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trader-signal

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更新日2026年5月25日 21:01

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

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Codex または Claude でインストールこの Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。

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sparkling

sparkling/ruflo

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関連職種SOC

SOC 職業分類に基づく

ソフトウェア開発者コンピュータ・数学職·SOC 15-1252

SKILL.md

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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__ruflo__memory_store mcp__ruflo__memory_retrieve mcp__ruflo__memory_search mcp__ruflo__memory_delete mcp__ruflo__neural_predict mcp__ruflo__agentdb_pattern-search
argument-hint	[--strategy NAME] [--symbols AAPL,MSFT]

Generate trading signals using neural-trader's anomaly detection engine.

Steps:

Ensure neural-trader is available: npm ls neural-trader 2>/dev/null || npm install neural-trader

Scan for signals:

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

With a specific strategy:

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

If --strategy specified, load strategy filters: mcp__ruflo__memory_retrieve({ key: "strategy-NAME", namespace: "trading-strategies" })
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
Use SONA for regime prediction: mcp__ruflo__neural_predict({ input: "anomaly types: [DETECTED], scores: [SCORES]" })
Search historical pattern matches: mcp__ruflo__agentdb_pattern-search({ query: "ANOMALY_TYPE score RANGE", namespace: "trading-signals" })
Present ranked signals: instrument, direction, confidence, anomaly type, entry/stop/target
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 @sparkleideas/memory@3.0.0-alpha.18 — sweeps them out after they expire): mcp__ruflo__memory_store({ key: "signal-TIMESTAMP", value: "SIGNALS_JSON", namespace: "trading-signals", expiresAt: Date.now() + 24 * 60 * 60 * 1000 })

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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__ruflo__memory_store mcp__ruflo__memory_retrieve mcp__ruflo__memory_search mcp__ruflo__memory_delete mcp__ruflo__neural_predict mcp__ruflo__agentdb_pattern-search
argument-hint	[--strategy NAME] [--symbols AAPL,MSFT]

Generate trading signals using neural-trader's anomaly detection engine.

Steps:

Ensure neural-trader is available: npm ls neural-trader 2>/dev/null || npm install neural-trader

Scan for signals:

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

With a specific strategy:

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

If --strategy specified, load strategy filters: mcp__ruflo__memory_retrieve({ key: "strategy-NAME", namespace: "trading-strategies" })
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
Use SONA for regime prediction: mcp__ruflo__neural_predict({ input: "anomaly types: [DETECTED], scores: [SCORES]" })
Search historical pattern matches: mcp__ruflo__agentdb_pattern-search({ query: "ANOMALY_TYPE score RANGE", namespace: "trading-signals" })
Present ranked signals: instrument, direction, confidence, anomaly type, entry/stop/target
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 @sparkleideas/memory@3.0.0-alpha.18 — sweeps them out after they expire): mcp__ruflo__memory_store({ key: "signal-TIMESTAMP", value: "SIGNALS_JSON", namespace: "trading-signals", expiresAt: Date.now() + 24 * 60 * 60 * 1000 })