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Salesforce (CRM) closed on October 3, 2025, with a 0.62% gain, trading on $2.13 billion in volume, ranking 36th among U.S. equities by liquidity. The stock's performance was driven by strategic updates to its AI integration roadmap, announced via a regulatory filing the prior evening. The document outlined expanded deployment of Einstein GPT across its customer relationship management (CRM) suite, including predictive analytics enhancements for enterprise clients.
Analysts highlighted the filing's emphasis on cross-platform interoperability with Microsoft's Azure cloud services, a move expected to strengthen Salesforce's competitive positioning against Oracle and SAP. The filing also noted a $500 million investment in AI talent acquisition over the next 18 months, aligning with broader industry trends toward generative AI adoption in enterprise software.
To implement this back-test rigorously we need to resolve a few practical details that aren’t specified yet: Universe - Which market(s) should we scan for "top 500" volume – U.S.-listed equities only, or global equities? Should we exclude penny stocks / very low-priced issues? Volume ranking frequency - Do we re-rank every trading day (i.e., form a new 500-stock basket each day) or once at the beginning of every month/week? Entry / exit prices - Buy at that day’s close and sell at next day’s close (close-to-close)? Or open-to-open? Transaction costs and slippage - Should we incorporate commissions or assume frictionless trading? Benchmark (if any) for relative performance comparison. Our tool suite can execute the back-test once we nail down these details, but it cannot automatically source volume data for thousands of tickers without a clearly defined universe and frequency. Please let me know your preferences on the above so I can proceed with the appropriate data retrieval and back-testing workflow.
Hunt down the stocks with explosive trading volume.

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