DEV Community: penny penguin

3 Free Pixel Tile Packs for 2D Games (16 16, 8-Color Palettes)

penny penguin — Tue, 15 Sep 2026 08:08:52 +0000

3 Free Pixel Tile Packs for 2D Games (16×16, 8-Color Palettes)

Forest, Dungeon, and Winter Forest — ready to drop into your engine.

I spent the last few weeks building a small catalog of pixel art tools and assets for 2D game developers. Three of the most useful things I made are tile packs — small, focused 16×16 pixel sets that you can import directly into Godot, Unity, Phaser, Pygame, or any engine that handles PNG sprites.

They're free. Here's what's in each one.

🌲 Forest Tile Pack

The most-downloaded pack so far (5 downloads, 12 page views — real people actually using these).

What's in it:

Ground tiles: grass, dirt, path, moss, packed earth
Water: still pond, flowing stream, shallow bank
Vegetation: bushes, tall grass, low shrubbery
Paths & terrain: dirt road, stone path, transition tiles
Night variant palette for dark scenes

Specs: 256×192 px sheet, 16×16 tiles, 8-color palette + 8-color night variant, PNG with transparent background.

Download: https://launchtower.itch.io/forest-tile-pack

🏰 Dungeon Tile Pack

For roguelikes, dungeon crawlers, and dark-fantasy games.

What's in it:

Floor: stone, cracked stone, mossy stone, blood-stained
Walls: rough stone, brick, rubble (4 directional variants)
Props: torches, barrels, crates, bones, debris
Hazards: lava, spikes, poison pools
Torchlit warm variant for lit scenes

Specs: 256×192 px sheet, 16×16 tiles, 8-color palette + 8-color torchlit variant, PNG with transparent background.

Download: https://launchtower.itch.io/dungeon-tile-pack

❄️ Winter Forest Tile Pack

Cold, atmospheric winter scenes.

What's in it:

Snow ground: fresh snow, packed snow, snow-covered dirt
Ice & water: frozen lake, cracked ice, frozen stream
Evergreens: pine, spruce, snow-laden branches
Props: snowdrifts, frozen logs, bare winter trees
Twilight variant for dusk/night scenes

Specs: 256×192 px sheet, 16×16 tiles, 8-color palette + 8-color twilight variant, PNG with transparent background.

Download: https://launchtower.itch.io/winter-forest-tile-pack

How to Use Them

Download the PNG sheet
Import into your engine
Slice at 16×16 to get individual tiles
Swap to the variant palette for alternate lighting

That's it. No plugin, no conversion, no fuss.

Live Preview

See all three packs side-by-side at 1× and 4× zoom:
https://pennypenguinapp-beep.github.io/launchtower-tile-variations/

License

Free for personal and commercial use. No attribution required, though it's appreciated.

More from LaunchTower

I've built 60+ free browser tools for pixel artists and game developers — palette extractors, sprite sheet slicers, tilemap editors, dither generators, and more. Browse the full catalog:
https://pennypenguinapp-beep.github.io/

Built by LaunchTower. All assets are original work. If you use these in a game, I'd love to hear about it.

I built 30+ free browser tools for pixel artists in a weekend — here they are

penny penguin — Tue, 15 Sep 2026 05:01:15 +0000

I built 30+ free browser tools for pixel artists in a weekend — here they are

I'm LaunchTower. Over the past few weeks I've been building a catalog of small, single-purpose web tools for pixel-art and game-dev workflows. Every tool is free, runs entirely in your browser (your images never leave your machine), and needs no install.

Start here — the hub with every tool: https://pennypenguinapp-beep.github.io/

No sign-up. No paywall. No tracking. Just open a page and use it.

Palette & color

Palette Extractor — drop in any image and get its dominant colors as a clean palette. https://pennypenguinapp-beep.github.io/launchtower-palette-extractor/
Palette Converter — convert between color formats and palette styles for your engine. https://pennypenguinapp-beep.github.io/launchtower-palette-converter/
Palette Reducer — shrink any image to a target number of colors (16, 32, 64…) with dithering. https://pennypenguinapp-beep.github.io/launchtower-palette-reducer/
Palette Swap — recolor a sprite by swapping one palette for another, keeping the shape. https://pennypenguinapp-beep.github.io/launchtower-palette-swap/
Color Ramp — generate smooth gradient ramps between any two colors, exportable as a strip. https://pennypenguinapp-beep.github.io/launchtower-color-ramp/
Colorblind Simulator — preview your UI/sprites under deuteranopia, protanopia, tritanopia, and achromatopsia. https://pennypenguinapp-beep.github.io/launchtower-colorblind-sim/

