Back to Details

trader-portfolio

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese
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 neural-trader
  2. Load current portfolio: 
    mcp__claude-flow__memory_search({ query: "current portfolio holdings", namespace: "trading-portfolio" })
  3. Run portfolio optimization:
    bash
    npx neural-trader --portfolio optimize
    With risk target:
    bash
    npx neural-trader --portfolio optimize --risk-target <number>
  4. 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
  5. Use SONA for expected return prediction: 
    mcp__claude-flow__neural_predict({ input: "expected returns for [HOLDINGS] given current regime" })
  6. Generate rebalancing plan:
    bash
    npx neural-trader --portfolio rebalance
    Output: trades needed, current vs target weights, estimated costs
  7. Search for similar allocations in history: 
    mcp__claude-flow__agentdb_pattern-search({ query: "optimized portfolio Sharpe > 1", namespace: "trading-portfolio" })
  8. Store optimized allocation: 
    mcp__claude-flow__memory_store({ key: "portfolio-optimal-TIMESTAMP", value: "ALLOCATION_JSON", namespace: "trading-portfolio" })
使用neural-trader的投资组合引擎优化投资组合配置。
步骤：
  1. 确保neural-trader已可用： 
    npm ls neural-trader 2>/dev/null || npm install neural-trader
  2. 加载当前投资组合： 
    mcp__claude-flow__memory_search({ query: "current portfolio holdings", namespace: "trading-portfolio" })
  3. 运行投资组合优化：
    bash
    npx neural-trader --portfolio optimize
    设置风险目标：
    bash
    npx neural-trader --portfolio optimize --risk-target <number>
  4. 获取风险指标：
    bash
    npx neural-trader --risk assess --portfolio current
npx neural-trader --var --portfolio current
npx neural-trader --correlation --portfolio current --flag-threshold 0.8
  5. 使用SONA进行预期收益预测： 
    mcp__claude-flow__neural_predict({ input: "expected returns for [HOLDINGS] given current regime" })
  6. 生成再平衡计划：
    bash
    npx neural-trader --portfolio rebalance
    输出内容：所需交易操作、当前权重与目标权重对比、预估成本
  7. 在历史记录中搜索相似配置： 
    mcp__claude-flow__agentdb_pattern-search({ query: "optimized portfolio Sharpe > 1", namespace: "trading-portfolio" })
  8. 存储优化后的配置： 
    mcp__claude-flow__memory_store({ key: "portfolio-optimal-TIMESTAMP", value: "ALLOCATION_JSON", namespace: "trading-portfolio" })