Scan selected market symbols for unusual price movements and rank potential trading signals. Combine anomaly scores, measures of departure from normal behavior, with strategy filters, historical pattern matches, and predictions about changing market conditions.
Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction
Examine price data for selected market symbols and organize unusual movements into trading signals. neural-trader classifies movements such as sudden changes, sustained deviations, and flat periods. It evaluates them with a Z-score, a measure of how far they depart from normal behavior. Saved strategy filters, SONA predictions of market conditions, and historical pattern matching help produce ranked results showing the instrument, direction, confidence, and other signal details.
Use this skill to review unusual price movements across several symbols or organize detected signals according to an existing trading strategy. Results are set to remain stored for 24 hours.
The skill uses npm, npx, neural-trader, and MCP tools for prediction, search, and memory. If the data source is anything other than yahoo-finance, results are treated as a demo, with no entry, stop-loss, or target prices proposed. Also watch for a switch to synthetic data when retrieval of real data fails.
This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.
AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.
The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 21, 2026. Treat it as a guide and read the original before installing.
Review the instructions the agent will receive before you install.
Generate trading signals using neural-trader's anomaly detection engine.
Steps:
npm ls neural-trader 2>/dev/null || npm install --ignore-scripts neural-tradernpx neural-trader --signal scan --symbols <TICKERS> --live
npx neural-trader --signal scan --strategy <name> --symbols <TICKERS> --live
--live the scan runs on deterministic synthetic bars (dataSource: "synthetic"). Check dataSource in the result; if it is not yahoo-finance (including when the live fetch failed and fell back), present the result as a demo and propose no entry, stop or target.mcp__plugin_ruflo-core_ruflo__memory_retrieve({ key: "strategy-NAME", namespace: "trading-strategies" })mcp__plugin_ruflo-core_ruflo__neural_predict({ input: "anomaly types: [DETECTED], scores: [SCORES]" })mcp__plugin_ruflo-core_ruflo__agentdb_pattern-search({ query: "ANOMALY_TYPE score RANGE", namespace: "trading-signals" })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", ttl: 86400 }) (ttl is in seconds; expiresAt is not a memory_store parameter and would be ignored, leaving the signal in memory forever)No reviews yet. Tell others what you thought.
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