How to Use Polymarket Prediction Market Data as Quant Signals

Why Prediction Markets Matter

Prediction Market Probability Data

Traditionally, macro event risk measurement relied on polls, analyst forecasts, or option-implied probabilities. However, these methods are often slow, biased, or difficult to interpret.

Prediction Markets differ. Since real money is involved, participants incorporate their knowledge into prices. For example, a price indicating “63% chance that A wins the US election” reflects a consensus from thousands of bettors.

Polymarket is currently the largest decentralized prediction market platform. Built on Polygon blockchain, it provides real-time probabilities on various events related to politics, economics, cryptocurrencies, technology, and sports.

Why It’s Useful for Quants

1. Real-Time Macro Event Probabilities

Events like Fed rate decisions, US elections, or regulatory changes greatly impact BTC and stock markets. Quantifying the actual likelihood of these events is challenging.

Polymarket allows direct access to these probabilities:

“Likelihood of Fed rate cut at upcoming FOMC” → Current price = market’s consensus probability
“Probability that BTC will exceed $100K by year-end” → Market consensus among bettors

By collecting these probability time series data, you can track changes in market sentiment.

2. More Intuitive Than Implied Option Probabilities

CME FedWatch similarly displays rate hike/cut probabilities but infers them from derivative prices, which can be complex to interpret. Polymarket offers direct bets on binary outcomes (“Yes/No”), making the probabilities straightforward.

3. Public Sentiment vs. Smart Money

Polymarket prices reflect “money on opinions.” They may differ from social media sentiment, and this divergence can present trading opportunities.

Collecting Data from Polymarket

Using CLOB API

Polymarket provides a CLOB (Central Limit Order Book) API to access real-time market data, order books, and trade history.

import requests
import pandas as pd

# Polymarket CLOB API
BASE_URL = "https://clob.polymarket.com"

def get_markets(tag: str = None) -> list:
    """Retrieve list of active markets"""
    params = {}
    if tag:
        params['tag'] = tag
    resp = requests.get(f"{BASE_URL}/markets", params=params)
    return resp.json()

def get_market_price(condition_id: str) -> dict:
    """Get current price (probability) of a specific market"""
    resp = requests.get(f"{BASE_URL}/price", 
                        params={"token_id": condition_id})
    return resp.json()

# Example: get crypto-related markets
crypto_markets = get_markets(tag="crypto")
for m in crypto_markets[:5]:
    print(f"{m['question']}: {m.get('outcomePrices', 'N/A')}")

Collecting Time Series Data

The change over time of probabilities is crucial. For example, if yesterday’s probability was 40% and today jumps to 65%, new information has entered the market.

import time
from datetime import datetime

def collect_price_timeseries(condition_id: str, 
                               interval_min: int = 60, 
                               duration_hours: int = 24):
    """Collect market price data at regular intervals"""
    data = []
    iterations = (duration_hours * 60) // interval_min
    for _ in range(iterations):
        price = get_market_price(condition_id)
        data.append({
            'timestamp': datetime.now(),
            'price': float(price.get('price', 0)),
            'condition_id': condition_id
        })
        time.sleep(interval_min * 60)
    return pd.DataFrame(data)

Using as Quant Signals

1. Event Risk Hedge Signal

If the probability of a Fed rate cut exceeds 70%, it may signal reduced risk for a BTC long position. Conversely, if the probability drops sharply, it could be a signal to reduce holdings.

def fed_cut_signal(cut_probability: float) -> str:
    """Signal based on Fed rate cut probability"""
    if cut_probability > 0.75:
        return "RISK_ON"  # Near certainty of cut → risk assets favorable
    elif cut_probability < 0.30:
        return "RISK_OFF"  # Low likelihood of cut → risk assets unfavorable
    else:
        return "NEUTRAL"

2. Sentiment Momentum

Rather than absolute probability values, the rate of change is more informative. Monitoring a 3-day moving average versus current probabilities helps identify rapid shifts.

def sentiment_momentum(prices: pd.Series, window: int = 3) -> pd.Series:
    """Calculate sentiment momentum based on probability changes"""
    ma = prices.rolling(window).mean()
    momentum = prices - ma
    return momentum

If the momentum exceeds +0.15, it suggests “new information is rapidly being reflected in the market.”

3. Correlation with BTC Price

Track real-time correlation between specific Polymarket market prices (e.g., “Likelihood of US crypto regulation easing”) and BTC price movements.

from scipy.stats import spearmanr

def btc_polymarket_correlation(btc_returns: pd.Series, 
                                   poly_changes: pd.Series,
                                   window: int = 20) -> pd.Series:
    """Rolling Spearman correlation between BTC returns and Polymarket probability changes"""
    correlations = []
    for i in range(window, len(btc_returns)):
        corr, _ = spearmanr(
btc_returns[i - window:i],
                         poly_changes[i - window:i])
        correlations.append(corr)
    return pd.Series(correlations)

Cautions

Liquidity

Not all markets have deep liquidity. Low-volume markets can be distorted by a few bettors. Always check trading volume and order book depth when using signals.

Regulations

Polymarket restricts US residents (CFTC regulation). Check availability in Korea. Data collection and analysis are possible via publicly available API.

Prediction Accuracy

Prediction markets are not infallible. For example, US elections in 2016 and 2024 saw probabilities diverge from actual outcomes. The “market consensus” differs from the “truth”.

Summary

Polymarket provides a new data source for quants: real-time macro event probabilities, sentiment change rates, and correlations with other assets like BTC. While useful standalone, integrating with on-chain data and technical analysis as complementary indicators is recommended.

Start exploring Polymarket here — check out prediction market data directly.