trader-portfolio

Original：🇺🇸 English

Translated

Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan

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Sourceruvnet/ruflo

Added on2026-05-03

NPX Install

npx skill4agent add ruvnet/ruflo trader-portfolio

SKILL.md Content

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Optimize portfolio allocation using neural-trader's portfolio engine.

Steps:

Ensure neural-trader is available:

npm ls neural-trader 2>/dev/null || npm install neural-trader

Load current portfolio:

mcp__claude-flow__memory_search({ query: "current portfolio holdings", namespace: "trading-portfolio" })

Run portfolio optimization:

bash

npx neural-trader --portfolio optimize

With risk target:

bash

npx neural-trader --portfolio optimize --risk-target <number>

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

Use SONA for expected return prediction:

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

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

Search for similar allocations in history:

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

Store optimized allocation:

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

trader-portfolio

NPX Install

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SKILL.md Content