After in-depth conversations with many quantitative analysis experts, I have summarized the following guiding opinions for your reference to learn quantitative trading. They are: financial literacy, mathematics knowledge and programming.
First of all, let's talk about the basic preparation of financial knowledge
Quantitative trading is rooted in the trading activities of the financial market, so for the new contact with quantitative trading, it is very important to understand the operation of the financial market, you need to understand the concept of the financial market, trading methods and various financial products, and to know that **, options, bonds, foreign exchange, etc. have their own risk characteristics, and at the same time need to distinguish the characteristics of the primary market and the secondary market, and master the corresponding skills. If you don't have a deep understanding of the nature of finance (e.g., matching trades, allocating funds through market mechanisms), it will be difficult to find the right trading strategy. Therefore, the first task is to learn the basic concepts of finance and lay a good foundation for yourself.
After learning the basic financial knowledge, let's further learn the knowledge related to computer programming
Due to the complex nonlinear behavior of the financial market and the unsteady nature of financial data, it is often difficult for traditional mathematical models to fully explore the connotation of the financial market, and it is not easy to achieve the ideal effect based on personal experience or mathematical models. This is because when exploring financial markets such as **, human research may only touch on a local optimal solution, and the real global optimal solution may be beyond the scope of traditional quantitative methods, but the introduction of programming has made quantitative trading truly possible. If you are a beginner, it is recommended to start learning with Python, learn how to process big data, and master the ability to transform mathematical models into computer programs.
After the above two parts are fully prepared, let's move on to the more advanced one, that is, mathematical modeling:
It is important to know that the classic quantitative investment strategy relies on the establishment of mathematical models to explore the laws of the market, so quantitative traders must master basic mathematical and statistical knowledge, especially probability theory, statistics, linear algebra, calculus, etc., which constitute the cornerstone of various models and algorithms in quantitative trading. If you are a college student, it is recommended to actively participate in mathematical modeling competitions, especially competitions on finance-related topics, which will help improve your modeling skills, and in the process of participating in the competition, you can also learn the trading strategies of other contestants, such as the principles, advantages and disadvantages of strategies such as mean reversion, trend following, arbitrage, and other strategies, advantages and disadvantages, and applicable scenarios.
Speaking of these trading strategies, I just want to give you a brief introduction to these strategies:
1.The mean reversion strategy uses the difference between the share price and ** to trade, and is suitable for environments where the stock price is less volatile.
2.The trend-following strategy trades in the direction of the stock price movement and is suitable for situations where the stock price fluctuates greatly.
3.The arbitrage strategy trades through the difference in different markets or time points, and is suitable for markets with ** differences.
In the process of learning quantitative trading strategies, it is recommended that you practice different strategies by simulating and practicing them by yourself, and evaluate their effectiveness and stability to understand the advantages and disadvantages of each strategy and the applicable environment. The above is my professional knowledge sharing, I hope it can help you.
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