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alphaear-signal-tracker

alphaear-signal-tracker

github.com/rkidingOther3.1k561 installsUpdated 31w ago
Repository

About alphaear-signal-tracker

alphaear-signal-tracker is a skill pattern for evaluating how new market information affects an existing investment signal. It guides an agent through research, initial analysis, and signal updates.

Key features

  1. 01Compare new information with a thesis
  2. 02Classify signal impact
  3. 03Update confidence and intensity
  4. 04Guide research and analysis prompts

Use cases

  1. 01Reassess a signal after new news
  2. 02Evaluate price changes against a thesis
  3. 03Update an existing signal's status

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/rkiding/awesome-finance-skills --skill alphaear-signal-tracker

After installing, type/alphaear-signal-trackerin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
PROMPTS.md
scripts/
__init__.py
fin_agent.py
scripts/prompts/
fin_agent.py
forecast_analyst.py
intent_agent.py
isq_prompt_generator.py
report_agent.py
trend_agent.py
visualizer.py
scripts/schema/
isq_template.py
models.py
scripts/tools/
__init__.py
toolkits.py
scripts/utils/
__init__.py
content_extractor.py
database_manager.py
hybrid_search.py
json_utils.py
logging_setup.py
md_to_html.py
news_tools.py
scripts/utils/llm/
capability.py
factory.py
router.py
scripts/utils/predictor/
evaluation.py
kline_generate.py
scripts/utils/predictor/model/
__init__.py
kronos.py
module.py

Details

From the project README

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.

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