Coinbase Stock Plunges Amid Volatility as $3.43 Billion Volume Surges to 24th in Market Activity

On October 7, 2025, Coinbase Global (COIN) closed with a 2.67% decline, trading a total volume of $3.43 billion, ranking it 24th in market activity. The drop followed mixed investor sentiment amid broader market volatility, though the high-volume session highlighted sustained trading interest in the cryptocurrency exchange platform.

Analysts noted that Coinbase’s performance remained sensitive to macroeconomic indicators and sector-specific dynamics. While no direct company-specific news influenced the move, the stock’s reaction aligned with broader risk-off trends observed in tech and crypto-related equities. The volume-to-price divergence suggested short-term positioning shifts but lacked clear directional signals for near-term trends.

To deliver an accurate back-test we need to clarify a few practical details: 1. Universe • Which market do you want to rank? (e.g., all U.S. listed common stocks, S&P 500 constituents, A-share market, etc.) • Are ADRs, ETFs, penny stocks or other special classes to be excluded? 2. Ranking and re-balancing rule • Is the ranking done on each day’s total dollar value traded (Volume × Close) or on share volume? • Do we buy the 500 names at that day’s close and hold them until the next day’s close (i.e., a one-day holding period with daily re-balancing)? • Equal-weight or volume-weighted portfolio? 3. Performance measure • Do you want pure portfolio return (ignoring transaction costs), or should we include trading costs and slippage? • Any risk control overlay (stop-loss, max draw-down, etc.)? Our current back-testing engine is designed for a single ticker or an event list. A daily cross-sectional “top-500” strategy can still be run, but we’ll first have to create a synthetic portfolio time-series that represents the strategy’s daily P&L. To build that series we need the specific details above and a defined security universe. Once you confirm, I can proceed to: 1. Retrieve the historical daily trading volumes for all tickers in the chosen universe. 2. Generate the daily membership list of the top-500 stocks and compute the portfolio’s next-day return series. 3. Feed that return series into the strategy_backtest_engine to obtain the performance statistics and visualization. Please let me know your preferences on the points above, and I will start the data retrieval and calculation.