Prediction Markets

Overview

Build tools for prediction market platforms — fetch data, analyze markets, create dashboards, and implement trading strategies. Cover Polymarket, Kalshi, Manifold, and Metaculus APIs.

Instructions

Platform overview

Platform     | Type              | Markets          | API       | Trading
-------------|-------------------|------------------|-----------|--------
Polymarket   | Crypto (Polygon)  | Binary/Multi     | REST+WS   | CLOB
Kalshi       | Regulated (US)    | Binary events    | REST+WS   | CLOB
Manifold     | Play money + Mana | Any question     | REST       | AMM
Metaculus    | Forecasting       | Probability est. | REST       | No trading

Polymarket API

Polymarket is the largest by volume. Data is publicly accessible without authentication.

# polymarket_client.py

import requests

GAMMA_API = "https://gamma-api.polymarket.com"
CLOB_API = "https://clob.polymarket.com"

def get_active_markets(limit: int = 100, offset: int = 0) -> list:
    """Fetch active markets sorted by 24h volume."""
    resp = requests.get(f"{GAMMA_API}/events", params={
        "limit": limit, "offset": offset,
        "active": True, "closed": False,
        "order": "volume24hr", "ascending": False
    })
    return resp.json()

def get_market_prices(condition_id: str) -> dict:
    """Get current prices (probabilities) for a market's outcomes."""
    return requests.get(f"{CLOB_API}/prices",
                        params={"token_ids": condition_id}).json()

def get_market_history(condition_id: str, interval: str = "1d") -> list:
    """Fetch price history for a market."""
    resp = requests.get(f"{CLOB_API}/prices-history", params={
        "market": condition_id, "interval": interval, "fidelity": 60
    })
    return resp.json().get("history", [])

Market analysis

# market_analyzer.py

def find_arbitrage_opportunities(markets: list, threshold: float = 0.02) -> list:
    """Find markets where outcome probabilities don't sum to ~1.0."""
    opportunities = []
    for market in markets:
        outcomes = market.get('outcomes', [])
        if len(outcomes) == 2:
            total = sum(float(o.get('price', 0)) for o in outcomes)
            if abs(total - 1.0) > threshold:
                opportunities.append({
                    'title': market['title'],
                    'deviation': abs(total - 1.0),
                    'volume_24h': market.get('volume24hr', 0)
                })
    return sorted(opportunities, key=lambda x: x['deviation'], reverse=True)

def calculate_expected_value(probability: float, buy_price: float,
                             fees: float = 0.02) -> float:
    """Calculate EV of a prediction market position."""
    cost = buy_price * (1 + fees)
    return probability * (1.0 - cost) - (1 - probability) * cost

Kalshi API

Kalshi is CFTC-regulated (US-accessible). Requires authentication:

# kalshi_client.py

KALSHI_API = "https://trading-api.kalshi.com/trade-api/v2"

class KalshiClient:
    def __init__(self, email: str, password: str):
        self.session = requests.Session()
        resp = self.session.post(f"{KALSHI_API}/login",
                                 json={"email": email, "password": password})
        self.session.headers["Authorization"] = f"Bearer {resp.json()['token']}"

    def get_events(self, status: str = "open", limit: int = 100) -> list:
        return self.session.get(f"{KALSHI_API}/events",
                                params={"status": status, "limit": limit}).json().get("events", [])

    def get_orderbook(self, ticker: str) -> dict:
        return self.session.get(f"{KALSHI_API}/orderbook",
                                params={"ticker": ticker}).json()

    def place_order(self, ticker: str, side: str, count: int, price: int) -> dict:
        return self.session.post(f"{KALSHI_API}/portfolio/orders", json={
            "ticker": ticker, "side": side, "count": count, "type": "limit",
            "yes_price": price if side == "yes" else None,
            "no_price": price if side == "no" else None
        }).json()

Manifold Markets API

Play money — great for experimenting, no auth required for reading:

MANIFOLD_API = "https://api.manifold.markets/v0"

def search_markets(query: str, limit: int = 20) -> list:
    return requests.get(f"{MANIFOLD_API}/search-markets",
                        params={"term": query, "limit": limit, "sort": "liquidity"}).json()

Dashboard building

Key visualizations for a prediction market dashboard:

Market cards: Title, probability (color-coded), 24h volume, time to resolution
Probability timeline: Line chart showing momentum over time
Volume bars: 24h volume history showing market attention
Alerts: Markets where probability moved >10% in 24 hours

# market_scorer.py — Score markets for dashboard prominence

def score_market(market: dict) -> float:
    score = 0.0
    volume = market.get('volume24hr', 0)
    prob = market.get('probability', 0.5)

    if volume > 100000: score += 30
    elif volume > 10000: score += 20
    elif volume > 1000: score += 10

    uncertainty = 1 - abs(prob - 0.5) * 2  # 1.0 at 50%, 0.0 at extremes
    score += uncertainty * 25

    prob_change = abs(market.get('probability_change_24h', 0))
    if prob_change > 0.10: score += 20
    elif prob_change > 0.05: score += 10

    return min(score, 100)

Examples

Build a prediction market dashboard

Build a real-time dashboard showing the top 50 Polymarket events sorted by 24-hour volume. Show each market as a card with: title, current probability (color-coded red-green), volume, time to resolution, and 7-day probability chart. Group by category (politics, crypto, sports, tech). Add alerts for markets where probability moved more than 10% in the last 24 hours. Use React and Chart.js.

Find mispriced prediction markets

Analyze all active Polymarket binary markets. Find markets where the Yes + No prices deviate more than 3% from $1.00 (indicating potential mispricing). Also find markets where the probability has been stable for weeks but a relevant news event just occurred. Output a ranked list of opportunities with expected value calculations.

Build a forecasting accuracy tracker

Build a system that tracks my prediction market bets across Polymarket and Kalshi, calculates my Brier score over time, and shows a calibration chart (predicted probabilities vs actual outcomes). Include position-size-weighted returns and compare my accuracy against the market's consensus probabilities.

Guidelines

Always check market liquidity (24h volume) before placing trades — low-liquidity markets have wide spreads
In binary markets, verify Yes + No prices sum to ~$1.00; deviations indicate mispricing or fees
Use Manifold (play money) for strategy testing before deploying capital on Polymarket or Kalshi
Compare your forecasts against market consensus to measure calibration over time
Monitor for >10% probability swings in 24 hours — these often signal new information or mispricing
Be aware that Polymarket is crypto-based (Polygon) while Kalshi is CFTC-regulated with different rules
Calculate expected value before every trade; don't trade based on conviction alone

prediction-markets