For those who've followed my prior posts: I've been running a quantitative model (Quantin) and sharing analysis of what it signals on individual names. A few of you asked to see the full portfolio. Starting today it's publicly accessible — no subscription, no login.

Here's the complete breakdown of what the model is holding right now and why.

Current portfolio — rebalanced June 30

Long AVLV Avantis US Large Cap Value +12.2% since Apr 2026

US large-cap value factor ETF from Avantis. Tilts toward stocks with high profitability and low valuation multiples. Strong risk-adjusted signal in the model's lookback window — one of the two long-running positions from before the June rebalance.

Long FNDB Schwab Fundamental US Broad Market ETF +10.0% since Apr 2026

Fundamentally-weighted broad US exposure — weights by revenue, dividends, and buybacks rather than market cap. Tilts toward value without going pure value. Held since April, consistent ensemble signal.

Long MGV Vanguard Mega Cap Value ETF +1.8% since Jun 2026

Tracks the largest value-oriented US stocks. High concentration in financials, healthcare, and industrials. Entered at the June rebalance — model sees the best risk-adjusted setup in mega-cap value right now.

Long SPHQ Invesco S&P 500 Quality ETF −2.6% since Jun 2026

Quality factor on the S&P 500 — screens for high ROE, stable earnings, and low leverage. Entered June. Slightly negative since entry but model's daily signal still LONG.

Long VTV Vanguard Value ETF +2.5% since Jun 2026

Broad US value exposure — one of the most liquid value ETFs. Entered at the June rebalance. The model's current strong tilt toward value factor is evident across several holdings.

Long DFLV Dimensional US Large Value ETF +4.5% since Jun 2026

Dimensional's systematic large-cap value fund. Similar philosophy to AVLV but from DFA — targets value and profitability factors with low turnover. Entered June, solid performance since entry.

Long AVUS Avantis US Equity ETF +3.2% since Jun 2026

Avantis's broad US equity fund — tilts toward small-cap and value without being a pure factor bet. Complements AVLV with broader market coverage.

Cash WDC Western Digital +372.1% since Oct 2025

NAND flash and HDD storage. The model entered this one back in Oct 2025 and rode it to +372% — now the daily signal has flipped to CASH. Position closed at the top of the move.

Cash VLUE iShares MSCI Value Factor ETF +32.4% since Feb 2026

Value factor ETF, held since February. Signal flipped to CASH — +32.4% captured from the value rotation that started earlier this year.

Cash EWY iShares MSCI South Korea ETF +43.1% since Feb 2026

Korean market ETF with heavy Samsung and SK Hynix concentration. Held since February, +43.1% — signal now CASH as the momentum fades.

Cash LRCX Lam Research +0.6% since Jun 2026

Semiconductor equipment (etch/deposition). Entered at the June rebalance but the daily signal quickly moved to CASH — risk-adjusted signal deteriorated.

Cash AMAT Applied Materials +1.6% since Jun 2026

Semiconductor equipment — deposition, etch, and inspection tools. In the portfolio since June but daily signal is CASH. Strong long-term business, model cautious short-term.

Cash ASML ASML Holding −4.7% since Jun 2026

The monopoly provider of EUV lithography machines — every leading-edge chip fab depends on ASML. Entered June, currently down 4.7%, signal CASH.

Cash CAT Caterpillar −22.0% since Jun 2026

Heavy equipment and machinery. The model's weakest performer since June — down 22%. Signal is CASH. Infrastructure cycle under pressure.

Cash TSM Taiwan Semiconductor Manufacturing +11.1% since Jun 2026

The world's leading contract chipmaker — makes chips for NVIDIA, Apple, AMD. +11.1% since June entry but signal has flipped to CASH. Geopolitical noise likely weighing on the short-term signal.

What stands out

The model's current allocation is a concentrated bet on US value and quality factors — AVLV, FNDB, MGV, VTV, DFLV, SPHQ, and AVUS are all variations of the same theme. That's not a coincidence: the ensemble Sharpe across 3/6/9/12-month windows is telling the model that value has been the best risk-adjusted place to be. The semiconductor names (AMAT, LRCX, ASML, TSM) are in the portfolio by selection but sitting in CASH on the daily signal — the model owns the thesis but is waiting on timing.

