alphaear-predictor: Скилл прогнозирования финансовых рынков

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