WDC |16-May |+21.7% |+23.1pp |+17.4pp
10 |LLY |18-May |+20.2% |+21.5pp |+16.5pp
11 |LLY |20-May |+18.3% |+20.0pp |+15.1pp
12 |DHI |24-May |+15.7% |+17.9pp |+13.9pp
13 |D |17-May |+12.4% |+13.8pp |+8.1pp
14 |SPG |11-May |+12.3% |+13.7pp |+9.2pp
15 |JNJ |16-May |+12.3% |+13.7pp |+8.0pp
16 |JNJ |17-May |+12.3% |+13.7pp |+8.0pp
17 |BIIB |17-May |+12.0% |+13.4pp |+7.7pp
18 |JPM |14-May |+9.7% |+12.3pp |+6.4pp
19 |JNJ |20-May |+10.6% |+12.3pp |+7.4pp
20 |WFC |24-May |+9.8% |+12.0pp |+8.0pp
21 |JPM |16-May |+10.5% |+11.9pp |+6.2pp
22 |JPM |17-May |+10.5% |+11.9pp |+6.2pp
23 |TSM |18-May |+10.1% |+11.4pp |+6.4pp
24 |VLO |07-May |+9.7% |+10.1pp |+6.4pp
25 |DOV |24-May |+7.7% |+9.9pp |+5.9pp
26 |UNH |18-May |+8.6% |+9.9pp |+4.9pp
27 |TSM |19-May |+9.2% |+9.8pp |+4.9pp
28 |RTX |18-May |+8.3% |+9.6pp |+4.6pp
29 |MAR |13-May |+7.7% |+9.5pp |+4.1pp
30 |RTX |15-May |+7.0% |+8.4pp |+2.7pp
31 |AWK |24-May |+6.0% |+8.2pp |+4.2pp
32 |AWK |14-May |+5.3% |+7.9pp |+2.0pp
33 |VLO |24-May |+5.0% |+7.2pp |+3.2pp
34 |ED |14-May |+4.6% |+7.2pp |+1.3pp
35 |VST |24-May |+4.6% |+6.8pp |+2.8pp
36 |ES |27-Apr |+7.5% |+5.6pp |+3.3pp
37 |MPC |14-May |+2.1% |+4.7pp |−1.2pp
38 |ANET |24-May |+2.3% |+4.5pp |+0.5pp
39 |MA |14-May |+1.9% |+4.5pp |−1.4pp
40 |SCHW |24-May |+0.6% |+2.8pp |−1.2pp
41 |NI |23-Apr |+4.8% |+1.9pp |+0.9pp
42 |OHI |30-Apr |+2.9% |+1.5pp |−0.5pp
43 |CEG |17-May |−1.2% |+0.2pp |−5.5pp
44 |LMT |15-May |−2.5% |−1.1pp |−6.8pp
45 |APPF |24-May |−5.6% |−3.4pp |−7.4pp
46 |NEE |17-May |−5.1% |−3.7pp |−9.4pp
47 |EPD |15-May |−6.8% |−5.4pp |−11.1pp
48 |CVX |14-May |−8.3% |−5.7pp |−11.6pp
49 |META |22-May |−9.4% |−7.2pp |−11.2pp
50 |FTI |15-May |−9.6% |−8.2pp |−13.9pp
51 |COP |14-May |−10.9% |−8.3pp |−14.2pp
52 |NVDA |24-May |−10.6% |−8.4pp |−12.4pp
53 |MSFT |24-May |−10.9% |−8.7pp |−12.7pp
54 |META |16-May |−10.4% |−9.0pp |−14.7pp
55 |MSFT |08-May |−10.4% |−9.2pp |−13.4pp
56 |AVGO |22-May |−11.9% |−9.7pp |−13.7pp
57 |GOOGL |24-May |−11.9% |−9.7pp |−13.7pp
58 |AVGO |24-May |−11.9% |−9.7pp |−13.7pp
59 |PARR |20-May |−11.4% |−9.7pp |−14.6pp
60 |REGN |13-May |−12.5% |−10.7pp |−16.1pp
61 |AVGO |19-May |−13.2% |−12.6pp |−17.5pp
62 |NVDA |19-May |−13.4% |−12.8pp |−17.7pp
63 |NVDA |16-May |−14.6% |−13.2pp |−18.9pp
64 |NVDA |18-May |−14.6% |−13.3pp |−18.3pp
65 |FDX |17-May |−15.2% |−13.8pp |−19.5pp
66 |PLTR |22-May |−17.8% |−15.6pp |−19.6pp \*Prices marked-to-market Jun 26, 2026. Benchmarks measured over each signal's own holding window.\*

