Computer science has been developed for so many years, and there have been many different development directions. For example, artificial intelligence, web development, big data computing, cloud storage, etc. These are all very popular directions, and today I want to talk about the combination of finance and computer science-quantitative trading software. I think quantitative software engineer is a very popular profession in the future, with deep development prospects. Quantitative trading software is a promising development direction for the computer industry in the future.
First of all, quantitative trading software refers to a stock trading strategy that can automatically execute artificially set stock trading strategies based on the data of stocks or futures in the financial market in real time. In other words, the quantitative trading software written by programmers can buy and sell stocks by themselves. This kind of software is a product of the combination of artificial intelligence and finance. The biggest advantage of quantitative trading software is that it can obtain all the stock data in the market in real time, and can analyze it based on these data, and then choose to execute the trading strategy recorded in its database that is most conducive to the current market conditions. It is rational and does not have any emotions. This can beat many stock traders. Because a trader, as a human, will feel fear when stocks fall-fear that his stock will continue to fall; when stocks rise, he will feel greedy-want to make more profits. These human emotions sometimes hinder traders from implementing rational trading strategies, leading to loss of more money, or loss of more profits. Quantitative trading software is not the case. As an artificial intelligence, it will only mechanically implement the optimal trading strategy it believes.
However, the financial market changes rapidly, and sometimes the strategy of quantitative trading software may not always bring profits. Sometimes sudden news in the financial market will break the routine, and quantitative trading software will lose money because it fails to keep up with the market changes in time. I think this is also the direction that quantitative trading software needs to work hard to develop and change. As a quantitative trading software engineer, his role is to develop stronger, faster, more accurate, and even predictable future quantitative trading software. Because there are many quantitative trading software in the financial market now, each software has a different trading strategy and code behind it. How to beat other software or other participants in the market? It is a question that every quantitative software engineer needs to consider.
Stronger! Quantitative trading software requires stronger trading strategies. The market is dynamic, and no trading strategy can always win. Therefore, the trading strategy of the software should be updated frequently, and the code responsible for the implementation of the strategy should also be updated frequently. In addition, the quantitative trading software engineer also needs to make his software have stronger artificial intelligence. Stronger artificial intelligence means that this software can analyze the market consensus behind each transaction and the current market trading sentiment based on the transaction data in the market. Then this stronger artificial intelligence can make better use of this information to defeat other opponents.
Faster! Quantitative trading software requires faster response speed. There are a lot of stocks listed now, and there is a lot of news about different stocks every day, and there is also the linkage between the futures market, the bond market, and the stock market. It can be said that there are many news and factors affecting the stock market. This means that engineers need to make quantitative trading software have a faster response speed. Because the financial market is lagging. Not everyone can know the news immediately, and can immediately respond to the news. If your software can respond more quickly means that it can quickly predict the impact of the news on the market, and then respond, then the software will be able to eat more profits, or even exceed the expected profits. On the technical level, this means that engineers need to write a stronger python crawler to get more updated news, and also need to write algorithms that require less time complexity to reduce the reaction time of the software.
More precise! A quantitative software that can accurately predict future market trends is very powerful. There are many researches on artificial intelligence. Some artificial intelligence can predict weather changes, some artificial intelligence can predict crop yields, and some can monitor factory emissions data and so on. In the future, these forecasting or monitoring environment, economic artificial intelligence software will be very many. And if the artificial intelligence of a quantitative trading software can obtain the data researched by these other types of artificial intelligence software, and build an economic model of the future world based on these data, so as to very accurately predict the future economic data, it will be very profitable of. Because everyone has different expectations for economic data, some people think it will be very good, some people think it will be very bad. But your quantitative trading software knows the most accurate data in the future, then you can use these data to design the best strategy. When the stock price is too high, short the stock, and when the stock price is too low, buy the stock. The dream of buying at the lowest point of the stock price and selling at the highest point of the stock price will become a reality.
In conclusion, the development of quantitative trading software is a very promising computer development direction. When it develops to the extreme, it is very possible to achieve a condition that in economics can never be achieved in reality-all traders in the market have exactly the same news. Because all quantitative trading software can be based on all current economic data and news. And when the future economic data can be predicted, then there will be no information gap between traders. This may also be the charm of computer science, creating new things and realizing impossible theories.
