Is Python used in high-frequency trading?

High-frequency trading (HFT) based on artificial intelligence (AI) has been quickly adopted as market practice and its impact on investors’ trading behavior and financial market has raised an increasing concern about managing AI in finance.

That means a computer with high-speed internet connections can execute thousands of trades during a day making a profit from a small difference in prices. This is called high-frequency trading . No human can compete with these algorithms, they’re extremely fast and more accurate.

Machine learning empowers traders to accelerate and automate one of the most complex, time-consuming, and challenging aspects of algorithmic trading , providing a competitive advantage beyond rules-based trading.

Thus feature selection or feature engineering becomes an important process in machine learning for HFT , and is one of our central themes.

HFT algorithms typically involve two-sided order placements (buy-low and sell-high) in an attempt to benefit from bid-ask spreads. HFT algorithms also try to “sense” any pending large-size orders by sending multiple small-sized orders and analyzing the patterns and time taken in trade execution.