# Alphaear Signal Tracker

Track finance investment signal evolution and update logic based on new finance market...

- Repository: https://github.com/RKiding/Awesome-finance-skills (3095 stars)
- Category: Business
- License: Apache-2.0
- Last updated: 2026-03-29
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add RKiding/Awesome-finance-skills --skill alphaear-signal-tracker -g -a claude-code -y`
Codex: `npx skills add RKiding/Awesome-finance-skills --skill alphaear-signal-tracker -g -a codex -y`
Cursor: `npx skills add RKiding/Awesome-finance-skills --skill alphaear-signal-tracker -g -a cursor -y`
Gemini CLI: `npx skills add RKiding/Awesome-finance-skills --skill alphaear-signal-tracker -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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):**

```python
# 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.