Kevin Reeves is a professional independent consultant operating out of Chicago, IL as a quantitative developer and senior software engineer. He graduated from Illinois Institute of Technology in the same city with High Honors in Electrical Engineering and Computer Engineering. His early work focused on actuarial modeling for insurance projections and large-scale financial simulations, however, his long-time passion for the field of machine learning left him convinced that computers were still often underutilized. His interests naturally converged in the financial derivatives markets under the notion of algorithmic trading.
Kevin’s experience includes research, development, and implementation of automated alpha-generation algorithms, specializing in statistical arbitrage and also (relatively) latency-insensitive strategies. He has worked for independent traders, prop shops, investment banks, and electronic trading firms in diverse roles including data mining, custom analytics, backtesting, development of contemporary options pricing models (stochastic volatility), and full-lifecycle trading automation.
Kevin firmly believes that machine learning techniques remain one of the largest untapped areas of future growth in automated trading. Kevin has successfully leveraged decision trees, neural nets, and other machine learning constructs to self-optimize algorithms, self-correct for natural model drift, predict signal confidence, manage risk, and otherwise improve profitability of existing algorithms. He is fluent in C++, C#, and Java.