Kalshi Trading Bot 🎯

A Python-based automated trading bot for weather derivatives on Kalshi. Features live P&L tracking, forecast fusion, risk management, and portfolio analytics.

Features

• Automated Trading: Real-time decision making based on weather forecasts and market prices
• Forecast Fusion: Combines multiple weather sources (NWS, OpenMeteo, ensemble methods)
• Risk Management: Portfolio-level risk constraints, position sizing, concentration limits
• Order Management: Smart order amendment for improved fill execution
• P&L Tracking: Real-time profit/loss analytics with daily breakdown
• Forecast Accuracy: Calibration tracking and forecast outcome resolution
• Correlation Hedging: Portfolio correlation analysis to reduce systematic risk
• Greeks-lite: Delta exposure and volatility detection
• Live Dashboard: Streamlit dashboard for performance visualization

Setup

Prerequisites

• Python 3.10+
• pip / conda
• Git

Installation

# Clone repository
git clone https://github.com/yourusername/kalshi-trading-bot.git
cd kalshi-trading-bot

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

# Install dependencies
pip install -r requirements.txt

Configuration

1. Create .env file with your settings:

# API Keys
KALSHI_API_KEY=your_key_here
KALSHI_API_SECRET=your_secret_here

# Trading Parameters
MAX_TOTAL_RISK_DOLLARS=60.0
TRADE_STEP=1
BASE_TARGET=1
MAX_TARGET=2
MIN_EDGE_CENTS=0.5

# Feature Toggles
ENABLE_CORRELATION_HEDGE=true
ENABLE_ORDER_AMENDING=true
ENABLE_GREEK_ALERTS=true

2. Place API keys in keys/ directory (never commit!)

Running

Main Bot

python src/run_bot.py

Dashboard (Streamlit)

streamlit run src/dashboard.py

Diagnostics

python src/check_pnl.py           # P&L analysis
python src/check_fills.py         # Trade statistics
python src/check_forecast.py      # Forecast accuracy
python src/check_correlation.py   # Portfolio diversification
python src/check_balance.py       # Account balance
python src/check_greeks.py        # Portfolio Greeks
python src/check_trading_activity.py  # Recent trades

Project Structure

├── src/
│   ├── run_bot.py              # Main trading loop
│   ├── db.py                   # Database schema & analytics
│   ├── kalshi_client.py        # Kalshi API client
│   ├── weather_sources.py      # Forecast providers
│   ├── nws.py                  # NWS integration
│   ├── dashboard.py            # Streamlit analytics UI
│   ├── check_*.py              # Diagnostic scripts
│   ├── liquidate_positions.py  # Emergency position liquidation
│   └── backfill_climo.py       # Historical data backfill
├── data/
│   └── kalshi_quotes.sqlite    # SQLite database (trading data)
├── keys/                        # API credentials (DO NOT COMMIT)
├── .env                         # Configuration (DO NOT COMMIT)
├── requirements.txt             # Python dependencies
└── README.md                    # This file

Database Schema

• quotes: Market price snapshots (bid/ask)
• trades: Executed trades (buys/sells)
• positions: Current holdings by ticker
• forecast_predictions: Model predictions
• provider_readings: Raw weather data
• outcomes: Resolved market outcomes
• correlations: Position correlations

Key Concepts

Edge Calculation

The bot identifies profitable trades by computing the expected value vs. market prices. Only trades with edge > MIN_EDGE_CENTS are executed.

Risk Management

• MAX_TOTAL_RISK_DOLLARS: Portfolio-level risk cap
• MAX_ABS_QTY_PER_TICKER: Concentration limit per market
• PERSIST_SECONDS: Minimum hold time to reduce noise

Forecast Accuracy

Predictions are tracked and compared to actual outcomes for model calibration. Accuracy metrics by provider/series help tune parameters.

P&L Components

• Realized: Closed positions (locked-in gains/losses)
• Unrealized: Open positions marked-to-market
• Daily breakdown: Performance by trading date

Deployment

Streamlit Cloud

1. Push code to GitHub
2. Go to streamlit.io
3. Connect repo and deploy src/dashboard.py
4. Add secrets in Streamlit dashboard settings

AWS / Azure

See DEPLOYMENT.md for cloud deployment options.

Performance Metrics

Real-time metrics displayed on dashboard:

• Sharpe Ratio: Risk-adjusted returns (annualized)
• Win Rate: % of profitable trades
• Portfolio Risk: Current risk usage vs. limit
• Equity Curve: Cumulative P&L over time
• Daily P&L: Breakdown by date

Trading Logic

1. Forecast Fusion: Combine multiple weather forecasts
2. Market Analysis: Get current quotes and compute fair value
3. Candidate Generation: Identify mispricings vs. forecasts
4. Risk Checks: Verify position doesn't exceed constraints
5. Order Execution: Place limit orders via Kalshi API
6. Order Management: Monitor and amend stale orders
7. P&L Tracking: Log trades and update positions

Safety Features

• Emergency Liquidation: liquidate_positions.py for rapid exit
• Stale Quote Detection: Skip trades when prices are old
• Liquidity Checks: Verify bid-ask depth before trading
• Manual Controls: Easy parameter tuning via .env

Troubleshooting

Bot not trading?

python src/check_greeks.py      # Check portfolio Greeks
python src/check_trading_activity.py  # Verify recent trades
python src/check_balance.py     # Confirm account funding

Database corruption?

rm data/kalshi_quotes.sqlite*
# Bot will reinitialize on next run

API connection issues?

• Verify KALSHI_API_KEY and KALSHI_API_SECRET in .env
• Check network connectivity and Kalshi API status
• Review logs in terminal output

Contributing

Contributions welcome! Please:

1. Create feature branch
2. Add tests for new functionality
3. Update docs and README
4. Submit pull request

Monitoring

Real-time alerts and diagnostics already built in:

• Volatility spike detection
• Portfolio concentration warnings
• Fill quality analytics
• Forecast calibration tracking

License

MIT License - See LICENSE file

Disclaimer

This bot trades real capital on Kalshi. Use at your own risk. Past performance is not indicative of future results. Always test thoroughly in paper trading first.

Support

For issues or questions:

1. Check diagnostics: python src/check_*.py
2. Review logs in terminal
3. Check .env configuration
4. Open GitHub issue with details

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Last Updated: February 2026
Status: Active (Trading Live)
Account: ~$20 with $60 risk limit

Kalshi_Weather_Bot

kalshi bot sells and buys stock based off statistics

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