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trader-signal

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Original

English
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Translation

Chinese
Generate trading signals using neural-trader's anomaly detection engine.
Steps:
  1. Ensure neural-trader is available: 
    npm ls neural-trader 2>/dev/null || npm install neural-trader
  2. Scan for signals:
    bash
    npx neural-trader --signal scan --symbols <TICKERS>
    With a specific strategy:
    bash
    npx neural-trader --signal scan --strategy <name> --symbols <TICKERS>
  3. If --strategy specified, load strategy filters: 
    mcp__claude-flow__memory_retrieve({ key: "strategy-NAME", namespace: "trading-strategies" })
  4. neural-trader classifies anomalies automatically:
    • spike (maxZ > 5): breakout — momentum entry or mean-reversion fade
    • drift (sustained high Z): trend forming — trend-following signal
    • flatline (low Z): consolidation — prepare for breakout
    • oscillation (alternating): range-bound — mean-reversion at extremes
    • pattern-break (multiple dims): regime change — close and reassess
    • cluster-outlier (>50% dims): multi-factor dislocation — arbitrage
  5. Use SONA for regime prediction: 
    mcp__claude-flow__neural_predict({ input: "anomaly types: [DETECTED], scores: [SCORES]" })
  6. Search historical pattern matches: 
    mcp__claude-flow__agentdb_pattern-search({ query: "ANOMALY_TYPE score RANGE", namespace: "trading-signals" })
  7. Present ranked signals: instrument, direction, confidence, anomaly type, entry/stop/target
  8. Store signals: 
    mcp__claude-flow__memory_store({ key: "signal-TIMESTAMP", value: "SIGNALS_JSON", namespace: "trading-signals" })
使用neural-trader的异常检测引擎生成交易信号。
步骤：
  1. 确保neural-trader可用： 
    npm ls neural-trader 2>/dev/null || npm install neural-trader
  2. 扫描信号：
    bash
    npx neural-trader --signal scan --symbols <TICKERS>
    使用特定策略：
    bash
    npx neural-trader --signal scan --strategy <name> --symbols <TICKERS>
  3. 如果指定了--strategy，加载策略过滤器： 
    mcp__claude-flow__memory_retrieve({ key: "strategy-NAME", namespace: "trading-strategies" })
  4. neural-trader会自动对异常进行分类：
    • spike（maxZ > 5）：突破——动量入场或均值回归离场
    • drift（持续高Z值）：趋势形成——趋势跟随信号
    • flatline（低Z值）：盘整——为突破做准备
    • oscillation（交替波动）：区间震荡——极值处进行均值回归操作
    • pattern-break（多维度）：市场状态转变——平仓并重新评估
    • cluster-outlier（>50%维度）：多因子错位——套利
  5. 使用SONA进行市场状态预测： 
    mcp__claude-flow__neural_predict({ input: "anomaly types: [DETECTED], scores: [SCORES]" })
  6. 搜索历史模式匹配： 
    mcp__claude-flow__agentdb_pattern-search({ query: "ANOMALY_TYPE score RANGE", namespace: "trading-signals" })
  7. 展示排名信号：交易工具、方向、置信度、异常类型、入场/止损/目标价位
  8. 存储信号： 
    mcp__claude-flow__memory_store({ key: "signal-TIMESTAMP", value: "SIGNALS_JSON", namespace: "trading-signals" })