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dr-manhattan Trade prediction markets (Polymarket, Kalshi, Opinion, Limitless, Predict.fun) using a unified CCXT-style API. Use when the user wants to browse, search, or trade prediction markets, check balances and positions, manage orders, run market-making strategies, or compare prices across exchanges.
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name dr-manhattan description Trade prediction markets (Polymarket, Kalshi, Opinion, Limitless, Predict.fun) using a unified CCXT-style API. Use when the user wants to browse, search, or trade prediction markets, check balances and positions, manage orders, run market-making strategies, or compare prices across exchanges. license Apache-2.0 compatibility Requires Python >= 3.11 and uv. Requires network access for exchange APIs. Optionally requires exchange credentials (private keys, API keys) for trading. metadata {"author":"guzus","version":"1.0"}
Dr. Manhattan - Prediction Market Trading
Dr. Manhattan is a unified API for prediction markets, similar to how CCXT works for cryptocurrency exchanges. It supports Polymarket, Kalshi, Opinion, Limitless, and Predict.fun through a single interface.
Setup
Install dependencies with uv:
uv venv && uv pip install -e .
For MCP server (Claude integration):
uv sync --extra mcp
Supported Exchanges
Polymarket Polygon Private key + funder address Kalshi Regulated CEX API key + RSA private key Opinion BNB Chain API key + private key + multi-sig address Limitless Base Private key Predict.fun BNB Chain API key + private key (EOA or smart wallet)
Usage as a Python Library
Read-Only (No Credentials) import dr_manhattan
polymarket = dr_manhattan.Polymarket({'timeout' : 30 })
markets = polymarket.fetch_markets()
for market in markets:
print (f"{market.question} : {market.prices} " )
With Authentication import dr_manhattan
polymarket = dr_manhattan.Polymarket({
'private_key' : '0x...' ,
'funder' : '0x...' ,
})
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' }
)
Exchange Factory from dr_manhattan import create_exchange, list_exchanges
print (list_exchanges())
exchange = create_exchange('polymarket' , {'timeout' : 30 })
Usage via MCP Server Dr. Manhattan exposes all trading capabilities as MCP tools. Configure in Claude Code (~/.claude/settings.json or .mcp.json):
{
"mcpServers" : {
"dr-manhattan" : {
"command" : "/path/to/dr-manhattan/.venv/bin/python" ,
"args" : [ "-m" , "dr_manhattan.mcp.server" ] ,
"cwd" : "/path/to/dr-manhattan"
}
}
}
MCP Tools Reference
list_exchanges - List all available prediction market exchanges.get_exchange_info(exchange) - Get metadata and capabilities for an exchange.validate_credentials(exchange) - Check if credentials are valid without trading.
search_markets(exchange, query) - Search markets by keyword. This is the fastest way to find markets about a topic.fetch_markets(exchange, limit?, offset?) - Fetch all markets with pagination.fetch_market(exchange, market_id) - Fetch a specific market by ID.fetch_markets_by_slug(exchange, slug) - Fetch markets by slug or URL (Polymarket, Limitless).find_tradeable_market(exchange, binary?, limit?, min_liquidity?) - Find a suitable market for trading.find_crypto_hourly_market(exchange, token_symbol?) - Find crypto hourly price markets (Polymarket).fetch_token_ids(exchange, market_id) - Get token IDs for a market.parse_market_identifier(identifier) - Extract slug from a Polymarket URL.get_tag_by_slug(slug) - Get Polymarket tag information.
get_orderbook(exchange, token_id) - Get full orderbook (bids and asks).get_best_bid_ask(exchange, token_id) - Get best bid and ask prices.
create_order(exchange, market_id, outcome, side, price, size) - Place a buy or sell order. Price is 0-1 (probability). Side is "buy" or "sell".cancel_order(exchange, order_id, market_id?) - Cancel a specific order.cancel_all_orders(exchange, market_id?) - Cancel all open orders.fetch_order(exchange, order_id, market_id?) - Get order details and fill status.fetch_open_orders(exchange, market_id?) - List all open orders.
fetch_balance(exchange) - Get account balance (USDC).fetch_positions(exchange, market_id?) - Get current positions with PnL.fetch_positions_for_market(exchange, market_id) - Get positions for a specific market.calculate_nav(exchange, market_id?) - Calculate net asset value (cash + positions).
create_strategy_session(strategy_type, exchange, market_id, ...) - Start a market-making strategy in the background.get_strategy_status(session_id) - Get real-time strategy status (NAV, positions, delta).get_strategy_metrics(session_id) - Get performance metrics (uptime, fills).pause_strategy(session_id) - Pause a running strategy.resume_strategy(session_id) - Resume a paused strategy.stop_strategy(session_id, cleanup?) - Stop a strategy and optionally cancel orders.list_strategy_sessions - List all active strategy sessions.
Common Workflows
Find and Analyze a Market
Use search_markets with a keyword to find relevant markets.
Pick a market from the results and note its id and metadata.clobTokenIds.
Use get_orderbook with a token ID to see current bids and asks.
Use get_best_bid_ask for a quick spread check.
Place a Trade
Find the market using search_markets or fetch_markets_by_slug.
Check fetch_balance to confirm available funds.
Get the orderbook with get_orderbook to see current prices.
Use create_order with the market ID, outcome ("Yes" or "No"), side ("buy" or "sell"), price (0-1), and size.
Monitor with fetch_order or fetch_open_orders.
Run a Market-Making Strategy
Find a market with search_markets or find_tradeable_market.
Start with create_strategy_session(strategy_type="market_making", exchange, market_id).
Monitor with get_strategy_status and get_strategy_metrics.
Control with pause_strategy, resume_strategy, or stop_strategy.
Check Portfolio
fetch_balance to see cash.fetch_positions to see all open positions with unrealized PnL.calculate_nav for total portfolio value (cash + positions).
Key Concepts
Prices are probabilities ranging from 0 to 1 (exclusive). A price of 0.65 means the market implies a 65% chance.Outcomes are typically "Yes" and "No" for binary markets. Their prices sum to approximately 1.Token IDs are exchange-specific identifiers for each outcome of a market. Needed for orderbook queries.Slugs are human-readable URL identifiers (e.g., "trump-2024") used by Polymarket and Limitless.Order types supported: GTC (Good-Til-Cancel), FOK (Fill-Or-Kill), IOC (Immediate-Or-Cancel).
Running Examples uv run python examples/list_all_markets.py polymarket
uv run python examples/spread_strategy.py --exchange polymarket --slug fed-decision
uv run python examples/spike_strategy.py -e opinion -m 813 --spike-threshold 0.02
Data Models Market fields: id, question, outcomes, prices, volume, liquidity, close_time, tick_size, description, metadata (contains slug, clobTokenIds).
Order fields: id, market_id, outcome, side (BUY/SELL), price, size, filled, status (PENDING/OPEN/FILLED/CANCELLED), time_in_force.
Position fields: market_id, outcome, size, average_price, current_price. Properties: cost_basis, current_value, unrealized_pnl.
Orderbook fields: bids (price, size descending), asks (price, size ascending). Properties: best_bid, best_ask, mid_price, spread.
