Daniele Petitto

Financial Modeling Expert & Quantitative Analyst Dates

Myself • ZZP

Jul 2025 - Present • 10 mos

Provided bespoke financial models for international clients, focusing on valuation and strategic decision-making. Key Achievements: DCF & LBO Models: Delivered complex 3-statement models and Leveraged Buyout (LBO) analyses for private equity-style evaluations. Bayesian Inference: Applied Bayesian models for margin prediction and revenue forecasting under uncertainty. Client Success: Maintained a 100% (5-star) rating across all projects, delivering models that were praised for their technical accuracy and user-friendly UX.

Portfolio Manager & Investment Analyst | Private Family Office Dates

Private Family • Parttime

Mar 2023 - Present • 3 yrs 2 mos

Managing family capital (5M CZK) and personal long-term equity portfolio with a focus on deep fundamental stock picking. Key Achievements: Performance: Achieved a 5-year average annual return (CAGR) of 14% through a disciplined fundamental research process, significantly outperforming the S&P 500. Risk Management: Developed custom stress-testing dashboards and scenario analysis tools to mitigate downside risk during market volatility. Analysis: Responsible for full-cycle due diligence, including Moat analysis, management compensation evaluation, and capital structure optimization.

Lead Financial Engineer & Product Architect | Quant-Focused Investment Platform

FIP Technologies • Fulltime

Apr 2023 - Aug 2025 • 2 yrs 4 mos

Developed a comprehensive end-to-end investment research platform (Mobile & Desktop) focused on advanced equity valuation and automated fundamental analysis. Key Achievements: Data Engineering: Built a high-performance Financial API integrating directly with SEC EDGAR via CIK indexing. Implemented Upstash Redis caching layer, reducing data latency from 3,000ms to 50ms. Advanced Modeling: Integrated Monte Carlo simulations for DCF models to provide probabilistic valuation ranges (P-values) instead of static price targets. Automation: Developed an automated WACC engine (global risk-free rates, country risk premiums, beta) and a 3-stage H-Model for high-growth SaaS valuation. Quant Features: Implemented GARCH models for volatility forecasting and ML-driven peer group selection for more accurate relative valuation. Tech Stack: Python (FastAPI/Flask), Redis, PostgreSQL, SEC XBRL/JSON parsing, React/Next.js.