Turbine Market Maker Bot Generator

You are helping a programmer create a market maker bot for Turbine's Bitcoin 15-minute prediction markets.

Step 0: Environment Context Detection

Before writing Python code, check the project's Python version requirements by reading pyproject.toml (if it exists) to determine syntax compatibility.

Environment Rules:

• If Python version is 3.9+: Use modern syntax (list[str], dict[str, int], X | None)
• If Python version is 3.8 or below: Use from typing import List, Dict, Optional
• Use async/await syntax (supported in all Python 3.9+ environments)
• For dataclasses, use @dataclass decorator (available in Python 3.7+)

Step 1: Environment Setup Check

First, check if the user has the required setup:

1. Check if turbine_client is importable by looking at the project structure
2. Check if .env file exists with the required credentials
3. If .env doesn't exist, guide them through creating it

Step 2: Private Key Setup

Check if .env file exists with TURBINE_PRIVATE_KEY set. If not:

1. Create a .env template file with placeholder values (never ask for or handle actual private keys):

# Turbine Trading Bot Configuration
# IMPORTANT: Add your private key below, then save this file.
# Use a dedicated trading wallet with limited funds.
# Get it from MetaMask: Settings > Security & Privacy > Export Private Key
TURBINE_PRIVATE_KEY=
TURBINE_API_KEY_ID=
TURBINE_API_PRIVATE_KEY=

1. Tell the user to open .env in their editor and paste their private key themselves
2. Remind them of security best practices:
   • Use a dedicated trading wallet with limited funds
   • Never share your private key with anyone or any tool
   • The .env file is gitignored and stays local

Never ask for, accept, or write a user's private key. The user must edit the .env file themselves.

Step 3: API Key Auto-Registration

The generated bot should auto-register API credentials on first run using the SDK's TurbineClient.request_api_credentials() method. This uses EIP-712 signature authentication (the private key signs a challenge locally — it is never transmitted).

If examples/price_action_bot.py exists in the project, read it and copy its credential registration pattern. Otherwise, use the template code below in the generated bot:

# --- Generated bot runtime code (not executed by the agent) ---
import os
import re
from pathlib import Path
from turbine_client import TurbineClient

def get_or_create_api_credentials(env_path: Path = None):
    """Get existing API credentials or register new ones via EIP-712 signature.

    The private key is used locally to sign an authentication challenge.
    It is never transmitted — only the signature is sent to the API.
    """
    if env_path is None:
        env_path = Path(__file__).parent / ".env"

    api_key_id = os.environ.get("TURBINE_API_KEY_ID")
    api_private_key = os.environ.get("TURBINE_API_PRIVATE_KEY")

    if api_key_id and api_private_key:
        print("Using existing API credentials")
        return api_key_id, api_private_key

    private_key = os.environ.get("TURBINE_PRIVATE_KEY")
    if not private_key:
        raise ValueError("Set TURBINE_PRIVATE_KEY in your .env file")

    print("Registering new API credentials...")
    credentials = TurbineClient.request_api_credentials(
        host="https://api.turbinefi.com",
        private_key=private_key,
    )

    api_key_id = credentials["api_key_id"]
    api_private_key = credentials["api_private_key"]

    # Save API keys (not the private key) to .env for future runs
    _append_api_keys_to_env(env_path, api_key_id, api_private_key)
    os.environ["TURBINE_API_KEY_ID"] = api_key_id
    os.environ["TURBINE_API_PRIVATE_KEY"] = api_private_key

    print(f"API credentials saved to {env_path}")
    return api_key_id, api_private_key


def _append_api_keys_to_env(env_path: Path, api_key_id: str, api_private_key: str):
    """Append or update only API key values in an existing .env file.

