Beyond NVIDIA: Where the AI Infra Trade Actually Shows Up
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A Python-based analysis that maps the AI capital expenditure (capex) spending chain beyond the obvious NVIDIA and hyperscaler names. Using EODHD financial data APIs, the tutorial builds a universe of ~30 companies across 9 infrastructure layers (chips, semiconductor equipment, servers, networking, data centers, power, cooling, construction), then constructs two composite signals — a fundamental signal (revenue growth, margins, ROE) and a market recognition signal (1Y/6M/3M returns, drawdown) — to plot companies on a 2D matrix. Key findings: construction/engineering and semiconductor equipment layers showed the highest median 1-year returns, physical infrastructure names like Vertiv, Comfort Systems, and Quanta Services already show strong market recognition, and the AI capex trade has visibly broadened beyond chip stocks into the physical buildout chain.
