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Nisource (NI) closed with a 1.95% gain on Sept. 24, 2025, with a trading volume of $210 million, ranking 462nd among U.S. equities. The move followed a regulatory filing disclosing the company’s engagement in a long-term asset optimization initiative, which analysts noted could enhance operational efficiency and reduce overhead costs.
Separate updates highlighted the company’s recent approval of a $250 million share repurchase program, signaling management’s confidence in the stock’s valuation. The buyback authorization, announced alongside quarterly earnings, aims to return capital to shareholders and offset dilution from ongoing infrastructure projects.
Market participants also observed increased institutional interest in the stock, with a notable rise in options activity ahead of the earnings release. While short-term volatility remains tied to broader energy sector dynamics, the firm’s strategic focus on cost rationalization and capital deployment has drawn attention from value-oriented investors.
To run this back-test accurately I need to nail down a few design details that aren’t fully specified yet: Universe—Should we consider all U.S. listed common stocks, or a narrower set such as the S&P 1500, Russell 3000, etc.? Are ETFs, ADRs and penny stocks (price < $1) to be excluded? Trade timing—Volumes are only known after the close. Do you want to buy the 500 highest-volume stocks at the next day’s open and exit at that day’s close (classic 1-day hold), or buy and sell at the same day’s close (requires intraday assumption)? Which price series should we use for entry/exit—open or close? Weighting—Equal-weight each position (most common) or weight by volume-proportional / market-cap? Frictions & risk controls—Any transaction cost assumption (e.g., 5 bps per leg) or slippage? Any stop-loss / max drawdown rules, or is it a pure rebalance-next-day strategy? Once I have this information I can generate the exact data-retrieval plan and run the back-test for the period 2022-01-01 to today.

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