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Draft evaluation
Build a Financial News Anomaly Monitoring Network with AutoGen — evaluation
Evaluate multi-agent system efficiency in classifying genuine news vs market manipulation signals.
Evaluation type
task based
Challenge
Build a Financial News Anomaly Monitoring Network with AutoGen
Difficulty
Advanced
Rigor
Not declared
The author has not specified a rigor level.
Evaluation overview
How the linked challenge is judged: tasks, benchmarks, and criteria count.
Tasks
1
Benchmarks
0
Criteria
0
Task templates
Inputs and expected outputs.
Task 1
Analyze News Volatility
Evaluate headline paired with order book snapshot and flag manipulation risk.
Input format
JSON containing news_headline, publication_timestamp, ticker, price_change_pct, and order_volume_spike.
Output format
JSON containing manipulation_detected (boolean), sentiment_impact ('HIGH', 'MEDIUM', 'LOW'), and explanation.