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Challenge

Build a Macroeconomic Sentiment Extraction Pipeline with Google ADK

Create an automated sentiment and macro indicator extraction pipeline for wealth managers following global inflation announcements. Using Google ADK, your agent will process speeches and CPI data, extract interest rate signals, and output structured portfolio guidance.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop a Google ADK agent to extract interest rate sentiment and economic indicators from central bank speeches and inflation releases.

Delivery guide

How work is evaluated

Evaluation

Evaluate classification accuracy of monetary policy stance (Hawkish/Dovish/Neutral).

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

stance_classification_check

Stance matches expected hawkish classification

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

stance_score_mae

Mean absolute error of numerical stance score • target: 0.05 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Build a Google ADK agent for continuous text intelligence parsing

  • Extract hawkish/dovish sentiment polarity scores from official speeches

  • Correlate economic indicators (CPI) with monetary policy expectations

  • Format advisory outputs for wealth management relationship managers

Resources and assets

Reference links and supporting material

Dataset notes

Transcripts of central bank speeches and monetary authority press releases paired with analyst consensus scores.

Challenge setup

Evaluate classification accuracy of monetary policy stance (Hawkish/Dovish/Neutral).

Challenge input

JSON containing transcript_text and speaker

Google ADK

Google Agent Development Kit for Vertex AI integration.

Evaluated output

JSON with stance ('HAWKISH'|'DOVISH'|'NEUTRAL'), stance_score (-1.0 to +1.0), key_phrases

Evaluation checks
  • Stance matches expected hawkish classification
  • Mean absolute error of numerical stance score • target: 0.05 • range: 0-1
Configured evaluation evidence
  • Benchmark: FinNLP_Sentiment_Bench
  • Stance Score Mae target: 0.05
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Google ADK · Required
  • Google · Optional
  • Guidance · Optional

Evaluation contract

  • stance_classification_check · Weight 1
  • stance_score_mae · Weight 1

Metrics

  • stance_score_mae · Target 0.05

Benchmarks

  • FinNLP_Sentiment_Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing transcript_text and speaker

    Output: JSON with stance ('HAWKISH'|'DOVISH'|'NEUTRAL'), stance_score (-1.0 to +1.0), key_phrases

    Execution harness: Python

Evaluation tools

  • Google ADK · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

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