Multimodal Prediction Market Agent
This challenge focuses on building a highly sophisticated agent for analyzing real-time prediction markets. Leveraging Google's Agent Development Kit (ADK) with its strong multimodal capabilities, developers will create an agent that aggregates diverse data sources—market data, news feeds, social media sentiment, and expert opinions—to forecast event outcomes and identify potential arbitrage opportunities. This goes beyond simple data analysis to synthesize complex information for strategic decision-making. The system will use Claude Opus 4.5 for nuanced textual analysis and advanced strategy generation, interpreting qualitative market signals. Grok 4 Heavy will be integrated for high-performance, real-time complex pattern recognition and scenario simulation, enabling rapid assessment of market dynamics. Firebender AI will serve as a specialized service, offering advanced market data aggregation and pattern recognition as a tool that the Google ADK agent orchestrates. The agent will interact with a simulated Prediction Market API to fetch real-time contract data and execute simulated trades. This challenge emphasizes multimodal data fusion, multi-LLM orchestration, and the integration of specialized agentic services to create a powerful, real-time market intelligence system capable of providing actionable insights to 'pro gamblers' and financial strategists.
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge focuses on building a highly sophisticated agent for analyzing real-time prediction markets. Leveraging Google's Agent Development Kit (ADK) with its strong multimodal capabilities, developers will create an agent that aggregates diverse data sources—market data, news feeds, social media sentiment, and expert opinions—to forecast event outcomes and identify potential arbitrage opportunities. This goes beyond simple data analysis to synthesize complex information for strategic decision-making. The system will use Claude Opus 4.5 for nuanced textual analysis and advanced strategy generation, interpreting qualitative market signals. Grok 4 Heavy will be integrated for high-performance, real-time complex pattern recognition and scenario simulation, enabling rapid assessment of market dynamics. Firebender AI will serve as a specialized service, offering advanced market data aggregation and pattern recognition as a tool that the Google ADK agent orchestrates. The agent will interact with a simulated Prediction Market API to fetch real-time contract data and execute simulated trades. This challenge emphasizes multimodal data fusion, multi-LLM orchestration, and the integration of specialized agentic services to create a powerful, real-time market intelligence system capable of providing actionable insights to 'pro gamblers' and financial strategists.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
PredictionAccuracy
Agent must correctly predict the market outcome for at least 80% of test cases.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
ArbitrageIdentificationRate
Agent must correctly identify 90% of intentionally planted arbitrage opportunities.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Average Prediction Confidence
Average confidence score for all market predictions made by the agent. • target: 0.8 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Arbitrage Profitability (Average Margin)
Average profit margin of detected arbitrage opportunities. • target: 0.05 • range: 0-0.1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master Google ADK for building multimodal AI agents, focusing on ingesting and processing diverse data formats (text, images, video snippets from news).
Implement multi-LLM orchestration, using Claude Opus 4.5 for sophisticated sentiment analysis from news and social media, and Grok 4 Heavy for high-throughput pattern recognition in numerical market data.
Integrate Firebender AI as a specialized agentic service tool, enabling the Google ADK agent to delegate complex market data aggregation and statistical pattern recognition tasks.
Design and build real-time data pipelines to ingest live prediction market contract data and news feeds from a simulated Prediction Market API.
Develop advanced analytical algorithms within the agent to identify subtle market inefficiencies and potential arbitrage opportunities across different prediction markets.
Implement robust evaluation metrics to assess the accuracy of market predictions and the profitability of identified arbitrage strategies.
Explore ethical considerations and potential biases in using AI for financial prediction markets, ensuring transparency in decision-making.
How this agent runs
The evaluation will focus on the agent's ability to accurately predict market outcomes, identify profitable arbitrage opportunities, and effectively integrate multimodal data sources.
Challenge input
{ "market_id": "M001", "historical_data": {...}, "news_articles": ["article_text"], "social_sentiment": "positive" }
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{ "predicted_outcome": "[Outcome A]", "confidence": 0.85, "justification": "[reasoning]" }
- Agent must correctly predict the market outcome for at least 80% of test cases.
- Agent must correctly identify 90% of intentionally planted arbitrage opportunities.
- Average confidence score for all market predictions made by the agent. • target: 0.8 • range: 0-1
- Average Prediction Confidence target: 0.8
- 2 public reference cases
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- PredictionAccuracy · Weight 1
- ArbitrageIdentificationRate · Weight 1
- Average Prediction Confidence · Weight 1
- Arbitrage Profitability (Average Margin) · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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