Overview

Fetch high-frequency financial signals, including titles, summaries, confidence scores, and the transmission-chain analyses behind them, directly from the DeepEar Lite platform's real-time data source. This helps users quickly grasp market trends and the factors driving stock performance.

How This Skill Works

Utilize the Python tool scripts/deepear_lite.py via DeepEarLiteTools to call fetch_latest_signals(). It pulls data from https://deepear.vercel.app/latest.json and returns a formatted report with signal titles, sentiment metrics, summaries, and source links.

When to Use It

• You need immediate insight into market trends before making trades.
• You want to understand the drivers behind a stock's recent moves.
• You need a quick sanity check of signals from the DeepEar Lite dashboard.
• You're monitoring sentiment shifts during volatile sessions.
• You want a lightweight validation of data before deeper analysis.

Quick Start

1. Step 1: Install Python and dependencies (requests, loguru).
2. Step 2: Run the fetch script: python scripts/deepear_lite.py.
3. Step 3: Review the formatted report with signal titles, metrics, and source links.

Best Practices

• Verify that the latest.json timestamp is current before acting.
• Cross-check signal source links to confirm data provenance.
• Interpret confidence scores and the accompanying reasoning rather than taking signals at face value.
• Use these signals alongside technical/fundamental analysis for decisions.
• Run python scripts/deepear_lite.py to confirm connectivity and fetch success.

Example Use Cases

• Pre-market briefing: pull the latest signals to orient trades.
• Review top signals and summaries to understand drivers behind a stock's moves.
• During earnings volatility, check high-confidence signals and their rationale.
• Monitor sentiment across related assets to spot cross-asset shifts.
• Dev/QA: run the script to ensure the endpoint is reachable.

Frequently Asked Questions