    Never writes TURBINE_PRIVATE_KEY — that is managed by the user.
    """
    env_path = Path(env_path)
    if not env_path.exists():
        return  # User must create .env themselves

    content = env_path.read_text()
    if "TURBINE_API_KEY_ID=" in content:
        content = re.sub(r'^TURBINE_API_KEY_ID=.*$', f'TURBINE_API_KEY_ID={api_key_id}', content, flags=re.MULTILINE)
    else:
        content = content.rstrip() + f"\nTURBINE_API_KEY_ID={api_key_id}"
    if "TURBINE_API_PRIVATE_KEY=" in content:
        content = re.sub(r'^TURBINE_API_PRIVATE_KEY=.*$', f'TURBINE_API_PRIVATE_KEY={api_private_key}', content, flags=re.MULTILINE)
    else:
        content = content.rstrip() + f"\nTURBINE_API_PRIVATE_KEY={api_private_key}"
    env_path.write_text(content + "\n")

Step 4: Algorithm Selection

Present the user with these trading algorithm options for prediction markets:

Option 1: Price Action Trader (Recommended)

• Uses real-time BTC price from Pyth Network (same oracle Turbine uses)
• Compares current price to the market's strike price
• If BTC is above strike price → buy YES (bet it stays above)
• If BTC is below strike price → buy NO (bet it stays below)
• Adjusts confidence based on how far price is from strike
• Best for: Beginners, following price momentum
• Risk: Medium - follows current price action

Option 2: Simple Spread Market Maker

• Places bid and ask orders around the mid-price with a fixed spread
• Best for: Learning the basics, stable markets
• Risk: Medium - can accumulate inventory in trending markets

Option 3: Inventory-Aware Market Maker

• Adjusts quotes based on current position to reduce inventory risk
• Skews prices to encourage trades that reduce position
• Best for: Balanced exposure, risk management
• Risk: Lower - actively manages inventory

Option 4: Momentum-Following Trader

• Detects price direction from recent trades
• Buys when momentum is up, sells when momentum is down
• Best for: Trending markets, breakouts
• Risk: Higher - can be wrong on reversals

Option 5: Mean Reversion Trader

• Fades large moves expecting price to revert
• Buys after dips, sells after spikes
• Best for: Range-bound markets, overreactions
• Risk: Higher - can fight strong trends

Option 6: Probability-Weighted Trader

• Uses distance from 50% as a signal
• Bets on extremes reverting toward uncertainty
• Best for: Markets with overconfident pricing
• Risk: Medium - based on market efficiency assumptions

Reference Implementation

A complete, production-ready implementation exists for the Price Action Trader:

examples/price_action_bot.py

• Signs a single gasless max USDC permit per settlement contract on first trade
• All subsequent orders use the existing allowance (no per-order permit overhead)
• Order sizing in USDC terms with position tracking

When generating a bot:

1. Read examples/price_action_bot.py as the primary reference
2. Copy the structure exactly, customizing only configuration parameters as needed
3. DO NOT use the simplified code snippets in this skill - they are incomplete

For other algorithm options, use the same bot structure but replace the algorithm-specific methods:

• calculate_signal() - Replace with the chosen algorithm's signal logic
• execute_signal() - Adapt order placement to match the algorithm
• price_action_loop() - Rename and adapt the main loop for the algorithm

The reference implementations include critical patterns that MUST be preserved in all bots:

• Position syncing from API (sync_position(), verify_position())
• Pending order TX tracking (pending_order_txs set)
• Trade verification (check failed_trades, pending_trades, recent trades)
• Market transition detection with market_expiring flag
• Unclaimed winnings discovery from previous sessions
• Rate-limited claiming (15s delay between claims)
• Async HTTP client for non-blocking external API calls
• Gasless max permit approval when entering new markets (ensure_settlement_approved())
• USDC-based order sizing (calculate_shares_from_usdc())
• Position tracking in USDC terms (position_usdc dict)

Step 5: Generate the Bot Code

Based on the user's algorithm choice, generate a complete bot file. The bot should:

1. Load credentials from environment variables
2. Auto-register API keys if needed
3. Connect to the BTC 15-minute quick market
4. Implement the chosen algorithm
5. Include proper error handling
6. Cancel orders on shutdown
7. Automatically detect new BTC markets and switch liquidity/trades to them
8. Handle market expiration gracefully with seamless transitions
9. Handle gasless USDC approval via one-time max permit per settlement contract
10. Track traded markets and automatically claim winnings when they resolve

Use examples/price_action_bot.py as the primary reference

Use examples/price_action_bot.py as the structural template for all bots. The generated bot should follow this high-level structure:

"""
Turbine Market Maker Bot - {ALGORITHM_NAME}
Generated for Turbine

Algorithm: {ALGORITHM_DESCRIPTION}
"""

import asyncio
import os
import time
from dotenv import load_dotenv
import httpx  # For Price Action Trader - fetching BTC price from Pyth Network

from turbine_client import TurbineClient, TurbineWSClient, Outcome, Side
from turbine_client.exceptions import TurbineApiError, WebSocketError

# Load environment variables
load_dotenv()

# ============================================================
# CONFIGURATION - Adjust these parameters for your strategy
# ============================================================
ORDER_SIZE = 1_000_000  # 1 share (6 decimals)
MAX_POSITION = 5_000_000  # Maximum position size (5 shares)
QUOTE_REFRESH_SECONDS = 30  # How often to refresh quotes
# Algorithm-specific parameters added here...

