trader-signal
Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
Best for quantitative tradersWorks with GitHubHigh risk
⌘source
- author
@ruvnet- repo
- ruvnet/ruflo
- language
- TypeScript
★46kstars
5.1kforks
May 5, 2026
✦overview.md
Key Features
- ·Z-score anomaly classification
- ·Multiple signal types: spike, drift, flatline, oscillation
- ·Strategy-based filter loading
- ·Regime prediction via SONA model
- ·Portfolio weighting suggestions
Use Cases
- →Scan a set of stock tickers for breakout momentum signals
- →Detect trend formation using drift anomaly classification
- →Identify consolidation zones before breakout entries
- →Trade mean-reversion in oscillating markets at extremes
Best for
- ✓quantitative traders
- ✓algorithmic trading pipelines
Not ideal for
- !manual discretionary traders
- !long-term investors
FAQs
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__claude-flow__memory_store mcp__claude-flow__memory_retrieve mcp__claude-flow__memory_search 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:
- Ensure neural-trader is available:
npm ls neural-trader 2>/dev/null || npm install neural-trader - Scan for signals:
With a specific strategy:npx neural-trader --signal scan --symbols <TICKERS>npx neural-trader --signal scan --strategy <name> --symbols <TICKERS> - If --strategy specified, load strategy filters:
mcp__claude-flow__memory_retrieve({ key: "strategy-NAME", namespace: "trading-strategies" }) - 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
- Use SONA for regime prediction:
mcp__claude-flow__neural_predict({ input: "anomaly types: [DETECTED], scores: [SCORES]" })
$install
1-click copynpx skills add ruvnet/ruflo --skill trader-signalSafety assessment
★
Clarity score
How clear and easy to understand the SKILL.md instructions are, rated from 1 to 5.
4/ 5
very goodClear and well structured, with only minor parts that might need a second read.
◎
Actionability score
How directly an agent can act on the SKILL.md instructions, rated from 1 to 5.
4/ 5
highMostly actionable with clear steps; only a few small gaps remain.