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.
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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.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Fetch high-frequency financial signals, including titles, summaries, confidence scores, and reasoning directly from the DeepEar Lite platform's real-time data source.
Use scripts/deepear_lite.py via DeepEarLiteTools.
fetch_latest_signals()
https://deepear.vercel.app/latest.json.requests, loguruRun the test script to verify the connection and data fetching:
python scripts/deepear_lite.py
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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.
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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.
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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.
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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.
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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.
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