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event-driven

Generate sentiment-based trading signals from financial news and macro events.

Updated Jun 30, 2026
Search Tags:#llm#finance#signals#backtesting#trading#sentiment
One-click install
npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill event-driven-0xzknw
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: event-driven
Source: https://github.com/0xZKnw/vibe-trading-tap/tree/main/agent/src/skills/event-driven
Command: npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill event-driven-0xzknw

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy.

What problem does it solve?

This Skill addresses the challenge of integrating qualitative market news and macro events into quantitative trading strategies, preventing missed opportunities caused by slow manual analysis.

Core Features & Use Cases

  • Sentiment Analysis: Uses LLMs to score news and announcements from -1.0 to 1.0 based on market impact.
  • Signal Aggregation: Combines technical indicators with event-driven sentiment using time-decay logic.
  • Use Case: Automatically adjust your trading strategy by feeding recent earnings reports and central bank policy updates into the engine to weight your technical signals.

Quick Start

Use the event-driven skill to analyze the latest news for the current portfolio and update the signal engine with the new sentiment scores.

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 and macro events?▼

You can generate trading signals from financial news by using LLM-driven scoring to evaluate market impact from -1.0 to 1.0, then applying time-decay aggregation to process the qualitative data into quantitative sentiment signals.

How do I combine sentiment analysis with technical indicators for a multi-factor trading strategy?▼

To combine sentiment analysis with technical indicators, this Skill integrates LLM-scored event data with existing technical signals using time-decay logic, allowing you to automatically adjust your trading strategy based on recent earnings or policy updates.

Can I use pandas and numpy to calculate time-decay sentiment aggregation for backtesting?▼

Yes, you can use pandas and numpy for data manipulation and signal calculation. The Skill requires these dependencies to compute time-decay aggregation and structure the event-driven sentiment outputs for backtesting across various asset classes.

Does this approach work for analyzing central bank policy updates and earnings reports?▼

Yes, this approach works for analyzing central bank policy updates and earnings reports by feeding the latest announcements into the engine to update sentiment scores and weight your technical signals accordingly.

What is the best way to automate market event analysis for quantitative trading?▼

The best way to automate market event analysis for quantitative trading is using an LLM-driven engine that scores news sentiment and aggregates it with time-decay logic, preventing missed opportunities from slow manual analysis.

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