Algorithmic Trading

Algorithmic trading - also called automated or systematic trading - replaces discretionary decision-making with rule-based computer programs. In the institutional world, algorithms handle the vast majority of order flow; in retail forex, the primary vehicles are MetaTrader Expert Advisors, Python scripts using broker APIs or libraries such as OANDA v20, and platforms such as cTrader's cAlgo or TradingView's Pine Script.

Algorithms are used across a wide spectrum of strategy types. Trend-following algorithms generate signals when technical conditions (moving average crossovers, breakouts above resistance) align and hold positions until opposite signals emerge. Mean-reversion algorithms bet that a price that has moved far from its historical average will return - pairs trading and statistical arbitrage are examples. Market-making algorithms continuously post limit orders on both sides of the spread to collect the bid-ask difference. High-frequency algorithms exploit latency advantages measured in microseconds and require co-location near exchange matching engines.

For retail traders, the practical benefits of algorithmic trading are consistency (the algorithm executes identically every time, eliminating emotional interference) and scale (it can monitor multiple pairs and timeframes simultaneously). The risks are that the algorithm's edge may erode as market conditions change, and poorly written code can create runaway trades or miss exit signals during connectivity issues. Risk controls - maximum open lots, daily loss limits, and connection monitoring - are essential components of any production EA or trading bot.