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On September 25, 2025, General Electric (GE) reported a trading volume of $0.36 billion, marking a 106.99% increase from the previous day's activity. The stock ranked 317th in trading volume among U.S. equities. Meanwhile, its healthcare division (GEHC) declined by 3.35%.
Recent developments highlight strategic shifts within the conglomerate.
announced a restructuring plan to streamline its energy business, focusing on renewable power generation and grid solutions. This move aims to accelerate growth in carbon-free energy markets while reducing exposure to fossil fuel-dependent segments. The decision follows regulatory pressures and evolving investor demands for decarbonization.Analysts noted the restructuring could impact short-term earnings but aligns with long-term market trends. The company also revealed partnerships with two European utilities to develop offshore wind projects, signaling expanded international ambitions. These collaborations are expected to drive revenue diversification and enhance operational efficiency in high-growth regions.
To run this back-test accurately I need to make sure we’re working with the right data universe and assumptions. Could you please confirm the following? 1. Market universe • Do you want the daily ranking performed across all U.S. common stocks (i.e., the full U.S. equity universe), or a specific universe such as the S&P 500, Russell 3000, or another exchange list? 2. Trade price • Enter at the day’s close and exit at the next day’s close (my default), or would you prefer open-to-open or another convention? 3. Weighting & re-balancing • Equal-weight each of the 500 names selected each day (my default), or use another weighting scheme (e.g., volume-weighted)? 4. Transaction costs & slippage • Should we assume zero costs (my default) or apply a commission/slippage estimate? 5. Benchmark (optional) • Do you want results compared to a benchmark such as SPY? Once these points are clarified I can generate the daily trading signals, run the back-test from 2022-01-01 to today, and present the return and risk statistics.

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