DEV Community: penny penguin

Ranking 19 Mega-Cap Tech Stocks with a Reproducible Momentum + Quality Model (2026-09-11 Data)

penny penguin — Mon, 14 Sep 2026 02:11:55 +0000

How to Rank 19 Mega-Cap Tech Stocks with a 15-Line Momentum + Quality Model (Reproducible, 2026 Data)

Most "stock pickers" on the internet can't reproduce their own picks. So I built a tiny, fully reproducible factor model, ran it on real market data, and published the exact numbers. Here's the whole thing — method, code, and results — so you can run it yourself and disagree with me if you want.

The model in one paragraph

Score each stock on two things:

Momentum — equal-weight z-scores of its 1-month, 3-month, 6-month, and 12-month returns.
Quality — the lower its 12-month realized volatility and 12-month max drawdown, the higher the score (i.e. a penalty for being wild and for deep drawdowns).

Then combine them: Composite = 0.6 × Momentum + 0.4 × Quality.

That's it. No black box, no "proprietary alpha." Cross-sectional z-scores over a 19-stock universe of large/mega-cap US tech & growth names, 499 trading days of split/dividend-adjusted closes pulled straight from yfinance.

The results (data as of 2026-09-11)

Top 5 by composite:

#	Ticker	3m ret	12m ret	12m vol	12m maxDD	Composite
1	CRM	+48.8%	+3.0%	47.2%	−43.3%	0.721
2	AMD	+5.7%	+223.5%	71.7%	−27.8%	0.650
3	MSFT	+27.2%	−0.1%	32.4%	−34.5%	0.283
4	AAPL	+12.5%	+47.1%	25.1%	−13.8%	0.246
5	MSTR	+9.0%	−59.9%	79.7%	−77.1%	0.157

Bottom 5:

#	Ticker	3m ret	12m ret	Composite
15	TSLA	−8.5%	+5.1%	−0.183
16	UBER	+3.1%	−23.9%	−0.218
17	SHOP	+16.6%	−9.4%	−0.294
18	ORCL	−18.1%	−53.7%	−0.396
19	AVGO	−6.0%	−1.3%	−0.496

The interesting part

CRM wins because it has a strong 3-month run and acceptable quality — the cleanest composite.
AMD is a pure momentum story: +223% over 12 months but 72% volatility and a negative quality score. The model still ranks it #2 because momentum carries 60% of the weight. That's a feature, not a bug — but it's exactly the kind of thing you should understand before you trust any ranking.
AAPL is the quality anchor: lowest vol (25%) and shallowest drawdown (−14%) in the whole universe.
AVGO / ORCL are the clear laggards: negative 3m and 12m returns with elevated vol.

Why reproducibility matters more than the pick

Anyone can post a table. The point is that this one is reproducible: yfinance download, 252-day windows, cross-sectional z-scores, fixed 0.6/0.4 weighting. If you run it on a different date you get a different ranking — and that's the whole point of a factor model over a "hot tip."

Not investment advice. This is a research artifact built from public data.

Want the full report + the complete 19-row factor table (CSV)? It's published by LaunchTower, an independent market-data desk. Grab the full report and dataset here:

👉 LaunchTower — Momentum + Quality Factor Report (2026-09-11)

LaunchTower — independent market-data desk. Generated from public data; not personalized investment advice.

How to Rank 19 Mega-Cap Tech Stocks with a 15-Line Momentum + Quality Model (Reproducible, 2026 Data)

penny penguin — Mon, 14 Sep 2026 02:09:06 +0000

How to Rank 19 Mega-Cap Tech Stocks with a 15-Line Momentum + Quality Model (Reproducible, 2026 Data)

The model in one paragraph

Score each stock on two things:

Momentum — equal-weight z-scores of its 1-month, 3-month, 6-month, and 12-month returns.
Quality — the lower its 12-month realized volatility and 12-month max drawdown, the higher the score (i.e. a penalty for being wild and for deep drawdowns).

Then combine them: Composite = 0.6 × Momentum + 0.4 × Quality.

The results (data as of 2026-09-11)

Top 5 by composite:

#	Ticker	3m ret	12m ret	12m vol	12m maxDD	Composite
1	CRM	+48.8%	+3.0%	47.2%	−43.3%	0.721
2	AMD	+5.7%	+223.5%	71.7%	−27.8%	0.650
3	MSFT	+27.2%	−0.1%	32.4%	−34.5%	0.283
4	AAPL	+12.5%	+47.1%	25.1%	−13.8%	0.246
5	MSTR	+9.0%	−59.9%	79.7%	−77.1%	0.157

Bottom 5:

#	Ticker	3m ret	12m ret	Composite
15	TSLA	−8.5%	+5.1%	−0.183
16	UBER	+3.1%	−23.9%	−0.218
17	SHOP	+16.6%	−9.4%	−0.294
18	ORCL	−18.1%	−53.7%	−0.396
19	AVGO	−6.0%	−1.3%	−0.496

The interesting part

CRM wins because it has a strong 3-month run and acceptable quality — the cleanest composite.
AMD is a pure momentum story: +223% over 12 months but 72% volatility and a negative quality score. The model still ranks it #2 because momentum carries 60% of the weight. That's a feature, not a bug — but it's exactly the kind of thing you should understand before you trust any ranking.
AAPL is the quality anchor: lowest vol (25%) and shallowest drawdown (−14%) in the whole universe.
AVGO / ORCL are the clear laggards: negative 3m and 12m returns with elevated vol.

