Leopold X Serenity Investment Research Framework

Problem

AI infrastructure investing often gets stuck between macro trend calls and micro stock evidence. Watching hot tickers such as NVDA or SMH can mistake already priced-in demand for alpha, while scanning small-cap supply-chain names can miss real bottlenecks in power, memory, and packaging. This skill combines Leopold's physical-bottleneck trend discovery with Serenity's Chokepoint five-factor screening into a reusable research workflow.

How It Works

The framework runs a two-layer funnel: Layer 1 identifies the physical constraints required for AGI deployment, what the market has not priced in, and the t0 to t4 bet sequence; Layer 2 checks Confirmed Demand, Constrained Supply, Low Market Attention, Value Capture, and Catalysts, producing evidence grades and position caps. Built-in risk scoring covers liquidity, technology path, customer concentration, expansion certainty, and geopolitical exposure. The alert center first checks 13F data freshness, leverage, crowding, and reflexivity before deciding whether a conclusion is still usable.

Boundaries

It is intended for supply-chain research, risk checklists, and observation nodes, not for generating buy or sell instructions. Cited 13F holdings, prices, and market data carry cut-off dates; Leopold's Q1/Q2 13F filings do not represent current positions, and Serenity's past returns cannot be extrapolated. All external data should be independently verified, and position sizing or stop-loss decisions must remain subject to the user's own risk constraints.