I built an AI multi-agent system to pick stocks — here's the real ROI after tracking every recommendation

Over the past month I've been running ProspectAI, a pipeline I built using Claude + CrewAI that analyzes Reddit sentiment, technical indicators, and fundamental data to recommend long-buy entries. I finally sat down and pulled every recommendation with its entry price and compared it against today's market close.

Results across 51 unique tickers:

• Hit rate: 76% — 39 out of 51 picks are in positive territory
• Average ROI: +4.04% — across a holding period of mostly 4–30 days
• No cherry-picking — this is the full export, losers included

Full results sorted by ROI:

Takeaways:

• 76% hit rate — 39 winners out of 51 picks
• 24% loss rate — 12 losers out of 51 picks
• +4.04% average ROI across the full portfolio, holding period mostly 4–30 days
• Semiconductors were the standout sector — MU, AMD, WDC, and TSM all posted double-digit gains in under 3 weeks
• Energy was the weakest — structurally mixed with both winners (MPC, VLO) and losers (PARR, COP, FTI, CVX)
• REGN is the only real outlier at -13%. Every other loser is under -7%
• 4% average ROI over days, not months, is genuinely strong on an annualized basis

The system identifies momentum + fundamental setups and flags entry zones — it doesn't predict the future. REGN is a good reminder that no pipeline is bulletproof. But a 76% hit rate with this kind of upside skew is a result I'm happy to put out publicly.

Happy to answer questions about how it works.

Ran a full technical + fundamental + sentiment analysis on the Semiconductors sector. Here's what stands out:

NVDA — strongest setup in the sector

• VERY_EXPENSIVE at 43.8x PE but 95.6% earnings growth + 73.2% revenue growth justify the premium
• Momentum score 8.5/10 — MACD bullish, RSI 65.86 (room to run)
• Reddit sentiment 0.72 — bullish consensus
• Entry zone: $199.14–$202.17 | Target: $214.11 | Stop: $193.17
• Current price $215.20 sits 6.4% above zone — wait for pullback

MPWR — exceptional fundamentals, extreme valuation

• 114.1x PE and 88.8x EV/EBITDA — you're paying for perfection
• But: 39.5% earnings growth, 26.1% revenue growth, 0.005 debt-to-equity (fortress)
• ADX 40.20, SMA20 > SMA50 > SMA200 — very strong trend confirmed
• Entry zone: $1505.59–$1584.83 | Target: $1675.17 | Stop: $1460.42
• Current price $1600.84 — 1% above zone, pullback entry setup

AVGO — monitor, not entry

• Composite 84.7 but Bearish MACD with negative histogram contradicts bullish narrative
• 84.0x PE and 0.83 debt-to-equity make this hard to justify at current levels
• Strong margins (76.7% gross, 36.6% net) but need MACD confirmation before entry
• Watch for retracement to $425.70 with RSI below 60

QCOM — extreme overbought, stay away

• RSI 85.85 — 15+ points above overbought threshold
• Current price 28.7% above entry zone of $156.30
• Negative sentiment (-0.15) on top of overbought technicals = material downside risk
• Strong fundamentals (22.3% net margin, $12.8B FCF) but declining revenue (-3.5% YoY) doesn't support chasing here

AEHR — avoid

• Revenue down 43.7% YoY, net margin -25.2%, burning cash at -$12.4M FCF
• 67.6x PS ratio on a loss-making company = pure speculation
• Positive sentiment (0.68) and decent momentum don't override fundamental deterioration

Key takeaway: sector is broadly overbought post-earnings. Only 2 of 5 names have actionable entries, both requiring pullbacks. 60% cash reserve is the right call here.

