Argenx Stock Surges 4.04 on 56.09 Volume Jump to 325th Market Rank

On October 1, 2025, argenx (ARGX) surged 4.04% to close trading, with a daily trading volume of $390 million, marking a 56.09% increase from the previous day's activity. This performance placed the stock at rank 325 in overall market volume, highlighting a notable uptick in investor engagement.

Recent developments surrounding argenx point to growing institutional interest and strategic positioning ahead of key clinical data releases. Analyst activity has intensified, with several firms upgrading price targets following updated trial timelines. Short-term momentum appears driven by positive sentiment around the company's pipeline diversification and potential catalysts in the coming quarter.

Market participants are closely monitoring capital flows into the stock, as the elevated volume suggests increased positioning by both retail and institutional players. Technical indicators show the stock is trading near critical resistance levels, with options activity reflecting heightened expectations for near-term volatility.

To back-test this idea rigorously I need to pin down a few implementation details and be sure the test can be carried out with the available data engines: 1. Universe • Do you want to limit the stock universe to U.S. listed equities only? • Should we exclude OTC / penny stocks or include everything? 2. Ranking logic • For each trading day, should we rank by that day’s dollar volume (price × shares traded) or by share volume? • Are corporate-action-adjusted volumes acceptable (these are what most data feeds provide)? 3. Trade-price convention • Enter at the close of the ranking day and exit at the next day’s close (T + 1 hold)? -- or enter next day’s open and exit that same day’s close? • Equal-weight across the 500 names each day? 4. Practical constraint Our back-test engine runs at the single-ticker level, so to replicate a 500-stock daily-rebalanced portfolio we would need to stitch the holdings together at the portfolio level. – If you are mainly interested in the “average” performance, we can approximate by sampling a representative subset (e.g., the 50 highest-volume names) or by using a proxy ETF such as S&P 500 (SPY). – If you need the exact 500-stock portfolio return, we’ll have to build a custom routine, which will take longer and require more compute resources.