QTA Syllabus
- PART I
- PART II
Section Weight Introduction Basic knowledge in Quantitative Trading 15% This part consists of the basic knowledge to be applied in quantitative investment, including quantitative analysis, portfolio theory, financial statement analysis, equity investment products, fixed income investment products, derivatives, etc. Statistics Fundamentals 10% This section introduces the purpose of learning statistics, explains how to describe data sets, and gives some basic concepts of probability theory. Classified data analysis, regression analysis and model construction, analysis of variance of experimental design, nonparametric statistics and other statistic methods are introduced in this part as well. Quantitative Investment Theory 15% This part gives a comprehensive introduction to the principle of quantitative investment which leads candidates to build quantitative transaction analysis thinking and lay a solid theoretical foundation. In addition, through the common cases of quantitative trading strategy model, candidates should fully understand the whole process of quantitative investment. Python Fundamentals 20% This part gives a basic introduction to the core syntax of Python programming language to establish basic programming skills and programming thinking. It also gives an explanation to the commonly used data analysis libraries numpy and pandas, which would be used in quantitative strategy development and back testing. Python Data Analysis Basis 20% This section introduces the python common data acquisition and storage methods, common data processing methods, time series analysis methods, and through data processing analysis of actual combat cases, which establishes candidates a good understanding of common methods in data analysis. Quantitative Trading Back Testing Method 20% This part introduces the python implementation and back testing process of classic quantitative trading strategy which establishes candidates a basic development ideas of quantitative trading strategy, and getting familiar with the development process and back testing method. Meanwhile, options pricing, optimal portfolio, value at risk and other cases are introduced as well in this section to help candidates fully understand the practical methods of quantitative trading. Section Weight Introduction The Foundation of Database 25% This section introduces the database, specially the SQLite, and the method on using SQL language to access the database. By well explaining the design, coding and applying method of the automatic repository toolkit, it helps candidates to understand how to use SQL language so as to prepare for the use of quantitative transaction database. Implementation and back-testing of Python in quantitative trading strategy 30% Based on the basic of quantitative trading – "back-testing on quantitative trading strategy", it improves the candidate's ability of development and back-testing on quantitative trading strategy. It requires candidates to complete the development of strategy and back testing process independently, including acquiring data, developing and evaluating strategy, etc. Artificial Intelligence and Machine Learning Strategies 25% This section systematically introduces the popular strategies in AI and machine learning. It helps candidates get familiar with the basic knowledge of AI and machine learning algorithms, and apply in the development of quantitative trading strategies as well. Quantitative Firm Offer Transaction 10% This section introduces the idea of object-oriented programming (OOP) and the method of building quantitative trading platform by using OOP. It introduces the way of operating on Uqer and IB platform to help candidates understand the operation methods of frequently-used quantitative trading platform and construct object-oriented way of thinking. Quantitative Risk Management 10% This section introduces the method of modeling the credit risk and market risk in banks, Internet Finance and other financial institutions. It helps candidates understand the method on quantitative risk management.