TeraWulf Surges 2.94% on $450M Volume as 241st Most Active Stock

TeraWulf (WULF) closed 2.94% higher on October 3, 2025, with a trading volume of $450 million, ranking 241st in market activity for the day. The stock's performance reflects renewed investor focus on its operational updates and strategic positioning in the cryptocurrency mining sector.

Recent developments highlight TeraWulf's efforts to optimize its mining operations through hardware upgrades and energy efficiency improvements. The company has also been actively engaging with stakeholders to address liquidity challenges, though no concrete resolutions have been announced. Market participants are closely monitoring these initiatives for potential long-term impacts on profitability.

Analysts note that the stock's short-term volatility remains tied to broader market sentiment toward crypto-related equities. While TeraWulf's fundamentals have not shown material changes, the sector's cyclical nature continues to influence trading dynamics. Institutional activity in the name appears limited, with retail investors driving much of the recent volume.

To conduct this back-test accurately I need to pin down a few practical details about the strategy’s implementation and the investment universe. Could you please confirm (or modify) the following? 1. Universe • Should the “top-500” be selected from all U.S. common stocks (NYSE + NASDAQ + AMEX), or from a narrower list such as the S&P 500 constituents? • Do we need any filters (e.g., minimum price ≥ $1, exclude ETFs, ADRs, etc.)? 2. Trade timing • Signal formation takes place after the close: we rank the day’s volume, pick the 500 names, and then place market orders at the next day’s open (to avoid look-ahead bias). • The position is held for exactly one session and liquidated at that next day’s close. Is this sequence acceptable? 3. Weighting scheme • Equal-weight each of the 500 names, or weight by volume, or by market cap? 4. Performance aggregation • The output you expect is the daily P&L of the whole 500-stock basket, along with cumulative return, drawdown, and risk metrics. (The current back-test engine handles one ticker at a time, so I will generate a synthetic “index” series from the 500-stock basket and feed that into the engine.) Once I have this information I can proceed with data retrieval, construction of the daily basket, and the back-test.