mcpField
T
trader-signal

trader-signal

github.com/ruvnetOther80.0k744 installsUpdated today
Repository

About trader-signal

trader-signal is a skill that generates trading signals using neural-trader's anomaly detection engine. It combines SONA regime prediction with historical pattern matching and presents ranked signals with entry, stop, and target details.

Key features

  1. 01Scan instruments for anomalies
  2. 02Load filters for a chosen strategy
  3. 03Predict market regimes with SONA
  4. 04Search historical pattern matches
  5. 05Generate and store expiring signals

Use cases

  1. 01Scan multiple instruments for trading signals
  2. 02Filter anomalies using strategy criteria
  3. 03Assess current signals against historical patterns

MCP support

It uses Ruflo MCP tools to retrieve strategy filters, predict market regimes, search historical patterns, and store signals with a 24-hour TTL.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/ruvnet/ruflo --skill trader-signal

After installing, type/trader-signalin Claude Code to confirm it loaded.

Details

From the project README

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/[email protected] — 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 })

Comments

Markdown supported