A clean, reproducible factor dataset for 151 US-listed large-cap stocks, scored on momentum and quality and blended into a single composite rank.

WHAT YOU GET (delivered automatically after purchase)

• factor_scores_151.csv — one row per ticker: 12m/3m/6m/1m returns, annualized volatility, max drawdown, distance from 52w high, momentum z-score, quality z-score, composite score, rank
• launchtower_factor_screen.py — the full Python script that fetches prices from Yahoo Finance, computes every column, and regenerates the CSV from scratch (no API key needed)
• factor_pack_README.md — methodology, exact column definitions, composite formula, usage examples

THE FACTORS

• momentum_score: cross-sectional z-score of 12-month trailing return
• quality_score: cross-sectional z-score of negative annualized volatility (low-vol rewarded)
• composite = 0.5 × z(ret_12m) + 0.5 × z(−vol_ann)

WHO THIS IS FOR

• Quant researchers building or backtesting factor models
• Momentum/quality traders who want a clean, reproducible screen
• Python developers who want working reference code, not just a spreadsheet

HOW TO USE
import pandas as pd
df = pd.read_csv(“factor_scores_151.csv”).sort_values(“composite_score”, ascending=False)
print(df.head(20)) # top 20 by composite score

Or regenerate the dataset yourself:
pip install yfinance pandas numpy
python launchtower_factor_screen.py

FREE TIER — TRY BEFORE YOU BUY
A free sample of the dataset is public on GitHub:
https://pennypenguinapp-beep.github