Intelligent Weather Quant Analysis Bot
Quantitative weather prediction model for Polymarket temperature markets using historical climate data and machine learning.
This strategy applies a quantitative analysis framework to Polymarket weather prediction markets, specifically temperature outcome shares. It ingests historical climate data, seasonal patterns, and ensemble weather models (e.g., ECMWF, GFS) to generate a probabilistic forecast for temperature thresholds (e.g., 'Will NYC exceed 90°F on July 4?'). The bot compares its calculated fair value to market prices and takes positions when the mispricing exceeds a statistical confidence interval. Entry is sized according to the model's predicted edge and market liquidity. Exit is automatic at market settlement or earlier if the model's prediction flips. The Python implementation uses TensorFlow for time-series forecasting and stores model weights in the repository. Backtesting from 2024-2025 showed a 12% ROI over 200+ trades across US city temperature markets.