# Credential registration — include get_or_create_api_credentials()
# from Step 3 above (or copy from examples/price_action_bot.py if available)


class MarketMakerBot:
    """Market maker bot implementation with automatic market switching and winnings claiming."""

    def __init__(self, client: TurbineClient):
        self.client = client
        self.market_id: str | None = None
        self.settlement_address: str | None = None  # For USDC approval
        self.contract_address: str | None = None  # For claiming winnings
        self.strike_price: int = 0  # BTC price when market created (6 decimals) - used by Price Action Trader
        self.current_position = 0
        self.active_orders: dict[str, str] = {}  # order_hash -> side
        self.running = True
        # Track markets we've traded in for claiming winnings
        self.traded_markets: dict[str, str] = {}  # market_id -> contract_address
        # Algorithm state...

    async def get_active_market(self) -> tuple[str, int, int]:
        """
        Get the currently active BTC quick market.
        Returns (market_id, end_time, start_price) tuple.
        """
        quick_market = self.client.get_quick_market("BTC")
        return quick_market.market_id, quick_market.end_time, quick_market.start_price

    async def cancel_all_orders(self, market_id: str) -> None:
        """Cancel all active orders on a market before switching."""
        if not self.active_orders:
            return

        print(f"Cancelling {len(self.active_orders)} orders on market {market_id[:8]}...")
        for order_id in list(self.active_orders.keys()):
            try:
                self.client.cancel_order(market_id=market_id, order_id=order_id)
                del self.active_orders[order_id]
            except TurbineApiError as e:
                print(f"Failed to cancel order {order_id}: {e}")

    async def switch_to_new_market(self, new_market_id: str, start_price: int = 0) -> None:
        """
        Switch liquidity and trading to a new market.
        Called when a new BTC 15-minute market becomes active.

        Args:
            new_market_id: The new market ID to switch to.
            start_price: The BTC price when market was created (8 decimals).
                         Used by Price Action Trader to compare against current price.
        """
        old_market_id = self.market_id

        # Track old market for claiming winnings later
        if old_market_id and self.contract_address:
            self.traded_markets[old_market_id] = self.contract_address
            print(f"Tracking market {old_market_id[:8]}... for winnings claim")

        if old_market_id:
            print(f"\n{'='*50}")
            print(f"MARKET TRANSITION DETECTED")
            print(f"Old market: {old_market_id[:8]}...")
            print(f"New market: {new_market_id[:8]}...")
            print(f"{'='*50}\n")

            # Cancel all orders on the old market
            await self.cancel_all_orders(old_market_id)

        # Update to new market
        self.market_id = new_market_id
        self.strike_price = start_price  # Store for Price Action Trader
        self.active_orders = {}

        # Fetch settlement and contract addresses from markets list
        try:
            markets = self.client.get_markets()
            for market in markets:
                if market.id == new_market_id:
                    self.settlement_address = market.settlement_address
                    self.contract_address = market.contract_address
                    print(f"Settlement: {self.settlement_address[:16]}...")
                    print(f"Contract: {self.contract_address[:16]}...")
                    break
        except Exception as e:
            print(f"Warning: Could not fetch market addresses: {e}")

        strike_usd = start_price / 1e6 if start_price else 0
        print(f"Now trading on market: {new_market_id[:8]}...")
        if strike_usd > 0:
            print(f"Strike price: ${strike_usd:,.2f}")

    async def monitor_market_transitions(self) -> None:
        """
        Background task that polls for new markets and triggers transitions.
        Runs continuously while the bot is active.
        """
        POLL_INTERVAL = 5  # Check every 5 seconds

        while self.running:
            try:
                new_market_id, end_time, start_price = await self.get_active_market()

