What does Algorithmic Trading primarily rely on?

Algorithmic trading primarily relies on mathematical modeling and data processing to execute trades efficiently and effectively. This approach leverages complex algorithms to analyze vast amounts of market data quickly and accurately, allowing traders to identify patterns and make informed decisions based on quantitative analysis rather than relying on intuition or emotional judgment.

Mathematical models aid in predicting price movements, managing risk, and optimizing trading strategies. This computational power enables algorithmic trading systems to execute thousands of trades in a matter of seconds, capitalizing on price discrepancies and market inefficiencies that may otherwise go unnoticed.

The other options highlight methods that are generally less systematic and data-driven. Relying on human intuition and experience can introduce bias and inefficiency, while randomized trading strategies lack a foundational basis for consistency. Market sentiment analysis, while valuable, traditionally focuses on psychological and qualitative factors rather than the rigorous quantitative analysis inherent to algorithmic trading. Thus, mathematical modeling and data processing stand out as the core components driving algorithmic trading strategies.