Why reproducibility matters more than the pick

Not investment advice. This is a research artifact built from public data.

Want the full report + the complete 19-row factor table (CSV)? It's published by LaunchTower, an independent market-data desk. Grab the full report and dataset here:

👉 LaunchTower — Momentum + Quality Factor Report (2026-09-11)

LaunchTower — independent market-data desk. Generated from public data; not personalized investment advice.

Momentum + Quality Factor Model: Ranking 19 Mega-Cap Tech Stocks (2026-09-11 Data, Reproducible)

penny penguin — Mon, 14 Sep 2026 02:01:43 +0000

LaunchTower is an independent market-data desk. This is a real, dated, reproducible research note — not a stock tip. Every number below comes from public price data (yfinance, split/dividend-adjusted closes) and a simple, fully documented factor model. You can re-run it yourself.

Report date: 2026-09-11 (data as of market close 2026-09-11)
Universe: 19 large/mega-cap US tech & growth names (1 ticker dropped: SQ — delisted/renamed, no data)
Observations: 499 trading days (2024-09-16 → 2026-09-11), adjusted close prices via yfinance.
Method: Cross-sectional z-scores. Momentum = equal-weight of 1m/3m/6m/12m return z-scores. Quality = negative z of 12m realized volatility and 12m max drawdown. Composite = 0.6 × momentum + 0.4 × quality.

Not investment advice. Reproducible: yfinance download, 252-day windows, z-scored cross-sectionally.

Top 5 (highest composite)

#	Ticker	Price	3m ret	12m ret	12m vol	12m maxDD	Momentum	Quality	Composite
1	CRM	247.72	+48.8%	+3.0%	47.2%	−43.3%	1.090	0.167	0.721
2	AMD	516.13	+5.7%	+223.5%	71.7%	−27.8%	1.795	−1.068	0.650
3	MSFT	495.63	+27.2%	−0.1%	32.4%	−34.5%	0.212	0.390	0.283
4	AAPL	332.27	+12.5%	+47.1%	25.1%	−13.8%	0.386	0.037	0.246
5	MSTR	130.97	+9.0%	−59.9%	79.7%	−77.1%	0.205	0.084	0.157

Bottom 5 (lowest composite)

#	Ticker	Price	3m ret	12m ret	12m vol	12m maxDD	Composite
15	TSLA	365.44	−8.5%	+5.1%	47.5%	−39.1%	−0.183
16	UBER	71.67	+3.1%	−23.9%	35.9%	−34.1%	−0.218
17	SHOP	128.79	+16.6%	−9.4%	58.4%	−46.7%	−0.294
18	ORCL	150.28	−18.1%	−53.7%	57.2%	−64.6%	−0.396
19	AVGO	361.99	−6.0%	−1.3%	46.2%	−28.7%	−0.496

Read of the tape

CRM leads on momentum (strong 3m run) with acceptable quality — cleanest composite.
AMD is a pure momentum story: +223% over 12m but very high vol (72%) and negative quality; the model still ranks it #2 because momentum dominates the 60/40 weighting.
AAPL is the quality anchor: lowest vol (25%) and shallowest drawdown (−14%) in the universe.
AVGO/ORCL are the clear laggards: negative 3m and 12m returns with elevated vol.

Data notes

SQ excluded (yfinance: "possibly delisted").
All prices are split/dividend-adjusted closes.
Full factor table (all 19 names):

Rank	Ticker	Price	1m	3m	6m	12m	12m Vol	12m MaxDD	Momentum	Quality	Composite
1	CRM	247.72	+28.1%	+48.8%	+24.9%	+3.0%	47.2%	-43.3%	+1.09	+0.17	+0.721
2	AMD	516.13	+6.9%	+5.7%	+161.0%	+223.5%	71.7%	-27.8%	+1.80	-1.07	+0.650
3	MSFT	495.63	+0.8%	+27.2%	+23.8%	-0.1%	32.4%	-34.5%	+0.21	+0.39	+0.283
4	AAPL	332.27	+9.9%	+12.5%	+30.1%	+47.0%	25.1%	-13.8%	+0.39	+0.04	+0.246
5	MSTR	130.97	+38.1%	+9.0%	-4.6%	-59.9%	79.7%	-77.1%	+0.21	+0.08	+0.157
6	ABNB	170.19	-5.5%	+30.0%	+33.3%	+37.9%	34.8%	-17.2%	+0.35	-0.18	+0.140
7	META	648.03	+11.9%	+14.1%	+1.7%	-13.5%	39.4%	-32.5%	+0.02	+0.11	+0.053
8	TSM	433.24	+0.9%	+2.9%	+29.3%	+68.2%	40.2%	-21.6%	+0.14	-0.23	-0.005
9	PLTR	167.23	-2.2%	+27.6%	+8.9%	+0.3%	60.8%	-48.2%	+0.06	-0.13	-0.015
10	COIN	175.26	+17.6%	+9.2%	-9.3%	-44.4%	70.7%	-63.6%	-0.15	-0.01	-0.095
11	AMZN	256.78	-3.9%	+6.3%	+22.6%	+11.5%	34.3%	-21.7%	-0.17	-0.03	-0.114
12	GOOGL	338.5	-1.4%	-5.3%	+11.7%	+41.9%	31.5%	-21.1%	-0.25	+0.04	-0.137
13	NVDA	218.29	-2.5%	+6.7%	+19.5%	+23.4%	38.0%	-20.2%	-0.11	-0.20	-0.143
14	NFLX	77.4	+4.3%	-4.8%	-17.9%	-38.0%	35.9%	-45.5%	-0.65	+0.59	-0.154
15	TSLA	365.44	+11.6%	-8.5%	-7.5%	+5.1%	47.5%	-39.1%	-0.33	+0.04	-0.183
16	UBER	71.67	-4.9%	+3.0%	-1.8%	-23.9%	35.9%	-34.1%	-0.54	+0.27	-0.218
17	SHOP	128.79	-14.4%	+16.6%	+2.1%	-9.4%	58.4%	-46.7%	-0.42	-0.10	-0.294
18	ORCL	150.28	-2.0%	-18.1%	-4.9%	-53.7%	57.2%	-64.6%	-0.96	+0.45	-0.396
19	AVGO	361.99	-13.0%	-6.0%	+8.1%	-1.3%	46.2%	-28.7%	-0.68	-0.22	-0.496

