/trader-signal

name: trader-signal
description: Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
allowed-tools: Bash Read mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_retrieve mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__memory_delete mcp__plugin_ruflo-core_ruflo__neural_predict mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search
argument-hint: "[--strategy NAME] [--symbols AAPL,MSFT]"

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 --ignore-scripts neural-trader` 2. Scan for signals:

   npx neural-trader --signal scan --symbols <TICKERS> --live

With a specific strategy:

   npx neural-trader --signal scan --strategy <name> --symbols <TICKERS> --live

Without `--live` the scan runs on deterministic synthetic bars (`dataSource: "synthetic"`). Check `dataSource` in the result; if it is not `yahoo-finance` (including when the live fetch failed and fell back), present the result as a demo and propose no entry, stop or target. 3. If --strategy specified, load strategy filters: `mcp__plugin_ruflo-core_ruflo__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__plugin_ruflo-core_ruflo__neural_predict({ input: "anomaly types: [DETECTED], scores: [SCORES]" })` 6. Search historical pattern matches: `mcp__plugin_ruflo-core_ruflo__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 with a 24-hour TTL (intraday signals shouldn't pollute long-running memory; the `MemoryConsolidator.sweepExpired()` pass introduced in ADR-125 Phase 4 — shipped in `@claude-flow/memory@3.0.0-alpha.18` — sweeps them out after they expire): `mcp__plugin_ruflo-core_ruflo__memory_store({ key: "signal-TIMESTAMP", value: "SIGNALS_JSON", namespace: "trading-signals", ttl: 86400 })` (`ttl` is in seconds; `expiresAt` is not a `memory_store` parameter and would be ignored, leaving the signal in memory forever)