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alphaear-signal-tracker

Track investment signals, the evidence behind an investment idea, as news and prices change. Compare new information with the existing thesis, classify its impact, and update the signal’s confidence and strength.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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.

실행 점수 미검증

설치 방법 보기

이럴 때 유용합니다

  • Reassessing an investment thesis after new market news
  • Checking how price changes affect an existing signal
  • Creating an initial signal from researched facts
  • Updating signal confidence and strength

포함된 파일(36)

  • SKILL.md1.8 KB
  • references/PROMPTS.md2.0 KB
  • scripts/__init__.py0 B
  • scripts/fin_agent.py3.8 KB
  • scripts/prompts/fin_agent.py6.6 KB
  • scripts/prompts/forecast_analyst.py1.9 KB
  • scripts/prompts/intent_agent.py2.2 KB
  • scripts/prompts/isq_prompt_generator.py1.8 KB
  • scripts/prompts/report_agent.py18.1 KB
  • scripts/prompts/trend_agent.py7.4 KB
  • scripts/prompts/visualizer.py2.0 KB
  • scripts/schema/isq_template.py13.6 KB
  • scripts/schema/models.py6.1 KB
  • scripts/tools/__init__.py448 B
  • scripts/tools/toolkits.py18.7 KB
  • scripts/utils/__init__.py25 B
  • scripts/utils/content_extractor.py4.7 KB
  • scripts/utils/database_manager.py21.3 KB
  • scripts/utils/hybrid_search.py8.0 KB
  • scripts/utils/json_utils.py6.2 KB
  • scripts/utils/llm/capability.py2.8 KB
  • scripts/utils/llm/factory.py3.6 KB
  • scripts/utils/llm/router.py2.8 KB
  • scripts/utils/logging_setup.py1.0 KB
  • scripts/utils/md_to_html.py4.8 KB
  • scripts/utils/news_tools.py9.9 KB
  • scripts/utils/predictor/evaluation.py5.6 KB
  • scripts/utils/predictor/kline_generate.py7.6 KB
  • scripts/utils/predictor/model/__init__.py394 B
  • scripts/utils/predictor/model/kronos.py30.4 KB
  • scripts/utils/predictor/model/module.py22.3 KB
  • scripts/utils/predictor/training.py18.3 KB
  • scripts/utils/search_tools.py28.6 KB
  • scripts/utils/sentiment_tools.py9.9 KB
  • scripts/utils/stock_tools.py9.8 KB
  • tests/test_tracker.py565 B

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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:

  1. Research: Use FinResearcher Prompt to gather facts/price for a signal.
  2. Analyze: Use FinAnalyst Prompt to generate the initial InvestmentSignal.
  3. 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.

리뷰

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같은 저장소의 스킬

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Checking the latest financial signals
  • Reviewing reported factors affecting stock performance
  • Reading signal summaries alongside confidence scores
RKiding/Awesome-finance-skills3.1천2026년 3월 29일 업데이트

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Visualizing the reasoning behind an investment thesis
  • Mapping how financial signals transmit through markets
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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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Check trending financial news
  • Combine headlines from several sources into one report
  • Review a summary of active Polymarket prediction markets
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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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Forecasting financial market trends from time-series data
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  • Combining quantitative forecasts with news sentiment
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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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Turning investment signals into a financial report
  • Grouping related signals to plan report sections
  • Combining section drafts into a complete report
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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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Researching financial information on the web
  • Combining results from multiple search engines
  • Finding financial information in stored local news
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