Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
Scanned 9/4/2026
Install to Claude Code
npx -y skills add ruvnet/claude-flow --skill trader-signal --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Trader Signal?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/ruvnet-trader-signal-ruflo)More formats (shields.io, HTML) on the badges page.
---
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:
```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__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 })`
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!