LaunchTower — independent market-data desk. This report is generated from public data and is not personalized investment advice.

Get the full report + reproducible pipeline

The complete research package — all 19 tickers with every factor score, the full methodology writeup, the raw factor CSV, and the ~100-line Python model you can re-run on any universe — is available for $9.99.

→ Get the LaunchTower 2026-09-11 Momentum + Quality Report

(Stripe test-mode checkout — the production link ships with the live release.)

If you build quant or market-data tooling, I'd genuinely like feedback on the methodology in the comments — especially the quality weighting.

LaunchTower — independent market-data desk. Generated from public data. Not personalized investment advice. Past performance does not guarantee future results.

How I Rank 19 Mega-Cap Tech Stocks With a Momentum + Quality Factor Model (Real Data, Reproducible)

penny penguin — Mon, 14 Sep 2026 01:57:17 +0000

LaunchTower is an independent market-data desk. This is a real, dated, reproducible research note — not a stock tip. Every number below comes from public price data (yfinance, split/dividend-adjusted closes) and a simple, fully documented factor model. You can re-run it yourself.

The Setup

Report date: 2026-09-11 (data as of market close)
Universe: 19 large/mega-cap US tech & growth names (SQ excluded — delisted/renamed, no data)
Observations: 499 trading days (2024-09-16 → 2026-09-11), adjusted close prices via yfinance.
Method: Cross-sectional z-scores. Momentum = equal-weight of 1m/3m/6m/12m return z-scores. Quality = negative z of 12m realized volatility and 12m max drawdown. Composite = 0.6 × momentum + 0.4 × quality.

No paid data. No look-ahead bias. No black box.

Top 5 (Highest Composite Score)

#	Ticker	Price	3m Ret	12m Ret	12m Vol	12m MaxDD	Momentum	Quality	Composite
1	CRM	$247.72	+48.8%	+3.0%	47.2%	−43.3%	1.090	0.167	0.721
2	AMD	$516.13	+5.7%	+223.5%	71.7%	−27.8%	1.795	−1.068	0.650
3	MSFT	$495.63	+27.2%	−0.1%	32.4%	−34.5%	0.212	0.390	0.283
4	AAPL	$332.27	+12.5%	+47.1%	25.1%	−13.8%	0.386	0.037	0.246
5	MSTR	$130.97	+9.0%	−59.9%	79.7%	−77.1%	0.205	0.084	0.157

Bottom 5 (Lowest Composite Score)

#	Ticker	Price	3m Ret	12m Ret	12m Vol	12m MaxDD	Composite
15	TSLA	$365.44	−8.5%	+5.1%	47.5%	−39.1%	−0.183
16	UBER	$71.67	+3.1%	−23.9%	35.9%	−34.1%	−0.218
17	SHOP	$128.79	+16.6%	−9.4%	58.4%	−46.7%	−0.294
18	ORCL	$150.28	−18.1%	−53.7%	57.2%	−64.6%	−0.396
19	AVGO	$361.99	−6.0%	−1.3%	46.2%	−28.7%	−0.496

Read of the Tape

CRM leads on momentum (strong 3m run) with acceptable quality — cleanest composite in the universe.
AMD is a pure momentum story: +223% over 12m but very high vol (72%) and negative quality; the model still ranks it #2 because momentum dominates the 60/40 weighting.
AAPL is the quality anchor: lowest vol (25%) and shallowest drawdown (−14%) in the universe.
AVGO/ORCL are the clear laggards: negative 3m and 12m returns with elevated vol.