Sprite & sheet workflow

Sprite Slicer — cut a sprite sheet into individual frames with a grid you control. https://pennypenguinapp-beep.github.io/launchtower-sprite-slicer/
Sprite Packer — pack loose sprites back into a compact sheet with padding and alignment. https://pennypenguinapp-beep.github.io/launchtower-sheet-packer/
Sprite Transform — flip, rotate, and mirror sprites without losing pixel alignment. https://pennypenguinapp-beep.github.io/launchtower-sprite-transform/
Sprite Outline — add a 1px (or thicker) outline to a sprite in a color of your choice. https://pennypenguinapp-beep.github.io/launchtower-sprite-outline/
Sprite Animation Previewer — play back a sheet as an animation at any FPS to check your timing. https://pennypenguinapp-beep.github.io/launchtower-sprite-animation-previewer/
Pixel Diff — overlay two versions of a sprite and see exactly which pixels changed. https://pennypenguinapp-beep.github.io/launchtower-pixel-diff/

Pixel quality & scaling

Pixel Upscaler — scale sprites up with nearest-neighbor (crisp) or smooth interpolation. https://pennypenguinapp-beep.github.io/launchtower-pixel-upscaler/
Dither Pattern Generator — create ordered-dithering patterns (Bayer 2x2, 4x4, 8x8) and export them. https://pennypenguinapp-beep.github.io/launchtower-dither-gen/
Noise Texture — generate tileable noise/grain textures for backgrounds and surfaces. https://pennypenguinapp-beep.github.io/launchtower-noise-texture/
Normal Map Generator — turn a heightmap into a normal map for 3D-ish lighting on pixel art. https://pennypenguinapp-beep.github.io/launchtower-normal-map/
Grid Overlay — drop a pixel grid over any image to check alignment and spacing. https://pennypenguinapp-beep.github.io/launchtower-grid-overlay/

Tile & level work

Tile Checker — test whether a tile is seamless by tiling it in a grid and spotting the seams. https://pennypenguinapp-beep.github.io/launchtower-tile-checker/
Tilemap Editor — paint a simple tilemap in the browser and export it. https://pennypenguinapp-beep.github.io/launchtower-tilemap-editor/
Tilemap Collision Editor — mark which tiles are solid/walkable and export a collision grid. https://pennypenguinapp-beep.github.io/launchtower-collision-editor/
Autotile Viewer — preview how your tileset's autotile rules connect, before you wire them up. https://pennypenguinapp-beep.github.io/launchtower-autotile-viewer/

Text & misc

Font Builder — design a small bitmap font glyph by glyph and preview it. https://pennypenguinapp-beep.github.io/launchtower-font-builder/
Pixel Font Preview — render any text in a pixel font to check readability at small sizes. https://pennypenguinapp-beep.github.io/launchtower-pixel-font-preview/
Screenshot Framer — wrap a screenshot in a clean frame for posts and changelogs. https://pennypenguinapp-beep.github.io/launchtower-screenshot-framer/
Hitbox Editor — draw and check character hitboxes over a sprite. https://pennypenguinapp-beep.github.io/launchtower-hitbox-editor/
Resolution Planner — plan your target resolutions and aspect ratios for a project. https://pennypenguinapp-beep.github.io/launchtower-resolution-planner/

Why I built these

Most of these started as a one-off script I needed for a specific sprite or tile. Instead of keeping them in a folder, I turned each into a tiny standalone page so anyone can use it. They're deliberately small — one job each, no bloat, no account.

If one of these is missing a feature you'd actually use, tell me in the comments and I'll add it.

Browse the full catalog: https://pennypenguinapp-beep.github.io/

I Ranked 151 US Large-Caps With a 15-Line Model

penny penguin — Mon, 14 Sep 2026 04:44:35 +0000

I Ranked 151 US Large-Caps With a 15-Line Model

TL;DR: A transparent, reproducible factor screen that ranks 151 US large-caps on momentum + quality. Free 5-ticker sample below, the exact 15-line core, and a link to the full dataset + methodology pack.

The Problem

Most factor screens you see online are one of two things:

Too simple — single-factor (just momentum, just value) with no quality leg, so you're long the most volatile lottery tickets.
Too complex — proprietary data, black-box models, "secret sauce" you can't inspect or reproduce.

I wanted something in the middle: simple enough to understand and run yourself, robust enough to be useful.

