AQFTM Syllabus

• Basics
• Theories and Practice

1. Section	Weight	Introduction
   Financial Foundation	25%	This section explains the core financial basic knowledge that needs to be applied when making quantitative investments, including quantitative analysis, portfolio theory, financial statement analysis, equity investment products, fixed income investment products, derivative products, etc.
   Quantitative Investment Theories	15%	This part gives a comprehensive and basic introduction to the principles of quantitative investment, helping candidates to build quantitative financial analysis thinking and consolidate their theoretical basis. In addition, candidates could set up a helicopter view of the whole quantitative investment process by studying common quantitative trading models in this part.
   Introduction to Python Programming	25%	This part gives a comprehensive and basic introduction to the core grammars of Python programming, and helps candidates to master basic programming skills and develop programming thinking, which can be applied to the development and backtesting of quantitative strategies.
   Python for Financial Data Analysis	20%	This part gives a comprehensive and basic introduction to popular Python libraries for data analysis such as NumPy and Pandas to help candidates to master basic Python data analysis methods which can be applied to financial data analysis.
   Backtesting Methods of Quantitative Strategies	15%	This section demonstrates Python implementation and backtesting of classical quantitative trading strategies to help candidates obtain a general understanding of the development process and backtesting methods of quantitative trading strategies.

2. Section	Weight	Introduction
   Database Foundation	20%	This part introduces the popular databases used in quantitative finance, and methods of using SQL to operate in SQLite database. Through the detailed explanation of the design ideas and code implementation of an automatic repository toolkit, candidates can master the method of using SQL in practice, and lay the foundation for further use of database in quantitative transaction.
   Python Realization of Quantitative Strategies Backtesting	30%	This part further enhances candidates' abilities to develop and backtest quantitative strategies based on Backtesting Methods of Quantitative Strategies in Part I. Candidates are required to independently complete the compilation and backtesting process of quantitative strategies, including data acquisition, strategy development, strategy evaluation, etc.
   Artificial Intelligence and Machine Leaning Strategies	15%	This part systematically introduces the commonly used Artificial Intelligence (AI) and Machine Learning (ML) strategies, acquainting candidates with basic AI and ML algorithms, which can be applied to the development of quantitative trading strategies.
   Quantitative Trading Platforms	20%	This part discusses some popular quantitative trading platforms, helping candidates get familiar with and master the commonly used platform operation skills and develop object-oriented programming thinking.
   Practice of Quantitative Risk Management	15%	This section introduces the credit risk and market risk modeling methods in banks, internet finance and other financial institutions, promoting candidates’ understanding of quantitative risk control methods in practice.