Pattern recognition at machine scale. Strategy signals. Cloud automation. Education that ships.
Quantitative alpha factor research. Systematic signal discovery, backtesting, and portfolio construction.
EXPLORE →Tutorials, courses, and code walkthroughs. Learn to build automated trading systems from scratch.
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DEPLOY →"Working with Brian on a fintech application was a pleasure. Not only is Brian very knowledgeable as a Chartered Financial Analyst, but he was a fantastic resource when it came down to programming the application itself. His Python skills are top notch, and he was able to introduce me to developer tools and practices that I still use today."
"I had the pleasure of working with Brian on our internal fintech application overhaul, where he demonstrated exceptional skills in data analytics and workflow automation. My project requirements were complex and a little fluid. However, his clear communication and proactive approach made collaboration seamless, resulting in a dramatic increase in my trading system's performance."
A LightGBM model made money on NFL totals at both FanDuel and DraftKings. The same-side agreement filter looked stronger, but its historical edge is a lead for one locked 2026 test, not a betting strategy.
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Two MLB models were profitable in a corrected 2022–2025 backtest. That is a lead worth following, not proof of an edge. Their plausible profit ranges still included losses, so the right next move is one locked-down test on new games, not another model sweep, and not capital.
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A strategy redesign looked dramatically better in sample. A 2022 out-of-sample test exposed the hidden leverage, and the strategy was rejected before the final holdout.
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A walkforward optimizer objective test using geometric Martin as the out-of-sample path-quality judge.
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The first job of a serious research system is not to find alpha. It is to create the infrastructure where alpha can be trusted.
READ MORE →Three systems, one thesis: reliable judgment requires infrastructure.
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