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alphaear-signal-tracker — Claude Code skill for tracking investment

A skill that tracks investment signals over time: it weighs fresh news and prices and decides whether a signal got stronger, weaker or was falsified.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add RKiding/Awesome-finance-skills --skill alphaear-signal-tracker -a claude-code
Open on GitHub

What alphaear-signal-tracker does

The skill takes an existing signal plus new market information, compares that against the signal's thesis, and works out the direction of impact: positive, negative or neutral. You get an updated signal back with revised confidence and intensity. Four outcomes are possible: strengthened, weakened, falsified or unchanged.

It runs as a chain of prompts from references/PROMPTS.md. FinResearcher pulls the facts and prices, FinAnalyst builds the initial InvestmentSignal, and the Signal Tracking Prompt judges how an existing signal evolves. Data comes through the alphaear-search and alphaear-stock skills, and there is a _sanitize_signal_output helper in scripts/fin_agent.py for cleaning JSON.

You need the agno agent framework installed and a properly initialised DatabaseManager on sqlite3. Right now the tracking logic sits inside the track_signal method in scripts/fin_agent.py, so treat this as a pattern lifted from FinAgent rather than a standalone utility. That method is also what breaks first when the code is refactored.

What it helps with

  • Decide whether a thesis still holds after new data lands
  • Tell neutral news noise apart from a real hit to the thesis
  • Recalculate signal confidence after a price move
  • Spot the moment a signal is falsified and should be closed
  • Gather facts and price before building a new signal

Example prompts for the agent

  • Track my $AAPL signal against the new earnings numbers and tell me if it weakened
  • Compare today's oil news with the signal thesis and update my confidence
  • Is the Fed rate signal falsified by the latest data? Show your reasoning

How to install alphaear-signal-tracker

1Skills CLI (Vercel)

npx skills add RKiding/Awesome-finance-skills --skill alphaear-signal-tracker -a claude-code

2Manually: put the files in place

~/.claude/skills/alphaear-signal-tracker/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/alphaear-signal-tracker/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about alphaear-signal-tracker

How do I install alphaear-signal-tracker in Claude?

Run npx skills add RKiding/Awesome-finance-skills --skill alphaear-signal-tracker -a claude-code. The skill lands at ~/.claude/skills/alphaear-signal-tracker/SKILL.md.

What does alphaear-signal-tracker do?

It tracks how an investment signal evolves: compares new facts and prices with the thesis, labels the result strengthened, weakened, falsified or unchanged, and revises confidence.

What does the skill need to run?

The agno framework and an initialised DatabaseManager on sqlite3. Data gathering relies on the alphaear-search and alphaear-stock skills.

Source text

SKILL.md · Apache-2.0
---
name: alphaear-signal-tracker
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.
---

# 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.

© RKiding · Apache-2.0 · RKiding/Awesome-finance-skills

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