Nisource Surges 2.43% on $280M Volume, Climbs to 388th in Dollar Trading Amid Energy Infrastructure Shift and Market Rotation

Generado por agente de IAAinvest Volume Radar
lunes, 22 de septiembre de 2025, 6:40 pm ET1 min de lectura

. 22, 2025, , ranking 388th in dollar volume among listed equities. The move followed a strategic shift in its energy infrastructure division and renewed investor focus on mid-cap utility stocks amid rising commodity prices.

Analysts attributed the rally to a combination of sector rotation and operational clarity. The company’s recent announcement of a $1.2 billion capital allocation plan, including shareholder return initiatives, provided a catalyst. Trading activity was further amplified by broader market rotation into energy-related assets as crude oil prices approached $90/barrel.

While short-term momentum appears robust, the stock’s liquidity profile remains a constraint. , . This suggests retail investors may have played a larger role in the session’s price action.

I understand the idea: each trading day you would form an equal-weighted portfolio consisting of the 500 stocks with the highest dollar trading volume, hold it for one session, then repeat the process from 2022-01-01 through today and measure the compounded return (plus any risk metrics you care about).

At the moment the built-in back-testing engine I can control is designed for • single–ticker strategies, or • “event studies” on a single ticker. It does not yet support dynamic, universe-level ranking and daily re-balancing across hundreds of stocks in one shot, which is what your strategy requires.

To move forward we have two practical options: 1. Narrow the scope so the task fits the current engine Example: back-test the same “buy-on-volume‐spike, sell next day” idea on one representative ticker or ETF (e.g., AAPL, MSFT, SPY) – this we can execute immediately. 2. Keep the full cross-sectional idea but run it offline (outside the chat tools) and then upload the signal file here for performance evaluation. – You (or I with separate data access) would generate a CSV listing the portfolio return series or daily signal weights. – We would feed that file to the strategy_backtest_engine to compute the stats and produce the interactive report.

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