trader-portfolio

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:
   复制

   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" })

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:
   复制

   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" })