58f9380ec4
- alphaear-deepear-lite: DeepEar Lite API integration - alphaear-logic-visualizer: Draw.io XML finance diagrams - alphaear-news: Real-time finance news (10+ sources) - alphaear-predictor: Kronos time-series forecasting - alphaear-reporter: Professional financial reports - alphaear-search: Web search + local RAG - alphaear-sentiment: FinBERT/LLM sentiment analysis - alphaear-signal-tracker: Signal evolution tracking - alphaear-stock: A-Share/HK/US stock data Updates: - All scripts updated to use universal .env path - Added JINA_API_KEY, LLM_*, DEEPSEEK_API_KEY to .env.example - Updated load_dotenv() to use ~/.config/opencode/.env
58 lines
2.0 KiB
Markdown
58 lines
2.0 KiB
Markdown
---
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name: alphaear-sentiment
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description: Analyze finance text sentiment using FinBERT or LLM. Use when the user needs to determine the sentiment (positive/negative/neutral) and score of financial text markets.
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---
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# AlphaEar Sentiment Skill
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## Overview
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This skill provides sentiment analysis capabilities tailored for financial texts, supporting both FinBERT (local model) and LLM-based analysis modes.
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## Capabilities
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## Capabilities
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### 1. Analyze Sentiment (FinBERT / Local)
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Use `scripts/sentiment_tools.py` for high-speed, local sentiment analysis using FinBERT.
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**Key Methods:**
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- `analyze_sentiment(text)`: Get sentiment score and label using localized FinBERT model.
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- **Returns**: `{'score': float, 'label': str, 'reason': str}`.
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- **Score Range**: -1.0 (Negative) to 1.0 (Positive).
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- `batch_update_news_sentiment(source, limit)`: Batch process unanalyzed news in the database (FinBERT only).
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### 2. Analyze Sentiment (LLM / Agentic)
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For higher accuracy or reasoning capabilities, **YOU (the Agent)** should perform the analysis using the Prompt below, calling the LLM directly, and then update the database if necessary.
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#### Sentiment Analysis Prompt
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Use this prompt to analyze financial texts if the local tool is insufficient or if reasoning is required.
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```markdown
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请分析以下金融/新闻文本的情绪极性。
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返回严格的 JSON 格式:
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{"score": <float: -1.0到1.0>, "label": "<positive/negative/neutral>", "reason": "<简短理由>"}
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文本: {text}
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```
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**Scoring Guide:**
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- **Positive (0.1 to 1.0)**: Optimistic news, profit growth, policy support, etc.
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- **Negative (-1.0 to -0.1)**: Losses, sanctions, price drops, pessimism.
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- **Neutral (-0.1 to 0.1)**: Factual reporting, sideways movement, ambiguous impact.
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#### Helper Methods
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- `update_single_news_sentiment(id, score, reason)`: Use this to save your manual analysis to the database.
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## Dependencies
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- `torch` (for FinBERT)
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- `transformers` (for FinBERT)
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- `sqlite3` (built-in)
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Ensure `DatabaseManager` is initialized correctly.
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