Generate financial market forecasts with Kronos using time-series data, meaning observations recorded over time. An agent then adjusts the numerical forecast using recent news sentiment and reasoning, combining a quantitative baseline with a subjective assessment.
中文简介正在准备中。当前显示英语简介。
Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments.
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Retrieve the latest financial signals from DeepEar Lite as a formatted report. Review titles, summaries, sentiment indicators, confidence scores, and source links to understand reported market trends and factors affecting stock performance.
中文简介正在准备中。当前显示英语简介。
Turn investment theses and chains of financial market effects into Draw.io-compatible diagrams. The skill generates diagram XML and saves it in a viewable HTML file, helping readers follow complex relationships and reasoning.
中文简介正在准备中。当前显示英语简介。
Fetch current financial headlines and combine trending stories from multiple sources, including Weibo and WallstreetCN. You can also retrieve a summary of active Polymarket prediction markets to review alongside the news.
中文简介正在准备中。当前显示英语简介。
Turn financial analysis and investment signals into a structured report. Group related signals, draft sections, assemble and edit the report, and create chart configurations to support the written analysis when needed.
中文简介正在准备中。当前显示英语简介。
Search for financial information on the web or in a local news database. Use Jina, DDG, or Baidu, combine results from multiple engines, and assess whether cached results can answer a query before searching again.
中文简介正在准备中。当前显示英语简介。
Analyze financial text with local FinBERT or an LLM to identify positive, negative, or neutral sentiment. Return a numeric score and short reason, with support for batch processing unanalyzed news using FinBERT.
中文简介正在准备中。当前显示英语简介。