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. 16, . stocks in terms of liquidity. The stock’s performance reflects renewed institutional interest following strategic updates in its medical device segment and regulatory progress in key markets.
Recent developments highlight Abbott’s focus on expanding its diagnostics portfolio, including a newly launched point-of-care testing platform that has garnered positive feedback from healthcare providers. Analysts note this aligns with broader industry trends toward decentralized care models, potentially boosting the company’s market share in the short to medium term. Additionally, the firm’s ongoing cost optimization initiatives have reinforced investor confidence in maintaining profit margins amid inflationary pressures.
To run this back-test accurately I’ll need to pin down a few practical details and make sure the set-up matches what our current back-testing engine can handle. Universe scope • Are we talking about all U.S. listed common stocks (NYSE + NASDAQ) or a different universe? • Do we keep ADRs, ETFs, preferred shares,
. in or out? Portfolio construction • Equal-weight across the 500 names each day, or volume-weighted / market-cap-weighted? • Rebalance daily at the close (buy at today’s close, hold one day, exit next day’s close), or use next day’s open for entry/exit? Engine capability note Our current single-ticker back-testing engine can evaluate one instrument at a time. If your goal is the daily of a 500-stock basket, we have two choices: a) Aggregate returns outside the engine (I generate the daily list of tickers, compute equal-weight returns, then feed the synthetic index into the engine). b) Narrow the request to a representative ETF or index ticker (e.g., SPY) if an approximate proxy is acceptable. Let me know which route you prefer—or provide any universe/weighting details—so I can proceed with data retrieval and run the back-test.
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