Hackathon Quickstart Guide

from prediction_market_agent_tooling.deploy.agent import DeployableTraderAgent
from prediction_market_agent_tooling.gtypes import Probability
from prediction_market_agent_tooling.markets.agent_market import AgentMarket
from prediction_market_agent_tooling.markets.data_models import ProbabilisticAnswer
from prediction_market_agent_tooling.tools.google_utils import search_google_serper
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel

from prediction_market_agent.tools.web_scrape.markdown import web_scrape
from prediction_market_agent.utils import APIKeys


class YourCustomAgent(DeployableTraderAgent):
    bet_on_n_markets_per_run = 3

    def answer_binary_market(self, market: AgentMarket) -> ProbabilisticAnswer | None:
        # 1. Search for information
        urls = search_google_serper(market.question)
        
        # 2. Filter and scrape
        contents = []
        for url in urls[:5]:
            if "manifold" not in url:  # Skip copy-paste results
                if scraped := web_scrape(url):
                    contents.append(scraped[:5000])  # Limit length
        
        if not contents:
            return None
        
        # 3. Your custom analysis logic here!
        # Example: Use a better prompt
        probability, confidence = self.analyze_with_llm(
            market.question, 
            contents
        )
        
        return ProbabilisticAnswer(
            confidence=confidence,
            p_yes=Probability(probability),
            reasoning=f"Analyzed {len(contents)} sources",
        )
    
    def analyze_with_llm(self, question: str, contents: list[str]) -> tuple[float, float]:
        agent = Agent(
            OpenAIModel(
                "gpt-4o-mini",
                provider=get_openai_provider(api_key=APIKeys().openai_api_key),
            ),
            system_prompt="You are an expert prediction market analyst.",
        )
        
        # Your custom prompt engineering here!
        result = agent.run_sync(
            f"""Question: {question}
            
Evidence from web:
{chr(10).join(contents)}

Analyze the evidence and provide:
1. Probability (0.0-1.0) that the answer is YES
2. Your confidence (0.0-1.0) in this prediction

Respond with two numbers separated by space."""
        ).output
        
        prob, conf = map(float, result.split())
        return prob, conf

from prediction_market_agent.agents.your_agent.deploy import YourCustomAgent

# In the main() function:
benchmarker = Benchmarker(
    markets=markets_deduplicated,
    agents=[
        BenchmarkAgent(agent=AdvancedAgent()),
        BenchmarkAgent(agent=YourCustomAgent()),  # Add here
    ],
    cache_path=cache_path,
    only_cached=only_cached,
)