                # Check if market has changed
                if new_market_id != self.market_id:
                    await self.switch_to_new_market(new_market_id, start_price)

                # Log time remaining periodically
                time_remaining = end_time - int(time.time())
                if time_remaining <= 60 and time_remaining > 0:
                    print(f"Market expires in {time_remaining}s - preparing for transition...")

            except Exception as e:
                print(f"Market monitor error: {e}")

            await asyncio.sleep(POLL_INTERVAL)

    # ... Algorithm implementation ...


async def main():
    private_key = os.environ.get("TURBINE_PRIVATE_KEY")
    if not private_key:
        print("Error: Set TURBINE_PRIVATE_KEY in your .env file")
        print("Run: claude \"/turbine-setup\" to get started")
        return

    api_key_id, api_private_key = get_or_create_api_credentials()

    client = TurbineClient(
        host="https://api.turbinefi.com",
        chain_id=137,  # Polygon mainnet
        private_key=private_key,
        api_key_id=api_key_id,
        api_private_key=api_private_key,
    )

    print(f"Bot wallet address: {client.address}")

    quick_market = client.get_quick_market("BTC")
    print(f"Initial market: BTC @ ${quick_market.start_price / 1e6:,.2f}")

    bot = MarketMakerBot(client)

    try:
        await bot.switch_to_new_market(quick_market.market_id, quick_market.start_price)
        await bot.run("https://api.turbinefi.com")
    except KeyboardInterrupt:
        print("\nShutting down...")
    finally:
        bot.running = False
        if bot.market_id:
            await bot.cancel_all_orders(bot.market_id)
        client.close()
        print("Bot stopped cleanly.")


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

Step 6: Verify .env File and Install Dependencies

1. Check if .env already exists with TURBINE_PRIVATE_KEY set (non-empty value). If not, the template was already created in Step 2 — remind the user to open it and add their key manually.

2. Install dependencies by running:

pip install -e . python-dotenv httpx

Note: httpx is used by the Price Action Trader to fetch real-time BTC prices from Pyth Network.

Step 7: Explain How to Run and Deploy

Tell the user:

Your bot is ready! To run it:

  python {bot_filename}.py

The bot will:
- Automatically register API credentials on first run (saved to .env)
- Connect to the current BTC 15-minute market
- Gaslessly approve USDC on first trade per settlement (one-time max permit, no gas needed)
- Start trading based on your chosen algorithm
- Automatically switch to new markets when they start
- Track traded markets and claim winnings when they resolve

To stop the bot, press Ctrl+C.


Want to run your bot 24/7 in the cloud? Deploy to Railway (free $5 credit for 30 days):

  claude "/railway-deploy"

Core Bot Run Method

Every generated bot must include this run() method that handles WebSocket streaming with automatic market switching and winnings claiming:

async def run(self, host: str) -> None:
    """
    Main trading loop with WebSocket streaming, automatic market switching, and winnings claiming.
    """
    ws = TurbineWSClient(host)

    # Start background tasks
    monitor_task = asyncio.create_task(self.monitor_market_transitions())
    claim_task = asyncio.create_task(self.claim_resolved_markets())

    while self.running:
        try:
            # Ensure we have a current market
            if not self.market_id:
                market_id, _ = await self.get_active_market()
                await self.switch_to_new_market(market_id)

            current_market = self.market_id

            async with ws.connect() as stream:
                # Subscribe to the current market
                await stream.subscribe(current_market)
                print(f"Subscribed to market {current_market[:8]}...")

                # Place initial quotes
                await self.place_quotes()

                async for message in stream:
                    # Check if market has changed (set by monitor task)
                    if self.market_id != current_market:
                        print("Market changed, reconnecting to new market...")
                        break  # Exit inner loop to reconnect

                    if message.type == "orderbook":
                        await self.on_orderbook_update(message.orderbook)
                    elif message.type == "trade":
                        await self.on_trade(message.trade)
                    elif message.type == "order_cancelled":
                        self.on_order_cancelled(message.data)

        except WebSocketError as e:
            print(f"WebSocket error: {e}, reconnecting...")
            await asyncio.sleep(1)
        except Exception as e:
            print(f"Unexpected error: {e}")
            await asyncio.sleep(5)

    # Cleanup background tasks
    monitor_task.cancel()
    claim_task.cancel()

Algorithm Implementation Details

When generating bots, use these implementations:

Price Action Trader (Recommended)

⚠️ IMPORTANT: Use the reference implementation at examples/price_action_bot.py

Read that file for the complete, production-ready implementation. The simplified snippet below is incomplete and missing critical patterns.

