投资组合优化与再平衡

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

1. Load current portfolio:

mcp__plugin_ruflo-core_ruflo__memory_search({ query: "current portfolio holdings", namespace: "trading-portfolio" })

1. Run portfolio optimization:

```bash
npx neural-trader --portfolio optimize
```
With risk target:
```bash
npx neural-trader --portfolio optimize --risk-target
```

1. 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
```

1. Use SONA for expected return prediction:

mcp__plugin_ruflo-core_ruflo__neural_predict({ input: "expected returns for [HOLDINGS] given current regime" })

1. Generate rebalancing plan:

```bash
npx neural-trader --portfolio rebalance
```
Output: trades needed, current vs target weights, estimated costs

1. Search for similar allocations in history:

mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search({ query: "optimized portfolio Sharpe > 1", namespace: "trading-portfolio" })

1. Store optimized allocation:

mcp__plugin_ruflo-core_ruflo__memory_store({ key: "portfolio-optimal-TIMESTAMP", value: "ALLOCATION_JSON", namespace: "trading-portfolio" })