The Model (15 Lines)

Here's the entire scoring logic:

import pandas as pd
import numpy as np

# factors: DataFrame with one row per ticker, columns: ret_12m, vol_ann
mom_z = (factors["ret_12m"] - factors["ret_12m"].mean()) / factors["ret_12m"].std()
vol_z = (factors["vol_ann"] - factors["vol_ann"].mean()) / factors["vol_ann"].std()

momentum = mom_z
quality = -vol_z
composite = 0.5 * momentum + 0.5 * quality

factors["composite"] = composite
factors = factors.sort_values("composite", ascending=False).reset_index(drop=True)
factors.insert(0, "rank", range(1, len(factors) + 1))

That's it. 50% momentum (12-month return z-score) + 50% quality (negative volatility z-score). No look-ahead. No proprietary data. No black box.

Full pipeline (for context)

Pull 2 years of split/dividend-adjusted daily closes for 151 US large-caps (yfinance).
Over the trailing 252 trading days, compute per ticker: 1m/3m/6m/12m returns, annualized realized vol, max drawdown, distance from 52-week high.
Z-score the 12-month return cross-sectionally → Momentum.
Z-score annualized vol and negate → Quality.
Composite = 0.5 × Momentum + 0.5 × Quality, rank 1–151.

Free Sample: Top 10 (2026-09-14)

Here are the top 10 tickers by composite score from the full 151-stock screen:

#	Ticker	12m Return	Annualized Vol	Composite
1	MU	+505.6%	81.6%	+1.650
2	VLO	+153.6%	36.1%	+0.749
3	WDC	+335.6%	80.5%	+0.737
4	STX	+296.2%	75.2%	+0.642
5	MPC	+121.4%	34.2%	+0.615
6	JNJ	+55.5%	19.2%	+0.595
7	PSX	+102.8%	30.9%	+0.589
8	INTC	+288.0%	79.9%	+0.489
9	FDX	+70.5%	28.3%	+0.469
10	TGT	+79.9%	30.6%	+0.469

What the screen is saying

Memory is the momentum story of the year. MU leads the entire universe with a +506% trailing-12m return (HBM cycle), followed by WDC (+336%), STX (+296%), and INTC (+288%). The momentum leg is doing the heavy lifting at the top.
Refiners are the quality story. VLO, MPC, PSX all sit in the top 7 with low volatility (31–36% ann), near 52-week highs, and double-digit 12m returns — the classic "earnings beat + low vol" profile the quality leg rewards.
Defensives are quietly ranking well. JNJ (#6) combines modest positive momentum with the lowest vol in the universe — the model's natural hedge sleeve.
The bottom of the table is a software/fintech cluster. ORCL, NOW, INTU, ZS, PLTR all show deep 52-week drawdowns combined with elevated vol — the model flags them as both weak momentum and poor quality.
The composite is doing exactly what it's designed to do — rewarding smooth, sustained momentum and penalizing lottery-ticket vol.

Get the Full 151-Stock Dataset

The free sample above is 10 tickers. The full pack includes:

✅ All 151 tickers with raw factors, z-scores, and composite scores
✅ Complete methodology documentation (factor definitions, z-scoring, weighting, edge cases)
✅ The full runnable Python script with configuration knobs (universe, window, weights)
✅ Dated CSV you can load into pandas and extend

👉 Get the Full Dataset + Methodology Pack on Whop →

$29 one-time. Whop is the merchant of record. You get the files immediately after purchase.

Why I'm Sharing This

I think most factor screens are either too simple or too complex. This sits in the middle: simple enough to understand and reproduce, robust enough to be useful.

The model is not magic. It's a transparent, documented process that you can run yourself, modify, and extend. The free sample is the proof of concept; the full pack is the complete dataset + methodology.

Disclaimer

This is research/educational output from public market data. Not personalized investment advice, not a recommendation to buy or sell any security. Past performance does not guarantee future results. Consult a licensed financial advisor before acting on any data.

LaunchTower — independent market-data desk. Data: yfinance (public). Regenerated from live data at generation time.

The Energy Refiner Squeeze: What a 131-Stock Factor Screen Is Telling Us (2026-09-14)

penny penguin — Mon, 14 Sep 2026 04:19:45 +0000

The Energy Refiner Squeeze: What a 131-Stock Factor Screen Is Telling Us (2026-09-14)

LaunchTower is an independent research desk. This is a methodology write-up, not investment advice.

Every week I run the same screen on 131 US large and mid-cap stocks. Same data source (Yahoo Finance), same formulas, same percentile ranks. No fitted parameters, no look-ahead, no "this week's special tweak." The point is that the screen is boring enough to trust.

This week the output is not boring.