WDC at +372% from October is the headline trade. The model held it the entire way up and exited when the signal flipped.

Backtest (Feb 2018 – Aug 2026)

Metric	Quantin	S&P 500
Avg. annual return	+26.2%	+14.4%
Max drawdown	−9.5%	−33.7%
Sharpe ratio	2.00	0.80
Annual outperformance	+11.8pp	—

Portfolio updates every trading day. Full movement history — every position change with entry price and return — is also visible.

quantin.finance/portfolio

We run a quantitative stock selection model that ranks ~800 tickers by risk-adjusted performance across multiple time windows and outputs a concentrated portfolio of 15. Right now, three of those 15 are non-US positions: ASML (Netherlands), EWY (South Korea), and TSM (Taiwan). Here's what the model sees.

Taiwan Semiconductor (TSM) — Taiwan

The model has covered this one before but it keeps ranking. The quant reasoning isn't "AI is big" — it's about structural position. TSM manufactures at advanced nodes for everyone: NVIDIA, Apple, AMD, Qualcomm. You don't pick a winner in the AI arms race; you own the factory that arms both sides.

What makes it score well risk-adjusted rather than just high-return: TSM has low idiosyncratic volatility relative to its returns. The market treats it like high-beta semiconductor but its revenue behaves more like a utility with pricing power. That compression between perceived risk and actual risk-adjusted return is where the model finds alpha.

ASML (ASML) — Netherlands

This one gets less attention than it deserves outside quant circles.

ASML makes the EUV lithography machines that physically print transistors onto silicon. 100% of EUV systems shipped globally come from one company, in Veldhoven, Netherlands. It took 30 years and ~$10B in R&D to build, depends on sole-source suppliers (Carl Zeiss for optics, Cymer for lasers), and has 16,000+ active patents. TSMC, Samsung, and Intel cannot manufacture sub-7nm chips without ASML hardware. There is no alternative supplier.

The numbers: Q2 2026 revenue €9.3B, gross margin 54%, Q3 guided at €11-12B with 55-57% margins. Year-end backlog: €38.8B — nearly one full year of revenue already contracted before the year started.

EWY (iShares MSCI South Korea ETF) — South Korea

The model selected an ETF, not a single stock. Here's why that's interesting.

EWY's top two holdings are Samsung (21%) and SK Hynix (20%) — together 41% of the fund. In practice, you're primarily buying Korean memory and logic semiconductors wrapped in ETF structure. But the ETF selection over single stocks is deliberate from a risk-adjusted perspective: Korean individual stocks have wide bid-ask spreads for US investors, single-name execution risk, and concentrated chaebol exposure. EWY gives correlated AI upside with partially diversified downside.

The HBM thesis: SK Hynix holds ~62% of the global High Bandwidth Memory market. HBM is the performance-critical memory stack that sits directly on AI accelerators — without it, you cannot build a competitive GPU. SK Hynix's Q1 2026 operating margin: 72%. They were first to mass-produce HBM3E, hold the majority of NVIDIA's supply agreements, and are already shipping HBM4.

The governance catalyst: Korea has been running a Corporate Value-Up Program since 2024, following Japan's playbook. March 2026 saw formal Commercial Code amendments mandating fiduciary duty to minority shareholders, treasury share cancellation requirements, and tighter audit independence. The "Korea discount" — historically 30-50% P/E gap to global peers driven by chaebol governance concerns — is beginning to compress. Multiple expansion on top of an earnings supercycle is what EWY's +74% YTD in 2026 reflects.

The model entered EWY in February 2026, right as the governance reform implementation was accelerating and HBM demand confirmation was coming through NVIDIA's B200 supply agreements.

What these three have in common

None of these are speculative bets on future growth. They're monopoly or near-monopoly positions in the AI infrastructure supply chain — the layer that everyone in the chain depends on. ASML makes the machines that make the chips. TSM makes the chips. SK Hynix (via EWY) makes the memory that goes on the chips.

The model doesn't read earnings calls. It sees Sharpe ratios, revenue stability, and momentum signals. These three have all of that — plus fundamental moats that explain why the signal has been persistent.

