> alphaear-deepear-lite
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.
curl "https://skillshub.wtf/RKiding/Awesome-finance-skills/alphaear-deepear-lite?format=md"DeepEar Lite Skill
Overview
Fetch high-frequency financial signals, including titles, summaries, confidence scores, and reasoning directly from the DeepEar Lite platform's real-time data source.
Capabilities
1. Fetch Latest Financial Signals
Use scripts/deepear_lite.py via DeepEarLiteTools.
- Fetch Signals:
fetch_latest_signals()- Retrieves all latest signals from
https://deepear.vercel.app/latest.json. - Returns a formatted report of signal titles, sentiment/confidence metrics, summaries, and source links.
- Retrieves all latest signals from
Dependencies
requests,loguru- No local database required for this skill.
Testing
Run the test script to verify the connection and data fetching:
python scripts/deepear_lite.py
> related_skills --same-repo
> skill-creator
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
> alphaear-stock
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.
> alphaear-signal-tracker
Track finance investment signal evolution and update logic based on new finance market information. Use when monitoring finance signals and determining if they are strengthened, weakened, or falsified.
> alphaear-sentiment
Analyze finance text sentiment using FinBERT or LLM. Use when the user needs to determine the sentiment (positive/negative/neutral) and score of financial text markets.