pmxt 2.43.3

Unified prediction market data API - The ccxt for prediction markets

PMXT Python SDK

A unified Python interface for interacting with multiple prediction market exchanges (Kalshi, Polymarket).

Note: This SDK requires the PMXT sidecar server to be running. See Installation below.

Installation

pip install pmxt

Requirements: Python >= 3.8. The sidecar server is bundled automatically via the pmxt-core dependency -- no separate install needed.

Quick Start

import pmxt

# Initialize exchanges (server starts automatically!)
poly = pmxt.Polymarket()
kalshi = pmxt.Kalshi()

# Search for markets
markets = poly.fetch_markets(query="Trump")
print(markets[0].title)

# Get outcome details
outcome = markets[0].outcomes[0]
print(f"{outcome.label}: {outcome.price * 100:.1f}%")

# Fetch historical data (use outcome.outcome_id!)
candles = poly.fetch_ohlcv(
    outcome.outcome_id,
    resolution="1d",
    limit=30
)

# Get current order book
order_book = poly.fetch_order_book(outcome.outcome_id)
spread = order_book.asks[0].price - order_book.bids[0].price
print(f"Spread: {spread * 100:.2f}%")

How It Works

The Python SDK automatically manages the PMXT sidecar server:

1. First API call: Checks if server is running
2. Auto-start: Starts server if needed (takes ~1-2 seconds)
3. Reuse: Multiple Python processes share the same server
4. Zero config: Just import and use!

Manual Server Control (Optional)

If you prefer to manage the server yourself:

# Disable auto-start
poly = pmxt.Polymarket(auto_start_server=False)

# Or start the server manually in a separate terminal
# $ pmxt-server

Authentication (for Trading)

Polymarket

Requires your Polygon Private Key:

import os
import pmxt

poly = pmxt.Polymarket(
    private_key=os.getenv("POLYMARKET_PRIVATE_KEY"),
    proxy_address=os.getenv("POLYMARKET_PROXY_ADDRESS"),  # Optional
    # signature_type='gnosis-safe' (default)
)

# Check balance
balances = poly.fetch_balance()
print(f"Available: ${balances[0].available}")

# Place order (using outcome shorthand)
markets = poly.fetch_markets(query="Trump")
order = poly.create_order(
    outcome=markets[0].yes,
    side="buy",
    type="limit",
    amount=10,
    price=0.55
)

Kalshi

Requires API Key and Private Key:

import os
import pmxt

kalshi = pmxt.Kalshi(
    api_key=os.getenv("KALSHI_API_KEY"),
    private_key=os.getenv("KALSHI_PRIVATE_KEY"),
)

# Check positions
positions = kalshi.fetch_positions()
for pos in positions:
    print(f"{pos.outcome_label}: ${pos.unrealized_pnl:.2f}")

Limitless

Requires Private Key:

import os
import pmxt

limitless = pmxt.Limitless(
    private_key=os.getenv("LIMITLESS_PRIVATE_KEY")
)

# Check balance
balances = limitless.fetch_balance()
print(f"Available: ${balances[0].available}")

API Reference

Market Data Methods

• fetch_markets(params?) - Get active markets

  # Fetch recent markets
  poly.fetch_markets(limit=20, sort='volume')
  
  # Search by text
  poly.fetch_markets(query='Fed rates', limit=10)
  
  # Fetch by slug/ticker
  poly.fetch_markets(slug='who-will-trump-nominate-as-fed-chair')
  

• filter_markets(markets, query) - Filter markets by keyword
• fetch_ohlcv(outcome_id, params) - Get historical price candles
• fetch_order_book(outcome_id) - Get current order book
• fetch_trades(outcome_id, params) - Get trade history
• get_execution_price(order_book, side, amount) - Get execution price
• get_execution_price_detailed(order_book, side, amount) - Get detailed execution info

Trading Methods (require authentication)

• create_order(params) - Place a new order
• cancel_order(order_id) - Cancel an open order
• fetch_order(order_id) - Get order details
• fetch_open_orders(market_id?) - Get all open orders

Account Methods (require authentication)

• fetch_balance() - Get account balance
• fetch_positions() - Get current positions

Data Models

All methods return clean Python dataclasses:

@dataclass
class UnifiedMarket:
    market_id: str       # Use this for create_order
    title: str
    outcomes: List[MarketOutcome]
    volume_24h: float
    liquidity: float
    url: str
    # ... more fields

@dataclass
class MarketOutcome:
    outcome_id: str      # Use this for fetch_ohlcv/fetch_order_book/fetch_trades
    label: str           # "Trump", "Yes", etc.
    price: float         # 0.0 to 1.0 (probability)
    # ... more fields

See the full API reference for complete documentation.

Important Notes

Use outcome.outcome_id, not market.market_id

For deep-dive methods like fetch_ohlcv(), fetch_order_book(), and fetch_trades(), you must use the outcome ID, not the market ID:

markets = poly.fetch_markets(query="Trump")
outcome_id = markets[0].outcomes[0].outcome_id  # Correct

candles = poly.fetch_ohlcv(outcome_id, ...)  # Works
candles = poly.fetch_ohlcv(markets[0].market_id, ...)  # Wrong!

Prices are 0.0 to 1.0

All prices represent probabilities (0.0 to 1.0). Multiply by 100 for percentages:

outcome = markets[0].outcomes[0]
print(f"Price: {outcome.price * 100:.1f}%")  # "Price: 55.3%"

Timestamps are Unix milliseconds

from datetime import datetime

candle = candles[0]
dt = datetime.fromtimestamp(candle.timestamp / 1000)
print(dt)

Development

# Clone the repo
git clone https://github.com/pmxt-dev/pmxt.git
cd pmxt/sdks/python

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest

License

MIT