/trader-portfolio
Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
Claude Code
$ npx -y skills add ruvnet/ruflo --skill trader-portfolio --agent claude-code
Codex
$ npx skills add ruvnet/ruflo --skill trader-portfolio -a codex -y
Cursor
$ npx skills add ruvnet/ruflo --skill trader-portfolio -a cursor -y
Gemini CLI
$ npx skills add ruvnet/ruflo --skill trader-portfolio -a gemini-cli -y
GitHub Copilot
$ npx skills add ruvnet/ruflo --skill trader-portfolio -a github-copilot -y
OpenCode
$ npx skills add ruvnet/ruflo --skill trader-portfolio -a opencode -y
Windsurf
$ npx skills add ruvnet/ruflo --skill trader-portfolio -a windsurf -y
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 --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" })` 6. Rebalancing plan: the CLI has no rebalance computation (see step 3). Derive it yourself from the weights `/trader-portfolio-cg` returns and the user's stated holdings: trades needed, current vs target weights, estimated costs. Present it as a proposal for the user to act on; this plugin places no orders. 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" })`
Read more
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 --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" })` 6. Rebalancing plan: the CLI has no rebalance computation (see step 3). Derive it yourself from the weights `/trader-portfolio-cg` returns and the user's stated holdings: trades needed, current vs target weights, estimated costs. Present it as a proposal for the user to act on; this plugin places no orders. 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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Questions about trader-portfolio.
- Which coding agents can use trader-portfolio?
- trader-portfolio installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