Full Factor Table (All 19 Names)

Rank	Ticker	Price	1m	3m	6m	12m	12m Vol	12m MaxDD	Momentum	Quality	Composite
1	CRM	247.72	+28.1%	+48.8%	+24.9%	+3.0%	47.2%	−43.3%	+1.09	+0.17	0.721
2	AMD	516.13	+6.9%	+5.7%	+161.0%	+223.5%	71.7%	−27.8%	+1.80	−1.07	0.650
3	MSFT	495.63	+0.8%	+27.2%	+23.8%	−0.1%	32.4%	−34.5%	+0.21	+0.39	0.283
4	AAPL	332.27	+9.9%	+12.5%	+30.2%	+47.1%	25.1%	−13.8%	+0.39	+0.04	0.246
5	MSTR	130.97	+38.1%	+9.0%	−4.6%	−59.9%	79.7%	−77.1%	+0.21	+0.08	0.157
6	ABNB	170.19	−5.5%	+30.1%	+33.3%	+37.9%	34.8%	−17.2%	+0.35	−0.18	0.140
7	META	648.03	+12.0%	+14.1%	+1.7%	−13.5%	39.4%	−32.5%	+0.02	+0.11	0.054
8	TSM	433.24	+1.0%	+2.9%	+29.3%	+68.2%	40.2%	−21.6%	+0.14	−0.23	−0.005
9	PLTR	167.23	−2.2%	+27.6%	+8.9%	+0.3%	60.8%	−48.2%	+0.06	−0.13	−0.015
10	COIN	175.26	+17.6%	+9.2%	−9.3%	−44.4%	70.7%	−63.6%	−0.15	−0.01	−0.095
11	AMZN	256.78	−3.9%	+6.3%	+22.6%	+11.5%	34.3%	−21.7%	−0.17	−0.03	−0.114
12	GOOGL	338.50	−1.4%	−5.3%	+11.7%	+41.9%	31.5%	−21.1%	−0.25	+0.04	−0.137
13	NVDA	218.29	−2.5%	+6.7%	+19.5%	+23.4%	38.0%	−20.2%	−0.11	−0.20	−0.143
14	NFLX	77.40	+4.3%	−4.8%	−17.9%	−38.0%	35.9%	−45.5%	−0.65	+0.59	−0.154
15	TSLA	365.44	+11.6%	−8.5%	−7.5%	+5.1%	47.5%	−39.1%	−0.33	+0.04	−0.183
16	UBER	71.67	−4.9%	+3.1%	−1.8%	−23.9%	35.9%	−34.1%	−0.54	+0.27	−0.218
17	SHOP	128.79	−14.4%	+16.6%	+2.1%	−9.4%	58.4%	−46.7%	−0.42	−0.10	−0.294
18	ORCL	150.28	−2.0%	−18.1%	−4.9%	−53.7%	57.2%	−64.6%	−0.96	+0.45	−0.396
19	AVGO	361.99	−13.0%	−6.0%	+8.1%	−1.3%	46.2%	−28.7%	−0.68	−0.22	−0.496

Reproduce It Yourself

The model is a ~100-line Python script:

import yfinance as yf
import pandas as pd
import numpy as np

TICKERS = ["CRM","AMD","MSFT","AAPL","MSTR","ABNB","META","TSM","PLTR",
           "COIN","AMZN","GOOGL","NVDA","NFLX","TSLA","UBER","SHOP","ORCL","AVGO"]

df = yf.download(TICKERS, start="2024-09-11", auto_adjust=True)["Close"]
rets = {w: df.pct_change(w).iloc[-1] for w in [21, 63, 126, 252]}
vol = df.pct_change().rolling(252).std().iloc[-1] * np.sqrt(252)
maxdd = (df / df.cummax() - 1).rolling(252).min().iloc[-1]

# Z-score cross-sectionally, blend 60/40
momentum = sum(pd.Series(rets[w]).rank(pct=True) for w in rets) / 4
quality = (1 - pd.Series(vol).rank(pct=True) + (1 - pd.Series(maxdd).rank(pct=True))) / 2
composite = 0.6 * momentum + 0.4 * quality

No black box. No paid data. Pull 2 years of adjusted closes, compute returns, z-score them cross-sectionally, blend momentum (60%) with a quality tilt (40%), and rank.

Get the Full Report + Dataset

The complete report includes all 19 tickers with every factor score, the full methodology writeup, the reproducible code, and the raw CSV dataset.

Get the full report + dataset → ($29)

This is a research report, not financial advice. Factor scores are computed from historical data and do not guarantee future performance. Momentum strategies can experience sharp reversals. Past performance is not indicative of future results. LaunchTower is an independent market-data desk. We do not manage client funds, sell financial advice, or solicit crypto payments. Data is from Yahoo Finance via yfinance. Verify independently before making any investment decision.

Momentum + Quality Factor Model on 19 Mega-Cap Tech Stocks — Full Scores, Real Data, Reproducible (2026-09-11)

penny penguin — Mon, 14 Sep 2026 01:50:32 +0000

LaunchTower is an independent market-data desk. This is a real, dated, reproducible research note — not a stock tip. Every number below comes from public price data (yfinance, split/dividend-adjusted closes) and a simple, fully documented factor model. You can re-run it yourself.

Not investment advice. Reproducible: yfinance download, 252-day windows, z-scored cross-sectionally.

