I have utilized Pico Corvil Analytics extensively in my various roles. As an end user, I have employed it in the banking industry and as an instructor. Specifically, I was involved in level three production support and delved deep into analytics related to market data and fixed decoding. My primary responsibility was to scrutinize transactions from start to finish, identify weaknesses, and create dashboards to notify of any potential issues. Pico Corvil Analytics offers several useful features, such as regular processing and custom dashboards for different time frames, which can generate alerts when problems arise. To investigate and resolve any delays or irregularities, I frequently requested specific information such as the application and order number, and Pico Corvil Analytics helped me identify the root cause. Initially, I started using it for machine learning and AI, and at that time, I was ahead of Pico Corvil Analytics in that area. However, I have noticed that they have caught up now. I had a close relationship with the CTO of Pico Corvil Analytics, and I would use their data to conduct stack and time series analysis from multiple dimensions. This would involve analyzing the number of orders, cancellations, placement latency, order activity time, and maximum values, among other factors. I would then run neural networks and use an equation to help an investment bank predict when their dark pool would begin to fail, given a specific combination of dimensional inputs. Using Coral data at an investment bank, I was able to predict fill rates for the next day's market close with an accuracy of plus or minus four percent. Moreover, I leveraged the same data to create a decision tree model that could identify factors that positively or negatively affected fill rates. Lastly, I designed an anomaly engine at an investment bank, which mainly used Pico Corvil Analytics data, but also integrated other sources, to detect anomalies in trading, such as order or application anomalies, or hardware performance issues. Interestingly, now I notice that Pico Corvil Analytics has a similar feature. At the time, while working on these projects, I did not receive any support from Pico Corvil Analytics, although I believe they may have used some of my work. Nevertheless, I appreciate and admire their product.
Corvil transforms Network Data with speed and precision into the powerful real-time truth. Corvil captures, decodes, reassembles, and enriches vast amounts of data in motion, adding analytics and making the resulting enriched data and IT Operations Analytics available to humans, machines and other systems.
I have utilized Pico Corvil Analytics extensively in my various roles. As an end user, I have employed it in the banking industry and as an instructor. Specifically, I was involved in level three production support and delved deep into analytics related to market data and fixed decoding. My primary responsibility was to scrutinize transactions from start to finish, identify weaknesses, and create dashboards to notify of any potential issues. Pico Corvil Analytics offers several useful features, such as regular processing and custom dashboards for different time frames, which can generate alerts when problems arise. To investigate and resolve any delays or irregularities, I frequently requested specific information such as the application and order number, and Pico Corvil Analytics helped me identify the root cause. Initially, I started using it for machine learning and AI, and at that time, I was ahead of Pico Corvil Analytics in that area. However, I have noticed that they have caught up now. I had a close relationship with the CTO of Pico Corvil Analytics, and I would use their data to conduct stack and time series analysis from multiple dimensions. This would involve analyzing the number of orders, cancellations, placement latency, order activity time, and maximum values, among other factors. I would then run neural networks and use an equation to help an investment bank predict when their dark pool would begin to fail, given a specific combination of dimensional inputs. Using Coral data at an investment bank, I was able to predict fill rates for the next day's market close with an accuracy of plus or minus four percent. Moreover, I leveraged the same data to create a decision tree model that could identify factors that positively or negatively affected fill rates. Lastly, I designed an anomaly engine at an investment bank, which mainly used Pico Corvil Analytics data, but also integrated other sources, to detect anomalies in trading, such as order or application anomalies, or hardware performance issues. Interestingly, now I notice that Pico Corvil Analytics has a similar feature. At the time, while working on these projects, I did not receive any support from Pico Corvil Analytics, although I believe they may have used some of my work. Nevertheless, I appreciate and admire their product.