[PDF] Which level of the market is algorithmic exchanging? - openpr.com

The global algorithmic trading market was valued at US$ 10,346.6 million in 2018 and is projected to reach US$ 25,257.0 million by 2027, exhibiting a CAGR of 10.7% over the forecast period

Algorithmic trading, or algo trading, is useful for computer programs and software to execute trade based on pre-defined criteria and without any human intervention. In algorithmic trading, mathematical models are used to analyze every quote and trade in the stock market, turn the information into intelligent trading decisions, and identify liquidity opportunities. It is a computer directed trading, which cuts down transaction costs and allows investment managers to take control of their own trading processes. The major factor driving adoption of algo trading is rising number of financial organizations opting for automation in trading processes. Moreover, companies in economies such as India, South Korea, and others are focused on expanding their presence in the global market with diverse portfolio, which is expected to create high demand for algo trading processes. Additionally, increasing adoption of cloud-based solutions and services has further boosted growth of the algorithmic trading market.

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Key Takeaways of the Market:

According to ’ analysis, Europe is the second largest market in the global algorithmic trading market and is expected to witness high growth over the forecast period. This is attributed to increasing adoption of cloud-based solutions, services, and cloud computing in the region. According to , in March 2017, overall cloud adoption rate reached a record high of 88% in the U.K., an increase of 5% from 2016.

Some of the major players operating in the algorithmic trading market include AlgoTrader GmbH, Trading Technologies International, Inc., Tethys Technology, Inc., Tower Research Capital LLC, Lime Brokerage LLC, InfoReach, Inc., FlexTrade Systems, Inc., Hudson River Trading LLC, Citadel LLC, and Virtu Financial.

The common strategies used in algorithmic trading

Many strategies can be coded into trading algorithms, and different traders use different strategies. While there are many strategies out there for both discretionary and automated trading, the common strategies for the algorithmic trading category are the following:

Mean-reversion strategies
Trend-following strategies
Breakout strategies
Biased strategy
Key Trends and Analysis of the Global Algorithmic Trading Market:

Among applications, the commodity trading segment is expected to exhibit the highest growth rate during the forecast period. Trading firms mostly use algorithmic trading as it eliminate human errors. Algorithmic trading is used across various applications in the finance industry such as forex, stock markets, commodities, bonds and cryptocurrency. Commodity trading is one of the dominating markets in algorithmic trading. For instance, according to a paper published by Commodity Futures Trading Commission (CFTC), in the U.S. trading market, the level of automation and speed of trading is increasing. This increase has been more prevalent for asset classes or contract groups where automation penetration had been low compared to rest of the market. Furthermore, according to CFTC (The U.S. Commodity Futures Trading Commission), on the basis of trading activity from November 1, 2014 to October 31, 2016, trading in energy-related contracts in the prior period (before 2014) was around 47% automated. In 2016, this had increased to around 57%; agricultural commodities had increased similarly from 38 to 48%, FX (foreign exchange) increased from 80 to 83%.

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