Robinhood Surges 2.04% on $5.12B Volume Climbs to 13th Most Actively Traded Stock on October 3 2025

Robinhood Markets (HOOD) surged 2.04% on October 3, 2025, with a trading volume of $5.12 billion, securing its position as the 13th most actively traded stock on the day. The move followed a mix of operational updates and market dynamics that influenced investor sentiment. Recent developments highlighted the platform’s evolving user base and regulatory scrutiny, which remain key focal points for analysts.

The stock’s performance was partially driven by a strategic shift in its commission-free trading model, which has seen a gradual realignment of revenue streams toward subscription services and expanded product offerings. While the company continues to navigate challenges in user retention post-pandemic, recent data suggests a stabilization in monthly active users, offering a tentative floor for investor confidence. However, macroeconomic factors, including interest rate uncertainty, remain a drag on broader market risk appetite.

It looks like you’d like to construct a daily-rebalanced, 500-stock portfolio that is formed each day using the names with the highest trading volume, and then measure the 1-day-holding-period performance from 2022-01-01 through today. To implement this precisely we would need, for every trading day: 1. The full universe of stocks and their daily trading volume. 2. A ranking of that universe to pick the top 500 names. 3. A way to build an equal-weighted (or otherwise weighted) return series from those names, rolling it forward one day, and repeating. At the moment, the tool-set I have access to can: • Retrieve indicators or events for specified tickers. • Back-test a strategy on a single security (or a predefined list of signals) with the Strategy Backtest Engine. However, it doesn’t directly support a cross-sectional, multi-ticker, daily-rebalanced portfolio of this size in one step. There are a couple of ways we could proceed: A) Narrow the scope (e.g., limit the universe to the S&P 500 and pick the 50 most-active names each day) – still requires a data feed for volumes, but is lighter. B) Approximate the “high-liquidity factor” by using an index or ETF that already tilts toward high-volume stocks (e.g., an equal-weight ETF, or a “deep-liquidity” ETF) and back-test that proxy. C) If you have the daily list of tickers you want to own (or can provide them as a file), I can feed those signals straight into the back-test engine. Could you let me know which approach (or another) best matches what you need?