Glossary

Algorithmic trading

Using computer programs to automatically place and manage trade orders based on predefined rules.

Also called: algo trading

Algorithmic trading uses computer programs to automatically generate, route, and manage trade orders according to predefined rules, without a human deciding on each individual trade. The rules can range from simple, such as buying a fixed quantity at a scheduled time, to complex signals derived from quantitative trading research.

Execution algorithms are commonly designed to minimize market impact and cost by breaking a large order into smaller pieces spread over time or across venues, informed by the current state of the order book and the prevailing bid-ask spread. This differs from quantitative trading generally, which is about how a trade decision is generated; algorithmic trading is specifically about how an order, once decided, is executed in the market.

Algorithmic trading now accounts for the majority of volume in many liquid markets, used by institutional investors to reduce trading costs and by high-frequency firms to trade at speeds no human could match. Its performance is judged with tools like transaction cost analysis, and strategies are refined through backtesting before deployment. Risks include software bugs or misconfigured algorithms causing rapid, large losses, and the potential for algorithms to interact in ways that amplify volatility during periods of market stress, as has happened in several documented flash-crash episodes.

Last reviewed September 22, 2026

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Related terms

Quantitative trading Trading strategies built on mathematical models and statistical analysis rather than discretionary judgment. Order book A real-time list of all outstanding buy and sell orders for an asset, ranked by price. Bid-ask spread The gap between the highest price buyers will pay and the lowest price sellers will accept for an asset. Transaction cost analysis (TCA) Measuring the real cost of executing a trade, including spread, market impact, and timing, versus a benchmark. Backtesting Testing a trading or investment strategy against historical data to see how it would have performed.

Related tools

NautilusTrader Open-source, event-driven algorithmic trading platform in Python/Rust for high-performance backtesting and live deployment. QuantConnect Cloud algorithmic trading platform built on the open-source LEAN engine, with research notebooks and live deployment.

Related guides

How to backtest a trading strategy Data quality, look-ahead and survivorship bias, realistic costs, and the evaluation metrics that separate a real edge from an overfit curve. How to choose a charting and technical analysis platform Choose a charting platform by how much analysis it automates for you, whether it's tied to a broker, and whether you need it to trade or just to think. How to choose a quant research and backtesting platform The choice is really about who owns the code — a library you control, a desktop license, or a hosted platform with data included.