Algorithmic trading uses computer programs to execute trades based on predefined rules and mathematical models. These algorithms can process vast amounts of data, identify patterns, and execute trades far faster than any human trader. Today, algorithms account for the majority of all trading volume on major exchanges.
Algorithms range from simple rule-based strategies (buy when the 50-day average crosses above the 200-day average) to extraordinarily complex machine learning models that analyze satellite imagery, natural language, and alternative data sources.
Execution algorithms (TWAP, VWAP, implementation shortfall) optimize how large orders are filled, minimizing market impact and slippage. Alpha-generation algorithms seek to identify profitable trading opportunities using statistical models, machine learning, or fundamental signals.
High-frequency trading (HFT) is a subset that relies on extremely low latency and high throughput, executing thousands of trades per second to capture tiny price discrepancies. HFT firms invest heavily in co-located servers, specialized hardware, and network infrastructure to gain microsecond advantages.
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