pykalshi 1.0.6

A typed Python client for the Kalshi prediction markets API with WebSocket streaming, automatic retries, and ergonomic interfaces

PyKalshi

The Python client for Kalshi prediction markets. WebSocket streaming, automatic retries, pandas integration, and clean interfaces for building trading systems.

from pykalshi import KalshiClient, Action, Side

client = KalshiClient()

# Place a trade
order = client.portfolio.place_order("KXBTC-25MAR15-B100000", Action.BUY, Side.YES, count_fp="10", yes_price_dollars="0.45")
order.wait_until_terminal()  # Block until filled/canceled

Features

• WebSocket streaming - Real-time orderbook, ticker, and trade data with typed messages
• Automatic retries - Exponential backoff on rate limits and transient errors
• Domain objects - Market, Order, Event with methods like order.cancel(), market.get_orderbook()
• pandas integration - .to_dataframe() on any list of results
• Jupyter support - Rich HTML display for markets, orders, and positions
• Local orderbook - OrderbookManager maintains state from WebSocket deltas
• Type safety - Pydantic models and typed exceptions throughout

Installation

pip install pykalshi

# With pandas support
pip install pykalshi[dataframe]

Get your API credentials from kalshi.com and create a .env file:

KALSHI_API_KEY_ID=your-key-id
KALSHI_PRIVATE_KEY_PATH=/path/to/private-key.key

Quick Start

Interactive demo: examples/demo.ipynb or

Browse Markets

from pykalshi import MarketStatus, CandlestickPeriod

client = KalshiClient()

# Search markets
markets = client.get_markets(status=MarketStatus.OPEN, limit=100)
btc_markets = client.get_markets(series_ticker="KXBTC")

# Get a specific market
market = client.get_market("KXBTC-25MAR15-B100000")
print(f"{market.title}: ${market.yes_bid_dollars} / ${market.yes_ask_dollars}")

# Market data
orderbook = market.get_orderbook()
trades = market.get_trades(limit=50)
candles = market.get_candlesticks(start_ts, end_ts, period=CandlestickPeriod.ONE_HOUR)

Trading

from pykalshi import Action, Side, OrderStatus

# Check balance
balance = client.portfolio.get_balance()
print(f"${balance.balance / 100:.2f} available")

# Place an order
order = client.portfolio.place_order(market, Action.BUY, Side.YES, count_fp="10", yes_price_dollars="0.50")

# Manage orders
order.wait_until_terminal()  # Block until filled/canceled
order.modify(yes_price=45)   # Amend price
order.cancel()               # Cancel

# View portfolio
positions = client.portfolio.get_positions()
fills = client.portfolio.get_fills(limit=100)
orders = client.portfolio.get_orders(status=OrderStatus.RESTING)

Real-time Streaming

from pykalshi import Feed, TickerMessage, OrderbookSnapshotMessage

async with Feed(client) as feed:
    await feed.subscribe_ticker("KXBTC-25MAR15-B100000")
    await feed.subscribe_orderbook("KXBTC-25MAR15-B100000")
    await feed.subscribe_trades("KXBTC-25MAR15-B100000")

    async for msg in feed:
        match msg:
            case TickerMessage():
                print(f"Price: ${msg.price_dollars}")
            case OrderbookSnapshotMessage():
                print(f"Book: {len(msg.yes)} yes levels, {len(msg.no)} no levels")

Local Orderbook

from pykalshi import Feed, OrderbookManager

manager = OrderbookManager()

async with Feed(client) as feed:
    await feed.subscribe_orderbook(ticker)

    async for msg in feed:
        manager.apply(msg)
        book = manager.get(ticker)
        best_bid = book["yes_dollars"][0] if book["yes_dollars"] else None

pandas Integration

# Any list result has .to_dataframe()
positions_df = client.portfolio.get_positions().to_dataframe()
markets_df = client.get_markets(limit=500).to_dataframe()
fills_df = client.portfolio.get_fills().to_dataframe()

# Candlesticks and orderbooks too
candles_df = market.get_candlesticks(start, end).to_dataframe()
orderbook_df = market.get_orderbook().to_dataframe()

Error Handling

from pykalshi import InsufficientFundsError, RateLimitError, KalshiAPIError

try:
    order = client.portfolio.place_order(...)
except InsufficientFundsError:
    print("Not enough balance")
except RateLimitError:
    pass  # Client auto-retries with backoff
except KalshiAPIError as e:
    print(f"{e.status_code}: {e.error_code}")

Examples

See the examples/ directory:

• demo.ipynb - Interactive notebook with rich display examples
• basic_usage.py - Browse markets and check portfolio
• place_order.py - Place and manage orders
• stream_orderbook.py - WebSocket streaming patterns
• momentum_bot.py - Simple trading bot example

Web Dashboard

A real-time web dashboard is included for browsing markets, viewing orderbooks, and monitoring your portfolio. It serves as both a development tool and a reference implementation.

pip install pykalshi[web]
uvicorn web.backend.main:app --reload

See web/ for details.

Why pykalshi?

	pykalshi	kalshi-python (official)
WebSocket streaming	✓	—
Automatic retry/backoff	✓	—
Rate limit handling	✓	—
Domain objects	✓	—
pandas integration	✓	—
Jupyter display	✓	—
Local orderbook	✓	—
Typed exceptions	✓	—
Pydantic models	✓	—
Full API coverage	—	✓

The official SDK is auto-generated from the OpenAPI spec. pykalshi adds the infrastructure needed for production trading: real-time data, error recovery, and ergonomic interfaces.

Links

• Kalshi API Reference
• kalshi-starter-code-python (official examples)

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This is an unofficial library and is not affiliated with Kalshi.