GitLab’s $250M Volume Ranks 459th in U.S. Turnover Amid Hybrid Cloud Strategy Surge

On September 16, 2025, GitLab (GTLB) closed at a 0.44% gain with a trading volume of $250 million, ranking 459th among U.S. equities in daily turnover. The developer tools provider saw renewed investor interest following a strategic update highlighting its hybrid cloud roadmap and enterprise adoption trends.

Analysts noted the stock's resilience amid broader market volatility, attributing the positive momentum to improved enterprise software spending patterns and GitLab's competitive positioning in the DevOps sector. Recent client retention metrics and product pipeline disclosures have reinforced investor confidence in the company's long-term growth trajectory.

Technical indicators suggest the stock remains within a consolidation pattern, with key resistance levels near its 52-week high. Positioning data reveals increased institutional ownership in the previous quarter, indicating strategic capital allocation toward cloud infrastructure plays. Market participants continue to monitor enterprise software licensing trends as potential catalysts for further upside.

To run this back-test properly we first need to pin-down a few practical details that weren’t specified in the original request. Because the strategy requires building (and rebalancing) a 500-stock portfolio every trading day, the precise data universe and ranking metric are critical:

1. Market universe • Common default is all U.S. listed equities (NYSE, Nasdaq, NYSE-Arca). • If you had another region in mind, please specify. 2. "Trading volume" definition • Share volume (number of shares traded) – most common. • Dollar volume (shares × price) – sometimes preferred to avoid penny-stock bias. 3. Weighting scheme for the 500-stock basket • Equal-weight (default for liquidity-factor studies). • Volume-weighted or market-cap-weighted can also be used. 4. Execution price assumption • Buy today at the official close, sell the next day at the official close (T+1). • If you would rather simulate buying at tomorrow’s open (to reduce slippage risk), let me know.

Once those choices are confirmed I can: • Generate a data-retrieval plan. • Pull the daily volume (and price) history for the full universe from 2022-01-03 to present. • Build the day-by-day list of the 500 highest-volume stocks. • Construct the 1-day-holding return series for the equal-weight basket. • Run the back-test engine to produce performance statistics and an interactive chart.