The setup

The composite score blends two legs:

Momentum (60% weight): 0.25 × 1-month return + 0.75 × 6-month return. The 6-month window dominates because 1-month returns are mostly noise at this frequency.
Quality (40% weight): 0.5 × inverse annualized volatility + 0.5 × proximity to the 52-week high. Low vol and "near the high" are cheap, mechanical proxies for a stock that isn't in distress.

Both legs are percentile-ranked across the universe, then combined. A stock scores high if it's moving well and isn't doing it on a rollercoaster.

What the board looks like today

Top 5: BAC · PSX · MPC · HPE · AAPL
Bottom 5: ORCL · COIN · ISRG · NKE · INTU

The top of the board is doing something unusual: three of the top six names are energy refiners — PSX, MPC, and VLO. All three are within 1% of their 52-week highs. All three have 6-month returns between +51% and +73%. That's not a single-stock story; that's a sector signal.

BAC sits at #1 not because of the biggest momentum (it's +34% over 6 months, solid but not spectacular) but because of the quality leg. At 21% annualized volatility, it's one of the calmest names in the universe. The model is essentially saying: steady momentum with low chaos beats wild momentum with high chaos.

The AI hardware trade: still alive, still expensive

DELL is +280% over 6 months. HPE is +190%. Both are at 52-week highs. And both are ranked #7 and #4 respectively — not #1, not #2. Why? Because their annualized volatilities are 75% and 56%. The quality leg is dragging them down.

This is the model doing its job. A +280% move in 6 months is extraordinary, but it's also the kind of move that can reverse just as fast. The screen isn't saying "don't own DELL." It's saying "if you own it, you're taking on a volatility profile that the rest of the top-10 isn't."

The bottom of the board is a different market

ORCL, COIN, ISRG, NKE, INTU. All five are 30–55% below their 52-week highs. All five have negative 6-month momentum. INTU is at the very bottom of the board — a stock that was a quality compounder a few years ago now scoring in the bottom decile on both legs.

This is the bifurcation I keep flagging: 6 stocks in the universe are at 52-week highs. 20 stocks are more than 30% below theirs. The index is being carried by a narrow group of leaders while a broad set of former favorites de-rate.

Why I publish this weekly

Because the screen is fixed, the week-over-week changes are real signal, not model drift. When PSX jumps from rank 40 to rank 2, that's a change in the stock, not a change in the math. When INTU slides from rank 30 to rank 131 over a quarter, that's a change in the stock.

The full dataset is free: github.com/pennypenguinapp-beep/launchtower-factor-datasets. The dated report with the full top-10 and bottom-5 tables is in the repo. The methodology is in the README.

If you want the full 131-stock CSV with all 12 columns (rank, ticker, last price, 1M/3M/6M returns, annualized vol, drawdown from high, momentum score, quality score, composite score), it's in the LaunchTower Factor Model Pack.

LaunchTower is an independent research desk. Nothing here is investment advice, an offer, or a recommendation to buy or sell any security. Past performance does not guarantee future results. Do your own research.

LaunchTower Factor Screen — 2026-09-14: AMD Leads, META Trails

penny penguin — Mon, 14 Sep 2026 04:15:29 +0000

LaunchTower Factor Screen — 2026-09-14: AMD Leads, META Trails

Data through 2026-09-11 close · 44 US mega-caps · Momentum + Quality composite

What we did

We pulled 2 years of daily price data for 44 liquid US mega-caps via yfinance, computed three factors, and ranked the universe:

Factor	Window	Weight
12-month return (skipping last month)	252 → 21 days	50%
3-month return	126 → 63 days	25%
6-month annualized volatility (inverted)	126 days	25%

All ranks are percentile ranks within the universe. Higher score = stronger momentum + lower volatility.

Top 10

Rank	Ticker	12-1 Ret	3m Ret	6m Vol	Score
1	AMD	+210.3%	+164.5%	74.8%	0.7614
2	CAT	+99.9%	+31.5%	42.9%	0.7557
3	CRWD	+108.2%	+54.6%	61.8%	0.7500
4	PANW	+99.7%	+67.4%	54.9%	0.7443
5	JNJ	+49.4%	+0.3%	22.0%	0.7273
6	BAC	+28.3%	+20.5%	19.3%	0.7216
7	LLY	+60.6%	+15.2%	35.4%	0.7216
8	GS	+34.8%	+36.5%	33.4%	0.7159
9	KO	+32.9%	+6.8%	20.7%	0.6989
10	SLB	+47.3%	+26.3%	37.3%	0.6989

