In view of your good math and physics and a certain C foundation, my advice (and for all those who want to start quantitative analysis) is:

I. Mathematics

Continue to lay a good foundation in mathematics and learn the knowledge of set theory and statistics, so that you can transfer from primary economics to advanced economics in the future. If you haven't studied set theory and statistics in your freshman year, I suggest you take a course first. For example, Probability Theory and Mathematical Statistics (Douban) and Mathematical Statistics Course (Douban) written by Chen Xiru, a famous domestic statistician, are very well written. Please try to understand them carefully.

2. Economics &; finance

The math foundation is ok. If you think it's simple, read some books on econometrics and intermediate microeconomics to see if you can understand them.

For example, Introduction to Econometrics (Douban) is a good book, as well as the classic Fan Lian intermediate microeconomics textbook: Microeconomics (Douban) and the classic Bible: Options, Futures and Other Derivatives (5th Edition) (Douban).

Of course, if you want to go abroad for further study, reading the English version is also a good choice.

But if you want to make better use of your time, reading the Chinese version is also a good choice, because reading the Chinese version is definitely much faster than reading the English version.

Three. Computer and programming

1. computer

If you haven't studied computer-related knowledge before, I suggest you read a book to understand the general working principle of computers. Recommended reading: Introduction to Computer Science (1 1 Edition) (Douban).

2. Language

Well, since you want to learn financial knowledge, I suggest that you can choose python instead of learning C or C++ for the time being. Python is simpler and more powerful in mathematical analysis and scientific calculation. Recommended reading: Python Basic Course (Douban) is about python2.7. For beginners, it is better to read this book.

You can also refer to the statistical language R to understand python more deeply. I recommend R language programming art (Douban).

Then you can read a good book devoted to data analysis in python: Data Analysis in Python (Douban). (python and the corresponding Panda, scipy and numpy modules in this book are all based on python2.7-which is why python 2.7 is the introductory textbook I recommend. For beginners, the version problem is likely to be a pit, so it is better to learn python2.7. )

In addition, the Python standard library (Douban) is always available at home, and many problems do not need to be rebuilt.

3. In the future

Strictly speaking, the measurement method is just a traditional method. In order to cope with the future and become a liberal, it is recommended to learn knowledge about data mining, machine learning and artificial intelligence. Recommend books such as Introduction to Data Mining (Douban) and Machine Learning (Douban).

In addition: python, as an interpreted language, is not as good as compiled languages such as C, especially for high-frequency transactions. Just in case, it is recommended to enter the computer algorithm, data structure and computer system pit. . . . Of course, this is another big pit.

Four. practice

Now there are some online financial systems that can give you the opportunity to write your own model. You can pay more attention to it, or you can find a teacher to write a few trading models when you are free.

As far as I know, most people who write trading models are not comprehensive (comprehensive economy, finance, mathematics and programming). If you want to be better than them, you must lay a good foundation in these three aspects.

Finally, young man, I am surprised to see your bones. I give you this book "Douban". Write a trading model with genetic algorithm as the core, bottom optimization and evolutionary characteristics, especially when the data environment itself has evolutionary characteristics. -At least it sounds awesome ~ ~