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The Kuest Python SDK provides a high-level interface for building trading bots and automated strategies.
The Python SDK is currently in beta. Visit github.com/kuestcom for the latest version.

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Installation

pip install kuest-sdk

​
Quick start

from kuest import KuestClient

# Initialize client
client = KuestClient(
    api_key="your-api-key",
    api_secret="your-api-secret",
    passphrase="your-passphrase",
    testnet=True,  # Use Polygon Amoy for testing
)

# Get all markets
markets = client.get_markets()
print(f"Found {len(markets)} markets")

# Place a limit order
order = client.place_order(
    market_id="0x123...",
    outcome="YES",
    side="BUY",
    price=0.65,
    size=10.0,
)

print(f"Order placed: {order.id}")

​
Authentication

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Get API credentials

  1. Visit auth.kuest.com
  2. Connect your Polygon wallet
  3. Generate API key, secret, and passphrase
  4. Store credentials securely

​
Environment variables

import os
from kuest import KuestClient

client = KuestClient(
    api_key=os.getenv("KUEST_API_KEY"),
    api_secret=os.getenv("KUEST_API_SECRET"),
    passphrase=os.getenv("KUEST_PASSPHRASE"),
    testnet=os.getenv("KUEST_TESTNET", "true") == "true",
)

​
Core features

​
Market data

# Get all active markets
markets = client.get_markets(status="active")

for market in markets:
    print(f"{market.title}: {market.yes_price:.2%}")

# Get specific market
market = client.get_market("0x123...")
print(f"Volume: ${market.volume:,.2f}")

# Get orderbook
orderbook = client.get_orderbook("0x123...")
print(f"Best bid: {orderbook.best_bid}")
print(f"Best ask: {orderbook.best_ask}")
print(f"Spread: {orderbook.spread:.4f}")

​
Place orders

# Limit order
order = client.place_order(
    market_id="0x123...",
    outcome="YES",
    side="BUY",
    price=0.65,
    size=10.0,
    order_type="LIMIT",
)

# Market order (fills immediately at best price)
order = client.place_order(
    market_id="0x123...",
    outcome="NO",
    side="SELL",
    size=5.0,
    order_type="MARKET",
)

​
Manage orders

# Get open orders
orders = client.get_orders(market_id="0x123...")

for order in orders:
    print(f"{order.side} {order.size} @ {order.price}")

# Cancel order
client.cancel_order(order_id="abc123")

# Cancel all orders for a market
client.cancel_all_orders(market_id="0x123...")

​
Portfolio management

# Get current positions
positions = client.get_positions()

for position in positions:
    print(f"{position.market}: {position.size} @ {position.avg_price}")
    print(f"Current value: ${position.current_value:.2f}")
    print(f"PnL: ${position.pnl:.2f} ({position.pnl_percent:.2%})")

# Get trading history
trades = client.get_trades(limit=100)

for trade in trades:
    print(f"{trade.timestamp}: {trade.side} {trade.size} @ {trade.price}")

​
Advanced features

​
WebSocket streaming

import asyncio

async def main():
    # Subscribe to price updates
    async def on_price_update(market_id, price):
        print(f"{market_id}: {price}")
    
    await client.subscribe_prices(
        market_ids=["0x123...", "0x456..."],
        callback=on_price_update,
    )

asyncio.run(main())

​
Batch operations

# Place multiple orders at once
orders = [
    {"market_id": "0x123...", "side": "BUY", "price": 0.60, "size": 5},
    {"market_id": "0x456...", "side": "SELL", "price": 0.40, "size": 10},
    {"market_id": "0x789...", "side": "BUY", "price": 0.75, "size": 3},
]

results = client.place_orders_batch(orders)
print(f"Placed {len(results)} orders")

​
Error handling

from kuest.exceptions import (
    InsufficientBalanceError,
    OrderNotFoundError,
    RateLimitError,
)

try:
    order = client.place_order(
        market_id="0x123...",
        side="BUY",
        price=0.65,
        size=1000,  # Too large
    )
except InsufficientBalanceError:
    print("Not enough USDC")
except RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after} seconds")
except Exception as e:
    print(f"Error: {e}")

​
Example strategies

​
Market making bot

import time

def market_make(market_id, spread=0.02, size=5.0):
    """Place buy and sell orders around mid-price"""
    orderbook = client.get_orderbook(market_id)
    mid_price = (orderbook.best_bid + orderbook.best_ask) / 2
    
