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

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

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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.

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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.