Albemarle Gains 0.70% on 482nd-Ranked 220M Trading Volume as Lithium Producer Secures Expansion and European Partnership Deals

On October 3, 2025, Albemarle (ALB) closed with a 0.70% gain, trading on a volume of $0.22 billion, ranking 482nd in market activity for the day. The lithium and specialty chemicals producer has drawn attention amid evolving dynamics in the battery materials sector.

Recent developments highlight Albemarle’s strategic positioning in the global lithium supply chain. The company has announced plans to expand production capacity at its existing facilities, aligning with projected growth in electric vehicle demand. Analysts note that such moves could strengthen its competitive edge against peers, though near-term execution risks remain a watchpoint. Additionally, Albemarle’s recent partnership with a major European automaker to secure long-term lithium supply has been cited as a catalyst for investor confidence.

To run this back-test rigorously we need to nail down a few practical details that determine both the data we have to fetch and how the trade signals will be created: 1. Universe: • Do you mean all U.S. common stocks (e.g., the total listed universe on NYSE + NASDAQ + AMEX) or a narrower list (e.g., S&P 1500 constituents)? 2. Re-balancing logic: • The strategy sounds like: “Every trading day, rank the entire universe by that day’s dollar trading volume, go long an equal-weighted basket of the top 500 names at the next day’s open, close all positions at the same day’s close.” • Please confirm this one-day holding period (open→close) and the equal-weight assumption. 3. Practical constraints: • Our built-in back-test engine currently evaluates one ticker (or an event series) at a time. For a cross-sectional, daily-rebalanced 500-stock portfolio we’ll need to generate the universe-level signals first and then feed them into a multi-asset back-test module. • We can do that, but it requires an extra preprocessing step (pulling daily volume for the full universe, computing the daily top-500 list, and writing position weights). I’ll automate that for you once the universe and details above are confirmed. 4. Transaction costs & liquidity filters: • Do you want to include any slippage/commissions assumptions or minimum-price filters? Please let me know your preferences on the above so I can set up the data pulls and run the back-test.