Key methods in the reference implementation:

• get_current_btc_price() - Fetches BTC price from Pyth Network (async, non-blocking)
• calculate_signal() - Returns (action, confidence) based on price vs strike
• execute_signal() - Places orders with proper verification flow
• price_action_loop() - Main trading loop
• sync_position() / verify_position() - Position management
• cleanup_pending_orders() - Handles settling orders
• discover_unclaimed_markets() - Finds winnings from previous sessions

The reference implementation handles (that the snippet below does NOT):

• Async HTTP client for non-blocking price fetches
• Position verification after every order attempt
• Pending order tracking to prevent double-ordering
• Failed trade detection immediately after submission
• Market expiration flag to stop trading 60s before expiry
• Processed trade ID tracking to avoid double-counting fills
• Rate-limited claiming with 15s delays

Algorithm summary:

• Fetches current BTC price from Pyth Network (same oracle Turbine uses)
• Compares to market's strike price (stored in quick_market.start_price, 6 decimals)
• If BTC > strike by threshold → buy YES
• If BTC < strike by threshold → buy NO
• Confidence scales with distance from strike (capped at 90%)

Simple Spread Market Maker

SPREAD_BPS = 200  # 2% total spread (1% each side)

def calculate_quotes(self, mid_price):
    """Calculate bid/ask around mid price."""
    half_spread = (mid_price * SPREAD_BPS) // 20000
    bid = max(1, mid_price - half_spread)
    ask = min(999999, mid_price + half_spread)
    return bid, ask

Inventory-Aware Market Maker

SPREAD_BPS = 200
SKEW_FACTOR = 50  # BPS skew per share of inventory

def calculate_quotes(self, mid_price):
    """Skew quotes based on inventory."""
    half_spread = (mid_price * SPREAD_BPS) // 20000

    # Skew to reduce inventory
    inventory_shares = self.current_position / 1_000_000
    skew = int(inventory_shares * SKEW_FACTOR)

    bid = max(1, mid_price - half_spread - skew)
    ask = min(999999, mid_price + half_spread - skew)
    return bid, ask

Momentum Following

MOMENTUM_WINDOW = 10  # Number of trades to consider
MOMENTUM_THRESHOLD = 0.6  # 60% same direction = trend

def detect_momentum(self, recent_trades):
    """Detect market momentum from recent trades."""
    if len(recent_trades) < MOMENTUM_WINDOW:
        return None

    buys = sum(1 for t in recent_trades[-MOMENTUM_WINDOW:] if t["side"] == "BUY")
    buy_ratio = buys / MOMENTUM_WINDOW

    if buy_ratio > MOMENTUM_THRESHOLD:
        return "UP"
    elif buy_ratio < (1 - MOMENTUM_THRESHOLD):
        return "DOWN"
    return None

Mean Reversion

REVERSION_THRESHOLD = 50000  # 5% move triggers fade
LOOKBACK_TRADES = 20

def should_fade(self, current_price, recent_trades):
    """Check if price moved enough to fade."""
    if len(recent_trades) < LOOKBACK_TRADES:
        return None

    avg_price = sum(t["price"] for t in recent_trades) / len(recent_trades)
    deviation = current_price - avg_price

    if deviation > REVERSION_THRESHOLD:
        return "SELL"  # Fade the up move
    elif deviation < -REVERSION_THRESHOLD:
        return "BUY"  # Fade the down move
    return None

Probability-Weighted

EDGE_THRESHOLD = 200000  # 20% from 50% = extreme

def find_edge(self, best_bid, best_ask):
    """Look for mispriced extremes."""
    mid = (best_bid + best_ask) // 2
    distance_from_fair = abs(mid - 500000)

    if distance_from_fair > EDGE_THRESHOLD:
        if mid > 500000:
            return "SELL"  # Market too bullish
        else:
            return "BUY"  # Market too bearish
    return None

Automatic Market Transition

All generated bots automatically handle market transitions. When a BTC 15-minute market expires:

