trader-signal

Original：🇺🇸 English
Translated

Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction

6installs
Sourceruvnet/ruflo
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NPX Install

npx skill4agent add ruvnet/ruflo trader-signal

Tags

Translated version includes tags in frontmatter
trading-signalsanomaly-detectionneural-traderz-scoreneural-prediction

SKILL.md Content

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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" })