Opportunity Capture

alphaear-signal-tracker

AlphaEar Signal Tracker systematically evaluates how new market information affects existing investment signals, producing an updated signal state (Strengthened, Weakened, Falsified, or Unchanged).

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AlphaEar Signal Tracker systematically evaluates how new market information affects existing investment signals, producing an updated signal state (Strengthened, Weakened, Falsified, or Unchanged).

AlphaEar Signal Tracker systematically evaluates how new market information affects existing investment signals, producing an updated signal state (Strengthened, Weakened, Falsified, or Unchanged). Key features include an agentic workflow that combines FinResearcher for factual and price gathering, FinAnalyst for initial InvestmentSignal creation, and a Signal Tracking prompt to reassess thesis alignment. Inputs are the prior signal plus news and price updates; processing compares incoming data to the original thesis, determines impact direction (positive/negative/neutral), and adjusts confidence and intensity. Outputs are structured, sanitized JSON signals suitable for downstream automation. Integrations: alphaear-search and alphaear-stock for data, scripts/fin_agent.py helpers, and an agno/sqlite3-backed DatabaseManager. Use cases: portfolio monitoring, signal validation, automated alerting, risk control, and improving reproducibility of investment decisions.

Skill.md

So funktioniert diese Skill

AlphaEar Signal Tracker systematically evaluates how new market information affects existing investment signals, producing an updated signal state (Strengthened, Weakened, Falsified, or Unchanged).

SKILL.mdALPHIO / VERIFIZIERT

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.

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AlphaEar Signal Tracker systematically evaluates how new market information affects existing investment signals, producing an updated signal state (Strengthened, Weakened, Falsified, or Unchanged).