Methodology: composite scoring across 13+ technical indicators, fundamental metrics, and Reddit sentiment. Allocations are deterministic, not gut feel.

Full report generated by ProspectAI (open source): prospect-ai.moisesprat.dev

Ran a full technical + fundamental + sentiment analysis on the Semiconductors sector. Here's what stands out:

NVDA — strongest setup in the sector

• VERY_EXPENSIVE at 43.8x PE but 95.6% earnings growth + 73.2% revenue growth justify the premium
• Momentum score 8.5/10 — MACD bullish, RSI 65.86 (room to run)
• Reddit sentiment 0.72 — bullish consensus
• Entry zone: $199.14–$202.17 | Target: $214.11 | Stop: $193.17
• Current price $215.20 sits 6.4% above zone — wait for pullback

MPWR — exceptional fundamentals, extreme valuation

• 114.1x PE and 88.8x EV/EBITDA — you're paying for perfection
• But: 39.5% earnings growth, 26.1% revenue growth, 0.005 debt-to-equity (fortress)
• ADX 40.20, SMA20 > SMA50 > SMA200 — very strong trend confirmed
• Entry zone: $1505.59–$1584.83 | Target: $1675.17 | Stop: $1460.42
• Current price $1600.84 — 1% above zone, pullback entry setup

AVGO — monitor, not entry

• Composite 84.7 but Bearish MACD with negative histogram contradicts bullish narrative
• 84.0x PE and 0.83 debt-to-equity make this hard to justify at current levels
• Strong margins (76.7% gross, 36.6% net) but need MACD confirmation before entry
• Watch for retracement to $425.70 with RSI below 60

QCOM — extreme overbought, stay away

• RSI 85.85 — 15+ points above overbought threshold
• Current price 28.7% above entry zone of $156.30
• Negative sentiment (-0.15) on top of overbought technicals = material downside risk
• Strong fundamentals (22.3% net margin, $12.8B FCF) but declining revenue (-3.5% YoY) doesn't support chasing here

AEHR — avoid

• Revenue down 43.7% YoY, net margin -25.2%, burning cash at -$12.4M FCF
• 67.6x PS ratio on a loss-making company = pure speculation
• Positive sentiment (0.68) and decent momentum don't override fundamental deterioration

Key takeaway: sector is broadly overbought post-earnings. Only 2 of 5 names have actionable entries, both requiring pullbacks. 60% cash reserve is the right call here.

Methodology: composite scoring across 13+ technical indicators, fundamental metrics, and Reddit sentiment. Allocations are deterministic, not gut feel.

Full report generated by ProspectAI (open source): prospect-ai.moisesprat.dev

Ran a full technical + fundamental + sentiment analysis on the Energy sector this week. Here's what stands out:

VLO — strongest setup in the sector
• Fair valuation at 17.3x PE (rare in energy right now)
• Momentum score 8.8/10 — MACD bullish, RSI 47.8 (room to run)
• Reddit sentiment 0.78 — highest in the sector
• Entry zone: $228–$236 | Target: $250 | Stop: $221.85
• Brent at $138 directly benefits refining margins

CVX — interesting but expensive
• 32.3x PE with 44.4% earnings decline YoY is hard to justify
• Highest dividend yield in the group (3.70%) but payout
ratio >120% — not sustainable
• Only viable with a two-tranche entry to manage overpayment risk

XOM — fortress balance sheet, cyclical headwinds
• $23.61B free cash flow, debt-to-equity 0.18
• But 43.4% earnings decline YoY and 25x PE limit upside
• Valid entry $147–$150, conservative position sizing

WMB — wait
• Trading above entry zone, debt-to-equity 2.0,
current ratio 0.83
• $899M FCF trying to support $2.1B dividend — math doesn't work
• Only interesting below $72.27

COP — avoid for now
• Technical breakdown, MACD bearish, trading below entry zone
• -5.3% revenue, -20.2% earnings YoY
• Wait for stabilization signals

---

Methodology: composite scoring across technical indicators
(13+), fundamental metrics, and Reddit sentiment. Allocations
are deterministic, not gut feel.

Full report generated by ProspectAI (open source):
prospect-ai.moisesprat.dev