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Skill v1.0.1
currentAutomated scan100/100rkiding/awesome-finance-skills/alphaear-predictor
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version: "1.0.1" 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:
- Generate Base Forecast: Use
scripts/kronos_predictor.py(viaKronosPredictorUtility) to generate the technical/quantitative forecast. - Adjust Forecast (Agentic): Use the Forecast Adjustment Prompt in
references/PROMPTS.mdto subjectively adjust the numbers based on latest news/logic.
Key Tools:
-
KronosPredictorUtility.get_base_forecast(df, lookback, pred_len, news_text): ReturnsList[KLinePoint].
Example Usage (Python):
python
from scripts.utils.kronos_predictor import KronosPredictorUtilityfrom scripts.utils.database_manager import DatabaseManagerdb = DatabaseManager()predictor = KronosPredictorUtility()# Forecastforecast = predictor.predict("600519", horizon="7d")print(forecast)
Configuration
This skill requires the Kronos model and an embedding model.
- Kronos Model:
- Ensure
exports/modelsdirectory exists in the project root. - Place trained news projector weights (e.g.,
kronos_news_v1.pt) inexports/models/. - Or depend on the base model (automatically downloaded).
[!CAUTION]Model Security: This skill loads model weights fromexports/models. We useweights_only=Trueand only scan for thekronos_news_*.ptpattern. Ensure you only place trusted checkpoints in this directory.
- 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