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Get Started Free →Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | -51% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 56% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -19% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -49% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -6% | 0% |
Optimize portfolio allocation using neural-trader's portfolio engine.
Steps:
npm ls neural-trader 2>/dev/null || npm install --ignore-scripts neural-trader
mcp__plugin_ruflo-core_ruflo__memory_search({ query: "current portfolio holdings", namespace: "trading-portfolio" })
bash npx neural-trader --portfolio optimize With risk target: bash npx neural-trader --portfolio optimize --risk-target <number>
bash npx neural-trader --risk assess --portfolio current npx neural-trader --var --portfolio current npx neural-trader --correlation --portfolio current --flag-threshold 0.8
mcp__plugin_ruflo-core_ruflo__neural_predict({ input: "expected returns for [HOLDINGS] given current regime" })
bash npx neural-trader --portfolio rebalance Output: trades needed, current vs target weights, estimated costs
mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search({ query: "optimized portfolio Sharpe > 1", namespace: "trading-portfolio" })
mcp__plugin_ruflo-core_ruflo__memory_store({ key: "portfolio-optimal-TIMESTAMP", value: "ALLOCATION_JSON", namespace: "trading-portfolio" })
Other measured skills in the registry, with their headline benchmark lift.