What Is Algorithmic Trading?

An algorithmic strategy is built from a small number of components: a signal (the condition that triggers a trade — a moving average crossover, an RSI level, a breakout of a price range), an entry rule, an exit rule (stop loss, take profit, or a signal-based exit), and a position-sizing rule that determines how much to risk on each trade.

Once those rules are coded, the strategy can run continuously, scanning price data tick by tick or bar by bar, and executing trades the instant its conditions are met — without waiting for a human to notice the same pattern and click a button.

Trend-following systems aim to catch sustained directional moves, typically using moving averages, channel breakouts, or momentum indicators, and tend to have lower win rates offset by larger average wins.

Mean-reversion systems bet that price will revert toward an average after moving too far too fast — common in range-bound markets and often built around RSI, Bollinger Bands, or statistical z-scores.

Breakout systems enter when price moves outside a defined range (a Donchian channel, a session high/low, a volatility band), aiming to catch the start of a new directional move.

Scalping systems take many small, short-duration trades, aiming to capture small price movements with tight risk control — these are the most sensitive to spread, commission, and slippage.

The core difference isn't speed — a manual trader can place a single trade just as fast as an algorithm. The difference is consistency: an algorithm applies the exact same rules every time, at any hour, without fatigue, fear, or the temptation to deviate from a plan after a losing streak.

That consistency is a double-edged sword. A well-tested algorithmic strategy removes emotional decision-making from execution. A poorly-tested one will lose money with the same mechanical consistency it would otherwise make it — which is why backtesting and risk management aren't optional extras, they're the foundation the whole approach rests on.

Traditional algorithmic trading runs on fixed, human-written rules: 'if the fast EMA crosses above the slow EMA, buy.' The logic doesn't change unless a developer changes it.

AI-driven trading extends this by having a model learn patterns from data rather than following only hand-coded rules — for instance, using machine learning to score which market conditions historically preceded high-probability setups, or to dynamically adjust position sizing based on a changing volatility regime. We cover this distinction in more depth in AI Trading vs. Algorithmic Trading.