trader-portfolio

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: ```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__plugin_ruflo-core_ruflo__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__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" })`