CME's Derivatives Volume Plummets 24% to $0.39 Billion, Ranking 254th as Institutional Apathy and Cash Shift Overshadow Market Activity

On September 12, 2025, , . , .

Recent market dynamics highlight a broader trend of subdued institutional activity in derivatives markets, with CME's volume contraction aligning with reduced speculative positioning ahead of key macroeconomic data releases. Analysts noted that the decline could reflect a strategic shift in market participants toward cash instruments as volatility expectations moderate. However, the minimal share price movement suggests limited near-term pressure from algorithmic trading flows or arbitrage opportunities.

Strategic evaluations of volume-based trading frameworks require precise definitions of investable universes and rebalancing protocols. For instance, a strategy relying on daily volume rankings across a broad equity universe necessitates granular data inputs and . Practical implementation challenges include batch processing of tick-level volume data and constraints in handling , which may distort portfolio allocations if not explicitly excluded.

To evaluate this strategy rigorously I first need to be sure how you would like to define the investable universe. Key points that need clarification: 1. Universe of stocks • Do you want to rank across all listed U-S equities (many thousands), or a more focused group such as the Russell 3000, the S&P 1500, or another index universe? • If you already have a list of tickers you follow, please provide or upload it. 2. Re-balancing mechanics • On each trading day we rank the chosen universe by that day’s dollar trading volume (shares × price) and buy the top 500. • Positions are held for exactly one trading day and closed at the next day’s close. • Equal-weight portfolio unless you prefer volume-weighting or another allocation rule. 3. Practical simplifications • Computing and re-balancing an all-market strategy requires daily volume data for thousands of tickers. With the current tool set I can automate the back-test once the universe is fixed, but retrieving the raw data for every single ticker must be done in batches. • To keep the workflow tractable we can start with a representative universe (e.g., S&P 1500) and extend if necessary. Please let me know: • Which stock universe to use. • Whether equal weighting per trade date is acceptable. • Any other constraints (for example, exclude micro-caps, or impose a minimum price). Once these points are set I can generate the data-retrieval plan and run the back-test.