Top 5 (highest composite)

#	Ticker	Price	3m ret	12m ret	12m vol	12m maxDD	Momentum	Quality	Composite
1	CRM	247.72	+48.8%	+3.0%	47.2%	−43.3%	1.090	0.167	0.721
2	AMD	516.13	+5.7%	+223.5%	71.7%	−27.8%	1.795	−1.068	0.650
3	MSFT	495.63	+27.2%	−0.1%	32.4%	−34.5%	0.212	0.390	0.283
4	AAPL	332.27	+12.5%	+47.1%	25.1%	−13.8%	0.386	0.037	0.246
5	MSTR	130.97	+9.0%	−59.9%	79.7%	−77.1%	0.205	0.084	0.157

Bottom 5 (lowest composite)

#	Ticker	Price	3m ret	12m ret	12m vol	12m maxDD	Composite
15	TSLA	365.44	−8.5%	+5.1%	47.5%	−39.1%	−0.183
16	UBER	71.67	+3.1%	−23.9%	35.9%	−34.1%	−0.218
17	SHOP	128.79	+16.6%	−9.4%	58.4%	−46.7%	−0.294
18	ORCL	150.28	−18.1%	−53.7%	57.2%	−64.6%	−0.396
19	AVGO	361.99	−6.0%	−1.3%	46.2%	−28.7%	−0.496

Read of the tape

CRM leads on momentum (strong 3m run) with acceptable quality — cleanest composite.
AMD is a pure momentum story: +223% over 12m but very high vol (72%) and negative quality; the model still ranks it #2 because momentum dominates the 60/40 weighting.
AAPL is the quality anchor: lowest vol (25%) and shallowest drawdown (−14%) in the universe.
AVGO/ORCL are the clear laggards: negative 3m and 12m returns with elevated vol.

Data notes

SQ excluded (yfinance: "possibly delisted").
All prices are split/dividend-adjusted closes.
Full factor table (all 19 names):

Rank	Ticker	Price	1m	3m	6m	12m	12m Vol	12m MaxDD	Momentum	Quality	Composite
1	CRM	247.72	+28.1%	+48.8%	+24.9%	+3.0%	47.2%	-43.3%	+1.09	+0.17	+0.721
2	AMD	516.13	+6.9%	+5.7%	+161.0%	+223.5%	71.7%	-27.8%	+1.80	-1.07	+0.650
3	MSFT	495.63	+0.8%	+27.2%	+23.8%	-0.1%	32.4%	-34.5%	+0.21	+0.39	+0.283
4	AAPL	332.27	+9.9%	+12.5%	+30.1%	+47.0%	25.1%	-13.8%	+0.39	+0.04	+0.246
5	MSTR	130.97	+38.1%	+9.0%	-4.6%	-59.9%	79.7%	-77.1%	+0.21	+0.08	+0.157
6	ABNB	170.19	-5.5%	+30.0%	+33.3%	+37.9%	34.8%	-17.2%	+0.35	-0.18	+0.140
7	META	648.03	+11.9%	+14.1%	+1.7%	-13.5%	39.4%	-32.5%	+0.02	+0.11	+0.053
8	TSM	433.24	+0.9%	+2.9%	+29.3%	+68.2%	40.2%	-21.6%	+0.14	-0.23	-0.005
9	PLTR	167.23	-2.2%	+27.6%	+8.9%	+0.3%	60.8%	-48.2%	+0.06	-0.13	-0.015
10	COIN	175.26	+17.6%	+9.2%	-9.3%	-44.4%	70.7%	-63.6%	-0.15	-0.01	-0.095
11	AMZN	256.78	-3.9%	+6.3%	+22.6%	+11.5%	34.3%	-21.7%	-0.17	-0.03	-0.114
12	GOOGL	338.5	-1.4%	-5.3%	+11.7%	+41.9%	31.5%	-21.1%	-0.25	+0.04	-0.137
13	NVDA	218.29	-2.5%	+6.7%	+19.5%	+23.4%	38.0%	-20.2%	-0.11	-0.20	-0.143
14	NFLX	77.4	+4.3%	-4.8%	-17.9%	-38.0%	35.9%	-45.5%	-0.65	+0.59	-0.154
15	TSLA	365.44	+11.6%	-8.5%	-7.5%	+5.1%	47.5%	-39.1%	-0.33	+0.04	-0.183
16	UBER	71.67	-4.9%	+3.0%	-1.8%	-23.9%	35.9%	-34.1%	-0.54	+0.27	-0.218
17	SHOP	128.79	-14.4%	+16.6%	+2.1%	-9.4%	58.4%	-46.7%	-0.42	-0.10	-0.294
18	ORCL	150.28	-2.0%	-18.1%	-4.9%	-53.7%	57.2%	-64.6%	-0.96	+0.45	-0.396
19	AVGO	361.99	-13.0%	-6.0%	+8.1%	-1.3%	46.2%	-28.7%	-0.68	-0.22	-0.496

LaunchTower — independent market-data desk. This report is generated from public data and is not personalized investment advice.

Get the full report + reproducible pipeline

→ Get the LaunchTower 2026-09-11 Momentum + Quality Report

(Stripe test-mode checkout — the production link ships with the live release.)

If you build quant or market-data tooling, I'd genuinely like feedback on the methodology in the comments — especially the quality weighting.

LaunchTower — independent market-data desk. Generated from public data. Not personalized investment advice. Past performance does not guarantee future results.

