CaoYuan's Blog

This chapter introduces common convex and non-convex optimization problems in quantitative research and the corresponding solutions. For convex optimization problems, we use the `yalmip` framework; for non-convex optimization problems, we use the DE algorithm. At the application level, this chapter also introduces how to construct mean-reverting portfolios and detect insider trading in A-shares using these tools.

This chapter will mainly introduce the basics of strategy development, including Introduction to common types of strategies; Common strategy evaluation indicators; Strategy development process; Frequently asked questions about strategy development; Strategy development and backtesting using TqSdk.

This chapter will mainly introduce the basics of strategy development, including Introduction to common types of strategies; Common strategy evaluation indicators; Strategy development process; Frequently asked questions about strategy development; Strategy development and backtesting using TqSdk.

This chapter will mainly introduce the basics of strategy development, including Introduction to common types of strategies; Common strategy evaluation indicators; Strategy development process; Frequently asked questions about strategy development; Strategy development and backtesting using TqSdk.

This chapter will mainly introduce the basics of strategy development, including Introduction to common types of strategies; Common strategy evaluation indicators; Strategy development process; Frequently asked questions about strategy development; Strategy development and backtesting using TqSdk.

In this chapter, we will introduce the programming languages that may be used in strategy development, including MATLAB, Python, R, Julia, etc. In this chapter, we will compare their features (including syntax, speed, richness of libraries, etc.). In this chapter, we will compare their features (syntax, speed, library richness, etc.), and finally, we will give an example of multi-language collaborative development.

Most people don't really understand the marvelous markets we are in. Starting with the efficient market hypothesis, this chapter explains the fundamental characteristics of the markets we live in randomness, chaos, and time-varying nature, through the construction of stochastic portfolios, statistical tests of randomness, and the successes and failures of machine learning in the markets.

Most people don't really understand the marvelous markets we are in. Starting with the efficient market hypothesis, this chapter explains the fundamental characteristics of the markets we live in randomness, chaos, and time-varying nature, through the construction of stochastic portfolios, statistical tests of randomness, and the successes and failures of machine learning in the markets.

Most people don't really understand the marvelous markets we are in. Starting with the efficient market hypothesis, this chapter explains the fundamental characteristics of the markets we live in randomness, chaos, and time-varying nature, through the construction of stochastic portfolios, statistical tests of randomness, and the successes and failures of machine learning in the markets.

Most people don't really understand the marvelous markets we are in. Starting with the efficient market hypothesis, this chapter explains the fundamental characteristics of the markets we live in randomness, chaos, and time-varying nature, through the construction of stochastic portfolios, statistical tests of randomness, and the successes and failures of machine learning in the markets.