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LaunchTower: A Reproducible Momentum + Quality Factor Screen on 95 US Stocks

#finance #quant #python #data

LaunchTower — Factor Screen & Research Reports

LaunchTower is an independent, self-funded market-data desk. We run a
transparent, reproducible momentum + quality factor screen on a universe of
~95 liquid US mega-cap equities, and publish the full factor table, the
research report, and the exact code used to generate it — so anyone can
verify, reproduce, or build on our work.

Disclaimer: This is a research screen built from public market data.
It is not personalized investment advice and is not a recommendation
to buy or sell any security. Past momentum does not guarantee future
returns. Run at your own risk.

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 ($29)

Note: A live checkout link is pending a Whop store configuration fix
(default currency). The store page above is live. Once the checkout link
is active, it will appear here.

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.

What's in this repository

Path Description
launchtower_factor_screen_v2.py The complete, self-contained factor screen (v2, 2026-09-15). Runs end-to-end: downloads data, computes factors, prints top/bottom 10, writes a dated CSV.
reports/2026-09-15.md Latest research report (dated 2026-09-15, data as of 2026-09-11 close).
data/factor_scores_2026-09-15.csv Full 95-ticker factor table from the 2026-09-15 screen.

Methodology (v2, 2026-09-15)

The composite score is a z-scored blend of four components, computed over a
trailing 12-month (252 trading day) window:

Component Weight Definition
Momentum (12-1) 50% Return from t-252 to t-22 (skips the last month to avoid short-term reversal)
Quality (Sharpe) 30% Annualized mean daily return / annualized daily vol, trailing 252 days (gross Sharpe, rf = 0)
Low Volatility 10% Inverse z-score of annualized realized vol, trailing 252 days
Low Drawdown 10% Inverse z-score of max drawdown from peak, trailing 252 days

All components are z-scored across the universe before weighting. A higher
composite score indicates stronger momentum and better risk-adjusted
performance.

Data source: Yahoo Finance (via yfinance), auto-adjusted (split and
dividend adjusted) daily closes, trailing ~3 years.

Universe: 96 liquid US mega-cap names across tech, semis, industrials,
energy, and consumer. Names with fewer than 252 trading days of history are
dropped automatically.

Quick start 

# 1. Install dependencies
pip install yfinance pandas numpy

# 2. Run the screen
python launchtower_factor_screen_v2.py
Enter fullscreen mode Exit fullscreen mode

The script will:

  1. Download ~3 years of adjusted daily closes for the full universe.
  2. Compute the four components per ticker over the trailing 252-day window.
  3. Z-score each component cross-sectionally.
  4. Build the composite score and rank the universe.
  5. Print the top 10 and bottom 10 tickers.
  6. Write a dated CSV (factor_scores_YYYY-MM-DD.csv) to the current directory.

Latest results (2026-09-15)

Top 10 — Strongest Momentum + Quality

Rank Ticker Name 12M Ret 12-1 Mom Sharpe MaxDD 1Y Score
1 MU Micron +548.8% +506.2% 2.71 -39.1% +2.512
2 LITE Lumentum +462.2% +465.5% 2.27 -42.8% +2.160
3 WDC Western Digital +365.9% +373.0% 2.33 -41.8% +1.837
4 STX Seagate +325.3% +349.9% 2.31 -31.8% +1.698
5 INTC Intel +318.3% +310.2% 2.19 -41.9% +1.526
6 AMAT Applied Materials +169.8% +223.6% 1.97 -39.6% +1.166
7 TER Teradyne +229.2% +249.0% 1.95 -34.0% +1.160
8 MRVL Marvell +255.3% +226.7% 2.00 -48.4% +1.142
9 COHR Coherent +195.0% +243.6% 1.71 -48.0% +1.114
10 AMD AMD +231.6% +210.2% 2.03 -27.8% +0.992

Bottom 10 — Weakest Momentum + Quality

Rank Ticker Name 12M Ret 12-1 Mom Sharpe MaxDD 1Y Score
86 GRAB Grab -44.9% -34.7% -1.36 -53.3% -0.667
87 INTU Intuit -50.8% -48.8% -1.21 -63.4% -0.681
88 HUBS HubSpot -54.6% -57.7% -0.72 -67.4% -0.688
89 MSTR MicroStrategy -59.8% -70.9% -0.75 -77.1% -0.734
90 NKE Nike -48.9% -44.3% -1.67 -49.3% -0.803
91 RBLX Roblox -65.8% -73.3% -1.25 -74.9% -0.823
92 SMR NuScale -75.5% -72.7% -0.86 -85.8% -0.831
93 TTD Trade Desk -68.3% -70.2% -1.76 -75.9% -0.883
94 TME Tencent Music -68.0% -66.1% -2.19 -69.3% -0.968
95 MNSO Mens Sana -63.3% -51.8% -2.58 -63.3% -0.990

Interpretation: The top of the table is dominated by the memory /
storage / optical complex — Micron, Lumentum, Western Digital, Seagate,
Intel, Teradyne, Marvell, and Coherent. This is a meaningful rotation from
the prior screen (2026-09-14), where the top was led by semiconductor
equipment (AMAT, LRCX, KLAC) and energy (MPC). The new leaders are the
components of the AI data-center buildout: HBM memory (MU), optical
transceivers (LITE, COHR), and nearline storage (WDC, STX).

The bottom of the table is led by consumer software, gaming, and
China-exposed names that have underperformed over the trailing 12 months.
All 10 carry negative 12-month returns and negative Sharpe ratios.

CSV schema

data/factor_scores_2026-09-15.csv contains one row per ticker with the
following columns:

Column Description
rank Rank by composite score (1 = strongest)
ticker Ticker symbol
price Last adjusted close (USD)
ret_12m 12-month total return (decimal)
mom_12_1 12-1 momentum (decimal)
vol_12m Annualized realized volatility (decimal)
sharpe Annualized gross Sharpe ratio
maxdd_12m Maximum drawdown from peak (negative decimal)
momentum_z Z-score of mom_12_1 across the universe
sharpe_z Z-score of sharpe across the universe
lowvol_z Z-score of -vol_12m across the universe
lowdd_z Z-score of -maxdd_12m across the universe
composite Final composite score (higher = stronger)

Reproducibility

The screen is fully deterministic given the same input data. To reproduce
the 2026-09-15 report:

  1. Run python launchtower_factor_screen_v2.py on or after 2026-09-15.
  2. The output CSV will contain the same 95 tickers with the same factor values (prices may differ slightly if Yahoo Finance revises history).
  3. The top/bottom 10 tables in reports/2026-09-15.md are generated from the same composite score.

Note: Yahoo Finance data is point-in-time and subject to revision.
Minor differences in the last few days of history are normal.

Update cadence

The screen is re-run monthly (first trading day of each month). Each
run produces:

  • A new dated CSV in data/
  • A new dated report in reports/
  • The same self-contained script (no version churn)

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

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