aiopolymarket: Async Python Client for Polymarket

Complete guide to aiopolymarket - the async Python client for Polymarket with built-in retry logic, custom exceptions, and optimized performance. aiopolymarket provides a modern, async-first interface to Polymarket APIs, perfect for building high-performance trading bots, data aggregators, and real-time market monitoring applications. This comprehensive 2026 guide covers installation, usage patterns, error handling, and performance optimization.

Async Python provides significant performance advantages for Polymarket applications: concurrent requests for faster data fetching, non-blocking I/O for better resource utilization, and connection pooling for reduced latency. aiopolymarket wraps these benefits with built-in retry logic, proper error handling, and optimized connection management. High search volume: "aiopolymarket", "async polymarket python" - developers increasingly prefer async clients for production applications. Professional developers leverage analytics platforms like PolyTrack Pro which provides pre-built async infrastructure, allowing them to skip months of API integration work.

Installation

Install via pip

pip install aiopolymarket

# Or install latest from GitHub
pip install git+https://github.com/polymarket/aiopolymarket.git

# Install with optional dependencies for better performance
pip install aiopolymarket[all]

Python Version Requirements

• • Python 3.8+: Required for async/await syntax
• • Python 3.10+: Recommended for better type hints and performance
• • Dependencies: aiohttp, asyncio, typing_extensions (auto-installed)

Basic Usage

Simple Async Context Manager

import asyncio
from aiopolymarket import PolymarketClient

async def get_markets():
    """Fetch active markets using async client."""
    async with PolymarketClient() as client:
        markets = await client.get_markets(active=True, limit=10)
        return markets

# Run async function
markets = asyncio.run(get_markets())
print(f"Found {len(markets)} active markets")

Fetch Specific Market

async def get_market_by_slug(slug: str):
    """Fetch a specific market by slug."""
    async with PolymarketClient() as client:
        market = await client.get_market(slug=slug)
        return market

# Usage
market = asyncio.run(get_market_by_slug("trump-2028"))
print(f"Market: {market['question']}")

Concurrent Requests

Fetch multiple markets concurrently for better performance:

async def fetch_multiple_markets(slugs: list[str]):
    """Fetch multiple markets concurrently."""
    async with PolymarketClient() as client:
        # Create tasks for concurrent execution
        tasks = [client.get_market(slug=slug) for slug in slugs]
        markets = await asyncio.gather(*tasks, return_exceptions=True)
        
        # Filter out exceptions
        successful = [m for m in markets if not isinstance(m, Exception)]
        return successful

# Usage - fetches 5 markets simultaneously
slugs = ["trump-2028", "btc-100k", "eth-etf", "super-bowl-2026", "election-2024"]
markets = asyncio.run(fetch_multiple_markets(slugs))
print(f"Fetched {len(markets)} markets")

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Built-in Retry Logic

aiopolymarket automatically handles rate limits and retries with exponential backoff:

Automatic Retry Configuration

async with PolymarketClient(
    retry_on_rate_limit=True,  # Enable automatic retries
    max_retries=5,              # Maximum retry attempts
    retry_delay=2,              # Initial delay in seconds
    backoff_factor=2            # Exponential backoff multiplier
) as client:
    # All requests automatically retry on rate limit errors
    markets = await client.get_markets(active=True)
    orderbook = await client.get_orderbook(token_id="0x123...")

Custom Retry Strategy

from aiopolymarket import PolymarketClient
from aiopolymarket.exceptions import RateLimitError
import asyncio

async def fetch_with_custom_retry():
    """Custom retry logic with manual exception handling."""
    async with PolymarketClient(retry_on_rate_limit=False) as client:
        max_retries = 5
        delay = 2
        
        for attempt in range(max_retries):
            try:
                return await client.get_markets(active=True)
            except RateLimitError as e:
                if attempt < max_retries - 1:
                    print(f"Rate limited, waiting {delay}s before retry {attempt + 1}/{max_retries}")
                    await asyncio.sleep(delay)
                    delay *= 2  # Exponential backoff
                else:
                    raise

Error Handling

Custom Exception Types

aiopolymarket provides specific exception types for different error scenarios:

from aiopolymarket import PolymarketClient
from aiopolymarket.exceptions import (
    RateLimitError,      # 429 - Rate limit exceeded
    APIError,            # Generic API errors
    NetworkError,        # Network connection issues
    NotFoundError,       # 404 - Resource not found
    AuthenticationError  # 401 - Authentication failed
)

async def robust_market_fetch(slug: str):
    """Handle different error types appropriately."""
    try:
        async with PolymarketClient() as client:
            return await client.get_market(slug=slug)
    except RateLimitError:
        print("Rate limit hit, implement backoff or use built-in retry")
        raise
    except NotFoundError:
        print(f"Market '{slug}' not found")
        return None
    except NetworkError as e:
        print(f"Network error: {e}, check connection")
        raise
    except APIError as e:
        print(f"API error: {e}")
        raise

Advanced Usage Patterns

Long-Running Client

Reuse a single client for multiple requests to benefit from connection pooling:

async def monitor_markets(slugs: list[str], interval: int = 60):
    """Monitor multiple markets with long-running client."""
    async with PolymarketClient() as client:
        while True:
            tasks = [
                client.get_market(slug=slug) 
                for slug in slugs
            ]
            markets = await asyncio.gather(*tasks, return_exceptions=True)
            
            for i, market in enumerate(markets):
                if isinstance(market, Exception):
                    print(f"Error fetching {slugs[i]}: {market}")
                else:
                    print(f"{slugs[i]}: {market['question']}")
            
            await asyncio.sleep(interval)

