Weather NO-Side Betting with Gaussian Model

This strategy exploits overpriced YES shares in weather temperature prediction markets by systematically betting on the NO side. The bot uses live METAR (aviation weather) observations combined with Open-Meteo forecast data to build a Gaussian probability distribution around the expected temperature. If the market price for a YES outcome implies a probability significantly higher than the model's calculated probability, the bot enters a NO position. Entry is triggered when the model identifies a statistically significant mispricing. Exit occurs when the market corrects or at event expiration. The bot is currently paper-trading, focusing on temperature markets where weather forecasts provide a reliable edge over crowd sentiment.