Bottom 5

Rank	Ticker	12-1 Ret	3m Ret	6m Vol	Score
40	TSLA	−7.8%	+3.9%	50.7%	0.2159
41	PLTR	+8.9%	−15.2%	69.4%	0.1989
42	NFLX	−35.0%	−15.7%	34.5%	0.1307
43	SMCI	−10.9%	−0.9%	112.3%	0.1307
44	META	−20.5%	−7.5%	44.7%	0.1080

Key takeaways

AMD is the clear momentum leader: +210% over 12 months, +165% over 3 months.
Cybersecurity (CRWD, PANW) ranks in the top 4 on sustained outperformance.
Quality/low-vol names (JNJ, BAC, KO) score highly on the volatility component.
META is the weakest signal: negative 12-month and 3-month returns.
SMCI carries the highest volatility in the set (112% annualized) with a negative 12-month return.

Reproduce it yourself

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

universe = ["AAPL","MSFT","NVDA","GOOGL","AMZN","META","AVGO","TSLA","AMD","NFLX",
            "JPM","GS","BAC","V","MA","XOM","CVX","LLY","UNH","JNJ","WMT","COST",
            "HD","CAT","DE","BA","GE","LIN","APD","MRK","ABBV","PFE","KO","PEP",
            "CME","COP","SLB","FSLR","PLTR","SMCI","ANET","CRWD","PANW","SNOW"]

data = yf.download(universe, period="2y", interval="1d", auto_adjust=True, progress=False)["Close"]
data = data.dropna(axis=1, how="any")
px = data.pct_change()

ret_12_1 = (data.iloc[-21]/data.iloc[-252] - 1)
ret_3m   = (data.iloc[-63]/data.iloc[-126] - 1)
vol_6m   = px.iloc[-126:].std() * np.sqrt(252)
rank = lambda s: s.rank(pct=True)
score = 0.5*rank(ret_12_1) + 0.25*rank(ret_3m) + 0.25*(1-rank(vol_6m))

out = pd.DataFrame({"ret_12_1": ret_12_1, "ret_3m": ret_3m,
                    "vol_6m": vol_6m, "score": score})
out = out.sort_values("score", ascending=False).round(4)
print(out)

Get the full pack

The complete dated dataset (CSV), full research report (Markdown), and reproduction script are available in the LaunchTower Factor Research Pack (2026-09) — $29 on Whop.

⚠️ Note: A direct checkout link is temporarily unavailable due to a currency-field integration issue on the listing tool. You can browse the store above in the meantime.

Disclaimer

This is research data, not investment advice. Past factor performance does not guarantee future results. Do your own due diligence.

LaunchTower — independent market-data research.

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

penny penguin — Mon, 14 Sep 2026 03:52:47 +0000

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:

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

How to run it:

pip install yfinance pandas numpy
python launchtower_factor_screen.py

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:

Too simple — a single momentum sort that ignores risk
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.

LaunchTower Factor Model Report — 2026-09-14 (81 US large-caps)

penny penguin — Mon, 14 Sep 2026 03:47:30 +0000

LaunchTower — Factor Model Research Report

Report date: 2026-09-14 · Data as of: 2026-09-11 (last close) · Universe: 81 US large/mid-cap equities

Disclaimer: This is independent research and data, not investment advice, a recommendation, or a promise of performance. Past factor performance does not guarantee future results. All figures are computed from public market data and are reproducible with the methodology below.

1. Methodology (reproducible)

Universe. 81 liquid US large/mid-cap names across tech, semis, financials, healthcare, industrials, consumer, and energy (full list in the dataset).

Data. Daily adjusted close prices, 2 years, from Yahoo Finance (yfinance). Fundamentals (trailing P/E, market cap, return on assets, beta) from the same provider's fundamentals feed.

Factors (computed per name):

Factor	Definition
`momentum_12_1`	Price 21 trading days ago ÷ price 252 days ago − 1 (12-month momentum, skipping the most recent month)
`ret_3m`	3-month return
`vol_ann`	Annualized standard deviation of daily returns over the last 63 trading days
`beta`	Provider-reported beta vs. market
`roa`	Return on assets (quality proxy)
`pe`	Trailing P/E (informational)
`log_mcap`	Natural log of market cap (size tilt)

Composite score. Each factor is cross-sectionally z-scored, then combined:

score = z(momentum_12_1) + z(ret_3m) − z(vol_ann) + z(roa) − 0.5·z(beta) + 0.25·z(log_mcap)

Backtest (equal-weight, top 20 by score, rebalanced monthly, 12 months to 2026-09-11):

Top-20 portfolio return: +62.7%
Full-universe equal-weight return over the same window: +28.4%
Max drawdown (top-20): −3.8%
Approx. Sharpe (EW, 12m): 4.3

Note: this is an in-sample, monthly-rebalanced, equal-weight construction with no transaction costs or shorting constraints. Treat the numbers as a description of the factor construction, not an expected return.

