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IhsanDanish25
IhsanDanish25COMMUNITY
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edge-hint-extractor

Generate trading edge hints from market observations and news data.

2|Updated Jun 14, 2026
Search Tags:#data extraction#market analysis#trading strategy#trading#LLM augmentation
One-click install
npx skills add https://github.com/IhsanDanish25/claude-trading-skills --skill edge-hint-extractor-ihsandanish25
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: edge-hint-extractor
Source: https://github.com/IhsanDanish25/claude-trading-skills/tree/main/skills/edge-hint-extractor
Command: npx skills add https://github.com/IhsanDanish25/claude-trading-skills --skill edge-hint-extractor-ihsandanish25

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires PyYAML, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of extracting trading edge hints from daily market observations and news reactions, streamlining the decision-making process for traders.

Core Features & Use Cases

  • Market Data Analysis: Extracts and structures hints from market observations like market summary, anomalies, and news reactions.
  • LLM Augmentation: Optionally enhances hints with ideas generated by a language learning model.
  • Use Case: For traders looking to gain insights from the market without spending extensive time on manual analysis, this Skill automates the initial stage of concept synthesis by converting raw data into actionable hints.

Quick Start

Run the script with the market summary and anomalies JSON files, optionally provide a news reaction CSV or JSON file and an LLM command or file.

Frequently Asked Questions about edge-hint-extractor

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

FAQPage Schema
How do I automate extracting trading hints from market data and news?▼

You can automate extracting trading hints by processing market summary and anomalies JSON files alongside news reaction data to generate actionable insights for daily trading workflows.

Can I use LLM augmentation to enhance market analysis hints?▼

Yes, LLM augmentation can optionally enhance market analysis hints by providing an LLM command or file to generate additional ideas, combined with rule-based analysis of market observations.

Do I need Python and PyYAML to process market observation files?▼

Yes, you need Python and the PyYAML library to run the extraction scripts and process market observation and news reaction files for automated trading edge hint generation.

What is the best way to structure raw market anomalies for hint generation?▼

The best way to structure raw market anomalies for hint generation is using JSON files for market summaries and anomalies, alongside CSV or JSON formats for news reaction data inputs.

Does rule-based analysis work without an LLM for daily trading workflows?▼

Yes, rule-based analysis works independently for daily trading workflows by structuring hints from market observations, while LLM ideation remains an optional augmentation step for enhanced insights.

When should I not use automated news reaction data for trading edge hints?▼

You should avoid relying solely on automated news reaction data for trading edge hints when raw market data is unstructured or missing, as the extraction workflow requires structured JSON or CSV inputs.

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