匡醍量化|大富翁量化 匡醍量化|大富翁量化 Reading hubs by topic — counts reflect available English editions. Also browse the tag cloud.
Python is the native language of quantitative finance. This series begins with the core syntax of NumPy and Pandas, covering vectorized computation, data cleaning, visualization, and engineering practices (virtual environments, unit testing, continuous integration), and concludes with a 20-part series of comprehensive exercises. Ideal for beginners and advanced practitioners deploying Pandas in production.
Factors are the raw materials of quantitative strategies: how to find, validate, and combine them. This series covers the full practice of factor mining and backtesting—from momentum and low turnover to LOF arbitrage—including Alphalens factor analysis, quantstats performance attribution, A-share case studies, and common pitfalls (look-ahead bias, overfitting, crowding). Whether you’re building your first factor or optimizing multi-factor portfolios, this guide offers a difficulty-graded reading path.
Development Efficiency Spotlight: Practical testing and selection of AI coding assistants (Augment / OpenClaw / Cline), development toolchains, and terminal automation practices. Quantitative research is largely a competition in tool efficiency; this document records the tools and methods we have used.
Machine learning and LLMs are reshaping quantitative research. This special issue covers classic practices such as GBDT regression forecasting, XGBoost custom objective functions, and neural network time-series modeling, while also documenting real-world experiments using large models for factor mining, news analysis, and agent-based trading—including where they work and where they fall short.
Algorithms and High-Frequency Trading: Order book microstructure, options and volatility, market making and arbitrage, low-latency engineering. Covers risk management details from Monte Carlo simulations to HFT strategies, with a focus on engineering implementation and trading mechanisms, suitable for readers with programming skills seeking to deepen their understanding of trade execution.
News Special: Quantide Loop Daily Highlights and Weekly Reports covering market hotspots, major tech company updates, and quantitative career opportunities. Due to time-sensitive nature, these serve as entry points for market monitoring and topic selection; for systematic learning, please access other topics from the left sidebar.
A realistic portrait of the quant industry: job search strategies, interview guides, and skill checklists, alongside personal stories and industry insights. From first internships for new graduates to career direction choices for senior researchers; from hiring preferences at domestic private equity firms to work cultures at overseas hedge funds, this book offers actionable, experience-based guidance.
Quantitative Finance Essentials: Market Microstructure, Trading Mechanisms, Derivatives Fundamentals, and QMT Live Trading Integration. Ideal for beginners seeking a comprehensive conceptual framework; also includes practical guidance on configuring live trading interfaces (XtQuant/QMT) and common pitfalls, helping you build a solid foundation before getting started.
Backtesting is the most self-deceptive stage in quantitative research. This series focuses on backtesting framework selection, performance evaluation (Sharpe ratio, drawdown, turnover), risk control, and classic pitfalls like look-ahead bias—using real cases to explain why strategies that "look great" end up losing money.
Data is the foundation of quantitative research: selecting sources, defining storage formats, and choosing query engines. This series addresses selection-level questions—how to choose A-share data sources (Tushare/AkShare/QMT/BaoStock), determine storage formats (HDF5/Parquet/CSV), and select databases (DuckDB/SQLite/ClickHouse)—providing empirical comparisons and migration paths.
Course Overview: Quantitative Finance 24 Lessons – Series Overview, FAQs, and Learning Path. From Python basics to factor implementation, completing the courses in order will equip you with the ability to independently conduct a complete strategy research process.
Research Reports and Papers: Interpretations and reproductions of classic materials such as the Barra Risk Model Manual and "Seeking Alpha," along with practical methods for accessing academic resources for free. Ideal for researchers who want to thoroughly engage with primary sources rather than relying solely on secondary summaries.
Product and Ecosystem: Installation, configuration, maintenance, and troubleshooting of the Daifuweng open-source framework, along with the open-source tools and free resources we use. Ideal for readers seeking to quickly set up a quantitative research environment.