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Kunthawat Greethong 58f9380ec4 Import 9 alphaear finance skills

- alphaear-deepear-lite: DeepEar Lite API integration
- alphaear-logic-visualizer: Draw.io XML finance diagrams
- alphaear-news: Real-time finance news (10+ sources)
- alphaear-predictor: Kronos time-series forecasting
- alphaear-reporter: Professional financial reports
- alphaear-search: Web search + local RAG
- alphaear-sentiment: FinBERT/LLM sentiment analysis
- alphaear-signal-tracker: Signal evolution tracking
- alphaear-stock: A-Share/HK/US stock data

Updates:
- All scripts updated to use universal .env path
- Added JINA_API_KEY, LLM_*, DEEPSEEK_API_KEY to .env.example
- Updated load_dotenv() to use ~/.config/opencode/.env

2026-03-27 10:11:37 +07:00

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name, description

name	description
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 Signal Tracker Skill

Overview

This skill provides logic to track and update investment signals. It assesses how new market information impacts existing signals (Strengthened, Weakened, Falsified, or Unchanged).

Capabilities

1. Track Signal Evolution

1. Track Signal Evolution (Agentic Workflow)

YOU (the Agent) are the Tracker. Use the prompts in references/PROMPTS.md.

Workflow:

Research: Use FinResearcher Prompt to gather facts/price for a signal.
Analyze: Use FinAnalyst Prompt to generate the initial InvestmentSignal.
Track: For existing signals, use Signal Tracking Prompt to assess evolution (Strengthened/Weakened/Falsified) based on new info.

Tools:

Use alphaear-search and alphaear-stock skills to gather the necessary data.
Use scripts/fin_agent.py helper _sanitize_signal_output if needing to clean JSON.

Key Logic:

Input: Existing Signal State + New Information (News/Price).
Process:
1. Compare new info with signal thesis.
2. Determine impact direction (Positive/Negative/Neutral).
3. Update confidence and intensity.
Output: Updated Signal.

Example Usage (Conceptual):

# This skill is currently a pattern extracted from FinAgent.
# In a future refactor, it should be a standalone utility class.
# For now, refer to `scripts/fin_agent.py`'s `track_signal` method implementation.

Dependencies

agno (Agent framework)
sqlite3 (built-in)

Ensure DatabaseManager is initialized correctly.

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