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Polymarket Bot Guide: How to Build Trading Bots & Automate Your Strategy (2026)

Updated March 2026 — A complete guide to building and running Polymarket bots. Learn how automated trading works on Polymarket's CLOB exchange, explore bot strategies (market making, arbitrage, signal-based), and build your first Polymarket trading bot with Python.

What Is a Polymarket Trading Bot?

A Polymarket bot (also called a Polymarket trading bot) is an automated software program that places trades on Polymarket without manual intervention. Instead of clicking buttons on the website, the bot interacts directly with Polymarket's API to monitor prices, analyze market conditions, and execute buy and sell orders based on a predefined strategy.

Polymarket bots range from simple scripts that execute a single trading rule to sophisticated systems that combine multiple data sources, machine learning models, and risk management logic. What they all share is the ability to trade faster, more consistently, and more frequently than a human could manually.

Why Use a Bot on Polymarket?

• Speed: Bots react to price changes in milliseconds, far faster than manual trading
• Consistency: No emotions, no fatigue — the bot follows its rules every time
• 24/7 operation: Prediction markets trade around the clock; bots never sleep
• Scalability: A single bot can monitor and trade hundreds of markets simultaneously
• Precision: Execute exact position sizes and prices without rounding errors
• Arbitrage capture: Exploit fleeting price differences between Polymarket and other platforms before they disappear

Automated trading is a significant part of Polymarket's ecosystem. The platform was designed with programmatic access in mind, and a substantial portion of its trading volume comes from bots and algorithmic traders. This is a feature, not a bug — bot traders provide liquidity and tighter spreads that benefit all participants.

How Polymarket Bots Work (CLOB Architecture)

To understand how Polymarket bots operate, you need to understand the Central Limit Order Book (CLOB) — the exchange mechanism that powers all trading on Polymarket.

What Is the CLOB?

Unlike earlier prediction market designs that used automated market makers (AMMs), Polymarket uses a CLOB — the same order book model used by traditional stock exchanges like NYSE and Nasdaq. Here is how it works:

1. Bids and asks: Traders submit buy orders (bids) and sell orders (asks) at specific prices
2. Order matching: When a bid price meets or exceeds an ask price, a trade executes
3. Price discovery: The best bid and best ask define the current market price
4. On-chain settlement: Matched trades settle on the Polygon blockchain using USDC

The CLOB architecture is critical for bots because it provides limit orders, visible order book depth, and precise price control — features that AMM-based markets lack. A bot can place a limit order at an exact price and wait for it to fill, rather than accepting whatever price the AMM offers.

How a Bot Interacts with the CLOB API

A Polymarket bot follows this general flow:

1. Authenticate: Sign in using a Polygon wallet private key via the py_clob_client library
2. Fetch market data: Query the Gamma API for market metadata (question, token IDs, current prices)
3. Read order book: Call the CLOB API's /book endpoint to see current bids and asks
4. Apply strategy: Run the trading logic to determine whether to buy, sell, or hold
5. Place orders: Submit signed limit orders to the CLOB API's /order endpoint
6. Monitor fills: Track which orders have been filled via the API or WebSocket
7. Manage positions: Cancel stale orders, adjust prices, and manage risk
8. Repeat: Loop back to step 2 on a configurable interval

Types of Polymarket Bots

Polymarket bots fall into several categories based on their trading strategy. Here are the most common types:

1. Market Making Bots

Market making bots continuously place both buy and sell orders around the current market price, profiting from the bid-ask spread. They provide liquidity to the market and earn a small profit on each round-trip trade.

• How it works: Place a bid at $0.48 and an ask at $0.52. If both fill, you earn $0.04 per share.
• Profit source: The bid-ask spread
• Risk: Adverse selection (trading against informed participants who know the outcome)
• Capital needs: Moderate ($500–5,000 to start)
• Complexity: Medium — requires inventory management and dynamic pricing

2. Arbitrage Bots

Arbitrage bots exploit price differences between platforms. When the same event is priced differently on Polymarket and Kalshi, the bot buys the cheaper side and sells the more expensive side to lock in a risk-free profit.

• How it works: "Will X happen?" is 60 cents on Polymarket and 65 cents on Kalshi. Buy Yes on Polymarket, buy No on Kalshi. Guaranteed profit regardless of outcome.
• Profit source: Price discrepancies across platforms
• Risk: Settlement timing, fees, and execution risk
• Capital needs: Higher ($1,000+ across multiple platforms)
• Complexity: High — requires multi-platform integration and fast execution

3. Signal-Based / Sentiment Bots

Signal-based bots trade based on external data sources — news feeds, social media sentiment, polling data, or other signals that predict how market prices should move.

