PhD in Engineering. I turn complex, physics-driven industrial data into actionable insights—and increasingly, into working software. Since 2017 I combine data science, automation and disciplined investing to find opportunities in the S&P 500. ZIGNOLZ is the product of that work.
I have a PhD in Engineering and more than 15 years of experience working with complex industrial datasets. I've built software and analysis tools for large companies in the energy sector—applying the same rigor to my investment models. I use my own capital in the strategies I develop, so these tools are built for real-world performance, not just theory.
I bought my first stock in the early 2000s on impulse as online trading platforms gained traction. For many years I focused on academic and professional research, investing only occasionally. In 2017 I moved to Python for data analysis and began treating investing and trading more systematically.
I built technical indicators and trading algorithms using TradingView. Some systems worked; some didn't. What mattered most was the learning—applying data-science methods and software engineering to design, test and iterate on ideas rapidly.
I mix fundamental and technical analysis to identify solid companies with attractive entry points and long-term potential. I prefer durable fundamentals and dividends over speculation and high volatility. My goal is to balance growth with safety—so I can sleep well at night.
In 2025 my brother shared a first version of an S&P 500 fundamentals model. It was a messy—but effective—notebook of Python code. I proposed turning it into an automated web application. Over several nights and weekends we refined the logic, automated the pipeline (it now runs every Saturday), and started receiving consistent weekly recommendations by email.
Positive feedback from friends and family convinced us to build a proper product: better UI, user accounts, dashboards, and a historical archive. By the end of summer 2025 ZIGNOLZ launched as a data-driven stock recommendation service focused on undervalued S&P 500 companies with solid fundamentals.
It's the end of 2025 as I write this. ZIGNOLZ is live and the priority is continuous improvement: deeper backtests, expanded data sources, stronger ML models, and a smoother user experience. I'm also exploring models for GOLD and US30 as potential next steps.
I believe in decisions driven by data, not emotion. I value transparency, reproducibility and risk-aware approaches. My models are tools to help investors make clearer, more consistent choices—without relying on hype or guesswork.
I wanted a tool I would have used myself when I started: clear, evidence-driven, and automated. ZIGNOLZ exists so other investors can access disciplined, repeatable stock analysis without spending hours piecing together spreadsheets and notebooks.
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