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On September 30, 2025,
(WAT) closed with a 3.59% gain, driven by a 37.26% surge in trading volume to $250 million, ranking it 491st among active stocks. The rally followed regulatory updates and strategic positioning in the liquid chromatography market, with analysts noting short-term momentum from institutional buying patterns. Key catalysts included renewed interest in its HPLC systems amid biopharma sector demand shifts.Backtesting parameters for a WAT-focused strategy require defining universe constraints, portfolio construction rules, and execution assumptions. A synthetic index approach would be necessary given current platform limitations, with daily rebalancing assumptions and transaction cost modeling to be specified. The methodology hinges on volume-weighted price data alignment and liquidity assumptions for position closure at daily close prices.
To run this back-test rigorously I need to pin down a few practical details and check that the design matches the capabilities of our back-test engine: 1. Universe • Should we use all U.S. common stocks (NYSE + NASDAQ) as the ranking pool? • Or another market / exchange? 2. Portfolio construction • Do you want an equal-weight allocation across the 500 chosen names each day? • Should we assume positions are opened at that day’s close (after the volume data is known) and liquidated at the next day’s close? • Any transaction cost or slippage assumption? 3. Platform limitation & workaround • Our current single-ticker back-test engine is designed for one security at a time. • The usual workaround is to build a synthetic index that tracks the daily equal-weighted return of the selected basket, then back-test that index. • I can construct this synthetic series for you automatically, but it will take an extra step (pulling daily prices and volumes for every stock in the universe).

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