• How it works: Monitor a news API for election-related headlines. When a candidate receives major positive coverage, buy their Yes shares before the market fully adjusts.
• Profit source: Faster reaction to information than the market
• Risk: False signals, model inaccuracy, being slower than other bots
• Capital needs: Variable ($100–10,000+)
• Complexity: High — requires NLP, data pipelines, and model development

4. Mean Reversion Bots

Mean reversion bots assume that prices temporarily deviate from their true value and will return. They buy when prices drop sharply and sell when prices spike.

• How it works: Track a market's 24-hour moving average. When the price drops more than 10% below the average, buy. When it rises 10% above, sell.
• Profit source: Overreactions and temporary mispricings
• Risk: The price movement may not be temporary (a genuine shift in probability)
• Capital needs: Low to moderate ($200–2,000)
• Complexity: Low to medium

5. Portfolio Rebalancing Bots

Rebalancing bots maintain a target allocation across multiple prediction markets. When one position grows or shrinks relative to others, the bot trades to restore the target weights.

• How it works: Maintain equal exposure across 10 markets. When any position drifts more than 5% from target, rebalance.
• Profit source: Systematic diversification and discipline
• Risk: Correlated market movements, transaction costs from frequent rebalancing
• Capital needs: Moderate to high ($1,000+ for meaningful diversification)
• Complexity: Low

How to Build a Simple Polymarket Bot (Python)

This section walks through building a basic Polymarket trading bot using Python and the official py_clob_client library. We will build a simple market making bot that places buy and sell orders around the current midpoint price.

Prerequisites

• Python 3.9 or later
• A Polygon wallet with a private key (MetaMask or any EVM wallet)
• USDC funded on Polygon (start with $50–100 for testing)
• Basic understanding of the Polymarket API

Step 1: Install Dependencies

# Install the official Polymarket CLOB client and dependencies
pip install py_clob_client requests python-dotenv

Step 2: Configure Environment Variables

# Create a .env file (NEVER commit this to version control)
POLYMARKET_PRIVATE_KEY=your_polygon_wallet_private_key_here
POLYMARKET_HOST=https://clob.polymarket.com
CHAIN_ID=137

Step 3: Bot Initialization

import os
import time
import json
import requests
from dotenv import load_dotenv
from py_clob_client.client import ClobClient
from py_clob_client.clob_types import OrderArgs, OrderType

load_dotenv()

class PolymarketBot:
    """A simple Polymarket market making bot."""

    def __init__(self):
        self.host = os.getenv("POLYMARKET_HOST", "https://clob.polymarket.com")
        self.key = os.getenv("POLYMARKET_PRIVATE_KEY")
        self.chain_id = int(os.getenv("CHAIN_ID", "137"))
        self.gamma_api = "https://gamma-api.polymarket.com"

        if not self.key:
            raise ValueError("POLYMARKET_PRIVATE_KEY not set in environment")

        # Initialize the CLOB client
        self.client = ClobClient(
            host=self.host,
            key=self.key,
            chain_id=self.chain_id
        )

        # Derive API credentials
        self.client.set_api_creds(
            self.client.create_or_derive_api_creds()
        )

        print("Bot initialized successfully.")

    def get_midpoint(self, token_id: str) -> float:
        """Fetch the current midpoint price for a token."""
        response = requests.get(
            f"{self.host}/midpoint",
            params={"token_id": token_id}
        )
        response.raise_for_status()
        data = response.json()
        return float(data.get("mid", 0))

    def get_order_book(self, token_id: str) -> dict:
        """Fetch the full order book for a token."""
        response = requests.get(
            f"{self.host}/book",
            params={"token_id": token_id}
        )
        response.raise_for_status()
        return response.json()

    def get_spread(self, token_id: str) -> dict:
        """Calculate the current bid-ask spread."""
        book = self.get_order_book(token_id)
        best_bid = float(book["bids"][0]["price"]) if book.get("bids") else 0
        best_ask = float(book["asks"][0]["price"]) if book.get("asks") else 1
        mid = (best_bid + best_ask) / 2
        spread = best_ask - best_bid

        return {
            "best_bid": best_bid,
            "best_ask": best_ask,
            "mid": mid,
            "spread": spread,
            "spread_pct": (spread / mid * 100) if mid > 0 else 0
        }

