Kalshi Market Making Bot

An automated trading bot for market making on the Kalshi prediction market exchange. The bot participates in incentive programs by placing liquidity orders on selected markets.

Overview

This bot automates the process of:

• Monitoring and participating in Kalshi incentive programs
• Placing limit orders to provide liquidity on selected markets
• Managing open positions and orders
• Logging all trading activities for monitoring and analysis

Features

• Automated Trading: Automatically places limit orders based on incentive programs
• Position Management: Closes open positions and cancels existing orders before new trading sessions
• Incentive Tracking: Monitors and tracks incentive programs, updating trading strategies accordingly
• Error Handling: Robust error handling with automatic retry on transient failures
• Comprehensive Logging: Detailed logs of all trading activities written to trade.log
• Dual Environment Support: Works with both DEMO and PROD environments

Requirements

• Python 3.8+
• Kalshi API credentials (API Key ID and Private Key)
• Virtual environment (recommended)

Installation

1. Clone the repository (if applicable):

git clone <repository-url>
cd KalshiModel

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Configuration

Environment Variables

Create a .env file in the project root with the following variables:

# Demo Environment
DEMO_KEYID=your_demo_api_key_id
DEMO_KEYFILE=/path/to/demo_private_key.pem

# Production Environment
PROD_KEYID=your_prod_api_key_id
PROD_KEYFILE=/path/to/prod_private_key.pem

Trading Parameters

Edit the constants in market_bot.py to customize trading behavior:

TRADE_SIZE = 1              # Number of contracts per order
TRADE_DELTA = 0.01          # Price delta for order placement
WAIT_TIME = 60              # Seconds between trading cycles
EXPIRATION_TS = 1           # Order expiration time in minutes
TRADE_TICKER_SIZE = 2       # Number of tickers to trade simultaneously
INCENTIVE_SIZE = 300        # Incentive size parameter

Environment Selection

In market_bot.py, change the environment:

env = Environment.PROD  # Use Environment.DEMO for testing

Usage

Running the Bot

python market_bot.py

The bot will:

1. Cancel any existing open orders
2. Close any open positions
3. Check for available incentive programs
4. Place new orders based on incentives
5. Wait WAIT_TIME seconds before repeating

Stopping the Bot

Press Ctrl+C to stop the bot gracefully. The bot will log a shutdown message and exit.

Project Structure

KalshiModel/
├── market_bot.py      # Main bot logic and trading loop
├── clients.py         # Kalshi API client implementation
├── incentive.py       # Incentive program tracking and management
├── trade.py           # Order creation and trading logic
├── main.py            # Alternative entry point (if used)
├── requirements.txt   # Python dependencies
├── trade.log          # Trading activity logs (auto-generated)
└── .env               # Environment variables (not in repo)

Key Components

MARKET_BOT (market_bot.py)

Main bot class that orchestrates the trading workflow:

• start_trading(): Main trading cycle
• place_order(): Places orders for incentive tickers
• run(): Main loop with error handling
• log(): Unified logging to console and file

KalshiHttpClient (clients.py)

API client for interacting with Kalshi:

• Authentication with RSA signatures
• Rate limiting
• Order management (create, cancel, get orders)
• Position management
• Market data retrieval

INCENTIVE_PROGRAM (incentive.py)

Manages incentive program tracking:

• Loads market incentives
• Filters and selects tradable incentives
• Updates incentive status
• Maintains trade incentive dictionary

TRADE (trade.py)

Order creation logic:

• Calculates order prices based on order book
• Creates limit orders with proper pricing
• Manages trade size and balance

Logging

All trading activities are logged to trade.log with timestamps. Log entries include:

• Order Operations: Cancel, open, and close orders with details
• Position Management: Open positions, closing positions
• Order Books: Top 5 best prices for Yes/No sides
• API Responses: Order status, fills, and remaining quantities
• Errors: Detailed error messages with tracebacks
• Trading Sessions: New/updated incentives and tickers

Example log entries:

2026-01-16 18:05:57 [CANCEL ORDER] Ticker: KXHIGHCHI-26JAN16-B35.5 | Side: yes | Price: 0.4600
2026-01-16 18:05:57 [CLOSE POSITION] Ticker: KXHIGHCHI-26JAN16-B35.5 | Side: no | Price: 0.4600 | Count: 1
2026-01-16 18:05:58 [OPEN ORDER] Ticker: KXLOWTMIA-26JAN17-B60.5 | Side: yes | Action: buy | Count: 1 | Type: limit | Price: 0.5300

Error Handling

The bot includes comprehensive error handling:

• Transient Errors: 503 Service Unavailable errors are logged and retried on next cycle
• Client Errors: 400 Bad Request errors are logged with detailed API error messages
• Critical Errors: All exceptions are caught, logged with full tracebacks, and the bot continues running
• Graceful Shutdown: KeyboardInterrupt is handled for clean shutdown

Important Notes

⚠️ Risk Warning:

• This bot trades real money when using PROD environment
• Always test thoroughly in DEMO environment first
• Monitor trade.log regularly for errors
• Ensure sufficient account balance for trading

📝 Best Practices:

• Start with WAIT_TIME=60 or higher to avoid rate limiting
• Monitor trade.log for the first few hours of operation
• Keep API keys secure and never commit them to version control
• Use .env file for credentials (already in .gitignore)

🔧 Troubleshooting:

• 503 Errors: API server temporarily unavailable - bot will retry automatically
• 400 Errors: Check order parameters in logs (price format, required fields)
• 401 Errors: Verify API credentials in .env file
• 429 Errors: Reduce trading frequency (increase WAIT_TIME)

API Documentation

For detailed API documentation, refer to:

• Kalshi API Documentation

Author

Mai He

Disclaimer

This software is provided as-is for educational and research purposes. Trading involves risk of loss. Use at your own risk.