SKILL - Ruflo — ContextQMD

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.

Steps:

Ensure neural-trader is available:

bash

npm ls neural-trader 2>/dev/null || npm install --ignore-scripts neural-trader

Read the current covariance matrix Σ and expected-return vector μ from neural-trader's portfolio API:

bash

# Primary path (preferred — clean JSON):
npx neural-trader --portfolio current --json
# Fallback paths if the --json flag is unavailable on the installed version:
npx neural-trader --portfolio current  # parse the text output
# OR pull from AgentDB if a prior run stored the matrix there:

text

mcp__claude-flow__memory_search({ query: "covariance matrix current", namespace: "trading-risk", limit: 1 })

The skill expects the response to include covariance: number[][] (n × n) and expectedReturns: number[] (length n).

Solve Σ · x = μ via the SublinearAdapter (preferred path) when RUFLO_NEURAL_TRADER_DISABLE_CG is unset:

import { sublinearAdapter } from '../../src/sublinear-adapter.mjs';
const result = await sublinearAdapter.solveCG(COVARIANCE, EXPECTED_RETURNS, {
  tolerance: 1e-6,
  maxIterations: 200,
});
// result.solution    — optimal weights (number[])
// result.iterations  — CG iterations executed
// result.residual    — final ||A·x − b||₂
// result.latencyMs   — wall-clock latency
// result.method      — 'cg-sublinear-native' | 'cg-local'   <-- READ THIS
// result.solver      — '[email protected]' | 'local-js-cg'
// result.degraded    — true if input failed SPD checks (fall back to step 4)

The adapter does the dispatch itself: it probes for mcp__ruflo-sublinear__solve on globalThis (and honours RUFLO_SUBLINEAR_NATIVE=1 as a manual override), routes through the native kernel when reachable, and falls back transparently to the embedded ~50-LOC JS CG when not. The math is identical either way — CG, dense form, n × n SPD covariance. The operator reads result.method to know which backend produced the artifact.

The native MCP tool's wire shape (for direct callers who want to bypass the adapter):

text

mcp__ruflo-sublinear__solve({
  matrix: COVARIANCE,
  rhs: EXPECTED_RETURNS,
  algorithm: "cg",
  tolerance: 1e-6,
  maxIterations: 200
})

Output:

{ solution: number[], iterations: number, residual: number }

Fallback (legacy Neumann) — if step 3 reports degraded: true (non-SPD input, non-square matrix, MCP error) OR if RUFLO_NEURAL_TRADER_DISABLE_CG=1:
bash
```
npx neural-trader --portfolio optimize
```
Capture the weights output and tag the artifact metadata with method: 'neumann-fallback' and a reason field.

Store the optimal weights to trading-risk namespace with full provenance metadata. Take method and solver straight from the adapter's result so the operator can verify which backend ran:

text

mcp__claude-flow__memory_store({
  key: "portfolio-weights-PORTFOLIO_ID-TIMESTAMP",
  namespace: "trading-risk",
  value: JSON.stringify({
    weights: result.solution,           // number[] from step 3 (or weights from step 4 fallback)
    method: result.method,              // 'cg-sublinear-native' | 'cg-local' | 'neumann-fallback'
    solver: result.solver,              // '[email protected]' | 'local-js-cg' | 'neural-trader-cli'
    iterations: result.iterations,
    residual: result.residual,
    latencyMs: result.latencyMs,
    capturedAt: NEW_DATE_ISO,
    reason: FALLBACK_REASON || null
  })
})

The trading-risk namespace is canonical (ADR-126 Phase 1; the five-namespace alignment). Long-lived — no TTL — because portfolio weights are the audit trail Phase 4 will Ed25519-sign.

Cross-check against historical patterns (optional but recommended):
text
```
mcp__claude-flow__agentdb_pattern-search({
  query: "portfolio weights Sharpe regime:CURRENT_REGIME",
  namespace: "trading-risk"
})
```
If the new weights differ by more than 30% in any single asset from the historical median, flag for human review before applying. This is a guard-rail, not a hard block.

Acceptance criteria (ADR-126 Phase 3):

Latency < 1 ms on n = 256 covariance (local JS CG); native path target 40-60× faster (816 ns native vs 50 µs Neumann per [email protected]).
Parity with legacy Neumann within ||cg − neumann||_∞ < 1e-4 on a fixed seed.
Fallback path engages cleanly when native MCP unavailable / covariance non-SPD.
Artifact metadata distinguishes cg-sublinear-native, cg-local, and neumann-fallback.

Refs:

ADR-126 Phase 3 (this skill's authoring ADR)
ADR-123 §162 Row 8 (Wedge 8 speedup claim)
ADR-123 §262-289 (the SublinearAdapter contract)
plugins/ruflo-neural-trader/src/sublinear-adapter.ts (the adapter)
plugins/ruflo-neural-trader/benchmarks/portfolio-cg.bench.ts (the measured numbers)