A book about artificial intelligence, quantitative finance, and software development began as a board game hobby. At the end of my career travelling around the globe supporting technology projects for the US Department of Defense, I wanted an easy side hustle while I had nothing but time.
My original idea was to develop a board game called Market Leader through Market Leader Game, LLC. The concept was a Wall Street variation of Monopoly: players would acquire companies, respond to market events, allocate capital, and compete to build the strongest portfolio.
At first, I was simply trying to make the game feel realistic. That required research into how companies are evaluated, how markets respond to changing conditions, and what information investors use when deciding whether to buy, hold, or sell a security.
That research led me somewhere I had not expected.
I discovered real financial data through service providers like Alpha Vantage and Financial Modeling Prep. They enable software developers to retrieve market prices, company financial statements, valuation data, earnings information, and other forms of institutional-quality financial intelligence.
This discovery changed the direction of my project. I was no longer thinking about how to simulate investment research inside a board game. I began wondering whether I could build a system for real investment research.
Around the same time, three books emerged into a single idea.
I read Yves Hilpisch’s Python for Algorithmic Trading. The book demonstrated how Python could be used to retrieve financial data, analyze market behavior, test investment ideas, and construct systematic processes. It connected several subjects that had interested me throughout my career: programming, data analysis, decision support, and the practical application of emerging technology.
Python made it possible to move beyond theory. An investment idea could be expressed as a calculation. The calculation could be applied consistently across hundreds or thousands of companies. Its results could be tested, compared, revised, and repeated. Work that might once have required teams of analysts and specialized computer systems could increasingly be performed using widely available programming tools and financial-data services.
The board game began to recede into the background. In its place, a more ambitious idea emerged.
What if I could develop a product that helped democratize access to investment research—research that had once been practical only for eligible financial institutions with expensive data subscriptions, quantitative teams, specialized analysts, and large technology budgets?
A second book expanded my understanding of what systematic investment research could become. In The Man Who Solved the Market, Gregory Zuckerman tells the story of mathematician Jim Simons and the development of Renaissance Technologies. Simons did not assume that markets could be understood through traditional financial narratives alone. He brought together mathematicians, scientists, statisticians, and programmers to process enormous quantities of data searching for patterns that human observers might overlook.
The story was compelling not merely because of Renaissance Technologies’ extraordinary performance, but because of the process behind it. The researchers collected data, developed hypotheses, tested models, measured results, and continued refining their methods.
Benjamin Graham’s The Intelligent Investor provided a different but equally important foundation. Graham emphasized that investing should be grounded in the financial condition and underlying value of a business rather than in market excitement or short-term price movements. His principles—including fundamental analysis, a margin of safety, emotional discipline, and the distinction between investing and speculation—profoundly influenced Warren Buffett.
Buffett adapted those lessons as his experience grew. He retained Graham’s insistence on value and discipline while placing increasing emphasis on exceptional businesses, durable competitive advantages, capable management, and long-term ownership. That evolving philosophy became central to the investment approach through which Buffett transformed Berkshire Hathaway into one of history’s most successful companies.
The three books approached investing from different directions. Graham provided principles for evaluating businesses and protecting capital. The story of Jim Simons demonstrated the power of data, mathematics, experimentation, and systematic learning. Hilpisch showed how modern software could turn financial ideas into repeatable analytical workflows.
Together, they helped define the intellectual foundation of what would become American Market Analysis as a Service—AMAAS. My goal is to incorporate the enduring lessons of The Intelligent Investor, The Man Who Solved the Market, and Python for Algorithmic Trading into a practical decision-support system for informed investing.
From Benjamin Graham comes the discipline of examining financial quality, underlying value, risk, and the margin of safety. From Jim Simons and the researchers of Renaissance Technologies comes commitment to data, mathematical analysis, experimentation, measurement, and continuous learning. From Yves Hilpisch comes a practical framework for using Python to transform investment ideas into automated and repeatable analytical processes.
AMAAS attempts to bring those traditions together.
It uses fundamental information to examine the financial condition and economic characteristics of a company. It uses quantitative methods to compare companies across a broad investment universe. It uses predictive models to explore possible future outcomes without presenting those outcomes as certainties. It monitors rankings and conclusions as new evidence arrives. It uses artificial intelligence to organize the results, explain the evidence, and help people interact with complex research through natural language.
The purpose is not to imitate Graham, Buffett, or Simons, and it does not promise to reproduce their results. Their approaches developed under different circumstances, relied on different capabilities, and reflected different views of markets. The objective is to learn from each of them and incorporate compatible principles into a transparent, accountable research process.
The ultimate product is not a stock-picking machine. It is a decision-support system designed to help an investor ask better questions, compare alternatives, recognize uncertainty, examine conflicting evidence, and make more informed decisions.
This is not a retrospective account written after every problem was solved. The chapters were developed while AMAAS itself was evolving. They document ideas as they became working capabilities. They also document false starts, incorrect assumptions, design changes, operational failures, and the remedies that followed. Some lessons came from successful models. Others came from rankings that behaved unexpectedly, data that contradicted an assumption, features that users found confusing, or workflows that did not perform as intended.
Breakthrough technologies are often described after the uncertainty has been removed from the story. The final product appears inevitable, and the development process is reduced to a clean sequence of accomplishments, step-by-step instructions. Real innovation is rarely that orderly. It advances through experiments, errors, investigations, corrections, and repeated reconsideration of what the system is supposed to accomplish.
This book is being told as the work progresses. It records not only what was built, but why it was built, what went wrong, what was learned, and how each lesson influenced the next stage of development. It is intended to share that experience with a community interested in investment research, artificial intelligence, software development, and the changing relationship between people and machines.
The chapters that follow begin with simple concepts and progress toward a more complete investment-intelligence platform. The discussion then moves beyond individual calculations. It explores accountability over time, baseline measurement, event detection, Continuous Intelligence, Adaptive Rank, external corroboration, natural-language interaction, and AI agents capable of coordinating increasingly complex research workflows.
AMAAS began as a hobby and a Wall Street board game. It became an experiment in democratizing investment research. Along the way, it also became something more personal: a continuation of my lifelong relationship with technology and an opportunity to explore, in real time, how a person and an intelligent machine can learn to work together.
The goal is not to create a machine that never makes mistakes. The goal is to create a decision-support process that recognizes mistakes, investigates them, learns from them, and returns with better questions and better-supported conclusions.
That is the foundation of informed investing—and the journey documented in the chapters that follow.