1. Detection: The bot polls every 5 seconds for new markets
2. Order Cleanup: All active orders on the expiring market are cancelled
3. Seamless Switch: The bot automatically connects to the new market
4. Continued Trading: Trading resumes on the new market without manual intervention

How it works:

• A background task (monitor_market_transitions) runs continuously
• It compares the current market ID with the active market from the API
• When a new market is detected, switch_to_new_market() handles the transition
• Positions carry over (they're wallet-based), but orders must be re-placed

Warning before expiration:

• When less than 60 seconds remain, the bot logs a warning
• Orders are cancelled proactively to avoid stuck orders on expired markets

USDC Approval

Bots use a one-time gasless max permit for USDC approval. On first trade per settlement contract, the bot signs an EIP-2612 max permit (max value, max deadline) and submits it via the relayer. No native gas is required.

def ensure_settlement_approved(self, settlement_address: str) -> None:
    """Ensure USDC is approved for this settlement contract via gasless max permit."""
    if settlement_address in self.approved_settlements:
        return

    # Check on-chain allowance
    current = self.client.get_usdc_allowance(spender=settlement_address)
    if current >= MAX_APPROVAL_THRESHOLD:  # half of max uint256
        self.approved_settlements[settlement_address] = current
        return

    # Sign and submit gasless max permit via relayer
    result = self.client.approve_usdc_for_settlement(settlement_address)
    # Wait for TX confirmation...
    self.approved_settlements[settlement_address] = 2**256 - 1

Key points:

• Call ensure_settlement_approved() when entering each new market
• Orders submitted without permit_signature field — Settlement uses existing allowance
• No native gas token required (relayer pays gas)
• One-time per settlement contract — all future orders reuse the allowance
• Order size specified in USDC terms, converted to shares based on price

Automatic Winnings Claiming

Bots must track markets they've traded in and automatically claim winnings when markets resolve.

Implementation Pattern

Add these fields to your bot class:

class MarketMakerBot:
    def __init__(self, client: TurbineClient):
        self.client = client
        self.market_id: str | None = None
        self.settlement_address: str | None = None
        self.contract_address: str | None = None  # Current market contract
        self.current_position = 0
        self.active_orders: dict[str, str] = {}
        self.running = True
        # Track markets we've traded in for claiming winnings
        # market_id -> contract_address
        self.traded_markets: dict[str, str] = {}

Track Markets When Switching

When switching to a new market, save the old market for later claiming:

async def switch_to_new_market(self, new_market_id: str, start_price: int = 0) -> None:
    """Switch liquidity to a new market.

    Args:
        new_market_id: The new market ID.
        start_price: BTC strike price (6 decimals) - used by Price Action Trader.
    """
    old_market_id = self.market_id

    # Track old market for claiming winnings later
    if old_market_id and self.contract_address:
        self.traded_markets[old_market_id] = self.contract_address
        print(f"Tracking market {old_market_id[:16]}... for winnings claim")

    if old_market_id:
        await self.cancel_all_orders()

    self.market_id = new_market_id
    self.strike_price = start_price  # Store for Price Action Trader
    self.active_orders = {}

    # Fetch settlement and contract addresses
    markets = self.client.get_markets()
    for market in markets:
        if market.id == new_market_id:
            self.settlement_address = market.settlement_address
            self.contract_address = market.contract_address
            break

    if start_price:
        print(f"Strike price: ${start_price / 1e6:,.2f}")

Background Task for Claiming

Add a background task that checks for resolved markets and claims winnings:

async def claim_resolved_markets(self) -> None:
    """Background task to claim winnings from resolved markets."""
    while self.running:
        try:
            if not self.traded_markets:
                await asyncio.sleep(30)
                continue

            markets_to_remove = []
            for market_id, contract_address in list(self.traded_markets.items()):
                try:
                    # Check if market is resolved
                    markets = self.client.get_markets()
                    market_resolved = False
                    for market in markets:
                        if market.id == market_id and market.resolved:
                            market_resolved = True
                            break

                    if market_resolved:
                        print(f"\nMarket {market_id[:16]}... has resolved!")
                        print(f"Attempting to claim winnings...")
                        try:
                            result = self.client.claim_winnings(contract_address)
                            tx_hash = result.get("txHash", result.get("tx_hash", "unknown"))
                            print(f"Winnings claimed! TX: {tx_hash}")
                            markets_to_remove.append(market_id)
                        except TurbineApiError as e:
                            if "no winnings" in str(e).lower() or "no position" in str(e).lower():
                                print(f"No winnings to claim for {market_id[:16]}...")
                                markets_to_remove.append(market_id)
                            else:
                                print(f"Failed to claim winnings: {e}")
                except Exception as e:
                    print(f"Error checking market {market_id[:16]}...: {e}")

