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Overview

All trading agents in this framework inherit from the DeployableTraderAgent base class provided by the prediction-market-agent-tooling library. This base class provides a standardized interface for interacting with multiple prediction market platforms.

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The DeployableTraderAgent Class

The DeployableTraderAgent is the foundation for all prediction market trading agents. It handles:
  • Market discovery and filtering
  • Trade execution and position management
  • Rate limiting and trade intervals
  • Multi-platform support (Omen, Manifold, Polymarket, Metaculus)

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Core Methods

Every agent must implement specific methods to define its trading behavior:

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answer_binary_market()

The primary method that generates predictions for binary markets. This is where your agent’s logic lives. 
from prediction_market_agent_tooling.deploy.agent import DeployableTraderAgent
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.gtypes import Probability
import random

class DeployableCoinFlipAgent(DeployableTraderAgent):
    def answer_binary_market(self, market: AgentMarket) -> ProbabilisticAnswer | None:
        decision = random.choice([True, False])
        return ProbabilisticAnswer(
            confidence=0.5,
            p_yes=Probability(float(decision)),
            reasoning="I flipped a coin to decide.",
        )

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verify_market() (Optional)

Filter markets before processing them. Return False to skip a market. 
def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool:
    # Example: Only bet on markets with sufficient liquidity
    if market.total_liquidity < 100:
        return False
    return True

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get_betting_strategy() (Optional)

Define how much to bet on each market. See the Betting Strategies page for details. 
from prediction_market_agent_tooling.deploy.betting_strategy import (
    BettingStrategy,
    SimpleBinaryKellyBettingStrategy,
)

def get_betting_strategy(self, market: AgentMarket) -> BettingStrategy:
    return SimpleBinaryKellyBettingStrategy(
        max_position_amount=USD(3.3),
    )

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Configuration Properties

Agents can override class properties to customize behavior:
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bet_on_n_markets_per_run
int
default:"1"
Number of markets to trade on each execution
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get_markets_sort_by
SortBy
default:"SortBy.CLOSING_SOONEST"
How to sort available markets:
  • SortBy.CLOSING_SOONEST - Markets closing soon first
  • SortBy.HIGHEST_LIQUIDITY - Most liquid markets first
  • SortBy.NEWEST - Recently created markets first
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same_market_trade_interval
TradeInterval
default:"FixedInterval(days=7)"
How often to trade on the same market. See Trade Intervals for details.
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supported_markets
list[MarketType]
default:"All markets"
Limit which market platforms your agent supports

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Complete Example: Liquidity-Focused Agent

Here’s a complete agent that targets high-liquidity markets and trades frequently: 
from datetime import timedelta
from prediction_market_agent_tooling.deploy.agent import DeployableTraderAgent
from prediction_market_agent_tooling.deploy.trade_interval import (
    FixedInterval,
    TradeInterval,
)
from prediction_market_agent_tooling.markets.agent_market import AgentMarket, SortBy
from prediction_market_agent_tooling.markets.data_models import ProbabilisticAnswer
from prediction_market_agent_tooling.gtypes import Probability
import random

class DeployableCoinFlipAgentByHighestLiquidity(DeployableTraderAgent):
    # Configuration
    get_markets_sort_by = SortBy.HIGHEST_LIQUIDITY
    bet_on_n_markets_per_run = 2
    same_market_trade_interval: TradeInterval = FixedInterval(timedelta(days=14))
    
    def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool:
        # Accept all markets
        return True

    def answer_binary_market(self, market: AgentMarket) -> ProbabilisticAnswer | None:
        decision = random.choice([True, False])
        return ProbabilisticAnswer(
            confidence=0.5,
            p_yes=Probability(float(decision)),
            reasoning="I flipped a coin to decide.",
        )

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Running Your Agent

Once you’ve created an agent, add it to run_agent.py: 
from enum import Enum
from prediction_market_agent.agents.your_agent.deploy import YourAgent

class RunnableAgent(str, Enum):
    your_agent = "your_agent"

RUNNABLE_AGENTS: dict[RunnableAgent, type[DeployableAgent]] = {
    RunnableAgent.your_agent: YourAgent,
}
Then run it: 
python prediction_market_agent/run_agent.py your_agent omen
See the Quickstart guide for complete setup instructions and the Markets page to learn about supported market platforms.

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Agent Lifecycle

  1. Load: Agent initializes (optional load() method)
  2. Get Markets: Fetch available markets based on sorting and filtering
  3. Verify: Check each market with verify_market()
  4. Answer: Generate predictions with answer_binary_market()
  5. Calculate Bet: Determine bet size using betting strategy
  6. Execute: Place trades on the market
  7. Sleep: Wait for next run based on trade intervals

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Advanced Patterns

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Custom Market Fetching

Override get_markets() for complete control: 
def get_markets(
    self,
    market_type: MarketType,
    sort_by: SortBy = SortBy.CLOSING_SOONEST,
    filter_by: FilterBy = FilterBy.OPEN,
) -> Sequence[AgentMarket]:
    # Custom market fetching logic
    markets = super().get_markets(market_type)
    # Filter to only markets closing within 14 days
    max_close_time = utcnow() + timedelta(days=14)
    return [m for m in markets if m.close_time < max_close_time]

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Stateful Agents

Use the load() method to initialize state: 
def load(self) -> None:
    """Called once when agent starts"""
    super().load()
    # Load historical data, models, etc.
    self.model = load_my_model()
    self.historical_data = fetch_historical_data()

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Next Steps

Markets

Learn about supported market platforms

Betting Strategies

Optimize your bet sizing with Kelly criterion

Trade Intervals

Control when your agent trades on markets

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