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On September 5, 2025,
(UPS) traded with a volume of $0.73 billion, ranking 141st in market activity. The stock closed higher by 0.66% amid mixed analyst activity and institutional positioning shifts.Analyst coverage highlighted divergent views, with Redburn Atlantic lowering its price target to $117 and
adjusting to $100, while maintained a "buy" rating. Institutional investors showed renewed interest, with Brighton Jones LLC and Cetera Investment Advisers increasing holdings by 63.8% and 6.1%, respectively. Insider transactions also drew attention, as directors William R. Johnson and Christiana Smith Shi purchased shares totaling $476,585, signaling confidence in the company’s near-term prospects.UPS’s financials remain under scrutiny following a recent quarterly report showing $1.55 earnings per share, slightly below estimates. Despite a 2.7% revenue decline year-over-year, the firm’s 7.8% dividend yield and strong liquidity position continue to attract income-focused investors. The stock’s beta of 1.08 indicates moderate volatility relative to the market, while its 200-day moving average of $100.50 suggests a potential support level.
To run this back-test robustly, I need to lock down a few practical details and confirm that the scope matches what you have in mind: 1. Market
• Should I use all U.S. common stocks (NYSE + Nasdaq) as the candidate pool, or a different universe (e.g., only S&P 500 constituents)? 2. Ranking & trade timing • Typical implementation: – Rank stocks by the PREVIOUS day’s dollar trading volume. – Buy the top 500 at the next day’s open, equally weighted. – Close all positions at that day’s close (1-day holding period). • Is this sequence acceptable, or would you prefer a different entry/exit convention (e.g., buy at close and sell next-day close)? 3. Practical constraints • A full-universe, daily-rebalanced portfolio involves thousands of tickers and heavy data pulls. • Our current back-testing engine handles one ticker (or one synthetic series) at a time. – I can approximate the portfolio by constructing a synthetic daily return series offline, then feed that series into the engine. – Alternatively, we could narrow the universe (e.g., top 500 within the S&P 500) to keep the data volume reasonable.Hunt down the stocks with explosive trading volume.

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