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): 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

alphaear-predictor