sentiment-shift

name	sentiment-shift
description	Identify stocks where blogger sentiment has changed significantly. Use when users want to find who changed their mind or detect sentiment reversals.

Sentiment Shift Detector

Identify stocks where blogger sentiment has changed significantly.

Triggers

• "情绪变化最大的股票"
• "谁转向了"
• "sentiment shift"
• "who changed their mind"
• "态度转变"
• /sentiment-shift

Instructions

When the user wants to find sentiment shifts, follow these steps:

1. Get Multi-Day Summaries Call get_daily_summary for recent dates (today, yesterday, a few days ago) to compare sentiment over time.

2. Identify Changed Tickers Compare the summaries to find:

   • Stocks that moved from bullish to bearish
   • Stocks that moved from bearish to bullish
   • Stocks with increased/decreased mentions

3. Get Detailed Sentiment For tickers with notable changes, call get_ticker_sentiment to understand who changed their view.

4. Search for Explanations Call search_viewpoints for changed tickers to find the reasoning behind sentiment shifts.

5. Present Results Format the output as:

   ## 情绪转变追踪 🔄
   
   ### 转向看涨 📈
   
   #### TICKER1
   - **变化**: 看跌 → 看涨
   - **时间**: X天前开始转变
   - **关键转变博主**: 博主A, 博主B
   - **转变原因**: [摘要为什么改变看法]
   - **代表观点**: "[具体观点]" — 博主A
   
   ### 转向看跌 📉
   
   #### TICKER2
   - **变化**: 看涨 → 看跌
   - **时间**: X天前开始转变
   - **关键转变博主**: 博主C
   - **转变原因**: [摘要为什么改变看法]
   - **代表观点**: "[具体观点]" — 博主C
   
   ### 热度变化 🌡️
   
   | 股票 | 之前提及 | 现在提及 | 变化 |
   |------|----------|----------|------|
   | XXX | 5 | 25 | ⬆️ +400% |
   | YYY | 20 | 3 | ⬇️ -85% |
   
   ### 分析
   [总结市场情绪变化的整体趋势]
   

Tool Sequence

1. get_daily_summary(date=today) + get_daily_summary(date=yesterday) + get_daily_summary(date=3_days_ago) → Compare over time
2. Identify tickers with changed sentiment
3. get_ticker_sentiment(changed_ticker) → For each changed ticker
4. search_viewpoints(changed_ticker) → Find reasoning
5. Compile shift analysis

Notes

• Sentiment shifts can be leading indicators
• A blogger changing their view is often more significant than new bloggers joining
• Track both direction changes and intensity changes
• Include context for why shifts happened (news, earnings, etc.)