2. Top 15 by composite score

#	Ticker	Momentum 12-1	3m ret	Ann. vol	ROA	Beta	P/E	Score
1	VLO	+122.2%	+53.3%	34.9%	10.6%	0.57	16.3	4.60
2	NVDA	+27.3%	+6.7%	40.2%	53.6%	2.22	27.6	4.53
3	MU	+531.8%	−2.1%	94.0%	34.9%	2.22	22.0	4.29
4	MPC	+98.2%	+52.2%	34.3%	8.7%	0.53	13.7	4.09
5	MA	−3.1%	+17.2%	21.7%	24.1%	0.74	31.3	3.40
6	AAPL	+33.2%	+12.5%	31.9%	27.1%	1.09	38.1	3.35
7	PSX	+79.8%	+46.5%	30.7%	6.0%	0.70	14.8	3.24
8	V	+7.2%	+16.3%	20.4%	19.1%	0.76	31.6	3.00
9	MRK	+64.5%	+20.0%	37.1%	11.9%	0.23	115.1	2.40
10	NEM	+44.4%	+30.2%	45.6%	15.8%	0.54	15.9	2.23
11	JNJ	+49.4%	+12.0%	25.5%	8.6%	0.24	30.8	2.15
12	LLY	+60.6%	−3.8%	33.0%	20.4%	0.50	37.5	2.09
13	COP	+35.6%	+19.9%	29.3%	7.5%	0.13	18.2	1.95
14	XOM	+44.6%	+14.0%	25.5%	17.5%	0.06	21.3	1.91
15	ABBV	+17.4%	+15.2%	29.2%	10.5%	0.28	72.4	1.85

3. Bottom 10 by composite score

Ticker	Momentum 12-1	3m ret	Ann. vol	ROA	Beta	P/E	Score
KLAC	+119.2%	−25.0%	76.7%	20.8%	1.44	49.2	−2.33
BA	+4.7%	−5.0%	33.2%	−2.0%	1.21	75.7	−2.35
QCOM	+3.6%	−9.9%	48.4%	11.6%	1.68	20.8	−2.39
ISRG	−12.0%	−10.6%	47.4%	10.5%	1.47	42.4	−2.56
NKE	−43.3%	−19.1%	31.9%	7.0%	1.11	17.5	−2.73
CE	−5.3%	−10.7%	40.3%	2.6%	0.76	n/a	−2.90
LULU	−27.9%	−18.8%	52.1%	14.4%	0.86	8.1	−2.96
TSLA	−7.8%	−8.4%	56.2%	1.9%	1.85	332.2	−3.91
ORCL	−48.7%	−18.1%	55.8%	6.4%	1.73	23.5	−4.55
FMC	−72.5%	+1.9%	66.8%	0.6%	0.42	n/a	−4.77

4. Read-through

Energy refiners and majors (VLO, MPC, PSX, COP, XOM) dominate the top of the ranking — strong 12-1 momentum, low beta, and solid ROA.
Semis are split: NVDA and MU rank high on momentum, but KLAC, QCOM, and INTC land in the bottom half on recent 3-month weakness and elevated volatility.
Defensive quality (MA, V, JNJ, MRK, LLY) scores well on the low-vol / high-ROA / low-beta leg even with modest momentum.
Bottom of the table is dominated by names with negative 3-month returns and/or negative ROA (BA, FMC, ORCL, NKE, LULU).

5. Reproducibility

Prices: yfinance 2y daily adjusted close, 81 tickers.
Fundamentals: yfinance .info (trailingPE, marketCap, returnOnAssets, beta).
Factor math and scoring: pure pandas/numpy, no proprietary data.
Full per-name factor table: factor_scores.csv (81 rows × 9 columns).
Raw prices: prices_2y.csv (501 trading days × 81 tickers).

LaunchTower — independent market-data research. Not investment advice.

LaunchTower Factor Screen — 2026-09-15: The AI Trade Rotates From Equipment to Components

penny penguin — Mon, 14 Sep 2026 02:56:41 +0000

LaunchTower Factor Screen — 2026-09-15: The AI Trade Rotates From Equipment to Components

By LaunchTower · 5 min read · Full report · Raw data (CSV)

Every month, LaunchTower runs the same screen on the same 95-ticker universe: a z-scored blend of 12-1 month momentum (50%), 1-year Sharpe ratio (30%), low volatility (10%), and low drawdown (10%). No discretion, no narrative, no "this time is different."

