Evergy's 115% Volume Surge Elevates It to Mid-Tier Movers as Shares Dip 0.27%

On September 25, 2025, Evergy (EVRG) traded with a volume of $0.30 billion, marking a 115.34% increase from the previous day’s activity. The stock closed with a 0.27% decline, placing it within the mid-tier of volume-driven movers. The surge in trading interest suggests heightened investor attention, though the price trajectory indicates a bearish short-term sentiment.

Recent developments highlight regulatory and operational factors shaping Evergy’s performance. The company announced a revised infrastructure investment plan, deferring $200 million in grid modernization projects to 2026. Analysts note this adjustment may temporarily alleviate near-term capital expenditure pressures but could delay long-term efficiency gains. Additionally, a pending state legislative review of rate-setting mechanisms has introduced regulatory uncertainty, potentially affecting investor confidence.

Market participants are closely monitoring the interplay between Evergy’s operational adjustments and broader sector dynamics. While the deferred spending could improve short-term liquidity metrics, the absence of immediate earnings catalysts has limited upside potential. Institutional investors have shown mixed positioning, with some trimming exposure amid macroeconomic volatility, while others add to positions ahead of anticipated cost-of-service reviews in key service territories.

I understand you’d like to back-test a daily-rebalanced portfolio that, every day from 2022-01-01 to the present, buys (and holds for one trading day) the 500 stocks with the highest trading volume. To implement this precisely we need to be able to: 1. Pull volume data for the entire investable universe every day. 2. Rank all stocks each day and form an equal-weighted basket of the top-500 names. 3. Rebalance the basket daily and measure the one-day holding return. Our current tool set is designed for single-instrument and event-driven back-tests, and does not yet provide an automated way to construct or price a dynamic multi-stock basket of this size. To proceed, we have two practical alternatives: A. Narrow the scope (e.g., “top-50 by volume within S&P 500 constituents”) so we can manually enumerate the tickers and back-test each name in sequence, then aggregate the results. B. Approximate the strategy with a tradable proxy (for example, an ETF that represents high-turnover large-cap stocks) and back-test that instrument. Could you let me know which of these approaches (or perhaps another adjustment) would best meet your needs? Once I have your preference, I can lay out the detailed data-retrieval and back-test plan and run it for you.