behavioral-finance

Analyze China A-share market data to identify behavioral finance signals and biases.

Updated Apr 10, 2026

Search Tags:#momentum #sentiment #bias-detection #behavioral-finance #china-a-shares #quant-strategies

Ready to run

npx skills add https://github.com/ebrahim-sani/trading-automation --skill behavioral-finance-ebrahim-sani

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: behavioral-finance
Source: https://github.com/ebrahim-sani/trading-automation/tree/main/vibe-trading/agent/src/skills/behavioral-finance
Command: npx skills add https://github.com/ebrahim-sani/trading-automation --skill behavioral-finance-ebrahim-sani

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Traders often overlook how cognitive biases and sentiment extremes drive price movements, leading to missed opportunities and unmanaged risk in algorithmic strategies.

Core Features & Use Cases

Bias Detection: Identifies overreaction and underreaction patterns, disposition‑effect signals, and herding behavior in China A‑share equities.
Sentiment Scoring: Computes a composite sentiment indicator from turnover, margin growth, fund discounts, and limit‑up/down counts to guide exposure levels.
Strategy Optimization: Enhances momentum and contrarian strategies by integrating behavioral filters such as attention‑weighted holding periods and sentiment‑adjusted signal timing.
Practical Application: Use the skill to generate daily market sentiment reports, adjust position sizing, or back‑test behavioral‑adjusted trading models.

Quick Start

Ask the behavioral-finance skill to evaluate the current market sentiment and suggest bias‑adjusted trading actions.

Frequently Asked Questions about behavioral-finance

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

FAQPage Schema

How do I detect behavioral biases and sentiment signals in China A-share market data?▼

Compute behavioral finance signals by analyzing equity price, volume, and sentiment indicators in China A-share markets to identify overreaction, underreaction, and herding patterns. This generates bias-adjusted quantitative trading recommendations using Python.

What is a composite sentiment indicator and how does it guide trading exposure?▼

A composite sentiment indicator aggregates turnover, margin growth, fund discounts, and limit-up/down counts to quantify market sentiment. This score guides exposure levels and sentiment-adjusted signal timing for quantitative trading strategies.

How do I optimize momentum and contrarian strategies with behavioral filters?▼

Enhance momentum and contrarian strategies by applying behavioral filters such as attention-weighted holding periods and sentiment-adjusted signal timing. This integration accounts for cognitive biases to optimize quantitative trading models.

Does this behavioral finance signal generation require external API calls?▼

Behavioral finance signal generation requires no external API calls. It operates entirely offline using Python with pandas, numpy, and scipy to compute bias checklists and quantitative signals from local market data.

Can I back-test disposition effect and herding behavior signals for A-share equities?▼

Yes, you can back-test disposition effect and herding behavior signals for A-share equities. The system generates quantitative signals suitable for back-testing behavioral-adjusted trading models and adjusting position sizing.