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I believe that many investors have heard of quantitative trading, but for But I don’t know much about quantitative trading. This article will focus on the principles and benefits of quantitative trading, and why big funds and institutional investors like to use quantitative trading.
The concept of quantitative trading:
From a narrow perspective, quantitative trading is the use of very complex statistical methods and mathematical models to select a variety of "high probability" events that can bring excess returns from huge historical data to formulate strategies. Use quantitative models to verify and solidify these laws and strategies, and then use computers to strictly and efficiently implement the solidified strategies. This seems very lofty and seems to be only suitable for those who are proficient in statistics, finance and computers.
From a broad perspective, quantitative trading refers to the use of statistics, mathematics, computer technology and modern financial theory to help investors make better profits. These quantitative methods can be used only to analyze massive historical data, or they can be used to generate specific signals, or to control the size of positions, , risk control modules, etc. If you think about it from this perspective, you can be pleasantly surprised to find that quantitative trading and traditional subjective trading are no longer the binary opposition of and . Quantitative trading also includes subjective trading, such as the more common cross-market arbitrage strategies of futures and option volatility. Arbitrage and other semi-automatic transactions require traders to combine historical mean reversion and subjective interpretation of macro policies, adjust parameters before opening and let the computer strictly implement the strategy. Quantitative trading absorbs the essence of subjective trading and will inevitably become a historical development trend.
What is quantitative trading
Quantitative trading is a trading technology that derives trading strategies based on quantitative analysis. It identifies trading opportunities through mathematical calculations and numerical analysis . Past complete data are the basis for quantitative analysis, and price and quantity are the main variables in establishing mathematical models.
Understanding Quantitative Trading
Quantitative traders use computer programs , mathematics, statistics, and processing databases to make rational trading decisions.
uses mathematics to model it and then develops a computer program that applies the model to historical market data. The model is then tested and optimized. Implemented in actual real-time capital markets when favorable results are achieved.
The function of the quantitative trading model can be understood through analogies. At this moment when the sun is shining, the weather forecast says there is a 90% chance of rain. Meteorologists came to this illogical conclusion by collecting and analyzing climate data from sensors across the territory.
The computer's analysis program will produce these models, and when these models are the same as those in historical climate data (backtesting), if it rains 90 times out of 100, then meteorologists can confidently conclude that 90 out of 100 times it rains. The conclusion that there is a 90% probability that it will rain is the prediction. Quantitative traders apply the same process to make trading decisions in financial markets.
People usually explain q-Quant and P-Quant, or divide quantitative transactions from the perspective of seller/buyer quantification. Zhihu has many similar topics. Today, the author wants to talk about this topic from the definition of quantification itself.
1. Quantitative definition
Quantitative is the use of mathematical models (rather than the human brain) to determine the type, quantity, direction and timing of transactions. It is easy to understand that the core of quantification is to use mathematical models to replace the human brain, replace human perceptual and rational models, convert our investment logic into the mathematical language , strictly implement the model formulated by the trading rules, and determine the trading elements (varieties) , quantity, direction and timing).
The so-called heroes do not ask about their origin, and the same is true for the quantitative strategy of making heroes in real time.The quantitative strategy of
quality is to observe whether it is suitable for the current market, the current asset status and the current era background to record its real market, rather than judging by complex models, and its bottom is supported by the investment logic. This means that when people talk about quantification, they don't have to equate high frequency, deep learning, artificial intelligence , etc. A good strategy can be simple or complex, ultimately it is a consideration of the underlying investment logic.
How to Become a Quantitative Trader From the above statements, it is not difficult to find that its "quantification" is not as far-fetched as most people think. Does this mean that if we want to become a quantitative trader, it is not as difficult as we think?
As discussed in "The Definition of Quantification", most people tend to focus on the term "mathematical model", And ignores another core element of quantification-trading. In the entire construction process of quantitative strategies, it is first necessary to convert transaction logic into mathematical language, and then realize the conversion from mathematical language to program through tools such as programming languages.
At the bottom of this three-step two-step conversion process, it is backed by solid transaction logic. The cultivation of trading literacy takes time to accumulate, which is also a major advantage for traditional financial practitioners when they turn to quantitative work.
However, most quantitative teams have not yet achieved "fully automated trading". Due to the limitations of mathematical models, as well as the impact of black swan and cyclical cycles, most quantitative teams still maintain the artificial + intelligence approach to quantitative investment, which further improves quantitative researchers' understanding of transactions.
After realizing what underpinned the basic logic of quantification, I stopped worrying about rumors that quantization favored programmers over and started doing what I was doing.
In fact, with the development of AI intelligence in recent years, the foundation of quantitative trading has also made a qualitative leap and progress. Use profit quantification to see the future development trend. The world's first Yingshou "AI Stock Trading Robot Fully Automatic Trading Platform" that does not require programming and can be used by everyone is designed using innovative technologies such as big data, neural network , blockchain, special algorithms, and deep autonomous learning functions. .
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