Trader Signal
- 648 installs
- 67k repo stars
- Updated August 4, 2026
- ruvnet/ruflo
trader-signal is a finance CLI skill that generates trading signals using the npx neural-trader anomaly detection engine with Z-score scoring and neural prediction integrated with claude-flow memory and agentdb tools.
About
trader-signal is a ruflo skill from ruvnet that generates trading signals through neural-trader's anomaly detection engine with Z-score scoring and neural prediction. The workflow ensures neural-trader is installed via npm, then scans symbols with configurable strategy flags such as --strategy and --symbols for tickers like AAPL and MSFT. It integrates claude-flow MCP tools for memory_store, memory_retrieve, memory_search, neural_predict, and agentdb_pattern-search alongside Bash and Read permissions. Developers reach for trader-signal when building or operating agentic trading pipelines that need repeatable signal scans rather than manual chart review. Argument hints document strategy and symbol list parameters for scripted invocations.
- trader-signal
Trader Signal by the numbers
- 648 all-time installs (skills.sh)
- +10 installs in the week ending Jul 26, 2026 (Skillselion tracking)
- Ranked #562 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 648 |
|---|---|
| repo stars | ★ 67k |
| Last updated | August 4, 2026 |
| Repository | ruvnet/ruflo ↗ |
How do you generate trading signals with anomaly detection?
Use trader-signal for development tasks
Who is it for?
Developers operating agentic trading workflows who need automated neural-trader signal scans with Z-score anomaly scoring.
Skip if: Users without market data access, npm environments, or any need for algorithmic signal generation and anomaly detection pipelines.
When should I use this skill?
The user requests trading signals, anomaly detection scans, neural-trader runs, or Z-score scoring for symbol lists.
What you get
Trading signal scan results with Z-score scores, neural predictions, and optional claude-flow memory persistence.
- trading signal scan output
- Z-score anomaly results
Files
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__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 })
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Use trader-signal for executable anomaly-based signal scans; use equity report skills when structured thesis documents not live signals are needed.
FAQ
What engine does trader-signal use?
trader-signal uses the neural-trader anomaly detection engine via npx with Z-score scoring and neural prediction. The skill installs neural-trader with npm if it is not already present.
Which MCP tools does trader-signal allow?
trader-signal allows Bash, Read, and claude-flow MCP tools including memory_store, memory_retrieve, memory_search, neural_predict, and agentdb_pattern-search for signal workflows.