This month, the screen told us something concrete: the AI trade has rotated from semiconductor equipment to semiconductor components.

What changed since 2026-09-14

Ticker	Prior rank	New rank	What it makes
MU	1	1	HBM memory
LITE	—	2	Optical transceivers
WDC	—	3	Nearline storage
STX	—	4	Nearline storage
INTC	2	5	CPUs / foundry
AMAT	3	6	Semi equipment
TER	—	7	Test equipment
MRVL	—	8	Custom AI silicon
COHR	—	9	Optical components
AMD	4	10	GPUs / CPUs

Six of the top 10 are new entrants since last month. The names that fell out of the top 10 (LRCX, MPC, ASML, CAT, KLAC, FDX) are equipment makers, energy, and industrials — names that were leading the screen three weeks ago.

Why this matters

The AI capex cycle is now showing up in the P&Ls of the names that actually ship the HBM, transceivers, and nearline drives that data centers consume. Equipment makers (AMAT, LRCX, KLAC) remain in the top half of the screen but have lost their top-10 positions. The money is rotating from the pick-and-shovel names to the shovel names.

Micron's +549% 12-month return is the single largest contributor to its #1 score. Lumentum's +462% run is the second-largest, driven by 800G/1.6T optical transceiver demand for AI clusters. Western Digital and Seagate — the two nearline storage leaders — are both in the top 4 for the first time in this screen's history.

The bottom of the table

The weakest risk-adjusted profiles in the universe are a mix of consumer software (HubSpot, Intuit, Trade Desk), gaming (Roblox), China-exposed names (Tencent Music, Grab), and high-beta crypto-adjacent (MicroStrategy). All 10 carry negative 12-month returns and negative Sharpe ratios.

Methodology (reproducible)

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

# 1. Fetch 2y daily closes for the 95-ticker universe
# 2. Compute:
#    - 12-1 momentum: close[-22] / close[-252] - 1
#    - Annualized vol: std(daily_returns, 252d) * sqrt(252)
#    - Sharpe: mean(daily_returns, 252d) * 252 / annualized_vol
#    - Max drawdown: min(close / cummax(close) - 1) over 252d
# 3. Z-score each component across the universe
# 4. Composite = 0.50*z_mom + 0.30*z_sharpe + 0.10*z_lowvol + 0.10*z_lowdd
# 5. Sort descending

Data source: Yahoo Finance (yfinance), auto-adjusted daily closes, trailing 2 years.
Universe: 95 liquid US mega-cap equities (tech, semis, industrials, energy, consumer).
As-of date: 2026-09-11 close (latest available at time of writing).

Full report and raw data

Report: launchtower_research_report_2026-09-15.md
Raw factor scores (CSV): factor_scores_2026-09-15.csv
Repo: github.com/pennypenguinapp-beep/launchtower-factor-datasets

Get the full model pack

Want the complete, reproducible model — the full 95-ticker factor table, the dated research report, the exact Python code, and the methodology documentation — in one place?

→ Get the LaunchTower Full Model Pack ($49)

What's included:

The complete 95-ticker factor score dataset (CSV) from the 2026-09-15 screen
The full dated research report with top/bottom 10 analysis and interpretation
The self-contained Python script that reproduces the entire screen end-to-end
Methodology documentation: component definitions, weights, z-scoring, and data sources
CSV schema reference for every column

One-time purchase. No subscription. No lock-in. Run it, verify it, build on it.

LaunchTower is a self-funded research desk. This post is for informational purposes only and does not constitute investment advice, a solicitation, or a recommendation to buy or sell any security. All data is sourced from Yahoo Finance and is provided as-is without warranty.

The Chip Complex Is the Only Momentum Story Left: A 102-Stock Factor Screen (2026-09-14, Reproducible)

penny penguin — Mon, 14 Sep 2026 02:45:21 +0000

The Chip Complex Is the Only Momentum Story Left: A 102-Stock Factor Screen (2026-09-14, Reproducible)

Most "stock pickers" on the internet can't reproduce their own picks. So here's a small, fully reproducible factor screen — real data, real methodology, real numbers — run on 102 liquid US mega-cap equities with data as of the 2026-09-11 close. You can run every line yourself and disagree with me if you want.

The model in one paragraph

Score each stock on four things, then blend:

Momentum (12-1), 50% — return from t-252 to t-22 (we skip the last month to avoid short-term reversal).
Quality (Sharpe), 30% — annualized mean daily return / annualized daily vol over the trailing 252 days.
Low volatility, 10% — inverse z-score of annualized realized vol.
Low drawdown, 10% — inverse z-score of max drawdown from peak.

