I founded and scaled Armada Landscaping LLC from a localized startup into a multi-vehicle service operation. By managing full-cycle P&L, resource allocation, and customer acquisition strategies, I built the business infrastructure from the ground up with a focus on unit economics and operational efficiency. This journey culminated in a successful strategic business exit, providing me with first-hand experience in the capital management and grit required to run and value a sustainable enterprise.
Through TWA Capital, I translate this entrepreneurial perspective into technical rigor using sophisticated financial modeling. My framework utilizes dynamic 4-Quadrant macro models to identify economic regime shifts and multi-stage DCF valuations to determine the intrinsic value of equities. To institutionalize this research, I engineered the TWA Optimizer, a Python-based suite that leverages Modern Portfolio Theory and real-time SEC data to automate portfolio stress-testing and risk diagnostics.
Engineered in Python to automate the auditing of risk-adjusted returns for any custom equity basket. By integrating real-time SEC fundamental data via yahoo finance, the engine calculates asset-to-asset correlation matrices and weighted Beta relative to the S&P 500.
This tool utilizes Modern Portfolio Theory (MPT) and Monte Carlo simulations to identify the 'Efficient Frontier,' allowing for data-driven allocation that maximizes the Sharpe Ratio.
I leverage a sophisticated Excel framework to bridge the gap between top-down macro trends and bottom-up equity research. By utilizing a Dynamic 4-Quadrant Model and Weekly Equity Clock, I identify optimal economic regimes—shifting between Growth and Inflation trends to determine strategic asset positioning. This macro overlay is paired with comprehensive, multi-stage Discounted Cash Flow (DCF) models to calculate intrinsic equity value. This dual-track methodology ensures that every portfolio allocation is supported by both systemic economic tailwinds and rigorous fundamental analysis.
Beyond the spreadsheets, I have hands-on experience scaling a physical service operation from a localized startup to a multi-vehicle business. This background provides a 'real-world' filter for my financial modeling—understanding that every number in a DCF represents operational realities like P&L management, resource allocation, and customer acquisition. The journey culminated in a successful strategic exit, allowing me to pivot full-time into quantitative investment research.
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