    def place_limit_order(self, token_id: str, price: float,
                          size: float, side: str) -> dict:
        """Place a limit order on Polymarket."""
        order_args = OrderArgs(
            price=price,
            size=size,
            side=side,
            token_id=token_id
        )

        signed_order = self.client.create_order(order_args)
        response = self.client.post_order(signed_order, OrderType.GTC)

        print(f"  Order placed: {side} {size} @ ${price:.4f}")
        return response

    def cancel_all_orders(self):
        """Cancel all open orders."""
        open_orders = self.client.get_orders()
        for order in open_orders:
            self.client.cancel(order_id=order["id"])
        print(f"  Cancelled {len(open_orders)} open orders.")

Step 4: Market Making Strategy

    def run_market_maker(self, token_id: str, spread_width: float = 0.04,
                          order_size: float = 50, interval: int = 30):
        """
        Simple market making strategy.

        Places a bid below the midpoint and an ask above it,
        earning the spread when both sides fill.

        Args:
            token_id: The Polymarket token ID to trade
            spread_width: Total spread width (e.g., 0.04 = 4 cents)
            order_size: Number of shares per order
            interval: Seconds between order refreshes
        """
        half_spread = spread_width / 2

        print(f"Starting market maker for token {token_id[:20]}...")
        print(f"Spread: {spread_width:.2f} | Size: {order_size} | Interval: {interval}s")
        print("-" * 60)

        try:
            while True:
                # Cancel existing orders
                self.cancel_all_orders()

                # Get current midpoint
                mid = self.get_midpoint(token_id)
                if mid <= 0.01 or mid >= 0.99:
                    print(f"  Midpoint {mid:.4f} too extreme. Skipping.")
                    time.sleep(interval)
                    continue

                # Calculate bid and ask prices
                bid_price = round(max(0.01, mid - half_spread), 2)
                ask_price = round(min(0.99, mid + half_spread), 2)

                print(f"  Mid: ${mid:.4f} | Bid: ${bid_price:.2f} | Ask: ${ask_price:.2f}")

                # Place orders
                self.place_limit_order(token_id, bid_price, order_size, "BUY")
                self.place_limit_order(token_id, ask_price, order_size, "SELL")

                # Wait before refreshing
                time.sleep(interval)

        except KeyboardInterrupt:
            print("\nStopping bot...")
            self.cancel_all_orders()
            print("Bot stopped. All orders cancelled.")

Step 5: Run the Bot

# main.py
if __name__ == "__main__":
    bot = PolymarketBot()

    # Replace with an actual token_id from the Gamma API
    # You can find token IDs at:
    # https://gamma-api.polymarket.com/markets?active=true&limit=10
    TOKEN_ID = "YOUR_YES_TOKEN_ID_HERE"

    bot.run_market_maker(
        token_id=TOKEN_ID,
        spread_width=0.04,   # 4-cent spread
        order_size=50,        # 50 shares per side
        interval=30           # Refresh every 30 seconds
    )

Finding Token IDs for Your Bot

Your bot needs a token_id to trade. Here is how to find one:

import requests
import json

def find_market_tokens(search_query: str):
    """Search for Polymarket markets and return their token IDs."""
    response = requests.get(
        "https://gamma-api.polymarket.com/markets",
        params={"limit": 20, "active": "true", "order": "volume24hr", "ascending": "false"}
    )
    markets = response.json()

    for market in markets:
        question = market.get("question", "")
        if search_query.lower() in question.lower():
            tokens = market.get("tokens", [])
            print(f"Market: {question}")
            for token in tokens:
                print(f"  {token['outcome']}: token_id = {token['token_id']}")
            print()

# Example: Find Bitcoin-related markets
find_market_tokens("bitcoin")

Popular Polymarket Bot Frameworks & Tools

You do not have to build everything from scratch. Several frameworks and tools exist to accelerate Polymarket bot development.

