/trader-portfolio

name: trader-portfolio
description: Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
allowed-tools: Bash Read mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_retrieve mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__neural_predict mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search
argument-hint: "[--risk-target NUMBER]"

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__plugin_ruflo-core_ruflo__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__plugin_ruflo-core_ruflo__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__plugin_ruflo-core_ruflo__agentdb_pattern-search({ query: "optimized portfolio Sharpe > 1", namespace: "trading-portfolio" })` 8. Store optimized allocation: `mcp__plugin_ruflo-core_ruflo__memory_store({ key: "portfolio-optimal-TIMESTAMP", value: "ALLOCATION_JSON", namespace: "trading-portfolio" })`