Percival Birchwood
Percival Birchwood is a Portland-born quantitative trader, fund manager, and educator who turned early pattern-spotting talent into disciplined systems. He now channels that experience into Mindzo Investment Union, guiding thousands of learners through real-market, rule-based training.
Overview
Percival Birchwood, born in 1968 in Portland, Oregon, built his career at the intersection of business, computer science, and markets. After graduating in business management and later completing advanced studies in computer science, he moved from intuitive trading into fully encoded rule sets and systematic models. Recognition in emerging markets fund management reinforced his belief that robust processes, not charisma, should sit at the center of investing. In 2011 he co-founded Mindzo Investment Union with friends, designing a live-market program that teaches learners to operate with discipline, document their rules, and treat every trade as data. Today he continues to refine scalable decision-support architectures while mentoring the next generation of practitioners.
- Strengths: Pattern recognition, quantitative rigor, calm decision-making under stress.
- Focus: Systematic investing, emerging markets, practice-led financial education.
- Responsibilities: Guiding strategy at Mindzo Investment Union and shaping real-market training frameworks.
Practical Highlights
Career Highlights
Systematic Trading & Market Anomalies
Birchwood studies how subtle patterns in price action can be converted into robust, testable strategies. His work emphasizes rule encoding, validation, and risk-aware position sizing across equities, futures, and multi-asset portfolios.
Intelligent Decision-Support Engines
He focuses on building AI-enhanced decision-support systems that integrate machine learning with disciplined quant frameworks, aiming to shorten feedback loops while keeping human oversight and prudence at the core.
Practice-Led Finance Education
At Mindzo Investment Union, Birchwood designs live-market training environments where learners trade under supervision, analyze real outcomes, and develop long-term habits grounded in data, reflection, and structured experimentation.