Quantitative trading is actually like the species in "The Origin of Species" . With the development of computers and intelligent robots , investment quantification is an inevitable product of the development of the times . The so-called survival of the fittest is the survival of the most capable . In the thriving development environment of modern technology , the various advantages of quantitative exchanges have made global investors have to spend huge manpower, material and financial resources on the research and development of robot quantitative trading.
Due to the characteristics of the quantitative trading strategy itself, it first has relatively strong plasticity . As a trading strategy constructed and made by quantitative means, it can be accurately measured in the construction process and decision-making process. Relatively speaking, subjective trading and quantitative trading , although quantitative trading results can be obtained in the process of review, etc., due to the lack of overall accurate measurement ; Does not have a stable depiction ability.
This feature brings another advantage of quantitative trading strategies, namely verifiability . Since future data is not available , in fact traders largely rely on test results on historical data when judging a trading strategy . However, subjective traders include human judgment in the review or other testing process, so historical verification becomes a part that cannot be accurately repeated, lacks stability and persuasiveness, which is a serious problem for this type of trading strategy Defects.
Quantitative trading strategies do not have this problem. On the basis of constructing and expressing quantification, a large amount of historical data backtesting can obtain consistent results. If the test result is positive, it can at least show that the quantitative strategy has profitability in the historical test.
The above two characteristics make quantitative trading strategies more objective. Since quantitative means dominate the process of building trading strategies, and trading decisions have clear quantitative rules, quantitative trading strategies can largely avoid the subjective assumptions of strategy developers, and always obtain Treat it objectively.
At the same time, the characteristic of clear quantitative trading decision-making rules also makes it possible for traders to completely eliminate the interference and influence of emotions on the entire trading process when implementing quantitative trading strategies. Based on quantitative rules, the quantitative trading strategy itself is quite independent, and can completely guide the entire trading process without the subjective judgment of traders. However, subjective trading strategies inevitably have a certain bias because they require continuous human control and judgment in trading operations. Many related issues have been studied in behavioral economics, including loss aversion bias, overconfidence bias, reference point bias , etc., which are some inherent behavioral biases of human beings. When these objectively existing biases affect the actual execution of trading strategies, it is inevitable that the expectation of trading results will deviate from the optimal point.
Although quantitative trading strategies cannot help us completely avoid these problems, quantitative frameworks and rules can indeed minimize the damage caused by these unstable factors.
The exclusion of emotional operations mentioned above actually includes the consistency that traders often talk about, that is, to ensure that the same trading rules are used during the execution of trading strategies, including buying points, selling points, trading Determination of position size and so on. If traders do not take the initiative to add human judgment when implementing quantitative trading strategies, quantitative trading rules can help them complete this task easily. More importantly, the quantitative trading strategy can achieve the consistency between the historical verification process and the actual trading behavior, because whether it is a real trading decision or a historical backtest , the trading rules referred to are all precisely defined by quantitative expressions . This overall consistency cannot be guaranteed by most subjective trading strategies.
The quantitative characteristics of the quantitative trading strategy and the consistency it brings make this strategy more portable. Unless specific quantitative factors are used, generally speaking, quantitative trading strategies are easier to transplant to other markets or assets after they are proven effective in one market or asset.
For example, when the quantitative data used by the strategy is limited to price, all markets where quotations exist can use historical data to verify this quantitative trading strategy. When the quantitative data used includes price and trading volume, except for a few cases such as the foreign exchange market, quantitative trading strategies can also be transplanted to most other on-site trading markets. The stronger the availability of data used by quantitative trading strategies, the stronger their portability.
At the same time, since quantitative trading strategies can easily obtain quantitative verification results, strategic characteristics such as returns and risks can be presented in the form of data . Therefore, when quantitative trading strategies are applied to multiple markets or assets, strategy developers can conduct horizontal comparisons through the intuitive form of quantitative results, so as to select markets suitable for a specific quantitative trading strategy, or Matching across multiple markets. Since the execution of the quantitative trading strategy does not require the subjective judgment of the trader, compared with the subjective trading strategy, it has the ability to cover a large number of markets and assets at the same time, which is very good for the diversification of the investment portfolio. auxiliary role. In reality, most quantitative funds hold a large number of assets and asset types to form investment portfolios , which is to use the characteristics of quantitative trading strategies to more conveniently disperse risks .
