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
61 lines
1.9 KiB
Markdown
61 lines
1.9 KiB
Markdown
---
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name: alphaear-predictor
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description: Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments.
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---
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# AlphaEar Predictor Skill
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## Overview
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This skill utilizes the Kronos model (via `KronosPredictorUtility`) to perform time-series forecasting and adjust predictions based on news sentiment.
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## Capabilities
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### 1. Forecast Market Trends
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### 1. Forecast Market Trends
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**Workflow:**
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1. **Generate Base Forecast**: Use `scripts/kronos_predictor.py` (via `KronosPredictorUtility`) to generate the technical/quantitative forecast.
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2. **Adjust Forecast (Agentic)**: Use the **Forecast Adjustment Prompt** in `references/PROMPTS.md` to subjectively adjust the numbers based on latest news/logic.
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**Key Tools:**
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- `KronosPredictorUtility.get_base_forecast(df, lookback, pred_len, news_text)`: Returns `List[KLinePoint]`.
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**Example Usage (Python):**
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```python
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from scripts.utils.kronos_predictor import KronosPredictorUtility
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from scripts.utils.database_manager import DatabaseManager
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db = DatabaseManager()
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predictor = KronosPredictorUtility()
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# Forecast
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forecast = predictor.predict("600519", horizon="7d")
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print(forecast)
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```
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## Configuration
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This skill requires the **Kronos** model and an embedding model.
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1. **Kronos Model**:
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- Ensure `exports/models` directory exists in the project root.
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- Place trained news projector weights (e.g., `kronos_news_v1.pt`) in `exports/models/`.
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- Or depend on the base model (automatically downloaded).
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2. **Environment Variables**:
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- `EMBEDDING_MODEL`: Path or name of the embedding model (default: `sentence-transformers/all-MiniLM-L6-v2`).
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- `KRONOS_MODEL_PATH`: Optional path to override model loading.
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## Dependencies
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- `torch`
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- `transformers`
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- `sentence-transformers`
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- `pandas`
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- `numpy`
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- `scikit-learn`
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