Full 15-stock portfolio and methodology at Quantin. Happy to answer questions on the selection process in the comments.

Walk-forward validated since 2018. Not financial advice.

We run a quantitative stock selection model that ranks \~800 tickers by risk-adjusted performance across multiple time windows and outputs a concentrated portfolio of 15.

Three picks that keep consistently ranking at the top: **TSM**, **LRCX**, and **TRGP**. Here's what the model sees and why it's interesting.

**Taiwan Semiconductor (TSM)**

The obvious AI pick, but the quant reasoning is less obvious than "AI is big." TSM's Sharpe advantage comes from a structural position that almost no competitor can replicate: they manufacture at advanced nodes for *everyone* — NVIDIA, Apple, AMD, Qualcomm. You don't pick a winner in the AI arms race; you own the factory that arms both sides.

What makes it score well risk-adjusted rather than just high-return: TSM has low idiosyncratic volatility relative to its returns. The market treats it like a high-beta semiconductor play but its fundamentals behave more like a regulated utility with pricing power. That compression between perceived risk and actual risk-adjusted return is where the model finds alpha.

**Lam Research (LRCX)**

This one surprises people. While everyone buys fabless chip designers, the model keeps flagging equipment. Lam dominates deposition and etch — processes needed in *every* new semiconductor fab regardless of node generation or geography.

The key signal: Lam has an installed base of tens of thousands of systems that generate recurring service revenue. Every chip fab that gets built, anywhere in the world — CHIPS Act fabs in Arizona, Samsung in Texas, TSMC expansion in Japan — creates perpetual Lam service contracts. The model picks this up as abnormally stable revenue relative to price volatility. In the current low-volatility bull regime, that stability gets rewarded disproportionately.

**Targa Resources (TRGP)**

The one that raises eyebrows in a tech-heavy portfolio. Targa is midstream energy — they gather, process, and fractionate natural gas from the Permian Basin.

Here's the angle most quant models miss: AI data centers need power. Natural gas is filling that gap faster than renewables can scale. Targa sits directly on the infrastructure that moves that gas. Additionally, NGL exports (natural gas liquids, a Targa specialty) are at multi-year highs driven by petrochemical demand in Asia.

What makes it rank: Targa has delivered equity returns comparable to growth tech over the past year with *significantly* lower volatility. In a model that weights Sharpe across 3-12 month windows, that combination is hard to beat. It also provides genuine diversification — when semiconductors had their August correction, Targa didn't move.

**What the portfolio looks like overall**

These three sit inside a 15-stock equal-weight portfolio selected by a model that runs every two months, rebalancing based on updated Sharpe signals and a macro regime classifier (currently: bull market, low volatility). The full portfolio includes semiconductor equipment, international small cap, silver, energy infrastructure, and a couple of ETFs that the model treats as liquid alternatives.

CAGR since backtested inception: \~26%, Sharpe \~2.1, max drawdown \~10%.

If you're curious about the other 12 picks or the methodology, the model runs at [**quantin.finance**](http://quantin.finance) — it's a retail-accessible quant portfolio tracker updated every two months. Happy to answer questions about the selection methodology in the comments.

We run a quantitative stock selection model that ranks ~800 tickers by risk-adjusted performance across multiple time windows and outputs a concentrated portfolio of 15.

Three picks that keep consistently ranking at the top: TSM, LRCX, and TRGP. Here's what the model sees and why it's interesting.

Taiwan Semiconductor (TSM)

The obvious AI pick, but the quant reasoning is less obvious than "AI is big." TSM's Sharpe advantage comes from a structural position that almost no competitor can replicate: they manufacture at advanced nodes for everyone — NVIDIA, Apple, AMD, Qualcomm. You don't pick a winner in the AI arms race; you own the factory that arms both sides.

What makes it score well risk-adjusted rather than just high-return: TSM has low idiosyncratic volatility relative to its returns. The market treats it like a high-beta semiconductor play but its fundamentals behave more like a regulated utility with pricing power. That compression between perceived risk and actual risk-adjusted return is where the model finds alpha.

Lam Research (LRCX)

This one surprises people. While everyone buys fabless chip designers, the model keeps flagging equipment. Lam dominates deposition and etch — processes needed in every new semiconductor fab regardless of node generation or geography.