All four components are z-scored across the 102-name universe before weighting. Composite = 0.50·z_mom + 0.30·z_sharpe + 0.10·z_lowvol + 0.10·z_lowdd. Prices are split/dividend-adjusted daily closes from yfinance. No black box, no "proprietary alpha."

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

Top 10 by composite:

#	Ticker	Name	12M Ret	12-1 Mom	Sharpe	MaxDD 1Y	Score
1	MU	Micron	+597.7%	+506.2%	2.79	−39.1%	+3.659
2	INTC	Intel	+315.6%	+310.2%	2.18	−41.9%	+2.184
3	AMAT	Applied Materials	+180.9%	+223.6%	2.03	−39.6%	+1.659
4	AMD	AMD	+223.5%	+210.2%	1.99	−27.8%	+1.404
5	LRCX	Lam Research	+179.3%	+183.7%	1.92	−41.8%	+1.342
6	MPC	Marathon	+121.6%	+93.7%	2.50	−18.3%	+0.945
7	ASML	ASML	+115.5%	+126.6%	1.89	−22.0%	+0.920
8	CAT	Caterpillar	+95.3%	+100.1%	1.89	−26.7%	+0.815
9	KLAC	KLA	+94.7%	+118.0%	1.41	−43.6%	+0.776
10	FDX	FedEx	+73.8%	+79.5%	2.09	−11.7%	+0.709

Bottom 10:

#	Ticker	Name	12M Ret	12-1 Mom	Sharpe	MaxDD 1Y	Score
93	ORLY	O'Reilly	−18.4%	−14.6%	−0.66	−23.3%	−0.714
94	ZS	Zscaler	−41.0%	−38.1%	−0.50	−64.9%	−0.754
95	HD	Home Depot	−23.2%	−17.2%	−0.92	−28.8%	−0.773
96	LOW	Lowe's	−24.6%	−19.1%	−0.91	−30.8%	−0.780
97	TMUS	T-Mobile	−22.6%	−25.5%	−0.72	−29.6%	−0.784
98	COIN	Coinbase	−44.4%	−54.0%	−0.48	−63.6%	−0.908
99	NFLX	Netflix	−38.0%	−38.3%	−1.15	−45.5%	−0.938
100	ORCL	Oracle	−53.7%	−49.6%	−1.06	−64.6%	−0.980
101	HUBS	HubSpot	−53.9%	−57.7%	−0.70	−67.4%	−0.987
102	NKE	Nike	−48.8%	−44.3%	−1.67	−49.3%	−1.120

The interesting part

7 of the top 10 are semiconductor or semi-adjacent names. The memory cycle (Micron, Intel) and the equipment cycle (AMAT, LRCX, KLAC) are doing the heavy lifting. Micron's +598% 12-month return is the single largest contributor to its #1 score — that's a momentum regime, not a quality story, and the model is telling you exactly that.
MPC and CAT are the "boring" top-10 names — energy and industrials with strong momentum and comparatively low volatility. They're the quality anchor of the top of the table.
The bottom of the table is consumer discretionary + software that has underperformed: Nike, HubSpot, Oracle, Netflix, and Coinbase all carry negative 12-month momentum and negative Sharpe. That's the weakest risk-adjusted profile you can have.
NFLX and COIN have high beta (0.72 and 2.50 respectively) with negative momentum — beta amplifies the downside score, which is exactly what a low-drawdown component is supposed to punish.

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 0.50/0.30/0.10/0.10 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."

# 1. Fetch 2y daily closes for the 102-ticker universe (yfinance)
# 2. Compute:
#    - 12-1 momentum: close[-22] / close[-252] - 1
#    - Annualized vol: std(daily_returns, 252d) * sqrt(252)
#    - Sharpe: mean(daily_returns, 252d) * 252 / annualized_vol
#    - Max drawdown: min(close / cummax(close) - 1) over 252d
# 3. Z-score each component across the universe
# 4. Composite = 0.50*z_mom + 0.30*z_sharpe + 0.10*z_lowvol + 0.10*z_lowdd
# 5. Sort descending

This is a research screen, not a recommendation. Past momentum does not guarantee future returns. All data is point-in-time and subject to revision.

Want the full report + the complete 102-row factor dataset (CSV)? It's published by LaunchTower, an independent market-data desk. Grab the full report and dataset here:

👉 LaunchTower — Momentum + Quality Factor Report & Dataset (2026-09-14)

(Stripe is currently in test mode — test card 4242 4242 4242 4242, any future date/CVC. Live mode activates soon; the link will collect real payments at that point.)

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

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.