QuantPhi was created with a clear intention: to approach Quantitative Finance as a discipline that deserves depth, structure, and thoughtful engagement.

In an environment increasingly driven by speed, automation, and surface-level explanations, we believe it is essential to articulate the principles that guide how knowledge is created, curated, and shared on this platform.

The following principles reflect our view on learning, rigor, and the role of human understanding in quantitative disciplines. They shape every decision we make at QuantPhi, from content creation to the resources we choose to share.

Principle 1: Human intelligence

The emergence of generative artificial intelligence has profoundly transformed the way knowledge is produced and disseminated. At QuantPhi, we acknowledge this transformation and recognize the benefits that these technologies can offer.

However, we believe that, in its current state, generative artificial intelligence is not yet capable of providing the depth, coherence, and pedagogical structure required for meaningful human learning—particularly in technically demanding fields such as Quantitative Finance. Much of the content generated today prioritizes speed and ease of consumption over conceptual foundations, rigorous explanations, and proper referencing.

For this reason, QuantPhi adopts a classical approach to content creation. Our materials are developed through careful collection, study, and structured synthesis of knowledge drawn from academic sources, and are presented with clarity and intention by human authors.

Generative artificial intelligence is used selectively and transparently, primarily for the creation of images, graphs, and non-technical website content, while all technical and educational materials remain the result of human intellectual work.

Principle 2: Depth

In contrast to much of the content available online—which often delivers a flood of superficial information without explanation of sources or methodology—QuantPhi prioritizes quality over quantity.

We ensure that the knowledge we share is meaningful, rigorously structured, and accompanied either by detailed explanations or, at a minimum, references to authoritative sources.

This approach guarantees both the authenticity of our content and a clear trail for verification, helping to minimize errors and fostering trust in the material we provide.

Principle 3: No result without proof

In academic mathematics, it is common to encounter results presented without proof, often labeled as “trivial.” At QuantPhi, we recognize that what may appear obvious to one reader can be challenging to another, and that this practice can unintentionally discourage learning.

For this reason, all technical results presented on the platform—except for truly immediate ones—are accompanied either by a complete and detailed proof, rather than a sketch, or by a clear reference to where such a proof can be found.

This principle is applied with particular rigor in our books, where careful reasoning and transparency are essential to building solid mathematical foundations.

Principle 4: Rigor

In Quantitative Finance, it is common to encounter results—often with apparent practical relevance—presented without a clear statement of the assumptions and hypotheses on which they rely. When applied outside their valid conditions, such results can be misleading or even incorrect.

At QuantPhi, we are committed to presenting results with the necessary level of rigor, making explicit the assumptions that underpin them, while avoiding excessive formalism that would hinder learning and progress.

This balance is especially important in our courses, where understanding the scope and limitations of a result is as essential as understanding the result itself.

Principle 5: Emphasis on mathematical modeling

At QuantPhi, we acknowledge that Quantitative Finance relies on many empirical facts that are successfully applied in the daily work of finance professionals.

Nevertheless, our primary focus is on the study of mathematical models. This includes both models built upon a priori axioms and those that seek to explain and leverage empirical observations through mathematical structures.

This emphasis is motivated by three main reasons. First, mathematical models allow us to derive concrete quantitative results with clarity and precision. Second, they enable the use of a rich and powerful mathematical toolkit to obtain new insights that are not always directly accessible through empirical analysis alone.

Third, the practical aspects of model implementation through computer programs are already widely covered across online platforms and, to some extent, by generative artificial intelligence. In contrast, significantly less attention is devoted to the mathematical modeling that underlies these implementations, despite the fact that sound implementation is impossible without a proper understanding of the model itself. QuantPhi aims to address this gap by placing mathematical modeling at the center of its content.

Principle 6: No spam

Unlike many content platforms that prioritize publishing frequency over substance, QuantPhi will not follow a policy of daily updates regardless of quality or relevance.

New content is shared only when it meets our standards of depth and accuracy, typically on a weekly basis.

For those who wish to stay informed more regularly, a newsletter subscription provides curated updates directly to your inbox, ensuring that communication remains purposeful, relevant, and respectful of your time.

Principle 7: Respect

QuantPhi is guided by a fundamental principle of respect—both for our community members and for the broader field of Quantitative Finance.

This commitment is reflected in all of our publications and contributions, as well as in the moderation policies applied across courses, the Forum, and individual posts (see the Forum section for details).

We recognize that mathematical models are created by people, and every contribution with a solid scientific foundation deserves acknowledgment. QuantPhi does not favor any particular work or author, ensuring that all ideas are evaluated on their merits and presented with fairness and integrity.

Principle 8: Interactivity

Meaningful learning requires interaction between those who create knowledge and those who seek to understand it, particularly in online environments where the personal dimension of face-to-face learning is absent.

To foster this essential interaction, all topics within our courses include dedicated comment sections where students can ask questions, exchange ideas, and engage directly with the instructor and with one another.

This principle extends beyond course content and is reflected across the platform, including the Forum and the comment sections of individual posts, reinforcing QuantPhi’s role as a space for dialogue, clarification, and shared understanding.

Principle 9: Community:

QuantPhi cannot truly serve as a meeting point for enthusiasts of Quantitative Finance without fostering a genuine sense of community.

We believe that a central element in building this community is the dedicated Forum, where members can propose new topics, engage in ongoing discussions, and share perspectives in an open and constructive environment.

Through this space, QuantPhi aims to encourage sustained dialogue, collective learning, and meaningful connections among students, professionals, and enthusiasts alike.

Principle 10: Interdisciplinarity

The mathematical modeling that underpins Quantitative Finance does not originate exclusively within the financial domain. Disciplines such as Mathematics, Physics, Computer Science and Engineering have historically been—and continue to be—major sources of foundational ideas and methods.

This is reflected in the backgrounds of many professionals and researchers working in Quantitative Finance, a significant number of whom come from these fields.

At QuantPhi, we actively welcome and encourage contributions from all related areas of knowledge, including but not limited to those mentioned above. By embracing interdisciplinarity, we aim to enrich perspectives, strengthen modeling approaches, and foster a deeper understanding of quantitative finance as a whole.