/trader-signal
Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
$ npx -y skills add ruvnet/ruflo --skill trader-signal --agent claude-codeHow it fires
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/trader-signal
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Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
SKILL.md
trader-signal.SKILL.mdname: 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>
With a specific strategy:
npx neural-trader --signal scan --strategy <name> --symbols <TICKERS>
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", expiresAt: Date.now() + 24 * 60 * 60 * 1000 })`
Read more
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>
With a specific strategy:
npx neural-trader --signal scan --strategy <name> --symbols <TICKERS>
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", expiresAt: Date.now() + 24 * 60 * 60 * 1000 })`
An agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.
Repo: ruvnet/ruflo
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