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On October 2, 2025,
(VRTX) closed at $1.15 higher, with a trading volume of $0.67 billion ranking it 176th among U.S. equities. The biotech firm’s shares gained traction amid renewed focus on its therapeutic pipeline and regulatory developments. Analysts noted that recent updates on clinical trials for its rare disease treatments contributed to the upward momentum, though sector-wide volatility remained a factor.Vertex’s performance was primarily driven by investor confidence in its upcoming data readouts for cystic fibrosis therapies. The company has maintained a disciplined approach to R&D spending, which has been highlighted as a differentiator in its industry. While broader market conditions showed mixed signals, Vertex’s stock demonstrated resilience, supported by its strong cash reserves and a robust intellectual property portfolio.
To set up an accurate back-test, I need to pin down a few practical details about your strategy: 1. Universe—Should we rank all U.S. listed common stocks each day, or do you want to limit the universe (e.g., only stocks in the Russell 3000, only NYSE-listed shares, etc.)? 2. Ranking time-stamp—Do we select the top-500 by the previous day’s trading volume (i.e., rank on day t-1’s close, trade on day t’s open), or do we rank at today’s close and assume we can buy at that same close? 3. Weighting & rebalancing—Equal-weight all 500 names each day, or value-weight by volume or market-cap? 4. Transaction costs / slippage—Should we include an estimate (e.g., 5 bps per side) or assume zero costs? 5. Benchmark (optional)—Do you want the back-test to be evaluated against a benchmark such as SPY or an equal-weight index? Once I have those points, I can assemble the data fetch plan and run the strategy back-test from 2022-01-03 through today.

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