> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/gnosis/prediction-market-agent/llms.txt
> Use this file to discover all available pages before exploring further.

# DeployableCoinFlipAgent

> Random prediction agent for binary markets

## Overview

The `DeployableCoinFlipAgent` is a simple agent that makes random binary predictions using a coin flip mechanism. This agent serves as a baseline for testing and comparison purposes.

## Class: DeployableCoinFlipAgent

A basic trading agent that randomly predicts market outcomes with 50/50 probability.

### Inheritance

```python theme={null}
DeployableCoinFlipAgent(DeployableTraderAgent)
```

### Methods

#### verify\_market

```python theme={null}
def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool
```

Verifies if the agent should process a given market. This implementation accepts all markets.

<ParamField path="market_type" type="MarketType">
  The type of prediction market
</ParamField>

<ParamField path="market" type="AgentMarket">
  The market to verify
</ParamField>

<ResponseField name="return" type="bool">
  Always returns `True`
</ResponseField>

#### answer\_binary\_market

```python theme={null}
def answer_binary_market(self, market: AgentMarket) -> ProbabilisticAnswer | None
```

Generates a random prediction for a binary market by flipping a coin.

<ParamField path="market" type="AgentMarket">
  The market to predict on
</ParamField>

<ResponseField name="return" type="ProbabilisticAnswer | None">
  A probabilistic answer with:

  * `p_yes`: 0.0 or 1.0 (randomly chosen)
  * `confidence`: 0.5
  * `reasoning`: "I flipped a coin to decide."
</ResponseField>

## Class: DeployableCoinFlipAgentByHighestLiquidity

An enhanced version of the coin flip agent that targets high-liquidity markets.

### Inheritance

```python theme={null}
DeployableCoinFlipAgentByHighestLiquidity(DeployableCoinFlipAgent)
```

### Configuration Properties

<ParamField path="get_markets_sort_by" type="SortBy" default="SortBy.HIGHEST_LIQUIDITY">
  Sorts markets by liquidity, targeting the most liquid markets first
</ParamField>

<ParamField path="bet_on_n_markets_per_run" type="int" default="2">
  Number of markets to trade on per execution run
</ParamField>

<ParamField path="same_market_trade_interval" type="TradeInterval" default="FixedInterval(timedelta(days=14))">
  Minimum time interval between trades on the same market (14 days)
</ParamField>

## Usage Examples

### Basic CoinFlip Agent

```python theme={null}
from prediction_market_agent.agents.coinflip_agent.deploy import DeployableCoinFlipAgent
from prediction_market_agent_tooling.markets.markets import MarketType

# Initialize the agent
agent = DeployableCoinFlipAgent()

# Deploy locally for testing
agent.deploy_local(
    market_type=MarketType.OMEN,
    sleep_time=180,  # 3 minutes
)
```

### High Liquidity CoinFlip Agent

```python theme={null}
from prediction_market_agent.agents.coinflip_agent.deploy import (
    DeployableCoinFlipAgentByHighestLiquidity
)
from prediction_market_agent_tooling.markets.markets import MarketType

# Initialize the agent with liquidity targeting
agent = DeployableCoinFlipAgentByHighestLiquidity()

# Deploy to production
agent.deploy(
    market_type=MarketType.OMEN,
    enable_langfuse=True,
)
```

### Custom Configuration

```python theme={null}
from prediction_market_agent.agents.coinflip_agent.deploy import DeployableCoinFlipAgent
from prediction_market_agent_tooling.markets.agent_market import AgentMarket

# Subclass for custom behavior
class MyCoinFlipAgent(DeployableCoinFlipAgent):
    def verify_market(self, market_type: MarketType, market: AgentMarket) -> bool:
        # Only trade on markets with specific criteria
        return market.volume > 100

agent = MyCoinFlipAgent()
agent.deploy_local(market_type=MarketType.OMEN)
```

## Implementation Details

### Random Selection Mechanism

The agent uses Python's `random.choice()` to make binary decisions:

```python theme={null}
import random

decision = random.choice([True, False])
p_yes = Probability(float(decision))  # 0.0 or 1.0
```

### Source Location

```
prediction_market_agent/agents/coinflip_agent/deploy.py
```

## Use Cases

1. **Baseline Testing**: Compare more sophisticated agents against random predictions
2. **Market Activity**: Generate trading activity for testing market infrastructure
3. **Control Group**: Use as a control in agent performance studies
4. **Educational**: Simple example for understanding the agent architecture

## Related

* [AdvancedAgent](/api/agents/advanced-agent) - Evidence-based prediction agent
* [DeployableTraderAgent](/api/core/trader-agent) - Base class for trading agents
* [AgentMarket](/api/markets/agent-market) - Market interface