Agnico's $430M Volume Ranks 245th as Gold Equities Face Turbulence

Agnico (AGN.TO) saw a trading volume of $0.43 billion on Sept. 24, 2025, ranking it 245th in market activity for the day. The gold producer’s peer, IAMGOLD (IAG.TO), fell 2.75% amid sector-wide pressure from fluctuating gold prices and macroeconomic uncertainties.

Recent developments highlight mixed sentiment toward gold equities. A planned expansion at Agnico’s Kittila mine in Finland faces regulatory delays, potentially impacting 2026 production forecasts. Meanwhile, the company’s Q2 2025 earnings report underscored stable cash flow generation, with all-in sustaining costs remaining below $1,200/oz. Analysts noted that while operational resilience is a positive, near-term growth hinges on resolving permitting challenges in key jurisdictions.

Market participants are monitoring central bank policies and gold price volatility, which have historically influenced mining stocks. Agnico’s share price has underperformed broader gold ETFs this quarter, reflecting investor caution around capital expenditure risks. However, its low leverage and strong balance sheet position it to withstand short-term sector corrections.

I understand the strategy conceptually, but to implement it rigorously I need a bit more detail and to make sure our tool-set can meet your expectations.

1. Universe definition • Are we looking at all U.S. listed common stocks (NYSE + NASDAQ + AMEX), or a narrower universe such as the S&P 1500 constituents, Russell 3000, etc.? 2. Re-balancing mechanics • Do we form the list of the 500 highest-volume names using today’s full-day volume (implying we can only enter at the next day’s open), or do you want to assume perfect knowledge of intraday volume and buy at the same day’s close? 3. Position sizing • Equal-weight each name at entry? (That is the usual approach, but please confirm.) 4. Transaction costs / slippage • Should we assume zero trading cost, or would you like to apply a commission or spread? 5. Tool-set constraints • The backtest engine we have available works on a single ticker at a time. Portfolio-level, cross-sectional ranking strategies like this one would normally require a custom script that loops over the universe each day and aggregates returns. • If your main goal is to see whether the concept adds value, one workaround is to approximate it with an exchange-traded fund (for instance, equal-weighting the S&P 500 via RSP) or by running the strategy on a fixed list of high-volume tickers. Another option is that I prepare the data and share a Python backtest template you can run locally.

Could you let me know: • Which universe you’d like, • Your preferred entry/exit timing convention, • Whether equal-weighting and zero fees are acceptable, and • Whether you are comfortable with the approximation approaches described above?

Once I have that information, I can propose the best way forward (either via the built-in tools or with an external code snippet).