What is AlphaEar Signal Tracker?

A logic component that tracks and updates investment signals based on new market information, classifying evolution as Strengthened, Weakened, or Falsified.

What outputs does it produce?

An Updated Signal that includes the current state, direction of impact, and adjusted confidence/intensity.

What dependencies are required?

agno (Agent framework), sqlite3 (built-in), and proper initialization of DatabaseManager to persist signal state.

alphaear-signal-tracker

npx machina-cli add skill RKiding/Awesome-finance-skills/alphaear-signal-tracker --openclaw

Files (1)

SKILL.md

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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.

Source

git clone https://github.com/RKiding/Awesome-finance-skills/blob/main/skills/alphaear-signal-tracker/SKILL.mdView on GitHub

Overview

AlphaEar Signal Tracker monitors how fresh market information alters an existing investment signal. It assesses whether the thesis is strengthened, weakened, or falsified, and updates confidence and intensity accordingly.

How This Skill Works

The skill takes an existing signal state and new information (news or price). It compares the new data against the signal thesis, determines the impact direction (Positive/Negative/Neutral), and updates confidence and intensity to yield an Updated Signal. It uses an agent workflow: research with FinResearcher Prompt, analyze with FinAnalyst Prompt to form the initial InvestmentSignal, then track with Signal Tracking Prompt to classify evolution, and finally sanitize outputs with scripts/fin_agent.py if needed.

When to Use It

Monitoring an existing investment signal as new price data arrives
Assessing the impact of breaking news on a thesis
Revising a thesis after quarterly results or earnings calls
Comparing multiple signals to detect falsification or divergence
Maintaining an auditable history of signal evolution over time

Quick Start

Step 1: Load the Existing Signal State and input new information (news/price).
Step 2: Use FinResearcher Prompt to gather facts, then FinAnalyst Prompt to form the initial InvestmentSignal.
Step 3: Apply Signal Tracking Prompt to determine Strengthened/Weakened/Falsified and output the Updated Signal; sanitize with scripts/fin_agent.py if needed.

Best Practices

Define a clear signal thesis and explicit decision thresholds before tracking
Ingest reliable price and news data from vetted sources via FinResearcher and FinAnalyst prompts
Keep versioned signal states to support traceability and audits
Automate the tracking workflow and sanitize outputs with scripts/fin_agent.py
Review Strengthened/Weakened/Falsified decisions with the underlying thesis rationale

Example Use Cases

Tracking a momentum stock signal (e.g., AAPL) after an earnings release
Weighing a value signal when macro data shifts and alters the thesis
Falsifying a growth thesis when price trends diverge from fundamentals
Updating a sector rotation signal after geopolitical or policy news
Adjusting signals during high-volatility market sessions and regime changes

Frequently Asked Questions

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