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event-driven

Analyze financial news and macro events to generate sentiment-based trading signals.

Updated Jun 30, 2026
Search Tags:#llm#finance#signals#backtesting#trading#sentiment
One-click install
npx skills add https://github.com/20YN04/vibe-trading-macos --skill event-driven-20yn04
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: event-driven
Source: https://github.com/20YN04/vibe-trading-macos/tree/main/agent/src/skills/event-driven
Command: npx skills add https://github.com/20YN04/vibe-trading-macos --skill event-driven-20yn04

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy.

What problem does it solve?

This Skill solves the challenge of manually monitoring and interpreting fragmented news, macro data, and social sentiment to make timely trading decisions.

Core Features & Use Cases

  • Sentiment Analysis: Uses an LLM to score news and announcements on a standardized scale from -1.0 to 1.0.
  • Signal Aggregation: Combines event-driven signals with technical indicators using time-decay logic to ensure relevance.
  • Use Case: Automatically ingest earnings reports and central bank announcements to adjust your portfolio exposure based on the calculated sentiment impact.

Quick Start

Use the event-driven skill to analyze the latest news content and generate a sentiment score for the current market event.

Frequently Asked Questions about event-driven

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate trading signals from financial news using LLM sentiment analysis?▼

To generate trading signals from financial news, this Skill scores news and macro events using an LLM on a scale from -1.0 to 1.0. It processes market sentiment and aligns the data using pandas and numpy for backtesting frameworks.

What is the best way to combine event-driven signals with technical indicators for backtesting?▼

The best way to combine event-driven signals with technical indicators is through weighted aggregation using exponential time decay models. This ensures recent market events maintain relevance during backtesting signal processing.

Can I use pandas and numpy to process LLM sentiment scores for portfolio exposure adjustments?▼

Yes, you can use pandas and numpy to process LLM sentiment scores for portfolio exposure adjustments. The Skill requires these dependencies for data alignment and signal processing when ingesting earnings reports and central bank announcements.

How does exponential time decay work for event-driven trading signals?▼

Exponential time decay works for event-driven trading signals by applying weighted aggregation to gradually reduce the impact of older news. This ensures sentiment scores remain relevant to current market conditions during backtesting.

Does event-driven backtesting support automated ingestion of central bank announcements?▼

Yes, event-driven backtesting supports automated ingestion of central bank announcements. The Skill analyzes macro events and earnings reports to calculate sentiment impact and adjust your portfolio exposure automatically.

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