Portfolio Optimization And Rebalancing Engine

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" })