The concentration reference: the seven megacaps that drive index-level returns. Equal-weighted here (deliberately, vs their cap weight in the index) so the basket measures the cohort, not just NVDA and MSFT. Divergence between this basket and the equal-weight index is the concentration trade in one line.
Basket total returns · Annualized using 252 trading sessions · 0% risk-free rate and downside target. As of 2026-10-21 · close.
How these statistics are calculated
Risk methodology
Sharpe: average daily simple return divided by its sample standard deviation, multiplied by √252. The risk-free rate is assumed to be 0%; this is not a cash-adjusted ratio.
Sortino: average daily simple return divided by downside deviation, multiplied by √252. Downside deviation is the square root of the average squared shortfall below 0%, counting all sessions, including zeros for gains.
Historical volatility: √(252 × average squared daily log return), matching the Vol page’s realized-volatility convention. This differs from the sample standard deviation used in Sharpe.
Each period requires complete history and at least 5 sessions. MTD and YTD follow the calendar; 1y is 252 sessions. Missing data or an undefined ratio displays — with a reason. Short periods can produce unstable ratios; intraday snapshots include a partial session. History before this basket’s creation is backtested. If a new basket has no stored daily return series yet, estimates from its rounded published chart levels are marked ≈.
Basket returns include dividends. “vs FW 3000” compares the basket’s compounded growth with the benchmark’s. Green means positive; red means negative.
Cross-checked against MAGS. Daily return correlation: 0.99 across 886 sessions. near-exact analogue — the same seven names, equal-weighted
Membership history & changes
Membership history
reference
Widening pass: membership is complete by definition. Reference pinned to MAGS, an equal-weight Magnificent Seven ETF — the cleanest external check of any basket here.
create
Standard seven, equal-weighted as a cohort monitor.
Software businesses positioned to monetize AI through existing distribution: copilots attached to installed bases, agent platforms, and the EDA/design tools that themselves get an AI productivity kicker. The bet is that distribution beats model quality.
Large caps with minimal leverage and strong free cash flow — net-cash or near it. The rate-shock and credit-crunch hedge: when refinancing windows slam shut, these names fund themselves. Currently curated qualitatively; the planned upgrade is a quantitative screen from the fundamentals pipeline (lowest debt/equity with FCF and size floors).
The compute, networking, memory, storage, and power layer of the AI buildout. Owners of the bottlenecks: accelerators, custom silicon, switching, optics, HBM, fab equipment, test, and the server/power chain that turns capex into installed compute.