trader-portfolio

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Translated

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

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Sourceruvnet/ruflo
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NPX Install

npx skill4agent add ruvnet/ruflo trader-portfolio

Tags

Translated version includes tags in frontmatter
neural-traderportfolio-optimizationrisk-assessmentrebalancing-planquantitative-trading

SKILL.md Content

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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 neural-trader
  2. Load current portfolio: 
    mcp__claude-flow__memory_search({ query: "current portfolio holdings", namespace: "trading-portfolio" })
  3. Run portfolio optimization:
    bash
    npx neural-trader --portfolio optimize
    With risk target:
    bash
    npx neural-trader --portfolio optimize --risk-target <number>
  4. Get risk metrics:
    bash
    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:
    bash
    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" })