Snap's 6.6% Surge Defies 32% Volume Drop to $0.96B Ranking 114th in U.S. Equity Turnover as Strategic Shift and Regulatory Wins Bolster Resilience

On October 2, 2025, Snap (SNAP) surged 6.61% despite a 32.35% drop in trading volume to $0.96 billion, ranking 114th among U.S. equities by dollar turnover. The move followed a strategic shift in ad platform algorithms and a partnership with a major U.S. cloud provider to optimize real-time user engagement metrics. Analysts noted the volume contraction contrasted with recent volatility patterns, suggesting short-term positioning adjustments ahead of Q3 earnings in November.

Market participants highlighted the stock's resilience against sector-wide sell-offs in tech advertising shares. A key catalyst emerged from regulatory updates in the EU's digital services framework, which granted Snap temporary exemptions for its ephemeral content models. This contrasted with ongoing antitrust litigation in California, where the company secured a favorable ruling on data usage terms. Both developments narrowed the risk premium embedded in options contracts, with 30-day implied volatility dropping to 78% from 92% the prior week.

To ensure I design the back-test exactly the way you have in mind, could you please confirm a few practical details? 1. Market universe: Should I look at all U.S. listed common stocks (NYSE + NASDAQ + AMEX), or a different universe? 2. Ranking rule and timing: Daily ranking by that day’s dollar trading volume (price × shares traded), correct? Portfolio is formed at today’s close and liquidated at tomorrow’s close (i.e., close-to-close return), or would you prefer open prices? 3. Weighting scheme: Equal weight across the 500 names each day (default), or value-weighted? 4. Trading frictions: Assume zero transaction costs and perfect liquidity, or should I include an estimated cost/slippage? 5. Risk controls (stop-loss, max holding days, etc.): None for this simple 1-day-hold strategy, unless you’d like to add some. Once I have these clarifications, I can retrieve the necessary data, generate the daily trade lists, run the back-test, and present the results.