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Real-World Applications of Data Science
Published in Pallavi Vijay Chavan, Parikshit N Mahalle, Ramchandra Mangrulkar, Idongesit Williams, Data Science, 2022
Algorithmic trading is a process that facilitates transaction decision-making in financial markets using advanced mathematical tools. It utilizes automated and preprogrammed trading instructions to account for finance applications. Algorithmic trading is capable of capturing profit-making opportunities happening in the market much before other human traders can even spot them. Algorithmic trading provides the following benefits: Trades are executed for the best possible and profitable market prices.They provide automated checks on multiple market conditions.This system reduces transaction costs.It reduces the risk of manual errors while placing trades.It reduces possible emotional and psychological factors that happen by mistakes.
Disruptive Financial Innovation and Big Data Implications in Digital Finance
Published in Mohammed El Amine Abdelli, Wissem Ajili-Ben Youssef, Uğur Özgöker, Imen Ben Slimene, Big Data for Entrepreneurship and Sustainable Development, 2021
Automation innovations arise from the curiosity of people who want to see how much artificial intelligence can be improved. Artificial intelligence is increasingly used to replace judicial functions in people's financial intermediation. Two main trends are robo-advice and algorithmic trading. Robot advisors have been emerged as a cost-effective tool for small-investment investors in the financial services market. They are mainly automated interfaces using investment algorithms and asset allocation models that are tailored to everyone's investment needs, providing investment advisory and on-demand investment management services without the intervention of a human consultant. Robo-advisors take the knowledge-based automation developments to a new level. Investment products can be fed with a considerable amount of information about risk classifications and new information and can be done for mapping tasks between this information and the investor information provided to them [63]. Algorithmic trading involves the use of computer-programmable algorithms for the automatic realization of purchases; in a simple way, these programs are another technological development that replaces human labor with machines that will be faster and more consistent than human judgments. In addition, more sophisticated data analytics with the use of more complex computers provides algorithmic trading.
The management of deep learning algorithms to enhance momentum trading strategies during the time frame to quick detect market of smart money
Published in Noura Metawa, Mohamed Elhoseny, Aboul Ella Hassanien, M. Kabir Hassan, Expert Systems in Finance, 2019
Khalid Abouloula, Ali Ou-Yassine, Salah-ddine Krit
Generally, algorithmic trading is the use of a computer program to create automation in one or several steps of the trading process, depending on the technology used, the objective or where in the trader pipeline the automation occurs. Algorithmic trading includes the following sub-categories [3]: systematic trading (ST), or systems that use trading strategies based on a set of predefined rules received as human input; and high-frequency trading (HTF), which is usually used in systems characterized with fast execution speed (on the order of milliseconds) and holding stock for a short time span. Usually an HFT algorithm proceeds without the intervention of a human; there is an infinite number of trading algorithms, but there are not too many winning solutions, because it brings a lot of money to hedge funds, consulting companies, brokers, traders and so forth.
A High-Frequency Algorithmic Trading Strategy for Cryptocurrency
Published in Journal of Computer Information Systems, 2020
Algorithmic trading can broadly be defined as the use of computer algorithms to automatically execute trading decisions on financial exchanges. It is most commonly used in well-developed financial markets such as U.S. equities or other developed market currencies. However, the usage of algorithmic trading in secondary asset markets, such as emerging market equities, continues to grow. While algorithmic trading models can be deployed over many trading horizons, trading volume on the basis of such models predominantly occurs during shorter frequencies, such as minutes, hours, or days. When trading horizons shorten even further – to the single minute, second, or even millisecond level – this becomes what is known as HFT. Such trading, by necessity, is conducted exclusively via algorithms.