Accessing Polymarket.com Data in Python

Recently I started exploring arbitrage opportunities in the Polymarket.com world. I quickly realized I would need faster access to the data. So I wrote some Python programs to access the platform for data analytics. The documents for doing this are located in the Polymarket docs. Here’s how it works.

Install py-clob-client

pip install py-clob-client<br><br>

Coinbase Wallet

To perform this first block of code you need a Coinbase wallet. Once you have it setup you need to export your private key.

from py_clob_client.client import ClobClient

host = "https://clob.polymarket.com"
private_key = "YOUR_PRIVATE_KEY_GOES_HERE"
chain_id = 137  # Polygon Mainnet

# Initialize the client with private key
client = ClobClient(host, key=private_key, chain_id=chain_id)

api_key_data = client.create_api_key()

print(api_key_data)

Creating keys.py

We can take this outputted data and create our keys.py which will hold our keys.For some reason, the api_passphrase and rename it api_key while commenting out the original api_key. I left the api_secret and original api_passphrase. Now that I have my keys.py I can reference it in my main code

Extracting all of the data

import csv
import json
from py_clob_client.client import ClobClient
from keys import api_key  # Import only the API key
from py_clob_client.clob_types import OpenOrderParams

# Replace with your actual host and chain ID
host = "https://clob.polymarket.com"
chain_id = 137  # Polygon Mainnet

# Initialize the client with only the host, key, and chain_id
client = ClobClient(
    host,
    key=api_key,
    chain_id=chain_id
)

# Initialize variables for pagination
markets_list = []
next_cursor = None

# Fetch all available markets using pagination
while True:
    try:
        # Print the cursor value for debugging
        print(f"Fetching markets with next_cursor: {next_cursor}")

        # Make the API call based on the cursor value
        if next_cursor is None:
            response = client.get_markets()
        else:
            response = client.get_markets(next_cursor=next_cursor)

        # Print the raw response for debugging
        print(f"API Response: {json.dumps(response, indent=2)}")

        # Check if the response is successful and contains data
        if 'data' not in response:
            print("No data found in response.")
            break

        markets_list.extend(response['data'])
        next_cursor = response.get("next_cursor")

        # Exit loop if there's no next_cursor indicating no more data to fetch
        if not next_cursor:
            break

    except Exception as e:
        # Print the exception details for debugging
        print(f"Exception occurred: {e}")
        print(f"Exception details: {e.__class__.__name__}")
        print(f"Error message: {e.args}")
        break

# Debugging step: Print out the raw data to understand its structure
print("Raw Market Data:")
print(json.dumps(markets_list, indent=2))

# Dynamically extract all keys from the markets to create the CSV columns
csv_columns = set()
for market in markets_list:
    csv_columns.update(market.keys())
    # Also include nested keys like tokens
    if 'tokens' in market:
        csv_columns.update({f"token_{key}" for token in market['tokens'] for key in token.keys()})

csv_columns = sorted(csv_columns)  # Sort columns alphabetically for consistency

# Writing to CSV
csv_file = "markets_data.csv"
try:
    with open(csv_file, 'w', newline='') as csvfile:
        writer = csv.DictWriter(csvfile, fieldnames=csv_columns)
        writer.writeheader()
        for market in markets_list:
            row = {}
            for key in csv_columns:
                # Handling nested 'tokens' structure
                if key.startswith("token_"):
                    token_key = key[len("token_"):]
                    row[key] = ', '.join([str(token.get(token_key, 'N/A')) for token in market.get('tokens', [])])
                else:
                    row[key] = market.get(key, 'N/A')
            writer.writerow(row)
    print(f"Data has been written to {csv_file} successfully.")
except IOError as e:
    print(f"Error writing to CSV: {e}")

Output CSV

If all goes as planned you will now have a giant CSV file named markets_data.csv with all the different markets and their values. You can now build on this code to manipulate it to your needs.