Trading Automation Demystified: Strategies, Advantages, and Disadvantages

In the world of finance, algorithmic trading has emerged as a game-changer, revolutionising the way trades are executed. Michael Lewis, in his book Flash Boys, shed light on this transformation, highlighting the Wall Street traders and entrepreneurs who built companies that defined U.S. electronic trading.

The rise of algorithmic trading has been significant, with over 60% of all trades in the U.S. being executed by computers as early as 2009. This rapid automation has been made possible by high-speed networks, providing quicker access to markets and exchanges.

However, the use of black box algorithms, a subset of algorithmic trading characterised by goal-oriented approaches and opaque decision-making processes, raises questions about accountability and risk management within the financial world. Traders and investors might not fully grasp the basis of the algorithmic systems being used, leading to concerns about transparency and control.

Despite these concerns, the open-source approach in algorithmic trading has led to increased collaboration and knowledge-sharing within the financial community. This approach allows individual traders and amateur programmers to participate in what was once the domain of specialized professionals.

Fintech firms are at the forefront of research in black box algorithms, looking to take major advances in machine learning and artificial intelligence in recent years and apply them to financial trading. About a quarter of financial service professionals are involved in open-source data science and artificial intelligence/machine learning platforms.

The shift towards algorithmic trading has also provided potential for increased transparency, as investors know exactly how their shares will be traded in the market. Hedge funds and investment firms like Two Sigma and PanAgora have leveraged this shift by crowdsourcing algorithms and releasing improvements to open-source applications.

Moreover, algorithmic trading enables the execution of orders in highly specified conditions while reducing the probability of human error. Algorithmic trading strategies include arrival price algorithms, basket algorithms, implementation shortfall algorithms, percentage of volume algorithms, single-stock algorithms, volume-weighted average price (VWAP), time-weighted average price (TWAP), and algorithms with a risk-aversion parameter.

Interestingly, black box algorithms are popular in high-frequency trading and other advanced investment strategies because they can outperform more transparent and rule-based approaches. However, concerns about using open-access systems due to the need to safeguard proprietary knowledge persist, with two-thirds of those surveyed by the Fintech Open Source (FINOS) Foundation expressing such concerns.

Despite these challenges, the benefits of algorithmic trading, such as faster order execution and increased transparency, continue to drive its adoption in the financial industry. As technology continues to advance, we can expect to see algorithmic trading play an even more significant role in shaping the future of finance.