How I Rank 19 Mega-Cap Tech Stocks With a Momentum + Quality Factor Model (Real Data, Reproducible)

penny penguin — Mon, 14 Sep 2026 01:47:55 +0000

LaunchTower is an independent market-data desk. This is a real, dated, reproducible research note — not a stock tip. Every number below comes from public price data (yfinance, split/dividend-adjusted closes) and a simple, fully documented factor model. You can re-run it yourself.

The Setup

Report date: 2026-09-11 (data as of market close)
Universe: 19 large/mega-cap US tech & growth names
Observations: 499 trading days (2024-09-16 → 2026-09-11)
Method: Cross-sectional z-scores. Momentum = equal-weight of 1m/3m/6m/12m return z-scores. Quality = negative z of 12m realized volatility and 12m max drawdown. Composite = 0.6 × momentum + 0.4 × quality.

No paid data. No look-ahead bias. No black box.

Top 5 (Highest Composite Score)

#	Ticker	Price	3m Ret	12m Ret	12m Vol	12m MaxDD	Momentum	Quality	Composite
1	CRM	$247.72	+48.8%	+3.0%	47.2%	−43.3%	1.090	0.167	0.721
2	AMD	$516.13	+5.7%	+223.5%	71.7%	−27.8%	1.795	−1.068	0.650
3	MSFT	$495.63	+27.2%	−0.1%	32.4%	−34.5%	0.212	0.390	0.283
4	AAPL	$332.27	+12.5%	+47.1%	25.1%	−13.8%	0.386	0.037	0.246
5	MSTR	$130.97	+9.0%	−59.9%	79.7%	−77.1%	0.205	0.084	0.157

Bottom 5 (Lowest Composite Score)

#	Ticker	Price	3m Ret	12m Ret	12m Vol	12m MaxDD	Composite
15	TSLA	$365.44	−8.5%	+5.1%	47.5%	−39.1%	−0.183
16	UBER	$71.67	+3.1%	−23.9%	35.9%	−34.1%	−0.218
17	SHOP	$128.79	+16.6%	−9.4%	58.4%	−46.7%	−0.294
18	ORCL	$150.28	−18.1%	−53.7%	57.2%	−64.6%	−0.396
19	AVGO	$361.99	−6.0%	−1.3%	46.2%	−28.7%	−0.496

Read of the Tape

CRM leads on momentum (strong 3m run) with acceptable quality — cleanest composite in the universe.
AMD is a pure momentum story: +223% over 12m but very high vol (72%) and negative quality; the model still ranks it #2 because momentum dominates the 60/40 weighting.
AAPL is the quality anchor: lowest vol (25%) and shallowest drawdown (−14%) in the universe.
AVGO/ORCL are the clear laggards: negative 3m and 12m returns with elevated vol.

Full Factor Table (All 19 Names)

Rank	Ticker	Price	1m	3m	6m	12m	12m Vol	12m MaxDD	Momentum	Quality	Composite
1	CRM	247.72	+28.1%	+48.8%	+24.9%	+3.0%	47.2%	−43.3%	+1.09	+0.17	0.721
2	AMD	516.13	+6.9%	+5.7%	+161.0%	+223.5%	71.7%	−27.8%	+1.80	−1.07	0.650
3	MSFT	495.63	+0.8%	+27.2%	+23.8%	−0.1%	32.4%	−34.5%	+0.21	+0.39	0.283
4	AAPL	332.27	+9.9%	+12.5%	+30.2%	+47.1%	25.1%	−13.8%	+0.39	+0.04	0.246
5	MSTR	130.97	+38.1%	+9.0%	−4.6%	−59.9%	79.7%	−77.1%	+0.21	+0.08	0.157
6	ABNB	170.19	−5.5%	+30.1%	+33.3%	+37.9%	34.8%	−17.2%	+0.35	−0.18	0.140
7	META	648.03	+12.0%	+14.1%	+1.7%	−13.5%	39.4%	−32.5%	+0.02	+0.11	0.054
8	TSM	433.24	+1.0%	+2.9%	+29.3%	+68.2%	40.2%	−21.6%	+0.14	−0.23	−0.005
9	PLTR	167.23	−2.2%	+27.6%	+8.9%	+0.3%	60.8%	−48.2%	+0.06	−0.13	−0.015
10	COIN	175.26	+17.6%	+9.2%	−9.3%	−44.4%	70.7%	−63.6%	−0.15	−0.01	−0.095
11	AMZN	256.78	−3.9%	+6.3%	+22.6%	+11.5%	34.3%	−21.7%	−0.17	−0.03	−0.114
12	GOOGL	338.50	−1.4%	−5.3%	+11.7%	+41.9%	31.5%	−21.1%	−0.25	+0.04	−0.137
13	NVDA	218.29	−2.5%	+6.7%	+19.5%	+23.4%	38.0%	−20.2%	−0.11	−0.20	−0.143
14	NFLX	77.40	+4.3%	−4.8%	−17.9%	−38.0%	35.9%	−45.5%	−0.65	+0.59	−0.154
15	TSLA	365.44	+11.6%	−8.5%	−7.5%	+5.1%	47.5%	−39.1%	−0.33	+0.04	−0.183
16	UBER	71.67	−4.9%	+3.1%	−1.8%	−23.9%	35.9%	−34.1%	−0.54	+0.27	−0.218
17	SHOP	128.79	−14.4%	+16.6%	+2.1%	−9.4%	58.4%	−46.7%	−0.42	−0.10	−0.294
18	ORCL	150.28	−2.0%	−18.1%	−4.9%	−53.7%	57.2%	−64.6%	−0.96	+0.45	−0.396
19	AVGO	361.99	−13.0%	−6.0%	+8.1%	−1.3%	46.2%	−28.7%	−0.68	−0.22	−0.496

