$SHO's Hunter Agent Breakdown

As mentioned before, Hunter is no mere cookie cutter out-of-the-box agent. It takes a unique approach to TA and social data with custom logic and reinforced learning. Below is a layout of how Hunter will operate:

Core Hunter Design Principles

1. Dynamic Indicator Generation:

• Claude will dynamically request and combine technical indicators
• Indicators generated based on [redacted] data
• Experimental combinations encouraged
• Examples of potential indicators:
a) Custom MA variants
b) Volatility-adjusted momentum indicators
c) Hybrid trend-detection algos

2. Social Sentiment Analysis:

• X API integration for real-time data
• Multi-dimensional sentiment scoring:
a) Keyword analysis
b) Influencer credibility weighting
c) Technical chart image processing
d) Discussion volume tracking
e) TA-adjusted scoring

3. Performance Tracking Database:

• Comprehensive decision logging
• Statistical performance analysis
• Adaptive indicator recommendation system

4.  Learning Mechanism:

• Record every decision's outcome
• Analyze indicator performance
• Progressively refine strategy selection

5.  Risk Management:

• Dynamic risk tolerance parameters
• Position sizing algos
• SL mechanisms
• Diversification strategies
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