On the other hand, for quantitative trading strategies, the cost of strategy transplantation between multiple markets is very small , and in some cases it is not even necessary to change the original basic tools, thus reducing research and development costs and saving valuable resources. development time. Quantitative trading strategies can not only help save time and reduce costs in the process of strategy development, but also have very clear quantitative rules to guide transactions, so the strategy can be separated from human judgment in the actual use process, and the execution speed is faster . Fast, operational efficiency has been improved. Regardless of whether programmed execution methods are used, quantitative trading strategies can reduce the burden on people in actual transactions, and also reduce a lot of repetitive labor. For a controller of a trading strategy, he can focus more on the core strategy innovation.
In terms of strategy innovation, the quantitative trading strategy itself also has certain advantages. Today, with the continuous advancement of science, more and more advanced technologies have been created and applied to various situations. By combining the knowledge of multiple different disciplines and corresponding mathematical models, quantitative trading strategies are more likely to discover some hidden and complex data laws, which are often not easily perceived by subjective traders. Today, machine learning models such as neural networks, support vector machines, and hidden Markov models have begun to be frequently mentioned by quantitative trading practitioners.
In fact, as far as the industry as a whole is concerned, there is another advantage or characteristic of quantitative trading strategies that is not mentioned much, that is, it can help companies reduce their dependence on so-called star traders to a certain extent . Since in the trading process, it is no longer specific traders who make decisions, but a quantitative trading strategy with clear rules, so after the development of the quantitative trading strategy or even the construction of the core concept, the company's quantitative trading strategy The dependence on developers will drop rapidly, which can help the company reduce expenditures on staffing, and more importantly, improve the company's overall control over the transaction. The core advantage of the entire company system lies in the quantitative trading strategy itself, so it will not disappear very quickly due to personnel changes and other issues, which is conducive to the company's long-term stability in quantitative trading.
However, while this feature stabilizes the company, it actually harms the interests of developers of quantitative trading strategies.
Researchers of quantitative trading strategies lose certain value after the completion of strategy research and development, so they are not as powerful in bargaining as star traders in subjective trading in terms of salary and other aspects. This has also caused some quantitative trading practitioners to treat their work more negatively, and even choose to retain their core trading strategies, which implicitly damages the interests of the company. Fortunately, in many cases, even after the development of quantitative trading strategies is completed, they still need continuous improvement to adapt to the changing market environment. Therefore, there is still a continuous demand for quantitative trading strategy developers. Of course, to properly solve this problem still lies in the company's incentive policy.
Due to its clear and quantitative trading rules, the quantitative trading strategy will not only cause the above-mentioned shortcoming, but more importantly, these clear quantitative rules are very easy to copy. Quantitative trading strategies, like many technological innovations, are characterized by difficulties in research and development and easy replication . Even if the final trading rules are well kept secret, only some strategic concepts are leaked out. Compared with subjective trading strategies, quantitative trading strategies are more likely to be cracked by reverse engineering and leaked. On the one hand, this feature increases the cost of using quantitative trading strategies, and on the other hand, also increases the risks of quantitative trading strategies outside of trading. Some practitioners of quantitative trading are very cautious in the communication process . Some companies implement many rules and regulations when managing quantitative trading , or impose strict restrictions on employees ' contracts or even go to court , all of which are caused by the characteristics of quantitative trading strategies of.
There is another very important shortcoming of the quantitative trading strategy , which is the characteristic of quantification itself. It is true that this feature brings multiple advantages to quantitative trading strategies, but due to this feature, quantitative trading strategies can only adopt a more helpless approach of abandoning unquantifiable factors. Therefore, the quantitative trading strategy loses a lot of information that may actually bring profits, and also narrows the scope covered by the strategy when processing information. Of course, with the development of science and technology, some factors that could not be quantified before began to enter the research scope of quantitative trading strategies, such as investor sentiment portrayed in relation to network information and so on. However, even if technological means allow quantitative trading strategies to process a wider and deeper range of information, compared with subjective trading strategies, such defects are always something that quantitative trading strategies cannot completely get rid of . This kind of shortcoming derived from its own characteristics can only be improved but not cured.
At the same time, due to the quantitative method used in the construction process of quantitative trading strategies, a certain number of data samples are required for research, and the corresponding data are gradually generated over time. Therefore, when the construction form of quantitative trading strategies has no essential When the network changes, the quantitative features extracted from the data will only gradually change over time, and the transactions formed by the strategy can only change slowly. When there is a major change in the market situation, this slow-changing characteristic will cause the quantitative trading strategy to be unable to adapt to the market at the turning point, causing large losses in a short period of time. In comparison, some qualitative trading strategies are traded mainly based on logical thinking, so when the market situation changes, they can quickly make essential strategic adjustments based on subjective logic. The characteristic of slow turning is also a defect that is difficult to improve in quantitative trading strategies.