    # Place buy order
    client.place_order(
        market_id=market_id,
        side="BUY",
        price=mid_price - spread / 2,
        size=size,
    )
    
    # Place sell order
    client.place_order(
        market_id=market_id,
        side="SELL",
        price=mid_price + spread / 2,
        size=size,
    )
    
    print(f"Market making at {mid_price:.4f} +/- {spread/2:.4f}")

# Run market maker
while True:
    try:
        market_make("0x123...", spread=0.02, size=5.0)
        time.sleep(60)  # Update every minute
    except KeyboardInterrupt:
        break
    except Exception as e:
        print(f"Error: {e}")
        time.sleep(10)

​
Arbitrage detector

def check_arbitrage():
    """Find arbitrage opportunities across markets"""
    markets = client.get_markets(status="active")
    
    for market in markets:
        orderbook = client.get_orderbook(market.id)
        
        # YES + NO should equal 1.0
        yes_ask = orderbook.asks[0].price if orderbook.asks else 1.0
        no_ask = orderbook.asks[0].price if orderbook.asks else 1.0
        
        total = yes_ask + no_ask
        
        if total < 0.98:  # Arbitrage opportunity
            profit = 1.0 - total
            print(f"Arbitrage in {market.title}:")
            print(f"  Buy YES @ {yes_ask:.4f}")
            print(f"  Buy NO @ {no_ask:.4f}")
            print(f"  Profit: {profit:.4f} USDC")
            
            # Execute arbitrage
            client.place_order(market.id, "YES", "BUY", yes_ask, 1.0)
            client.place_order(market.id, "NO", "BUY", no_ask, 1.0)

# Run arbitrage detector
while True:
    check_arbitrage()
    time.sleep(30)

​
Momentum trading

import pandas as pd
import numpy as np

def momentum_strategy(market_id, lookback=60, threshold=0.01):
    """Trade based on recent price momentum"""
    # Fetch price history
    prices = client.get_price_history(
        market_id=market_id,
        interval="1m",
        limit=lookback,
    )
    
    df = pd.DataFrame(prices)
    df['returns'] = df['price'].pct_change()
    
    # Calculate momentum
    momentum = df['returns'].tail(10).mean()
    
    print(f"Momentum: {momentum:.4f}")
    
    if momentum > threshold:
        print("Strong upward momentum - buying")
        client.place_order(
            market_id=market_id,
            side="BUY",
            price=df['price'].iloc[-1] * 1.01,  # 1% above current
            size=10.0,
        )
    elif momentum < -threshold:
        print("Strong downward momentum - selling")
        client.place_order(
            market_id=market_id,
            side="SELL",
            price=df['price'].iloc[-1] * 0.99,  # 1% below current
            size=10.0,
        )

# Run momentum strategy
while True:
    momentum_strategy("0x123...", lookback=60, threshold=0.01)
    time.sleep(300)  # Every 5 minutes

​
Best practices

1

Test on Amoy first

Always test your bot on Polygon Amoy testnet before going live.
2

Implement risk management

MAX_POSITION_SIZE = 100  # USDC
MAX_DAILY_LOSS = 50      # USDC

def check_risk_limits():
    positions = client.get_positions()
    total_exposure = sum(p.size * p.avg_price for p in positions)
    
    if total_exposure > MAX_POSITION_SIZE:
        raise Exception("Position limit exceeded")
3

Handle rate limits

import time
from functools import wraps

def retry_on_rate_limit(max_retries=3):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):
                try:
                    return func(*args, **kwargs)
                except RateLimitError as e:
                    if attempt == max_retries - 1:
                        raise
                    time.sleep(e.retry_after)
            return wrapper
    return decorator
4

Log all trades

import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('trading.log'),
        logging.StreamHandler(),
    ],
)

logger = logging.getLogger(__name__)

def place_order_with_logging(**kwargs):
    logger.info(f"Placing order: {kwargs}")
    order = client.place_order(**kwargs)
    logger.info(f"Order placed: {order.id}")
    return order

​
Performance tips

  • Use asyncio for concurrent API calls
  • Cache orderbook data for 1-2 seconds
  • Batch order placements when possible
  • Use WebSocket for real-time price updates
  • Implement connection pooling

​
Next steps

Rust SDK

High-performance Rust SDK

Arbitrage

Advanced arbitrage strategies

API reference

Complete API documentation

Bot SDKs

SDK overview and comparison

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