What characterizes the practice of "algorithmic trading" in finance?

The practice of "algorithmic trading" in finance is primarily characterized by automating trading decisions using algorithms that follow specific criteria. Algorithms are designed to analyze vast amounts of data and execute trades at optimal times with high efficiency. This technological approach can include various strategies, such as statistical arbitrage, trend following, or market making, which all rely on predefined rules that typically incorporate quantitative methods.

Algorithmic trading leverages speed and accuracy, enabling traders to react to market changes much faster than a human would be able to. The automation aspect minimizes human emotional biases, allowing for consistent application of trading strategies based on data rather than instinct or experience. This results not only in potentially higher profitability but also in reduced risk through precise entry and exit points for trades.

In contrast, the other choices pertain to trading methods that do not utilize algorithms for decision-making, such as manual strategies by experts, instinct-based trading, or relying on customer feedback. These methods lack the automation and data-driven precision characteristic of algorithmic trading.