Computational science (CSE) develops mathematical models and algorithms and uses high-performance computing to simulate them. These models help us predict things from atoms and molecules to whole materials, complex engineering systems, and natural and man-made disasters.
Scientists and engineers develop computer programs, application software, that model the systems they study and then run these models with various sets of input parameters. This process requires massive amounts of calculations that are performed on supercomputers.
Algorithmic Trading
Computational science is a broad discipline that involves creating models and simulations of real-world changing conditions, like weather patterns, automobile body distortions in crashes, the motion of stars in a galaxy or an explosive device. Computer programs solve equations to model and predict these changes and then perform the simulated steps required to reach the next state.
Algorithmic trading is a way for traders to execute trades using pre-programmed instructions. It reduces transaction costs and allows traders to make multiple trades without having to stay glued to their computers throughout the market hour.
However, this method has its downsides. For example, it can be difficult to identify when an asset is poised for a mean reversion. Also, the initial investment to set up the software and infrastructure can be costly.
High-Frequency Trading
High-frequency trading is the practice of using computer algorithms to trade stocks and bonds at whiz-bang speeds. It's a lucrative field for those who can afford it, but critics argue that it's harmful to long-term investors.
For example, if the price of a euro in London drops two cents while it's going up in New York, high-frequency traders can take advantage of an arbitrage opportunity that lasts only 0.5 seconds. In that time, a high-frequency trader can buy euros in London and sell them almost instantly in New York, making two cents per coin.
Computational science is a Applications of Computational Science broad area of study that incorporates many different disciplines, including mathematics, applied math, data science, and engineering. It's also at the heart of modern technological fields like machine learning and artificial intelligence.
Computational Social Science
Since the rise of smartphones and social media, our ability to track human behaviour has expanded enormously. Researchers in computational social science take this huge pool of data and use it to answer long-standing questions about how people behave, and explore new questions too.
This involves analysing vast databases of information, often working closely with linguists, and requires knowledge of programming. One example is the study of tweets sent during Australia’s bushfire crisis to assess perceptions of blame, risk and levels of emergency response.
These projects, however, are not always easy to carry out ethically. Research that explores social dilemmas and game theory may not have any impact beyond scientific publication, while studies involving underserved groups face complicated ethical considerations. It’s important for CSS professionals to consider these issues when carrying out their work.
Computational Finance
Computational finance is a broad field of applied computational science that deals with practical financial issues. It utilizes math, statistics, and programming to handle the many complex systems involved in financial trading and asset management.
A large part of this involves analyzing big data and predictive economic models. This helps avoid investing huge amounts of money in something that doesn't have a solid chance of succeeding. Vast amounts of data can be retrieved from social media and other sources, and then used to analyze human behavior and predict future trends.
Using this method also speeds up the decision making process. This can save companies a lot of money, especially when it comes to making decisions about business restructuring. For example, it can help them see that a new product or business structure isn't going to work out the way they expected, which could save them millions.
Computational Biology
Computational biology uses computational modeling and data analysis to understand biological systems and relationships. It is a broad field that encompasses aspects of computer science, chemistry and biology.
The work of computational biologists often provides the basis for new experimental methods and techniques. For example, computer scientists have developed efficient algorithms for DNA sequencing and mapping. They have also helped improve protein modeling, which is used in drug design.
In addition to providing new tools, computational biology can help create a new paradigm of research. It can allow for more efficient experimentation by eliminating the need for manual repetition. It can also help ensure that results are valid by applying mathematical and statistical methodologies. However, it is important for researchers to keep in mind that computational results can be biased. They must be interpreted with caution, and the results should always be verified by laboratory experiments.
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