Individual skill
trader-portfolio
Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
plugins/ruflo-neural-trader/skills/trader-portfolio/SKILL.md
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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 --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 --symbols AAPL,MSFT,GOOGL --target-return 0.10
```
(`--risk-target` is accepted as an alias of `--target-return`.) In 2.8.11 this returns EQUAL weights and a risk figure for them, with a `note` saying the returns matrix is not wired; it does not optimise anything. For real mean-variance weights use `/trader-portfolio-cg`. `--portfolio current` and `--portfolio rebalance` run the same equal-weight demo and ignore the word after `--portfolio`.
4. Risk metrics: `--var` and `--correlation` are not CLI flags. Use `--risk assess --returns <series>` (see `/trader-risk`) with returns you derived from real data.
5. Use SONA for expected return prediction:
`mcp__plugin_ruflo-core_ruflo__neural_predict({ input: "expected returns for [HOLDINGS] given current regime" })`