This project is scheduled for launch
Launch date: Thursday, December 3, 2026 at 08:00 AM UTC

ForecastTrader is an automated trading bot designed to identify and capitalize on mispriced weather contracts within prediction markets like Kalshi. It provides a significant data-driven edge by leveraging advanced meteorological data to calculate real probabilities, offering a powerful tool for traders seeking consistent returns.
Ideal for prediction market traders, developers, and those seeking data-driven side income, ForecastTrader solves the problem of subjective forecasting. Unlike single-forecast weather APIs, it uses the GFS ensemble model's 31 simulations to generate a precise probability distribution for weather events. This allows users to accurately assess the likelihood of outcomes, such as a high temperature exceeding 80°F, and compare it against the market's implied probability.
By automating analysis and trade execution, ForecastTrader helps users capitalize on inefficiencies where the market's price deviates significantly from the scientifically derived probability. It's particularly beneficial for those who want to automate their trading strategy and compound small edges into consistent returns without constant manual monitoring.
ForecastTrader is available as a one-time purchase of its complete Python source code. There are no recurring software costs. It utilizes a free public weather data API and can run on a personal laptop or an inexpensive cloud server (e.g., $5/month), keeping operational expenses minimal. A paper trading walkthrough is included to validate the strategy before live trading.
Designed for ease of use, ForecastTrader features a straightforward setup process with a detailed, screenshot-rich guide, enabling non-developers to get it running in about 15 minutes. It supports SQLite for zero-config database setup and Postgres for advanced users. Comprehensive documentation, including strategy and parameter tuning guides, is provided. Personal email support from the developer ensures users receive assistance when needed.
The bot is primarily built with Python, utilizing a free public API for GFS ensemble weather data. It supports SQLite as a default, zero-configuration database for logging, with an option for PostgreSQL. Docker Compose is included for a simplified, one-command setup, streamlining environment management for users.
ForecastTrader offers a powerful, automated solution for gaining a significant, data-driven advantage in prediction markets by identifying and exploiting mispriced weather contracts. With its robust features, user-friendly setup, and dedicated support, it empowers users to transform complex data into actionable trading insights. Explore ForecastTrader today to enhance your prediction market strategy.
Comments will be available once the project is launched.
Steve Farmer
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