Algorithmic trading

Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. This type of trading attempts to leverage the speed and computational resources of computers relative to human traders. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders. A study in 2019 showed that around 92% of trading in the Forex market was performed by trading algorithms rather than humans.

It is widely used by investment banks, pension funds, mutual funds, and hedge funds that may need to spread out the execution of a larger order or perform trades too fast for human traders to react to. However, it is also available to private traders using simple retail tools. Algorithmic trading is widely used in equities, futures, crypto and foreign exchange markets.

The term algorithmic trading is often used synonymously with automated trading system. These encompass a variety of trading strategies, some of which are based on formulas and results from mathematical finance, and often rely on specialized software.

Examples of strategies used in algorithmic trading include systematic trading, market making, inter-market spreading, arbitrage, or pure speculation, such as trend following. Many fall into the category of high-frequency trading (HFT), which is characterized by high turnover and high order-to-trade ratios. HFT strategies utilize computers that make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe. As a result, in February 2013, the Commodity Futures Trading Commission (CFTC) formed a special working group that included academics and industry experts to advise the CFTC on how best to define HFT. Algorithmic trading and HFT have resulted in a dramatic change of the market microstructure and in the complexity and uncertainty of the market macrodynamic, particularly in the way liquidity is provided.

Before machine learning, the early stage of algorithmic trading consisted of pre-programmed rules designed to respond to that market's specific condition. Traders and developers coded instructions based on technical indicators - such as relative strength index, moving averages - to automate long or short orders. A significant pivotal shift in algorithmic trading as machine learning was adopted. Specifically deep reinforcement learning (DRL) which allows systems to dynamically adapt to its current market conditions. Unlike previous models, DRL uses simulations to train algorithms. Enabling them to learn and optimize its algorithm iteratively. A 2022 study by Ansari et al., showed that DRL framework "learns adaptive policies by balancing risks and reward, excelling in volatile conditions where static systems falter". This self-adapting capability allows algorithms to market shifts, offering a significant edge over traditional algorithmic trading.

Complementing DRL, directional change (DC) algorithms represent another advancement on core market events rather than fixed time intervals. A 2023 study by Adegboye, Kampouridis, and Otero explains that "DC algorithms detect subtle trend transitions, improving trade timing and profitability in turbulent markets". DC algorithms detect subtle trend transitions such as uptrend, reversals, improving trade timing and profitability in volatile markets. This approach specifically captures the natural flow of market movement from higher high to lows.

In practice, the DC algorithm works by defining two trends: upwards or downwards, which are triggered when a price moves beyond a certain threshold followed by a confirmation period(overshoot). This algorithm structure allows traders to pinpoint the stabilization of trends with higher accuracy. DC aligns trades with volatile, unstable market rhythms. By aligning trades with basic market rhythms, DC enhances precision, especially in volatile markets where traditional algorithms tend to misjudge their momentum due to fixed-interval data.

The technical advancement of algorithmic trading comes with profound ethical challenges concerning fairness and market equity. The key concern is the unequal access to this technology. High-frequency trading, one of the leading forms of algorithmic trading, reliant on ultra-fast networks, co-located servers and live data feeds which is only available to large institutions such as hedge funds, investment banks and other financial institutions. This access creates a gap amongst the participants in the market, where retail traders are unable to match the speed and the precision of these systems.