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behavioral-finance

Translate behavioral finance theories into quantifiable trading signals and risk-control rules.

Updated Jul 8, 2026
Search Tags:#quantitative analysis#risk management#cognitive bias#market sentiment#trading signals#behavioral finance
One-click install
npx skills add https://github.com/hxhyyy/Vibe-Trading --skill behavioral-finance-hxhyyy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: behavioral-finance
Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/behavioral-finance
Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill behavioral-finance-hxhyyy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy.

What problem does it solve?

This Skill addresses the challenge of identifying and mitigating systematic cognitive biases in trading, helping investors avoid emotional pitfalls like panic selling or overconfidence.

Core Features & Use Cases

  • Sentiment Analysis: Calculates a multi-dimensional sentiment score to gauge market greed or fear.
  • Bias Detection: Identifies cognitive biases such as the disposition effect, herding, and overreaction in real-time.
  • Use Case: Use this tool to analyze current market sentiment and receive actionable advice on whether to adjust your portfolio exposure based on behavioral indicators.

Quick Start

Use the behavioral-finance skill to generate a market sentiment diagnosis and debiasing checklist for the current trading session.

Frequently Asked Questions about behavioral-finance

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

FAQPage Schema
How do I quantify market sentiment for trading signals?▼

You can quantify market sentiment by calculating a multi-dimensional score using pandas and numpy to process market data, translating behavioral finance theories into actionable trading signals.

Can I detect cognitive biases like herding and overreaction in real-time market data?▼

Yes, you can detect cognitive biases such as herding, overreaction, and the disposition effect in real-time by applying behavioral indicators to current market data.

How do I mitigate emotional trading pitfalls like panic selling using quantitative analysis?▼

You mitigate emotional trading pitfalls by generating a debiasing checklist and adjusting portfolio exposure based on behavioral indicators computed via scipy.

Does this behavioral finance analysis tool require specific Python libraries?▼

Yes, the analysis requires pandas, numpy, and scipy to process market data and compute behavioral indicators for sentiment cycle tracking and risk-control rules.

What is the best way to track sentiment cycles in retail-driven markets?▼

The best way to track sentiment cycles is by applying momentum and reversal strategy optimization to identify systematic cognitive biases and gauge market greed or fear.

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