event-driven

Name: event-driven
Availability: InStock
Author: OpenSucker

Process news and macro event data into sentiment scores and impact signals.

15|2|Updated May 1, 2026

Search Tags:#signals #trading #news #sentiment #macro

Ready to run

npx skills add https://github.com/OpenSucker/OpenSucker --skill event-driven-opensucker

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: event-driven
Source: https://github.com/OpenSucker/OpenSucker/tree/main/skills/vibe_skills/event-driven
Command: npx skills add https://github.com/OpenSucker/OpenSucker --skill event-driven-opensucker

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables automated analysis of news, announcements, and macro events to inform trading strategies by assessing their sentiment and impact.

Core Features & Use Cases

Sentiment and event impact analysis: Uses NLP to score news and macro data, indicating bullish or bearish signals.
CSV event data management: Structures and processes event information following a standardized schema.
Use Case: Traders can integrate real-time macro or news events into their algorithmic trading models to catch short-term market shifts.

Quick Start

Use the event-driven skill to analyze news articles fetched via URL, generate sentiment scores, and incorporate the signals into your trading algorithm.

Frequently Asked Questions about event-driven

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

FAQPage Schema

How does sentiment analysis work for news and macroeconomic trading signals?▼

Sentiment analysis for trading signals uses NLP to score news and macro data, generating bullish or bearish impact scores. This process evaluates event data to inform algorithmic trading models and catch short-term market shifts.

How do I generate sentiment scores from news articles for a trading algorithm?▼

You generate sentiment scores by fetching news articles via URL and processing them through NLP and signal decay modeling. This outputs decoupled CSV event data structures that integrate directly into your trading algorithm.

Can I use CSV event data to monitor macroeconomic changes in quant trading?▼

Yes, you can process precise, decoupled CSV event data structures to monitor macroeconomic changes. The system structures event information following a standardized schema suitable for quant traders and analysts.

What is the best way to integrate macro signals into an automated trading strategy?▼

The best way to integrate macro signals is processing event data from news sources to generate impact signals and sentiment scores. This approach assesses event impact to inform strategic actions within automated trading models.

Does signal decay modeling affect how news impacts short-term market shifts?▼

Yes, signal decay modeling affects news impacts by modeling how sentiment scores diminish over time. This informs strategic trading actions by weighting the immediate relevance of macroeconomic changes and news announcements.