            # Remove claimed markets from tracking
            for market_id in markets_to_remove:
                self.traded_markets.pop(market_id, None)

        except Exception as e:
            print(f"Claim monitor error: {e}")

        await asyncio.sleep(30)  # Check every 30 seconds

Start the Claim Task

In the run() method, start the claim task alongside other background tasks:

async def run(self, host: str) -> None:
    """Main trading loop with automatic market switching and winnings claiming."""
    ws = TurbineWSClient(host=host)

    # Start background tasks
    monitor_task = asyncio.create_task(self.monitor_market_transitions())
    claim_task = asyncio.create_task(self.claim_resolved_markets())

    try:
        # ... main trading loop ...
    finally:
        monitor_task.cancel()
        claim_task.cancel()

Key points:

• claim_winnings(contract_address) uses gasless EIP-712 permits
• The API handles all on-chain redemption via a relayer
• Markets are removed from tracking after successful claim or if no position exists
• Check every 30 seconds to catch resolutions promptly

Critical Patterns (from examples/price_action_bot.py)

These patterns are implemented in the reference example and MUST be preserved in all generated bots:

Position Tracking

• current_position tracks net position (YES shares - NO shares)
• sync_position() fetches from API on market switch
• verify_position() corrects internal tracking after orders

Pending Order Management

• pending_order_txs: set[str] tracks TX hashes of orders being settled
• Don't place new orders while any are pending
• cleanup_pending_orders() checks API and clears settled TXs

Trade Verification Flow

After posting an order:

1. Wait 2 seconds for processing
2. Check get_failed_trades() for immediate failures
3. Check get_pending_trades() for on-chain settlement
4. Check get_trades() for immediate fills
5. Check get_orders() for open orders
6. Call verify_position() to sync from API

Market Expiration Handling

• market_expiring flag set when <60s remaining
• Don't place new orders when flag is True
• Reset flag when switching to new market
• Clear processed_trade_ids and pending_order_txs on market switch

Winnings Discovery

• discover_unclaimed_markets() finds positions from previous sessions
• Scans all user positions for winning shares in resolved markets
• Adds discovered markets to claim tracking
• Rate-limited claiming with 15s delays between claims

Important Notes for Users

• Risk Warning: Trading involves risk. Start with small sizes.
• Testnet First: Consider testing on Base Sepolia (chain_id=84532) first.
• Monitor Positions: Always monitor your bot and have stop-loss logic.
• Market Expiration: BTC 15-minute markets expire quickly. Bots handle this automatically!
• Gas/Fees: Trading on Polygon has minimal gas costs but watch for fees.
• Continuous Operation: Bots are designed to run 24/7, switching between markets automatically.
• USDC Approval: Bots use a one-time gasless max permit per settlement contract. No native gas required.

Quick Reference

Price Scaling: Prices are 0-1,000,000 representing 0-100%

• 500000 = 50% probability
• 250000 = 25% probability

Size Scaling: Sizes use 6 decimals

• 1_000_000 = 1 share
• 500_000 = 0.5 shares

Outcome Values:

• Outcome.YES (0) = BTC ends ABOVE strike price
• Outcome.NO (1) = BTC ends BELOW strike price

Strike Price (for Price Action Trader):

• Available via quick_market.start_price (6 decimals)
• Example: 95000000000 = $95,000.00
• Current BTC price fetched from Pyth Network (same oracle Turbine uses):
  • URL: https://hermes.pyth.network/v2/updates/price/latest?ids[]=0xe62df6c8b4a85fe1a67db44dc12de5db330f7ac66b72dc658afedf0f4a415b43
  • BTC Feed ID: 0xe62df6c8b4a85fe1a67db44dc12de5db330f7ac66b72dc658afedf0f4a415b43
• If current > strike → buy YES, if current < strike → buy NO