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QBEAR
From chaos to cash
Welcome to QBear – where data meets curiosity! We're a collective of passionate students exploring the intersection of finance, mathematics, and real life data through innovative quantitative research.
Our new project on the integrtion of basic Quantum Mecanics with Finance has been uploaded.
People often debate on how reliable the Technical Chart patterns are. Heres our take on this!
We're looking for passionate students to join our Marketing team.
At QBear, we blend traditional data analysis with cutting-edge technologies to uncover novel insights. Our approach combines rigorous quantitative methods with creative problem-solving to analyse "fun" real-world scenarios.
We're not your average quantitative research club. While we dive deep into classic topics like options pricing and risk management, our real passion lies in using machine learning and data analysis to explore unexpected patterns and relationships.
At QBear, we believe the most exciting discoveries happen when you ask "What if?" Whether we're examining traditional financial models or searching for hidden insights in unconventional datasets, we approach every project with a blend of technical skill and creative thinking.
Our team is a diverse group of students united by a shared curiosity and a desire to push the boundaries of what's possible in quantitative research. We thrive on collaboration, innovation, and the thrill of uncovering new insights that challenge conventional wisdom.
Pushing boundaries with creative approaches to quantitative problems
Diverse minds working together to solve fun challenges
Predictive relationships between random elements of the Universe.
Machine Learning Insights into Cross-Market Dynamics. Our research explores how different financial markets influence each other using advanced machine learning techniques to uncover hidden relationships and predictive patterns.
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We tested 10 classic chart patterns across 750+ global assets and 9 timeframes — the result: most patterns fail for short-term trading, but a few show consistent edge over multi-year horizons.
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Applying Particle-in-a-Box Models to Bitcoin Market Analysis
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