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trader-backtest

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Run a historical backtest using the 
neural-trader
Rust/NAPI engine.
Steps:
  1. Ensure neural-trader is available: 
    npm ls neural-trader 2>/dev/null || npm install neural-trader
  2. Check for saved strategy config: 
    mcp__claude-flow__memory_retrieve({ key: "strategy-STRATEGY_NAME", namespace: "trading-strategies" })
    If not found, list available: 
    mcp__claude-flow__memory_search({ query: "strategy", namespace: "trading-strategies", limit: 10 })
  3. Run backtest via neural-trader CLI:
    bash
    npx neural-trader --backtest --strategy <name> --symbol <TICKER> --period <range> --walk-forward
    For multi-indicator strategies:
    bash
    npx neural-trader --backtest --strategy multi-indicator --position-sizing kelly --symbol SPY --period 2020-2024
  4. Capture performance metrics from output: total return, annualized return, Sharpe ratio, Sortino ratio, max drawdown, win rate, profit factor, number of trades
  5. Store backtest results: 
    mcp__claude-flow__memory_store({ key: "backtest-STRATEGY-TIMESTAMP", value: "RESULTS_JSON", namespace: "trading-backtests" })
  6. If Sharpe > 1.5, store as successful pattern: 
    mcp__claude-flow__agentdb_pattern-store({ pattern: "profitable-STRATEGY_TYPE", data: "PARAMS_AND_RESULTS" })
  7. Train SONA on the outcome: 
    mcp__claude-flow__neural_train({ patternType: "trading-strategy", epochs: 10 })
使用
neural-trader
Rust/NAPI引擎进行历史回测。
步骤：
  1. 确保neural-trader已可用： 
    npm ls neural-trader 2>/dev/null || npm install neural-trader
  2. 检查已保存的策略配置： 
    mcp__claude-flow__memory_retrieve({ key: "strategy-STRATEGY_NAME", namespace: "trading-strategies" })
    如果未找到，列出可用策略：
    mcp__claude-flow__memory_search({ query: "strategy", namespace: "trading-strategies", limit: 10 })
  3. 通过neural-trader CLI运行回测：
    bash
    npx neural-trader --backtest --strategy <name> --symbol <TICKER> --period <range> --walk-forward
    对于多指标策略：
    bash
    npx neural-trader --backtest --strategy multi-indicator --position-sizing kelly --symbol SPY --period 2020-2024
  4. 从输出中获取性能指标：总收益、年化收益、夏普比率、索提诺比率、最大回撤、胜率、盈利因子、交易次数
  5. 存储回测结果： 
    mcp__claude-flow__memory_store({ key: "backtest-STRATEGY-TIMESTAMP", value: "RESULTS_JSON", namespace: "trading-backtests" })
  6. 如果夏普比率>1.5，将其存储为成功模式： 
    mcp__claude-flow__agentdb_pattern-store({ pattern: "profitable-STRATEGY_TYPE", data: "PARAMS_AND_RESULTS" })
  7. 基于结果训练SONA： 
    mcp__claude-flow__neural_train({ patternType: "trading-strategy", epochs: 10 })