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AI/MLruvnet

trader-portfolio

Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan

Stars
56,726
Source
ruvnet/claude-flow
Updated
2026-05-31
Slug
ruvnet--claude-flow--trader-portfolio
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/ruvnet/claude-flow/HEAD/plugins/ruflo-neural-trader/skills/trader-portfolio/SKILL.md -o .claude/skills/trader-portfolio.md

Drops the SKILL.md into .claude/skills/trader-portfolio.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

Optimize portfolio allocation using neural-trader's portfolio engine.

Steps:

  1. Ensure neural-trader is available: npm ls neural-trader 2>/dev/null || npm install --ignore-scripts neural-trader
  2. Load current portfolio: mcp__claude-flow__memory_search({ query: "current portfolio holdings", namespace: "trading-portfolio" })
  3. Run portfolio optimization:
    npx neural-trader --portfolio optimize
    With risk target:
    npx neural-trader --portfolio optimize --risk-target <number>
  4. Get risk metrics:
    npx neural-trader --risk assess --portfolio current
npx neural-trader --var --portfolio current
npx neural-trader --correlation --portfolio current --flag-threshold 0.8
  5. Use SONA for expected return prediction: mcp__claude-flow__neural_predict({ input: "expected returns for [HOLDINGS] given current regime" })
  6. Generate rebalancing plan:
    npx neural-trader --portfolio rebalance
    Output: trades needed, current vs target weights, estimated costs
  7. Search for similar allocations in history: mcp__claude-flow__agentdb_pattern-search({ query: "optimized portfolio Sharpe > 1", namespace: "trading-portfolio" })
  8. Store optimized allocation: mcp__claude-flow__memory_store({ key: "portfolio-optimal-TIMESTAMP", value: "ALLOCATION_JSON", namespace: "trading-portfolio" })