Southwest Airlines Gains 0.39% on Mixed Earnings, Daily Volume Ranks 477th in Market Activity

On Sept. 10, 2025, , , ranking 477th in market activity. The stock's performance followed a mixed earnings report highlighting improved transcontinental demand while regional routes showed weaker recovery trends compared to industry benchmarks.

Analysts noted that the carrier's volume ranking suggests limited short-term liquidity interest despite its modest price increase. Recent operational updates indicated capacity adjustments in response to shifting travel patterns, with a focus on premium cabin load factors. No material regulatory or fuel price developments were reported in the provided timeframe.

It sounds like you’d like to simulate a daily-rebalanced, 500-stock portfolio whose constituents change every day based on that day’s highest share-trading volume, . At the moment, the built-in back-test engines I have access to can: Back-test a strategy on one ticker at a time (strategy_backtest_engine), or Evaluate the impact of a series of event dates on one ticker (event_backtest_engine). Neither engine natively supports a multi-ticker, daily-changing portfolio such as “top-500-by-volume” across the entire market. There are two ways we could proceed: Approximation route Select a proxy universe (e.g., S&P 500 constituents). On each day, pick the 500 names with the highest dollar volume (effectively all of them). . This still requires custom aggregation that is beyond the single-ticker engines. Narrowed scope route Focus on a single ticker or a smaller fixed universe where we can generate buy/sell signals (for example, “buy SPY every day after unusually high volume, sell next day”). This can be executed with the existing tools. Could you let me know which path (or another) you’d like to follow? Once we have a workable scope for the available toolset, I can lay out the exact data-retrieval and back-testing steps and run the analysis.