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Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
---
name: trader-signal-6ccdb0c32eb5
description: Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
allowed-tools: Bash Read mcp__claude-flow__memory_store mcp__claude-flow__memory_retrieve mcp__claude-flow__memory_search mcp__claude-flow__memory_delete mcp__claude-flow__neural_predict mcp__claude-flow__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:
```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 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__claude-flow__memory_store({ key: "signal-TIMESTAMP", value: "SIGNALS_JSON", namespace: "trading-signals", expiresAt: Date.now() + 24 * 60 * 60 * 1000 })`
---
name: trader-signal-6ccdb0c32eb5
description: Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
allowed-tools: Bash Read mcp__claude-flow__memory_store mcp__claude-flow__memory_retrieve mcp__claude-flow__memory_search mcp__claude-flow__memory_delete mcp__claude-flow__neural_predict mcp__claude-flow__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:
```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 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__claude-flow__memory_store({ key: "signal-TIMESTAMP", value: "SIGNALS_JSON", namespace: "trading-signals", expiresAt: Date.now() + 24 * 60 * 60 * 1000 })`
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.