QuantWeft by Yuxiao Ma

QuantWeft

A Bilingual Course in Quantitative Finance and Systematic Research

Web textbook

Read 45 chapters with full-text search, same-chapter language switching, and day/night modes. The web edition is live. PDF downloads link to GitHub and require access while the repository is private.

From mathematical and financial foundations to reproducible systematic research.

A bilingual course for learners who want to understand models, implement them, and evaluate research carefully. The textbook, active exercises, and Notebooks connect concepts, derivations, and code.

45 chapters / 6 projects / 1 Capstone / Complete textbooks and workbooks in Chinese and English

Start here

Read	Practice	Run code
English learning guide Follow the prerequisites through 45 chapters	English workbook Work independently, then check complete answers	Notebook guide Start with constructed-data examples

Curriculum · Online reader and website preview · Chapter texts · Repository guide

Choose how to read

	English	中文
Textbook: concepts, derivations, and worked examples	Read the textbook	阅读中文教材
Workbook: active recall, exercises, and complete answers	Read the workbook	阅读中文练习册

Read the textbook, attempt the workbook independently, then run the Notebooks to check calculations and outputs. Build the online reader for chapter navigation, full-text search, and same-chapter language switching, or download the PDFs for offline reading. This repository does not include the editorial workspace's full PDF layout toolchain.

Learning route

Mathematical and financial foundations → Statistical models and asset pricing → Strategy testing and research engineering

Begin with Python, data, and timing contracts. Progress through regression, optimization, time series, and portfolios to out-of-sample validation, risk, costs, execution, and financial machine learning.

See the learning route and chapter dependency graph for prerequisites.

Connect ideas through projects

Foundational applications	Strategy research
Financial data analysis: returns, wealth, volatility, and drawdown	Multi-factor equity strategy: signals, universes, costs, and robustness
CAPM and Fama-French: regression, exposure, and inference	Pairs trading: training-window selection and out-of-sample ledgers
Portfolio optimization: rolling estimation and baseline comparison	Machine-learning alpha: date-based splits, baselines, and stability

The Capstone connects the research question, timing contracts, protocol, execution, and report. Negative results can pass; market research has separate data and authorization gates.

Run your first Notebook

Both languages share code, data contracts, dependencies, and a runner. Notebook code cells are identical; explanatory Markdown is available in each language.

Run these commands from the repository root. The project declares Python ≥ 3.12; this lock file targets the checked macOS arm64 / CPython 3.14.7 environment. Other platforms have not been validated.

python3 -m venv .venv
.venv/bin/python -m pip install -r requirements-macos-arm64-py314.lock
.venv/bin/python -m ipykernel install --prefix .venv --name python3 --display-name 'Reader Python'
.venv/bin/python run_notebook.py editions/en/notebooks/ch35_execution_ledger_constructed.ipynb

This is Chapter 35's constructed execution-ledger example. Dependency installation may use the network; default teaching execution is offline. The Chinese Notebook runs the same code. See the Notebook guide for more.

Keep exploring

Glossary · Notation and units · References · File navigation

Edition, validation scope, and publication status

This repository inherits the accepted local formal bilingual edition of October 19, 2026. See Local validation scope for existing textbook and offline engineering evidence. This homepage redesign does not constitute a new course or research run.

The default route uses constructed data. French market data are not distributed, and real-market research has not been run. Historical results and unresolved evidence gaps retain their recorded scope; see Remaining work and Optional data contracts.

By Yuxiao Ma. Original code uses MIT; original educational content uses CC BY-SA 4.0, subject to scope and third-party exceptions. The repository remains private pending the public-release rights review. See Repository scope and provenance.

Exercise design credit

Parts of the workbook’s exercise design and layout draw on the loom-notes LaTeX template, selectively adapted to the course. The template is not used in full. See Attribution and acknowledgements.

Contribute and support

Send learning feedback or read the contribution guide. The repository is private, so issues and PRs require access. Voluntary support helps maintain the bilingual course; learning and licensed reuse do not require payment.

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Choose any amount in the corresponding app and verify the recipient. Open either image for the original size. The repository does not process payments; availability depends on the provider.

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