/trader-portfolio
Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
$ npx -y skills add ruvnet/ruflo --skill trader-portfolio --agent claude-codeHow it fires
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- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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/trader-portfolio
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Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
SKILL.md
trader-portfolio.SKILL.mdname: trader-portfolio
description: Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
allowed-tools: Bash Read mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_retrieve mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__neural_predict mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search
argument-hint: "[--risk-target NUMBER]"
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" })`
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name: trader-portfolio description: Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan allowed-tools: Bash Read mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_retrieve mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__neural_predict mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search argument-hint: "[--risk-target NUMBER]"
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" })`
An agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.
Repo: ruvnet/ruflo
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