According to a market report, published by Sheer Analytics and Insights, the global algorithmic trading market was $15.6 billion in 2022 and it is expected to reach $119.9 billion at a CAGR of 19.2% between 2023 and 2033.
Algorithmic trading, also known as automated trading, black-box trading, or algo-trading, involves placing a deal using a computer program that adheres to a predetermined set of guidelines (an algorithm). Theoretically, the deal can produce profits at a pace and frequency that are beyond the capabilities of a human trader. The specified sets of instructions can be based on a mathematical model, time, pricing, quantity, or any other factor. In addition to providing the trader with prospects for profit, algo trading increases market liquidity and makes trading more organized by minimizing the influence of human emotions. In other words, using intricate formulae, mathematical models, and human monitoring, algorithmic trading makes judgments about whether to buy or sell financial instruments on an exchange. High-frequency trading technology, which allows a company to execute tens of thousands of trades per second, is frequently used by algorithmic traders.
The requirement for the algorithmic trading industry is anticipated to be driven by elements including favorable governmental rules, rising demand for quick, dependable, and efficient order execution, rising demand for market surveillance, and declining transaction costs. Algorithmic trading is used by large brokerage firms and institutional investors to reduce the expenses of bulk trading. For instance, in recent years, especially in the last ten years, FinTech tools have been developed to increase the capacity of the financial industry, and algorithmic trading has dominated the capital markets, particularly the trading business. Several market entry barriers were lowered as a result of the digital revolution. The general public now has access to data science tools, high-speed internet, and computing power. The proliferation of online trading platforms and apps has increased the accessibility of trading financial items. It now only takes a few mouse clicks to trade stocks, futures, and currencies.
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Moreover, the financial services industry's adoption of AI, ML, and big data is anticipated to play a significant role in the market growth for algorithmic trading. Because of the advancements in technology, regulators are also beginning to pay attention to the ways that people engage with the market. Some of the biggest institutions in the world began implementing such technologies to advance algorithmic trading. Throughout the projected period, the market for algorithmic trading will see significant potential due to the rising adoption of cloud-based services, cloud computing, and cloud-based trading solutions. When placing trades, traders employ cloud services for run-time series analysis, backtesting, and trading techniques. Because it is expensive to establish one's data centers for services like backup and recovery, data storage, trading networks, and data management, traders choose cloud computing. Renting space from the cloud is therefore more practical than creating hardware or software infrastructure.
On the other side, lack of observation and weak risk valuation skills could limit market growth during the forecasted timeframe. However, the market's expansion is being hampered by poor risk valuation skills. Furthermore, the factoring market is projected to experience profitable growth prospects in the years to come due to the increased adoption of machine learning, NLP, and artificial intelligence.
According to the study, key players dominating the global algorithmic trading market are Argo SE (U.S), Automated Trading Softtech (India), Financial Technologies Group (India), Hudson River Trading (U.S), iRage Capital (India), InfoReach (U.S), Kuberre Systems (U.S), MetaQuotes Software (Russia), Symphony Fintech (India), Software AG (Germany), Tata Group (India), The WoodBridge Company (Canada), UTrade Solutions (India), Virtu Financial (U.S), among others.
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