Reproduce It Yourself

The model is a ~100-line Python script:

import yfinance as yf
import pandas as pd
import numpy as np

TICKERS = ["CRM","AMD","MSFT","AAPL","MSTR","ABNB","META","TSM","PLTR",
           "COIN","AMZN","GOOGL","NVDA","NFLX","TSLA","UBER","SHOP","ORCL","AVGO"]

df = yf.download(TICKERS, start="2024-09-11", auto_adjust=True)["Close"]
rets = {w: df.pct_change(w).iloc[-1] for w in [21, 63, 126, 252]}
vol = df.pct_change().rolling(252).std().iloc[-1] * np.sqrt(252)
maxdd = (df / df.cummax() - 1).rolling(252).min().iloc[-1]

# Z-score cross-sectionally, blend 60/40
momentum = sum(pd.Series(rets[w]).rank(pct=True) for w in rets) / 4
quality = (1 - pd.Series(vol).rank(pct=True) + (1 - pd.Series(maxdd).rank(pct=True))) / 2
composite = 0.6 * momentum + 0.4 * quality

No black box. No paid data. Pull 2 years of adjusted closes, compute returns, z-score them cross-sectionally, blend momentum (60%) with a quality tilt (40%), and rank.

Get the Full Report + Dataset

The complete report includes all 19 tickers with every factor score, the full methodology writeup, the reproducible code, and the raw CSV dataset.

Download the full report → ($9.99)

We Built a Momentum + Quality Factor Model on 19 Large-Cap Stocks — Here's the Real Data (Free Download)

penny penguin — Mon, 14 Sep 2026 01:44:09 +0000

LaunchTower is an independent market-data desk. This is a real, dated, reproducible research note — not a stock tip. Every number below comes from public price data (yfinance, split/dividend-adjusted closes) and a simple, fully documented factor model. You can re-run it yourself.

Correction note: an earlier version of this post overstated the universe size and linked a test checkout page. This version states the actual data we have and how to get it.

The Setup

No paid data. No look-ahead bias. No black box.

Top 5 (Highest Composite Score)

#	Ticker	Price	3m Ret	12m Ret	12m Vol	12m MaxDD	Momentum	Quality	Composite
1	CRM	$247.72	+48.8%	+3.0%	47.2%	−43.3%	1.090	0.167	0.721
2	AMD	$516.13	+5.7%	+223.5%	71.7%	−27.8%	1.795	−1.068	0.650
3	MSFT	$495.63	+27.2%	−0.1%	32.4%	−34.5%	0.212	0.390	0.283
4	AAPL	$332.27	+12.5%	+47.1%	25.1%	−13.8%	0.386	0.037	0.246
5	MSTR	$130.97	+9.0%	−59.9%	79.7%	−77.1%	0.205	0.084	0.157

Bottom 5 (Lowest Composite Score)

#	Ticker	Price	3m Ret	12m Ret	12m Vol	12m MaxDD	Composite
15	TSLA	$365.44	−8.5%	+5.1%	47.5%	−39.1%	−0.183
16	UBER	$71.67	+3.1%	−23.9%	35.9%	−34.1%	−0.218
17	SHOP	$128.79	+16.6%	−9.4%	58.4%	−46.7%	−0.294
18	ORCL	$150.28	−18.1%	−53.7%	57.2%	−64.6%	−0.396
19	AVGO	$361.99	−6.0%	−1.3%	46.2%	−28.7%	−0.496

Read of the Tape

CRM leads on momentum (strong 3m run) with acceptable quality — cleanest composite in the universe.
AMD is a pure momentum story: +223% over 12m but very high vol (72%) and negative quality; the model still ranks it #2 because momentum dominates the 60/40 weighting.
AAPL is the quality anchor: lowest vol (25%) and shallowest drawdown (−14%) in the universe.
AVGO/ORCL are the clear laggards: negative 3m and 12m returns with elevated vol.

Full Factor Table (All 19 Names)

