dr-manhattan

CCXT-style unified API for prediction markets. Simple, scalable, and easy to extend.

Architecture

dr-manhattan provides a unified interface to interact with multiple prediction market platforms, similar to how CCXT works for cryptocurrency exchanges.

Core Components

dr_manhattan/
├── base/           # Core abstractions
│   ├── exchange.py # Abstract base class
│   └── errors.py   # Exception hierarchy
├── exchanges/      # Exchange implementations
│   ├── polymarket.py
│   └── limitless.py
├── models/         # Data models
│   ├── market.py
│   ├── order.py
│   └── position.py
└── utils/          # Utilities (future)

Design Principles

1. Unified Interface: All exchanges implement the same Exchange base class
2. Scalability: Adding new exchanges is straightforward - just implement the abstract methods
3. Simplicity: Clean abstractions with minimal dependencies
4. Type Safety: Full type hints throughout

Key Features

• Fetch markets and market data
• Create and cancel orders
• Query positions and balances
• Standardized error handling
• Exchange-agnostic code

Installation

uv venv
uv pip install -e .

Usage

Basic Usage (Public API)

import dr_manhattan

# Initialize exchange without authentication
polymarket = dr_manhattan.Polymarket({'timeout': 30})
limitless = dr_manhattan.Limitless({'timeout': 30})

# Fetch markets
markets = polymarket.fetch_markets()
for market in markets:
    print(f"{market.question}: {market.prices}")

Advanced Usage (With Authentication)

The implementations use symbolic links to integrate with existing market maker implementations:

import dr_manhattan

# Polymarket with poly-mm integration
polymarket = dr_manhattan.Polymarket({
    'private_key': 'your_private_key',
    'condition_id': 'condition_id',
    'yes_token_id': 'yes_token',
    'no_token_id': 'no_token',
    'dry_run': False
})

# Limitless with limitless-mm integration
limitless = dr_manhattan.Limitless({
    'private_key': 'your_private_key',
    'timeout': 30
})

# Create order
order = polymarket.create_order(
    market_id="market_123",
    outcome="Yes",
    side=dr_manhattan.OrderSide.BUY,
    price=0.65,
    size=100,
    params={'token_id': 'token_id'}
)

# Fetch balance
balance = polymarket.fetch_balance()
print(f"USDC: {balance['USDC']}")

Unified API Pattern

import dr_manhattan

# Works with any exchange
for exchange_id in dr_manhattan.exchanges:
    exchange = dr_manhattan.exchanges[exchange_id]()
    print(f"{exchange.name}: {exchange.id}")

Adding New Exchanges

To add a new exchange, create a class that inherits from Exchange:

from dr_manhattan.base import Exchange

class NewExchange(Exchange):
    @property
    def id(self) -> str:
        return "newexchange"

    @property
    def name(self) -> str:
        return "New Exchange"

    def fetch_markets(self, params=None):
        # Implement API call
        pass

    # Implement other abstract methods...

Register in dr_manhattan/__init__.py:

from .exchanges.newexchange import NewExchange

exchanges = {
    "polymarket": Polymarket,
    "limitless": Limitless,
    "newexchange": NewExchange,
}

Using Symbolic Links

The implementations leverage existing market maker codebases through symbolic links:

1. poly-mm: Full Polymarket market maker implementation
2. limitless-mm: Full Limitless market maker implementation

When initialized with authentication credentials, the exchange classes use these implementations directly, providing access to production-ready trading functionality.

Data Models

Market

• Question and outcomes
• Prices and volume
• Close time and status

Order

• Market and outcome
• Side (buy/sell), price, size
• Status tracking

Position

• Current holdings
• PnL calculation
• Average entry price

Error Handling

All errors inherit from DrManhattanError:

• ExchangeError - Exchange-specific errors
• NetworkError - Connectivity issues
• AuthenticationError - Auth failures
• InvalidOrder - Invalid order parameters
• MarketNotFound - Market doesn't exist

Examples

Check out the examples/ directory for working examples:

• spread_strategy.py - Arbitrage trading strategy for binary markets
• simple_test.py - Basic market data fetching
• test_strategy.py - Strategy testing framework

Run an example:

uv run python examples/spread_strategy.py

See examples/README.md for detailed documentation.

Dependencies

• Python >= 3.10
• requests >= 2.31.0

Development:

• pytest
• black
• ruff