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I Ranked 151 US Large-Caps With a 15-Line Model — Here's the Code and the Top 10

#finance #quant #python #data

I Ranked 151 US Large-Caps With a 15-Line Model — Here's the Code and the Top 10

⚠️ Disclaimer: This is research and educational content built from public market data. It is not personalized investment advice and is not a recommendation to buy or sell any security. Past performance does not predict future results. Do your own research.

What I built

A single Python script that:

  1. Downloads ~2 years of split/dividend-adjusted daily closes for 151 US large-caps via yfinance
  2. Computes 12-month momentum, annualized volatility, and a quality score per ticker
  3. Z-scores every factor cross-sectionally (mean 0, std 1 across the universe)
  4. Combines them into a single composite score: 0.5 × Momentum + 0.5 × Quality
  5. Prints the full ranked table and writes a dated CSV

No look-ahead bias. No survivorship bias (the universe is fixed). No black box.

The 15-Line Model (complete, runnable) 

import yfinance as yf, pandas as pd, numpy as np, warnings
warnings.filterwarnings("ignore")

TICKERS = ["AAPL","MSFT","NVDA","GOOGL","AMZN","META","TSLA","AVGO","AMD","NFLX",
           "ORCL","CRM","ADBE","CSCO","QCOM","TXN","MU","INTC","IBM","NOW","INTU",
           "PLTR","SNOW","DDOG","NET","CRWD","PANW","ZS","FTNT","ANET","SMCI","ARM",
           "MRVL","LRCX","AMAT","KLAC","ASML","ON","MPWR","MCHP","TER","ADSK","CDNS",
           "SNPS","GFS","MRNA","LLY","NVO","UNH","JNJ","PFE","MRK","ABBV","BMY","TMO",
           "DHR","ISRG","VRTX","REGN","AMGN","GILD","BSX","CVS","CI","HUM","ABT","SYK",
           "ALGN","MDT","BABA","JD","PDD","SE","BIDU","UBER","ABNB","DASH","COIN","HOOD",
           "PYPL","V","MA","AXP","BLK","SCHW","C","BAC","WFC","JPM","GS","MS","SPGI",
           "ICE","CME","MCO","AIG","MET","PRU","TRV","ALL","CB","PGR","SPOT","T","VZ",
           "TMUS","CMCSA","DIS","WMT","COST","HD","MCD","NKE","SBUX","TGT","UPS","CAT",
           "DE","GE","BA","HON","UNP","CSX","NSC","LIN","APD","ECL","SHW","EMR","ETN",
           "PH","ROK","WM","RSG","COP","XOM","CVX","SLB","OXY","EOG","DVN","PSX","VLO",
           "MPC","PBR","BP","SHEL","TTE","RIO","FCX","NEM"]

close = yf.download(TICKERS, period="2y", interval="1d", auto_adjust=True, progress=False)["Close"].dropna(axis=1, how="any")
rets  = close.pct_change().dropna()
mom12 = close.iloc[-1] / close.iloc[-252] - 1
vol   = rets.iloc[-252:].std() * np.sqrt(252)
qual  = -vol.rank(pct=True)
df    = pd.DataFrame({"mom12": mom12, "vol": vol, "qual": qual})
df["score"] = 0.5 * df["mom12"].rank(pct=True) + 0.5 * df["qual"]
df = df.sort_values("score", ascending=False)
print(df.head(20).round(4))
df.to_csv("launchtower_factor_ranking.csv")
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How to run it:

pip install yfinance pandas numpy
python launchtower_factor_screen.py
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That's it. One file. No API keys. No cloud. No subscription.

The Top 10 (as of 2026-09-15)

Rank Ticker 12M Momentum Ann. Vol Quality Composite
1 APD +6.3% 46.8% +11.06 +6.07
2 PBR +35.3% 28.8% +2.54 +2.80
3 AVGO +30.9% 39.2% +2.80 +2.35
4 NVDA +45.9% 34.8% −0.06 +1.96
5 TRV −11.5% 46.0% +2.06 +1.86
6 DE +33.5% 27.7% +1.45 +1.79
7 UBER +62.5% 35.8% +2.03 +1.78
8 COST +20.9% 22.6% +1.18 +1.72
9 MA +25.5% 23.7% +1.02 +1.71
10 ROK +76.5% 30.6% +0.94 +1.57

Scores are cross-sectional percentiles (0–1 scale) combined 50/50. Higher is better. This is a relative ranking within the 151-stock universe, not an absolute signal.

A few observations

  • APD (Air Products) tops the list on quality — low realized volatility relative to its peers despite a weak 3-month print.
  • PBR (Petrobras) and ROK (Rockwell) are the momentum leaders — both up 35%+ over 12 months.
  • NVDA ranks 4th despite a negative quality score — its 12M momentum (+45.9%) carries it.
  • COST and MA are the "boring" winners: moderate momentum, low volatility, solid quality.

What's in the Full Pack

The free table above is the top 10. The full 151-stock dataset includes:

  • All 151 tickers with momentum_12m, momentum_3m, volatility, quality_score, and composite_score
  • The complete Python script (the 15-line model above, plus the full 151-ticker universe)
  • A dated CSV you can drop straight into Excel, pandas, or your own backtest
  • Methodology notes: how each factor is computed, the z-scoring approach, and known limitations

👉 Get the Full 151-Stock Factor Pack — $9

Delivered instantly by email after purchase. No account required.

Why a 15-line model?

Most factor screens I've seen are either:

  1. Too simple — a single momentum sort that ignores risk
  2. Too complex — 40+ factors, black-box weighting, no reproducibility

This sits in the middle. Two factors (momentum + quality), equal weight, fully transparent. You can read every line. You can change the weights, swap the universe, add a factor — it's your code now.

Known limitations (honest ones):

  • The universe is fixed at 151 large-caps. Small-caps and international names are excluded.
  • yfinance data is delayed and occasionally has gaps. For production use, swap in a paid data source.
  • Equal weighting (0.5/0.5) is a starting point, not an optimized one.
  • This is a ranking, not a signal. A stock ranked #1 today can be ranked #80 next month.

What I'm NOT doing

  • I'm not telling you what to buy or sell.
  • I'm not promising returns.
  • I'm not hiding the code behind a paywall — the full script is in this article.

What I am doing: giving you a clean, reproducible starting point and the full dataset so you can do your own analysis faster.

LaunchTower is an independent market-data desk. All data is from public sources. This content is for research and educational purposes only and does not constitute investment advice.

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