Official Libraries

Community Frameworks

Supporting Tools

• pandas / numpy: Data analysis and strategy backtesting in Python
• websockets (Python): Real-time order book streaming via the CLOB WebSocket
• APScheduler: Python job scheduling for periodic bot tasks
• Grafana + InfluxDB: Dashboard and monitoring for bot performance metrics
• Sentry / Datadog: Error tracking and alerting for production bots
• PredScope: Aggregated market data across Polymarket and Kalshi for cross-platform strategies

import asyncio
import websockets
import json

async def stream_order_book(token_id: str):
    """Stream real-time order book updates via WebSocket."""
    uri = "wss://ws-subscriptions-clob.polymarket.com/ws/market"

    async with websockets.connect(uri) as ws:
        # Subscribe to order book updates
        subscribe_msg = {
            "type": "subscribe",
            "channel": "market",
            "assets_ids": [token_id]
        }
        await ws.send(json.dumps(subscribe_msg))
        print(f"Subscribed to order book for {token_id[:20]}...")

        async for message in ws:
            data = json.loads(message)
            event_type = data.get("event_type", "")

            if event_type == "book":
                bids = data.get("bids", [])
                asks = data.get("asks", [])
                best_bid = bids[0]["price"] if bids else "N/A"
                best_ask = asks[0]["price"] if asks else "N/A"
                print(f"Book update: Bid {best_bid} | Ask {best_ask}")

            elif event_type == "trade":
                price = data.get("price", "N/A")
                size = data.get("size", "N/A")
                side = data.get("side", "N/A")
                print(f"Trade: {side} {size} @ {price}")

# Run the stream
# asyncio.run(stream_order_book("YOUR_TOKEN_ID"))

Polymarket Bot Strategies

Here are detailed breakdowns of the most popular and profitable Polymarket bot strategies, including implementation considerations and example code.

Strategy 1: Market Making

Market making is the most common Polymarket bot strategy. The bot continuously quotes both sides of the market — offering to buy at a slightly lower price and sell at a slightly higher price. The difference (the spread) is profit.

Key Parameters for Market Making

The key challenge with market making is adverse selection — when someone trades against you because they know something you do not. For example, if a news event resolves a market and you have stale quotes, you will fill orders at the wrong price. Sophisticated market makers dynamically widen their spreads when volatility increases and narrow them when the market is calm.

def dynamic_spread(self, token_id: str, base_spread: float = 0.04) -> float:
    """Widen the spread when volatility is high."""
    # Get recent trades to estimate volatility
    response = requests.get(
        f"{self.host}/trades",
        params={"token_id": token_id, "limit": 50}
    )
    trades = response.json()

    if len(trades) < 10:
        return base_spread

    # Calculate price variance from recent trades
    prices = [float(t["price"]) for t in trades]
    mean_price = sum(prices) / len(prices)
    variance = sum((p - mean_price) ** 2 for p in prices) / len(prices)

    # Scale spread with volatility
    volatility_factor = min(3.0, max(1.0, variance * 1000))
    adjusted_spread = base_spread * volatility_factor

    return round(adjusted_spread, 4)

Strategy 2: Cross-Platform Arbitrage (Polymarket vs. Kalshi)

Cross-platform arbitrage is one of the most attractive bot strategies because it can be genuinely risk-free (in theory). When the same event is priced differently on Polymarket and Kalshi, you can lock in a profit regardless of the outcome.

import requests
import json

def find_cross_platform_arb():
    """
    Scan for arbitrage between Polymarket and Kalshi.
    Compares prices on matching markets across platforms.
    """
    # Fetch Polymarket markets
    poly_markets = requests.get(
        "https://gamma-api.polymarket.com/markets",
        params={"limit": 200, "active": "true"}
    ).json()

    opportunities = []

    for pm in poly_markets:
        prices = json.loads(pm.get("outcomePrices", "[]"))
        if len(prices) < 2:
            continue

        poly_yes = float(prices[0])
        poly_no = float(prices[1])

        # In a real bot, you would match this market against Kalshi's
        # API using keyword matching or a manual mapping table.
        # Here we check for internal arbitrage (Yes + No != 1.00)
        total = poly_yes + poly_no
        if total < 0.97:  # Potential arb if sum is significantly under 1
            opportunities.append({
                "market": pm.get("question", "Unknown"),
                "poly_yes": poly_yes,
                "poly_no": poly_no,
                "total_cost": total,
                "profit_per_share": round(1.0 - total, 4)
            })

    # For cross-platform arb, you would compare:
    # poly_yes + kalshi_no < 1.00  (buy Yes on Poly, No on Kalshi)
    # poly_no + kalshi_yes < 1.00  (buy No on Poly, Yes on Kalshi)

    opportunities.sort(key=lambda x: x["profit_per_share"], reverse=True)
    return opportunities

arb_opps = find_cross_platform_arb()
for opp in arb_opps[:5]:
    print(f"{opp['market'][:60]}")
    print(f"  Cost: ${opp['total_cost']:.4f} | Profit: ${opp['profit_per_share']:.4f}/share")
    print()

For a deep dive into arbitrage strategies, see our prediction market arbitrage guide.

