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What we publish
SelfMadeQuant publishes written programmes, worked exercises, practical projects, free tools, Dispatch articles and a quantitative finance glossary. The aim is to connect the reasoning behind a method with its implementation and limitations.
The material is published under the SelfMadeQuant name. SelfMadeQuant is an independent learning platform; it does not award a degree or professional qualification, and studying here does not guarantee employment or investment results.
Read the manifesto for the purpose behind the platform, or explore the curriculum and a complete sample lesson.
Our editorial standard
A useful quant explanation should make its assumptions visible and give the reader a way to check the result. These are the standards we use to develop and improve the material:
- Show the reasoning. Define the quantities, state the assumptions, work through the calculation and explain its financial meaning.
- Make examples checkable. Give numerical baselines, units and reproducible code where they help. Distinguish illustrative inputs from observed market data.
- Credit the source. Link or cite the textbooks, papers, datasets and documentation used. A reference supports the relevant claim; it is not an endorsement of the whole platform.
- Explain the limits. Separate a model result, historical experiment and practical trading decision. Discuss failure cases rather than treating a good-looking backtest as proof.
- Keep changes honest. Use publication and update dates where available. A date or a publisher byline does not claim independent peer review or professional certification.
Learning material can contain mistakes. If an assumption, formula, example or source needs correction, send the page and the specific issue so it can be investigated.
Sources, tools and generated answers
Original diagrams, worked examples and project results appear alongside cited third-party material. Follow the source notes and linked repository for the scope, data and licence of the particular item you use.
The optional margin-answer feature produces AI-generated explanations from selected text and your question. Those answers can be wrong and should be checked against the lesson, its references and an independent calculation. A generated answer is not a separately reviewed editorial article.
For account help, billing controls and corrections, visit Support. The privacy notice explains the data used to provide the service.