Building an Automated Weather Trading Bot with GPT-6 and WeatherNext

Architecture and Data Pipeline

The bot operates by bridging high-reasoning AI models with real-time meteorological data. The core data source is Google DeepMind's 'WeatherNext 3' model, accessed via Google Cloud's BigQuery and Earth Engine APIs. The developer uses the GPT-6 Astra model via the Codex CLI to handle reasoning, data extraction, and logic formulation. The system is designed to be headless, running on an AWS EC2 (T3 micro) instance to ensure 24/7 availability for market monitoring and execution.

Quantitative Strategy and Backtesting

The strategy relies on identifying discrepancies between the AI's weather forecast and the market-implied probabilities on Kalshi. The workflow involves:

1. Data Normalization: Fetching historical event data from Kalshi and aligning it with specific weather station coordinates (e.g., Central Park for NYC events).
2. Backtesting: Running a rigorous quantitative script that evaluates whether WeatherNext 3 provides predictive alpha beyond current market pricing, accounting for transaction fees and execution slippage.
3. Decision Logic: The bot executes trades only when a specific 'edge' threshold is met (e.g., a 5-cent advantage per contract). It freezes predictions 15 minutes before market opening to avoid data leakage and executes at the 1-minute mark post-opening to balance data freshness with liquidity.

Deployment and Execution

Deployment is handled via a CLI-driven workflow. The bot is first tested in 'paper mode' to verify API connectivity and logic stability before switching to live trading. The system uses environment variables to manage sensitive credentials, including Kalshi API keys and AWS access keys. The bot continuously polls for market updates, compares them against the frozen forecast, and triggers orders only when the expected value exceeds the defined risk-adjusted threshold.