// Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments.
Search A-Share/HK/US finance stock tickers and retrieve finance stock price history. Use when user asks about finance stock codes, recent price changes, or specific company finance stock info.
Fetch the latest financial signals and transmission-chain analyses from DeepEar Lite. Use when the user needs immediate insights into financial market trends, stock performance factors, and reasoning from the DeepEar Lite dashboard.
Plan, write, and edit professional financial reports; generate finance chart configurations. Use when condensing finance analysis into a structured output.
Create visualize finance logic diagrams (e.g., Draw.io XML) to explain complex finance transmission chains or finance logic flows.
Fetch hot finance news, unified trends, and prediction financial market data. Use when the user needs real-time financial news, trend reports from multiple finance sources (Weibo, Zhihu, WallstreetCN, etc.), or Polymarket finance market prediction data.
Perform finance web searches and local context searches. Use when the user needs general finance info from the web (Jina/DDG/Baidu) or needs to retrieve finance information from a local document store (RAG).
| name | alphaear-predictor |
| description | Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments. |
This skill utilizes the Kronos model (via KronosPredictorUtility) to perform time-series forecasting and adjust predictions based on news sentiment.
Workflow:
scripts/kronos_predictor.py (via KronosPredictorUtility) to generate the technical/quantitative forecast.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):
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)
This skill requires the Kronos model and an embedding model.
exports/models directory exists in the project root.kronos_news_v1.pt) in exports/models/.[!CAUTION] Model Security: This skill loads model weights from
exports/models. We useweights_only=Trueand only scan for thekronos_news_*.ptpattern. Ensure you only place trusted checkpoints in this directory.
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.torchtransformerssentence-transformerspandasnumpyscikit-learn