Michael L. Barna, Trading System Lab’s Founder



Michael L. Barna is Trading System Lab’s founder and President. Mike received a Bachelor of Science in Mathematics from Arizona State University, a Master of Science in Astronautical and Aeronautical Engineering from Stanford University, holds or has held a Series 3 Commodity Brokers License, a Series 30 Branch Office Manager License, a California Real Estate License, a National Futures Association Commodity Trading Advisor designation, an FCC Technicians License, a Commercial Drone Pilots License and 12 additional FAA Pilot Licenses or Ratings.


His background included work as a Senior Vice President for Regency Stocks and Commodity Fund, LP, and engineering and management positions in several large Fortune 500 defense firms where he developed ramjet, missile, and space based laser defense systems. His work included the use of Artificial Intelligence (AI) algorithms as a means to enhance various guidance and control systems.


Mr. Barna has utilized similar AI techniques in the design of modern Trading Systems and has pioneered AI and Trading System integration. Mike has created numerous popular Trading Systems that are employed by fund managers, brokerage houses and traders worldwide. His Legacy Trading System “Big Blue” is rated in the book: “The Ultimate Trading Guide”, by Hill, Pruitt and Hill. Mr. Barna is the creator and author of one of the most popular Legacy daytrading systems, The RMESA Trading System, which is his first trading system to include a basic Neural Network approximation filter. Mike’s background includes airline captain and flying management positions at one of the largest international airlines in the world. Mr. Barna has provided more Futures Truth top ranked trading systems than any other developer in the country. Mike has developed Trading Strategies for many different trading platforms including TradeStation™. His work has been published in numerous books and journals on Trading Systems.


Mike is a tournament level, indoor 4 wall handball player with California State, Canadian National, United States National and International Masters Championship Titles.

Frank D. Francone, President of RML Technologies, Inc.

Mr. Francone received a Bachelor of Arts in Economics from Claremont Men’s College, a Juris Doctor in Law from U.C. Berkley, a Technical Licensiate Degree in Earth and Energy Sciences from the Complex Systems Department of the Chalmers University of Technology in Sweden and is a 2012 PhD Candidate.

Mr. Francone designed RML’s linear genetic programming, optimization software, and statistical analysis and data preprocessing software packages, Discipulus™ and Notitia™. That software has been on the market since 1998.

For the past eight years, Mr. Francone has collaborated with SAIC in the design and testing of the UXO discrimination and residual risk analysis processes and has designed much of the system, including all the statistical classification and risk analysis portions and the bulk of the data pre-processing, feature extraction and QA/QC modules. He has been one of the leaders in developing statistical methodologies to convert the outputs of statistical classifiers into statistically supportable risk analysis and stop-digging decisions on UXO digs. He headed the JPG-V and F.E. Warren AFB MEC applied UXO discrimination projects and was principal investigator on the successful applied UXO discrimination projects at Camp Sibert and Camp San Luis Obispo in ESTCP project MM-0811.

Mr. Francone is one of the authors of a leading graduate-level textbook in artificial intelligence, machine learning, evolutionary computation and information theory entitled: Genetic Programming: an Introduction, Morgan Kaufmann (1998).

He has served for several years as an editor of the Journal of Genetic Programming and Evolvable Machines and as Senior Program Committee Member for the Genetic and Evolutionary Computation Conference. He has been a guest lecturer at West Point Military Academy, Colorado School of Mines, and University of Idaho on inductive learning and optimization.