Strategy 3: Sentiment-Based Trading

Sentiment bots monitor external data sources and trade before the market fully prices in new information. Common signal sources include:

• News APIs: NewsAPI, GDELT, or Google News for breaking event detection
• Social media: Twitter/X sentiment analysis for political and crypto markets
• Polling data: FiveThirtyEight, RealClearPolitics for election markets
• On-chain data: Blockchain analytics for crypto-related markets
• Weather APIs: For weather-related prediction markets

import requests
import json

class SentimentBot:
    """A simplified sentiment-based trading bot."""

    def __init__(self, news_api_key: str, polymarket_bot):
        self.news_api_key = news_api_key
        self.bot = polymarket_bot
        self.base_url = "https://newsapi.org/v2"

    def get_sentiment_score(self, query: str) -> float:
        """
        Fetch recent news and compute a simple sentiment score.
        Returns a value between -1 (very negative) and 1 (very positive).
        """
        response = requests.get(
            f"{self.base_url}/everything",
            params={
                "q": query,
                "sortBy": "publishedAt",
                "pageSize": 20,
                "apiKey": self.news_api_key
            }
        )
        articles = response.json().get("articles", [])

        if not articles:
            return 0.0

        # Simple keyword-based sentiment (replace with NLP model in production)
        positive_words = {"win", "surge", "gain", "rise", "lead", "ahead", "success"}
        negative_words = {"lose", "fall", "drop", "crash", "fail", "behind", "defeat"}

        score = 0
        for article in articles:
            text = (article.get("title", "") + " " + article.get("description", "")).lower()
            score += sum(1 for w in positive_words if w in text)
            score -= sum(1 for w in negative_words if w in text)

        # Normalize to [-1, 1]
        max_possible = len(articles) * len(positive_words)
        return max(-1, min(1, score / max(1, max_possible)))

    def trade_on_sentiment(self, token_id: str, query: str,
                            threshold: float = 0.3, size: float = 25):
        """
        Trade based on news sentiment.
        Buy if sentiment is strongly positive, sell if strongly negative.
        """
        sentiment = self.get_sentiment_score(query)
        mid = self.bot.get_midpoint(token_id)

        print(f"Sentiment for '{query}': {sentiment:.3f} | Market mid: ${mid:.4f}")

        if sentiment > threshold and mid < 0.85:
            print(f"  -> Strong positive sentiment. Buying {size} shares.")
            self.bot.place_limit_order(token_id, round(mid + 0.01, 2), size, "BUY")
        elif sentiment < -threshold and mid > 0.15:
            print(f"  -> Strong negative sentiment. Selling {size} shares.")
            self.bot.place_limit_order(token_id, round(mid - 0.01, 2), size, "SELL")
        else:
            print(f"  -> Neutral sentiment. No trade.")

For more on trading strategies, see our prediction market strategies guide.

Risks & Considerations

Running a Polymarket bot involves real financial risk. Before deploying capital, understand these key risks:

1. Smart Contract Risk

Polymarket runs on the Polygon blockchain. Your funds interact with smart contracts that could contain bugs or be exploited. While Polymarket's contracts have been audited, no smart contract is guaranteed to be bug-free. A critical vulnerability could result in loss of funds.

2. API and Infrastructure Risk

Your bot depends on Polymarket's API being available and functioning correctly. Risks include:

• API downtime: If the API goes offline, your bot cannot cancel stale orders or place new ones
• Stuck orders: Orders placed before an outage may fill at unfavorable prices
• Rate limiting: Aggressive bots may get rate-limited, causing missed opportunities
• API changes: Polymarket could change endpoints or authentication without notice

3. Slippage and Liquidity Risk

Many Polymarket markets have thin order books. A large order may move the price significantly (slippage), or you may not be able to exit a position at your desired price. Low-liquidity markets amplify this risk. Always check the order book depth before placing large orders.

4. Adverse Selection

Market making bots face adverse selection — the risk that your counterparty knows more than you. When a news event breaks that resolves a market, informed traders will immediately trade against your stale quotes. This is the primary way market making bots lose money.

5. Regulatory Risk

Polymarket operates offshore and is not regulated by US financial authorities (unlike Kalshi, which is CFTC-regulated). Regulatory changes could affect Polymarket's operations, your ability to access the platform, or the legality of prediction market trading in your jurisdiction.