The key signal: Lam has an installed base of tens of thousands of systems that generate recurring service revenue. Every chip fab that gets built, anywhere in the world — CHIPS Act fabs in Arizona, Samsung in Texas, TSMC expansion in Japan — creates perpetual Lam service contracts. The model picks this up as abnormally stable revenue relative to price volatility. In the current low-volatility bull regime, that stability gets rewarded disproportionately.

Targa Resources (TRGP)

The one that raises eyebrows in a tech-heavy portfolio. Targa is midstream energy — they gather, process, and fractionate natural gas from the Permian Basin.

Here's the angle most quant models miss: AI data centers need power. Natural gas is filling that gap faster than renewables can scale. Targa sits directly on the infrastructure that moves that gas. Additionally, NGL exports (natural gas liquids, a Targa specialty) are at multi-year highs driven by petrochemical demand in Asia.

What makes it rank: Targa has delivered equity returns comparable to growth tech over the past year with significantly lower volatility. In a model that weights Sharpe across 3-12 month windows, that combination is hard to beat. It also provides genuine diversification — when semiconductors had their August correction, Targa didn't move.

What the portfolio looks like overall

These three sit inside a 15-stock equal-weight portfolio selected by a model that runs every two months, rebalancing based on updated Sharpe signals and a macro regime classifier (currently: bull market, low volatility). The full portfolio includes semiconductor equipment, international small cap, silver, energy infrastructure, and a couple of ETFs that the model treats as liquid alternatives.

CAGR since backtested inception: ~26%, Sharpe ~2.1, max drawdown ~10%.

If you're curious about the other 12 picks or the methodology, the model runs at quantin.finance — it's a retail-accessible quant portfolio tracker updated every two months. Happy to answer questions about the selection methodology in the comments.

1) It's easy to find strategies that overperform certain past periods.
Simple rule based strategies can have outrageous performances.
Buy when the 50-day SMA crosses above the 200-day. RSI below 30, buy. Bollinger Band breakout. Every one of these, backtested on the right window, looks like a money printer. I ran 150+ combinations on US equities and some of them showed 40%+ CAGR. I thought I had cracked something.

I hadn't.

2) Almost all of them collapse the moment you test them out-of-sample.

The standard backtest is a lie. You pick the period, you pick the asset, you pick the parameters — of course it looks good. The moment you test those same strategies on data they've never seen, most fall apart. Some don't just underperform — they invert. The strategy that "worked" was just describing past noise.

The fix: walk-forward validation. Train on a window, test on the next one, move forward, repeat. It's brutal. Out of 150+ strategies, a much smaller subset survives with any consistency.

3) The ones that survive don't survive everywhere.
This was the most interesting finding. A strategy that crushes it in a low-volatility bull market can be genuinely destructive in a sideways or bear market. The market isn't one thing. It has regimes.

I ended up building a regime detection system that classifies each day into one of four states: bull low volatility, bull high volatility, sideways, and bear. The classification uses three independent methods — rule-based indicators, a Hidden Markov Model, and K-means clustering on volatility/momentum features.

4) The three regime detectors rarely agree perfectly.

When HMM says BEAR and rules say SIDEWAYS, that's meaningful signal on its own. I built a consensus layer (what I call Hybrid B) that resolves conflicts with a priority cascade. Unanimous agreement = high confidence. The strategies selected under high-confidence regime calls have materially better out-of-sample performance than the full set.

5) Even after all of this, the edge is real but modest.

This is the honest part. After walk-forward validation, regime filtering, and consensus-based selection — the quant ensemble beats buy-and-hold on risk-adjusted metrics. Not by some crazy margin. But consistently, across 9 years of out-of-sample data, with better Sharpe and lower drawdowns. That's what's actually achievable with systematic strategies on retail-accessible assets.

6) The hardest part isn't finding strategies. It's combining them across a real portfolio.
I ended up using a genetic algorithm to solve this. It runs thousands of portfolio configurations, each weighted differently, and evolves toward the combination that maximizes risk-adjusted return across the full walk-forward validation period. The output isn't just "buy these stocks" — it's a regime-aware rebalancing plan that tells you which strategy to run on each position, updated as the regime changes.