Algorithmic trading uses computer programs to execute trades according to predefined rules, and it accounts for roughly 70-80% of all equity trading volume in the U.S. When an institution needs to buy 5 million shares of Microsoft, they don't just hit 'market buy' — that would move the price against them catastrophically. Instead, they use execution algorithms that slice the order into thousands of small pieces and feed them into the market over hours or days, minimizing market impact and achieving a better average price. The three most common execution algorithms are TWAP, VWAP, and Implementation Shortfall.

TWAP (Time-Weighted Average Price) divides the order evenly across time. If you need to buy 1 million shares over 6 hours, TWAP executes roughly 2,778 shares per minute regardless of what the market is doing. VWAP (Volume-Weighted Average Price) is smarter — it concentrates execution during periods of high volume (like the open and close) and backs off during slow periods, matching the natural rhythm of the market. VWAP is the institutional benchmark — a trader who achieves a price better than VWAP is considered to have executed well. Implementation Shortfall algorithms are the most sophisticated, dynamically adjusting execution speed based on a trade-off between urgency and market impact.

Beyond execution algorithms, there are alpha-generating algorithms that make directional trading decisions. Momentum algorithms buy stocks showing upward price trends and short declining ones. Mean reversion algorithms bet that extreme price moves will reverse. Statistical arbitrage algorithms identify and exploit pricing discrepancies between related securities. These strategies differ from execution algos in a fundamental way: execution algos have a fixed target (buy X shares) and optimize HOW to do it, while alpha algos decide WHAT and WHEN to trade based on mathematical signals.

The evolution of algorithmic trading has created both opportunities and risks for all market participants. On the positive side, execution costs have plummeted — trading commissions have gone to zero and bid-ask spreads have narrowed dramatically. On the negative side, algorithms can interact in unexpected ways, creating flash crashes and sudden liquidity vacuums. The May 2010 Flash Crash and the August 2015 mini-crash both involved algorithmic trading spirals. Understanding algo trading isn't just for quants — it helps every investor understand why markets sometimes behave in seemingly irrational ways.