Meteo-logic.com Website Review

DISRUPTIVE ALGO-TRADING SOLUTION Leveraging the power of BIG DATA and MACHINE LEARNING technologies to measure and predict the impact of WEATHER on critical ENERGY parameters. WEATHER-DRIVEN ENERGY COMMODITY ALGO-TRADING Bringing our unique algo-trading and weather prediction capabilities into the energy commodity market, to leverage its enormous financial opportunities. Our core algorithms boast a winning track record with exceptional performance, spanning over more than a decade in the field of Our expertise lies in quantifiable weather prediction based on state-of-the-art, big data and machine learning technologies. Weather is inherently linked to energy supply and demand – both for renewable energy and other traditional energy as*ets. Our adaptive, self-learning algorithms produce highly accurate predictions based on millions of records of price history and synoptic data. Meteo-Logic collects relevant dynamic inputs, online data, historical info, global weather forecasts and more, building the best data When applied over big data, our most advanced machine learning an*lytics is producing highly accurate predictions, which only improve Our algorithmic engine is built to adjust to various types of data coming from numerous sources and measures the influence of each data Meteo-Logic’s core competence is in leveraging prediction capabilities for the impact of Mother Nature over energy commodities – to produce actual market positions with exceptional returns in commodity exchanges. Because our solution is location-agnostic, the technology is applicable for various exchanges and as*et clas*es around the world. With global weather proven to be a critical contributor to energy market as*et price fluctuations, our technology provides a significant, measurable advantage in energy commodities trading. Our proprietary field proven technology and skilled team give us a remarkable competitive edge. The company has a successful track record in taking big data from multiple sources and transforming it into an accurate prediction with