aqr-factor-investing

Implements statistical arbitrage trading strategies using machine learning techniques to identify and exploit market inefficiencies across financial instruments.

Implements Google Material Design components and styling guidelines for creating modern, consistent user interfaces in web and mobile applications.

Captures and encodes Paul Minervini's swing trading strategy and decision-making heuristics for identifying high-potential stock trading opportunities.

Captures computational finance expertise and decision-making strategies from De Shaw quantitative trading professionals, focusing on algorithmic trading and financial modeling techniques.

Captures Leslie Lamport's mental models and decision-making heuristics for designing and reasoning about distributed systems, focusing on consensus algorithms and distributed computing principles.

Enables continuous fuzzing of software systems using Google's advanced testing methodologies to automatically discover and report potential security vulnerabilities and edge-case bugs.

Captures and encodes Richard Stevens' expert mental models and design principles for understanding and implementing network protocols at a deep, intuitive level.

Captures and models the architectural insights and decision-making processes behind Google's search infrastructure design and scalability strategies.

Analyzes and maps cybersecurity threats and tactics using the MITRE ATT&CK framework to help security professionals understand and model potential attack techniques.

Accelerates high-frequency trading algorithms by implementing low-latency FPGA-based trading strategies for Jump Trading infrastructure.