Date: 2026-09-16 · Universe: 109 US large-caps · Data: yfinance (public, adjusted close)
⚠️ Honesty first. This is a transparent, reproducible research tool built from public data. It is not investment advice, and it does not promise returns. We include our own backtest — including the months where the signal lost money — because a factor model you can’t see the failure cases of is not a model, it’s a sales pitch.
Is: a 2-factor cross-sectional screen — 12-month momentum + low-volatility quality — applied to 109 US large-caps, fully reproducible from public data with ~15 lines of core logic.
Is not: a black box, an ML model, a “proprietary alpha” feed, or a return guarantee. The composite is simply:
composite = 0.5 · z(12m return) + 0.5 · z(−annualized vol)
z-scores are cross-sectional (mean 0, std 1) on the current universe. Rank 1 = highest composite.
We backtested the long top-20% / short bottom-20% of the composite, monthly rebalance, 24 periods over ~3 years (2023 → 2026), 109 tickers.
| Metric | Value |
|---|---|
| Avg top-20% forward return | +2.63% / month |
| Avg bottom-20% forward return | +3.21% / month |
| Avg long-short spread | −0.58% / month |
| Spread positive | 13 of 24 months |
| Best month (LS) | +12.41% (2025-12-31) |
| Worst month (LS) | −21.49% (2026-07-31) |
Read this honestly: over this window the naive long-short version of the signal did not produce a positive edge. That is a real result, not a bug. It means:
This is the difference between a research tool and a scam, and it’s why we publish the losing months.
Top 10 (highest composite):
| # | Ticker | 12m Return | Ann. Vol | Composite |
|---|---|---|---|---|
| 1 | MU | +597.7% | 81.5% | +1.838 |
| 2 | WDC | +371.5% | 80.2% | +0.740 |
| 3 | STX | +333.6% | 74.8% | +0.675 |
| 4 | VLO | +151.5% | 36.1% | +0.654 |
| 5 | JNJ | +54.5% | 19.2% | +0.560 |
| 6 | MPC | +121.6% | 34.2% | +0.549 |
| 7 | PSX | +103.3% | 30.9% | +0.534 |
| 8 | INTC | +315.6% | 79.5% | +0.478 |
| 9 | FDX | +73.8% | 28.3% | +0.446 |
| 10 | TGT | +78.6% | 30.6% | +0.418 |
Bottom 10 (lowest composite):
| # | Ticker | 12m Return | Ann. Vol | Composite |
|---|---|---|---|---|
| 100 | PLTR | +0.3% | 60.8% | −0.665 |
| 101 | NOW | −28.3% | 56.8% | −0.716 |
| 102 | RKT | −36.9% | 57.6% | −0.777 |
| 103 | ORCL | −53.7% | 57.2% | −0.853 |
| 104 | ZS | −41.0% | 63.1% | −0.925 |
| 105 | HOOD | −4.2% | 72.0% | −0.946 |
| 106 | COIN | −44.4% | 70.7% | −1.116 |
| 107 | RBLX | −65.7% | 67.1% | −1.139 |
| 108 | MRNA | +492.2% | 192.2% | −1.226 |
| 109 | SMCI | −8.7% | 91.4% | −1.412 |
What stands out:
pip install yfinance pandas numpy
python launchtower_factor_screen_2026-09-16.py
The script pulls 2 years of adjusted closes for the universe, computes 1m/3m/6m/12m returns, annualized vol, max drawdown, distance from 52w high, z-scores the 12m return (momentum) and negated vol (quality), averages them, and ranks. Config knobs at the top: universe, lookback, weights.
No subscription. No email gate. No “add to cart” upsell. One-time purchase.
LaunchTower — independent market-data desk. Data: yfinance (public). All figures regenerated from live data at generation time. Not investment advice; past performance does not guarantee future results.