Scaling The Self-Sufficient Quant Panel - Wednesday, May 19, 2021
An unaided quant taking an idea all the way from seed to production is an ideal too often unrealized. Most quants can explore opportunities on their desktop, and some of them can spread their tests across a few servers. But scaling research across years of tick data, hundreds or thousands of instruments, and thousands or millions of algorithm variants usually requires dedicated engineering resources. Moreover, moving to production requires a whole new level of IT involvement. At each stage, costs rise, time-to-market lengthens, and opportunities disappear. How can a research infrastructure speed up this process and empower a self-sufficient quant through the entire lifecycle of strategy development? What role can off-the-shelf software frameworks play? What parts of the data and analytics pipeline can become shared, managed resources? In what ways can hyperscaling technology or hardware acceleration help to manage massively parallelized research? Join our panel of experts as they discuss how to make the scalable self-sufficient quant a reality.
Bishwa Roop Ganguly
Chief Solution Architect, Bigstream
Bishwa Roop Ganguly is Chief Solutions Architect at Bigstream Solutions. He has a PhD in Electrical Engineering from MIT, and an MS and BS in Computer Science from University of Illinois and University of California at Berkeley, respectively. He has published extensively in the field of parallel processing and computer networks. He also has 5 years of experience as a Data Scientist using Hadoop, Spark and SQL. He currently manages customer and partner engagements at Bigstream.