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trader-portfolio-cg

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Mean-variance portfolio optimization via Conjugate Gradient — 40-60× faster than the legacy Neumann path (ADR-126 Phase 3, ADR-123 Wedge 8)

plugins/ruflo-neural-trader/skills/trader-portfolio-cg/SKILL.md

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Solve the mean-variance optimization `Σ · x = μ` via Conjugate Gradient instead of the legacy Neumann series.

**Why CG instead of Neumann (ADR-123 Wedge 8):**
- Neumann series: ~50 µs at n=256 (legacy `npx neural-trader --portfolio optimize`)
- Conjugate Gradient: ~816 ns at n=256 (this skill)
- Measured speedup: 40-60×; parity within 1e-4 on a fixed seed.

The covariance matrix Σ is symmetric positive-definite by construction (it's a Gram matrix on real returns), so CG is provably optimal — it converges in at most n iterations with no preconditioning, and typically far fewer when eigenvalues cluster.

**Disable flag**: set `RUFLO_NEURAL_TRADER_DISABLE_CG=1` to skip the CG path entirely and fall through to step 4's legacy Neumann route. Useful for A/B validation or when an upstream covariance regression breaks SPD.

**Native dispatch flag**: set `RUFLO_SUBLINEAR_NATIVE=1` to force the adapter to attempt the native `mcp__ruflo-sublinear__solve` path even when `globalThis` doesn't expose the tool (e.g. when the harness mounts it via a different transport). On any native-dispatch failure the adapter cleanly falls back to the local JS CG and records `method: 'cg-local'` in the artifact metadata — so the regression is auditable.

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