Prophet Agent Family

PredictionProphet-based agents for research-driven market predictions

Overview

The Prophet agent family uses the PredictionProphet library to perform sophisticated market research and generate evidence-based predictions. These agents represent production-quality implementations with various model configurations and betting strategies.

Base Classes

DeployableTraderAgentER

Base class for Prophet agents supporting binary markets.

class DeployableTraderAgentER(DeployableTraderAgent):
    agent: PredictionProphetAgent | OlasAgent
    bet_on_n_markets_per_run = 2

agent · PredictionProphetAgent | OlasAgent

The underlying prediction agent instance

bet_on_n_markets_per_run · int · default: "2"

Number of markets to trade on per execution run

Methods

answer_binary_market

def answer_binary_market(self, market: AgentMarket) -> ProbabilisticAnswer | None

Generates predictions using the PredictionProphet agent.

DeployableTraderAgentERCategorical

Base class for categorical market predictions.

class DeployableTraderAgentERCategorical(DeployableTraderAgent):
    agent: PredictionProphetAgent
    bet_on_n_markets_per_run = 2

answer_categorical_market

def answer_categorical_market(
    self, market: AgentMarket
) -> CategoricalProbabilisticAnswer | None

DeployableTraderAgentERScalar

Base class for scalar market predictions.

class DeployableTraderAgentERScalar(DeployableTraderAgent):
    agent: PredictionProphetAgent
    bet_on_n_markets_per_run = 2

answer_scalar_market

def answer_scalar_market(
    self, market: AgentMarket
) -> ScalarProbabilisticAnswer | None

Production Agents

DeployablePredictionProphetGPT4oAgent

Primary production agent using GPT-4o with Kelly criterion betting.

class DeployablePredictionProphetGPT4oAgent(DeployableTraderAgentER):
    bet_on_n_markets_per_run = 4
    agent: PredictionProphetAgent

bet_on_n_markets_per_run · int · default: "4"

Trades on 4 markets per run for increased activity

Configuration

• Research Model: gpt-4o-2024-08-06 (temperature 0.7)
• Prediction Model: gpt-4o-2024-08-06 (temperature 0.0)
• Betting Strategy: FullBinaryKellyBettingStrategy
  • Min bet: $1
  • Max bet: $5
  • Max price impact: 0.7

Usage

from prediction_market_agent.agents.prophet_agent.deploy import (
    DeployablePredictionProphetGPT4oAgent
)
from prediction_market_agent_tooling.markets.markets import MarketType

agent = DeployablePredictionProphetGPT4oAgent(
    enable_langfuse=True,
    place_trades=True,
)

agent.deploy(market_type=MarketType.OMEN)

DeployablePredictionProphetGPT4oAgent_B

Optimized version with reduced internet searches for lower Tavily API costs.

class DeployablePredictionProphetGPT4oAgent_B(DeployableTraderAgentER):
    bet_on_n_markets_per_run = 4
    agent: PredictionProphetAgent

Key Differences

• subqueries_limit: 3 (reduced from default)
• min_scraped_sites: 3 (reduced from default)
• Same model configuration as base GPT-4o agent

DeployablePredictionProphetGPT4oAgent_C

Experimental agent testing the impact of disabling take-profit functionality.

take_profit · bool · default: "False"

Disabled to test if larger final payouts increase profits

Model Variants

OpenAI Models

GPT-4o-mini

class DeployablePredictionProphetGPT4ominiAgent(DeployableTraderAgentER):
    # Uses gpt-4o-mini-2024-07-18
    # Cheaper but potentially less accurate

GPT-4 Turbo

class DeployablePredictionProphetGPT4TurboFinalAgent(DeployableTraderAgentER):
    # Uses gpt-4-turbo-2024-04-09
    # max_price_impact: None (no limit)

O-series Models

o1-2024-12-17

class DeployablePredictionProphetGPTo1(DeployableTraderAgentER):
    # Temperature: 1.0 (required by o1)
    # Max price impact: 0.418

o3-mini

class DeployablePredictionProphetGPTo3mini(DeployableTraderAgentER):
    # Uses MaxExpectedValueBettingStrategy
    # Min bet: $0.5, Max bet: $1

Anthropic Models

Claude 3.5 Sonnet

class DeployablePredictionProphetClaude35SonnetAgent(DeployableTraderAgentER):
    # Model: claude-3-5-sonnet-20241022
    # Max bet: $4.77, Max price impact: 0.63

Claude 3.5 Haiku

class DeployablePredictionProphetClaude35HaikuAgent(DeployableTraderAgentER):
    # Model: claude-3-5-haiku-20241022
    # Betting disabled (commented out)

OpenRouter Models

Gemini 2.0 Flash

class DeployablePredictionProphetGemini20Flash(
    DeployableTraderAgentProphetOpenRouter
):
    model = "google/gemini-2.0-flash-001"
    bet_on_n_markets_per_run = 4

DeepSeek Chat

class DeployablePredictionProphetDeepSeekChat(
    DeployableTraderAgentProphetOpenRouter
):
    model = "deepseek/deepseek-chat"

Categorical Markets

DeployablePredictionProphetGPT4oAgentCategorical

Specialized agent for categorical prediction markets.

