trader-portfolio-cg

npx skills add ruvnet/ruflo --skill trader-portfolio-cg --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

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 me

npm ls neural-trader 2>/dev/null || npm install --ignore-scripts neural-trader
npx neural-trader --portfolio current --json
npx neural-trader --portfolio current  # parse the text output
npx neural-trader --portfolio optimize