Quant Studio
Systematic Research for Independent Practitioners
Latest Update: April 24th 2026
About Us
Who Produces the Research?
My name is Claudio Catalani. I am an independent quantitative researcher and software engineer based in Argentina, working with practitioners worldwide.
I founded Quant Studio after more than six years of intensive, first-principles research and systems development. With a strong foundation in mathematics and physics, complemented by MIT MicroMasters in Finance and Data Science, I develop systematic research frameworks for financial markets.
Our Philosophy
Quant Studio exists at the intersection of academic rigor and real-time market reality. We transform complex market narratives into structured, transparent, and defensible analysis using probability, stochastic modeling, machine learning, and computational finance.
This is not another content platform. There are no AI-generated summaries or repackaged ideas. Every report is built from verified public data, reproducible code, and rigorously tested quantitative frameworks.
Our Commitment
I operate with full independence and zero conflicts of interest. My goal is simple: deliver high-quality, systematic research that independent market participants and analysts can trust and verify themselves.
Our Methods
Methodologies
The publications integrate asset pricing, derivatives pricing, machine learning, and computational finance methods — including probability distributions, time-series and stochastic models, Monte Carlo simulation, multi-factor frameworks, portfolio theory, neural networks, ensembles, and robust techniques for generalization and overfitting mitigation.Publications Standards
Conflict of Interest: The author has no personal positions, holdings, or financial interests in any of the assets, commodities, or securities discussed in this report and declares no conflicts of interest.Independence: All research is produced free from external commercial influence, ensuring complete objectivity and integrity of the analysis and scorecards.
Disclaimer: This report includes a standard disclaimer and does not constitute financial advice, investment recommendations, or performance guarantees.
Methodological Summary: Each publication provides a high-level explanation of the core methodology and logic (e.g., conditional probability frameworks).
Data Quality & Sourcing
All research is built exclusively on verified public data. We maintain that trustworthy quantitative analysis requires data that is accessible and reproducible by the public. Primary Sources:Market Data: OHLCV prices sourced via documented APIs from high-quality providers including EODHD, Yahoo Finance, and Binance.
Macro Indicators: Economic data (yield curves, inflation, GDP, etc.) sourced from the Federal Reserve Economic Data (FRED) – St. Louis Fed.
Diligence, Disclosure & Limitation of Liability
No Financial Advice
The information contained in this website and in any associated reports is for informational and educational purposes only. It does not constitute financial, investment, legal, or tax advice. Nothing presented should be interpreted as a recommendation to buy, sell, or hold any financial instrument or asset.Probabilistic Nature of Analysis
All models, forecasts, and conclusions are based on probabilistic frameworks and are inherently uncertain. Financial markets are complex, adaptive systems subject to unforeseen events, structural breaks, and regime changes. As such, no outcome is guaranteed, and past performance or model accuracy does not imply future results.Assumptions and Data Limitations
The analysis relies on historical data, model assumptions, and external data sources that may contain inaccuracies, delays, or revisions. While reasonable efforts are made to ensure data quality and methodological rigor, no representation or warranty is made regarding completeness or accuracy.User Responsibility
Any decisions made based on this information are the sole responsibility of the user. Users are encouraged to conduct their own independent research and, where appropriate, consult with licensed financial professionals before making investment decisions.No Liability
Under no circumstances shall the author or provider of this content be liable for any direct or indirect losses, damages, or consequences arising from the use of, or reliance on, any information presented in this project, including but not limited to financial losses.No Fiduciary Relationship
Use of this content does not establish any fiduciary, advisory, or client relationship between the user and the content provider.Forward-Looking Statements
Some content may include forward-looking statements or projections. These are based on current expectations and are subject to change without notice. Actual outcomes may differ materially.Jurisdiction
This content is intended for a general audience and may not comply with specific regulatory requirements in all jurisdictions. Users are responsible for ensuring compliance with their local laws and regulations.By accessing or purchasing any content from this project, you acknowledge and agree to the terms outlined in this statement.
Latest Reports
The Narrative Gap
2026 Supply Shock
Informational use only. Not financial advice. By purchasing, you agree to the Disclaimer below.
Diligence, Disclosure & Limitation of Liability
This project provides independent, data-driven analysis of financial markets, including but not limited to cryptocurrencies, macroeconomic trends, and commodities. All content is produced using quantitative methods, statistical models, and publicly available information.
No Financial Advice: The information contained in this website and in any associated reports is for informational and educational purposes only. It does not constitute financial, investment, legal, or tax advice. Nothing presented should be interpreted as a recommendation to buy, sell, or hold any financial instrument or asset.
Probabilistic Nature of Analysis: All models, forecasts, and conclusions are based on probabilistic frameworks and are inherently uncertain. Financial markets are complex, adaptive systems subject to unforeseen events, structural breaks, and regime changes. As such, no outcome is guaranteed, and past performance or model accuracy does not imply future results.
Assumptions and Data Limitations: The analysis relies on historical data, model assumptions, and external data sources that may contain inaccuracies, delays, or revisions. While reasonable efforts are made to ensure data quality and methodological rigor, no representation or warranty is made regarding completeness or accuracy.
User Responsibility: Any decisions made based on this information are the sole responsibility of the user. Users are encouraged to conduct their own independent research and, where appropriate, consult with licensed financial professionals before making investment decisions.
No Liability: Under no circumstances shall the author or provider of this content be liable for any direct or indirect losses, damages, or consequences arising from the use of, or reliance on, any information presented in this project, including but not limited to financial losses.
No Fiduciary Relationship: Use of this content does not establish any fiduciary, advisory, or client relationship between the user and the content provider.
Forward-Looking Statements: Some content may include forward-looking statements or projections. These are based on current expectations and are subject to change without notice. Actual outcomes may differ materially.
Jurisdiction: This content is intended for a general audience and may not comply with specific regulatory requirements in all jurisdictions. Users are responsible for ensuring compliance with their local laws and regulations.
By accessing or purchasing any content from this project, you acknowledge and agree to the terms outlined in this statement.
Contact Us
Research questions. Purchase issues.
Institutional and consulting inquiries.
Custom report requests.
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