class DeployablePredictionProphetGPT4oAgentCategorical(
    DeployableTraderAgentERCategorical
):
    bet_on_n_markets_per_run = 4

Betting Strategy (Omen Markets)

• Strategy: FullCategoricalKellyBettingStrategy
• Max position: $0.01 to$0.75
• Max price impact: 0.068
• allow_multiple_bets: False
• allow_shorting: False
• multicategorical: False

Usage

from prediction_market_agent.agents.prophet_agent.deploy import (
    DeployablePredictionProphetGPT4oAgentCategorical
)

agent = DeployablePredictionProphetGPT4oAgentCategorical()
agent.deploy(market_type=MarketType.OMEN)

Scalar Markets

DeployablePredictionProphetGPT4oAgentScalar

Agent for scalar range markets.

class DeployablePredictionProphetGPT4oAgentScalar(
    DeployableTraderAgentERScalar
):
    bet_on_n_markets_per_run = 4

Special Configuration

• Uses TopNOpenAINModel with n=5
• Temperature: 0.7 for prediction agent
• Generates multiple predictions and aggregates results

News-Based Trading

DeployablePredictionProphetGPT4oAgentNewMarketTrader

Agent that trades on new markets and re-evaluates positions when relevant news is published.

class DeployablePredictionProphetGPT4oAgentNewMarketTrader(
    DeployablePredictionProphetGPT4oAgent
):
    trade_on_markets_created_after = DatetimeUTC(2024, 10, 31, 0)
    get_markets_sort_by = SortBy.NEWEST
    same_market_trade_interval = MarketLifetimeProportionalInterval(max_trades=4)

trade_on_markets_created_after · DatetimeUTC

Only trades on markets created after this date

get_markets_sort_by · SortBy · default: "SortBy.NEWEST"

Sorts markets by creation date to target new markets

same_market_trade_interval · MarketLifetimeProportionalInterval

Re-trades up to 4 times per market based on news relevance

News Detection Logic

def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool:
    # Check for new relevant news since last trade
    last_trade_datetime = market.get_most_recent_trade_datetime(user_id=user_id)
    if last_trade_datetime:
        news = get_certified_relevant_news_since_cached(
            question=market.question,
            days_ago=(utcnow() - last_trade_datetime).days,
        )
        return news is not None
    return True

Embedding Models

DeployableOlasEmbeddingOAAgent

Agent using the Olas embedding model for market analysis.

class DeployableOlasEmbeddingOAAgent(DeployableTraderAgentER):
    agent: OlasAgent

• embedding_model: EmbeddingModel.openai
• Betting: FullCategoricalKellyBettingStrategy
• Max price impact: 0.7333

PredictionProphet Configuration

All agents initialize PredictionProphetAgent with:

research_agent · Agent

Agent for web research and information gathering

• Temperature: 0.7 (creative search)

prediction_agent · Agent

Agent for making final predictions

• Temperature: 0.0 (deterministic)

include_reasoning · bool · default: "True"

Include reasoning in prediction responses

logger · Logger

Logger instance for tracking agent activity

Optional Parameters

subqueries_limit · int · default: "3"

Maximum number of research subqueries (used in optimized agents)

min_scraped_sites · int · default: "3"

Minimum number of websites to scrape for research

Advanced Usage

Custom Prophet Agent

from prediction_market_agent.agents.prophet_agent.deploy import (
    DeployableTraderAgentProphetOpenRouter
)
from prediction_market_agent_tooling.deploy.betting_strategy import (
    FullBinaryKellyBettingStrategy,
)

class MyCustomProphetAgent(DeployableTraderAgentProphetOpenRouter):
    model = "your-model/model-name"
    bet_on_n_markets_per_run = 5
    
    def get_betting_strategy(self, market: AgentMarket) -> BettingStrategy:
        return FullBinaryKellyBettingStrategy(
            max_position_amount=USD(10),
            max_price_impact=0.5,
        )

agent = MyCustomProphetAgent()
agent.deploy(market_type=MarketType.OMEN)

Loading Existing Agent

from prediction_market_agent.agents.prophet_agent.deploy import (
    DeployablePredictionProphetGPT4oAgent
)

agent = DeployablePredictionProphetGPT4oAgent()
agent.load()  # Initialize the PredictionProphet agent

# Use the agent for prediction
from prediction_market_agent_tooling.markets.omen.omen import OmenAgentMarket

market = OmenAgentMarket.get_market(market_id="0x...")
answer = agent.answer_binary_market(market)
print(f"Prediction: {answer.p_yes}, Confidence: {answer.confidence}")

Required API Keys

OPENAI_API_KEY · str

Required for OpenAI model variants

ANTHROPIC_API_KEY · str

Required for Claude model variants

OPENROUTER_API_KEY · str

Required for Gemini, DeepSeek, and other OpenRouter models

TAVILY_API_KEY · str

Required for web research functionality

Source Location

prediction_market_agent/agents/prophet_agent/deploy.py

Related

• PredictionProphet Library - Underlying research library
• Think Thoroughly Agent - Multi-scenario reasoning agent
• Betting Strategies - Kelly criterion and other strategies