# Run monitoring loop
# asyncio.run(monitor_markets(["trump-2028", "btc-100k"], interval=60))

Batch Operations

async def fetch_all_active_markets():
    """Fetch all active markets efficiently with pagination."""
    async with PolymarketClient() as client:
        all_markets = []
        limit = 100
        offset = 0
        
        while True:
            markets = await client.get_markets(
                active=True,
                limit=limit,
                offset=offset
            )
            
            if not markets:
                break
            
            all_markets.extend(markets)
            
            if len(markets) < limit:
                break  # Last page
            
            offset += limit
        
        return all_markets

# Usage
markets = asyncio.run(fetch_all_active_markets())
print(f"Total active markets: {len(markets)}")

Performance Optimization

Connection Pooling

Reuse connections for better performance:

from aiopolymarket import PolymarketClient
import asyncio

async def optimized_batch_fetch(slugs: list[str]):
    """Use connection pooling for multiple requests."""
    async with PolymarketClient(
        pool_size=10,           # Connection pool size
        timeout=30              # Request timeout in seconds
    ) as client:
        # Create semaphore to limit concurrent requests
        semaphore = asyncio.Semaphore(10)
        
        async def fetch_with_limit(slug):
            async with semaphore:
                return await client.get_market(slug=slug)
        
        tasks = [fetch_with_limit(slug) for slug in slugs]
        return await asyncio.gather(*tasks, return_exceptions=True)

Caching

Cache responses for frequently accessed data:

from functools import lru_cache
from aiopolymarket import PolymarketClient
import asyncio

# Cache for synchronous wrapper
@lru_cache(maxsize=128)
def get_cached_market_sync(slug: str):
    """Synchronous wrapper with caching."""
    return asyncio.run(get_market(slug))

async def get_market(slug: str):
    async with PolymarketClient() as client:
        return await client.get_market(slug=slug)

# For async caching, use aiodis or similar
from aiodis import cache
import asyncio

@cache(ttl=300)  # Cache for 5 minutes
async def get_cached_market(slug: str):
    async with PolymarketClient() as client:
        return await client.get_market(slug=slug)

Complete Production Example

import asyncio
import logging
from aiopolymarket import PolymarketClient
from aiopolymarket.exceptions import RateLimitError, APIError

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class PolymarketMonitor:
    """Production-ready market monitor using aiopolymarket."""
    
    def __init__(self):
        self.client = PolymarketClient(
            retry_on_rate_limit=True,
            max_retries=5,
            pool_size=20
        )
    
    async def monitor_markets(self, slugs: list[str], interval: int = 60):
        """Monitor markets with error handling and logging."""
        async with self.client:
            while True:
                try:
                    tasks = [
                        self.client.get_market(slug=slug) 
                        for slug in slugs
                    ]
                    markets = await asyncio.gather(
                        *tasks, 
                        return_exceptions=True
                    )
                    
                    for slug, market in zip(slugs, markets):
                        if isinstance(market, Exception):
                            logger.error(f"Error fetching {slug}: {market}")
                        else:
                            logger.info(f"{slug}: {market.get('question', 'N/A')}")
                    
                except (RateLimitError, APIError) as e:
                    logger.warning(f"API error: {e}, retrying...")
                    await asyncio.sleep(60)
                
                await asyncio.sleep(interval)
    
    async def fetch_all_active(self):
        """Fetch all active markets efficiently."""
        async with self.client:
            all_markets = []
            limit = 100
            offset = 0
            
            while True:
                try:
                    markets = await self.client.get_markets(
                        active=True,
                        limit=limit,
                        offset=offset
                    )
                    
                    if not markets:
                        break
                    
                    all_markets.extend(markets)
                    offset += limit
                    
                    if len(markets) < limit:
                        break
                        
                except RateLimitError:
                    logger.warning("Rate limited, waiting...")
                    await asyncio.sleep(60)
            
            return all_markets

# Usage
async def main():
    monitor = PolymarketMonitor()
    
    # Monitor specific markets
    # await monitor.monitor_markets(["trump-2028", "btc-100k"], interval=60)
    
    # Or fetch all active markets
    markets = await monitor.fetch_all_active()
    print(f"Found {len(markets)} active markets")

if __name__ == "__main__":
    asyncio.run(main())

Best Practices

• • Use Context Managers: Always use async with to ensure proper resource cleanup
• • Enable Retry Logic: Set retry_on_rate_limit=True for production applications
• • Concurrent Requests: Use asyncio.gather() for parallel requests
• • Connection Pooling: Reuse client instances for better performance
• • Error Handling: Handle specific exception types appropriately
• • Timeouts: Set reasonable timeouts to prevent hanging requests
• • Logging: Log errors and retries for debugging
• • Rate Limit Awareness: Monitor for rate limit errors and adjust request frequency

Comparison: Sync vs Async

Conclusion

aiopolymarket is the recommended async Python client for Polymarket, providing significant performance advantages, built-in retry logic, and production-ready error handling. For applications requiring concurrent data fetching or real-time monitoring, aiopolymarket offers 10-100x performance improvements over synchronous clients.

For production applications requiring trading infrastructure, consider using enterprise platforms like PolyTrack Pro which provides pre-built async infrastructure and eliminates the need for low-level API integration.

Related Resources

• • gamma-api.polymarket.com Complete Guide: GraphQL & REST API Reference
• • Polymarket API JavaScript Tutorial: Complete Node.js Guide
• • py_clob_client Exponential Backoff: Rate Limit Handling Guide