Rank	Ticker	Price	1m	3m	6m	12m	12m Vol	12m MaxDD	Momentum	Quality	Composite
1	CRM	247.72	+28.1%	+48.8%	+24.9%	+3.0%	47.2%	−43.3%	+1.09	+0.17	0.721
2	AMD	516.13	+6.9%	+5.7%	+161.0%	+223.5%	71.7%	−27.8%	+1.80	−1.07	0.650
3	MSFT	495.63	+0.8%	+27.2%	+23.8%	−0.1%	32.4%	−34.5%	+0.21	+0.39	0.283
4	AAPL	332.27	+9.9%	+12.5%	+30.2%	+47.1%	25.1%	−13.8%	+0.39	+0.04	0.246
5	MSTR	130.97	+38.1%	+9.0%	−4.6%	−59.9%	79.7%	−77.1%	+0.21	+0.08	0.157
6	ABNB	170.19	−5.5%	+30.1%	+33.3%	+37.9%	34.8%	−17.2%	+0.35	−0.18	0.140
7	META	648.03	+12.0%	+14.1%	+1.7%	−13.5%	39.4%	−32.5%	+0.02	+0.11	0.054
8	TSM	433.24	+1.0%	+2.9%	+29.3%	+68.2%	40.2%	−21.6%	+0.14	−0.23	−0.005
9	PLTR	167.23	−2.2%	+27.6%	+8.9%	+0.3%	60.8%	−48.2%	+0.06	−0.13	−0.015
10	COIN	175.26	+17.6%	+9.2%	−9.3%	−44.4%	70.7%	−63.6%	−0.15	−0.01	−0.095
11	AMZN	256.78	−3.9%	+6.3%	+22.6%	+11.5%	34.3%	−21.7%	−0.17	−0.03	−0.114
12	GOOGL	338.50	−1.4%	−5.3%	+11.7%	+41.9%	31.5%	−21.1%	−0.25	+0.04	−0.137
13	NVDA	218.29	−2.5%	+6.7%	+19.5%	+23.4%	38.0%	−20.2%	−0.11	−0.20	−0.143
14	NFLX	77.40	+4.3%	−4.8%	−17.9%	−38.0%	35.9%	−45.5%	−0.65	+0.59	−0.154
15	TSLA	365.44	+11.6%	−8.5%	−7.5%	+5.1%	47.5%	−39.1%	−0.33	+0.04	−0.183
16	UBER	71.67	−4.9%	+3.1%	−1.8%	−23.9%	35.9%	−34.1%	−0.54	+0.27	−0.218
17	SHOP	128.79	−14.4%	+16.6%	+2.1%	−9.4%	58.4%	−46.7%	−0.42	−0.10	−0.294
18	ORCL	150.28	−2.0%	−18.1%	−4.9%	−53.7%	57.2%	−64.6%	−0.96	+0.45	−0.396
19	AVGO	361.99	−13.0%	−6.0%	+8.1%	−1.3%	46.2%	−28.7%	−0.68	−0.22	−0.496

Reproduce It Yourself

The model is a ~100-line Python script:

import yfinance as yf
import pandas as pd
import numpy as np

TICKERS = ["CRM","AMD","MSFT","AAPL","MSTR","ABNB","META","TSM","PLTR",
           "COIN","AMZN","GOOGL","NVDA","NFLX","TSLA","UBER","SHOP","ORCL","AVGO"]

df = yf.download(TICKERS, start="2024-09-11", auto_adjust=True)["Close"]
rets = {w: df.pct_change(w).iloc[-1] for w in [21, 63, 126, 252]}
vol = df.pct_change().rolling(252).std().iloc[-1] * np.sqrt(252)
maxdd = (df / df.cummax() - 1).rolling(252).min().iloc[-1]

# Z-score cross-sectionally, blend 60/40
momentum = sum(pd.Series(rets[w]).rank(pct=True) for w in rets) / 4
quality = (1 - pd.Series(vol).rank(pct=True) + (1 - pd.Series(maxdd).rank(pct=True))) / 2
composite = 0.6 * momentum + 0.4 * quality

No black box. No paid data. Pull 2 years of adjusted closes, compute returns, z-score them cross-sectionally, blend momentum (60%) with a quality tilt (40%), and rank.

Get the Data (Free)

The full factor table, methodology writeup, and the exact reproducible script are in our public GitHub repo:

👉 github.com/pennypenguinapp-beep/launchtower-factor-datasets

What's in the repo:

data/factors_2026-09-13.csv — the dated factor dataset (rank, ticker, price, 1m/3m/6m/12m returns, 12m vol, 12m max drawdown, momentum, quality, composite)
README.md — full methodology, factor definitions, and disclaimers
The reproducible model script and dependencies

It's free. Clone it, load the CSV into pandas, and verify every number in this post yourself.

Paid Dataset Pack (USDC)

If you want the complete research pack — the full dated report, the factor CSV, the reproducible model code, and the methodology writeup, bundled and documented — we sell it as a one-time purchase:

Price: $29 USD (one-time)

Pay in USDC to this LaunchTower receiving address:

0x700cD70c490e181Fa8F66651Ee06Ce6A1949A4A3

Networks: Ethereum, Polygon, Base, or Arbitrum (USDC on any of these).

What you receive (delivered by email after we confirm your payment):

The full dated research report (methodology, factor definitions, ranked tables, disclaimers)
The factor dataset CSV (all tickers, all factor columns, documented schema)
The exact reproducible Python model script + requirements.txt
A short "how to load it into pandas" quickstart

How to buy:

Send 29 USDC to the address above on any supported network.
Email your transaction hash and the email address you want the pack delivered to at the LaunchTower contact listed in the GitHub repo.
Once we confirm the transaction on-chain, we send the pack to your email.

No subscription. No recurring fee. One payment, full pack.

This is a research report, not financial advice. Factor scores are computed from historical data and do not guarantee future performance. Momentum strategies can experience sharp reversals. Past performance is not indicative of future results. LaunchTower is an independent market-data desk. We do not manage client funds or sell financial advice. Data is from Yahoo Finance via yfinance. Verify independently before making any investment decision.