6. Bot Code Bugs

The most common cause of bot losses is bugs in your own code. A misplaced decimal, incorrect order side, or missing error handler can lead to catastrophic losses. Test extensively before deploying real capital.

Legal Considerations for Bot Trading

The legality of running a Polymarket trading bot depends on your jurisdiction and the specific nature of your trading activity. Here are the key legal considerations:

Is Bot Trading Allowed on Polymarket?

Yes. Polymarket explicitly supports automated trading through its CLOB API. The platform was designed for programmatic access, and bot trading is both permitted and encouraged. There are no terms of service provisions prohibiting automated trading.

Jurisdictional Considerations

• United States: Polymarket settled with the CFTC in 2022 and currently blocks US-based users from trading on most markets. US residents should use CFTC-regulated alternatives like Kalshi for legal prediction market trading.
• European Union: Prediction market regulation varies by country. Some EU nations treat them as gambling, others as financial instruments. Check your local laws.
• United Kingdom: Prediction markets may fall under gambling or financial services regulation depending on the structure.
• Other jurisdictions: Many countries have no specific prediction market regulation. Polymarket is generally accessible from most non-US locations.

Tax Obligations

Bot trading profits are taxable income in most jurisdictions. Prediction market gains may be classified as:

• Capital gains: In jurisdictions that treat prediction market shares as financial assets
• Gambling winnings: In jurisdictions that classify prediction markets as gambling
• Ordinary income: For frequent/professional traders in some jurisdictions

Keep detailed records of all trades for tax reporting. Your bot should log every order, fill, and cancellation with timestamps and prices.

Market Manipulation

While prediction markets have fewer regulations than traditional financial markets, some forms of market manipulation may still carry legal risk:

• Wash trading: Trading with yourself to inflate volume is prohibited on most platforms
• Spoofing: Placing orders you intend to cancel to manipulate prices
• Insider trading: Trading on material non-public information about market outcomes

Build your bot to trade honestly and transparently. Legitimate strategies like market making, arbitrage, and signal-based trading are perfectly acceptable.

Best Practices for Running a Polymarket Bot

Based on the experience of successful Polymarket bot operators, here are the best practices for building and running automated trading systems:

Development Best Practices

1. Use environment variables for secrets. Never hardcode private keys or API credentials in source code.
2. Implement comprehensive logging. Log every API call, order placement, fill, and error with timestamps.
3. Build a dry-run mode. The bot should be able to run its full logic and log what it would do without actually placing orders.
4. Write unit tests. Test your strategy logic with historical data before deploying.
5. Handle all error cases. API timeouts, 429 responses, malformed data, network failures — your bot must handle them all gracefully.
6. Use typed data structures. Python dataclasses or Pydantic models prevent subtle bugs from dictionary key typos.

Operational Best Practices

1. Deploy on a cloud server. AWS, DigitalOcean, or Hetzner provide reliable 24/7 uptime. A $5–20/month VPS is sufficient for most bots.
2. Set up monitoring and alerts. Use Telegram or Slack webhooks to notify you of errors, large fills, or unusual market conditions.
3. Implement a kill switch. A fast way to stop the bot and cancel all open orders immediately.
4. Track performance metrics. P&L, fill rate, spread captured, inventory levels, and Sharpe ratio over time.
5. Review and adjust weekly. Markets change. Review your bot's performance weekly and adjust parameters.

Capital Management

1. Never risk more than you can afford to lose. Bot trading is risky. Treat your bot capital as high-risk allocation.
2. Start with minimum viable capital. Prove the strategy works with $100 before scaling to $1,000+.
3. Diversify across markets. Do not concentrate all capital in a single market.
4. Keep reserves. Maintain a USDC buffer above what the bot needs for orders.
5. Set daily loss limits. If the bot loses more than X% in a day, shut it down and investigate.

Frequently Asked Questions

Related Guides

• Polymarket API Guide — Complete developer tutorial with Python and JavaScript examples
• Prediction Market Strategies — Proven approaches to profitable trading
• Prediction Market Arbitrage — Find risk-free profit opportunities across platforms
• Polymarket Fees Explained — Complete breakdown of all trading costs
• Best Prediction Markets 2026 — Top platforms ranked and compared
• How to Trade on Polymarket — Step-by-step trading guide
• How to Make Money on Prediction Markets — Strategies and tips
• Crypto Prediction Markets — BTC, ETH, and crypto trading markets
• Prediction Market API Overview — Compare APIs across all major platforms
• Prediction Market Glossary — 40+ key terms explained

See also: Kalshi API — learn more about Kalshi API.