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Algorithmic trading is the use of computer programs to execute buy and sell orders in financial markets based on predefined rules, which include conditions such as price thresholds, timing, volume, or mathematical signals. The program monitors markets continuously and fires orders the instant those conditions are met, without human intervention. It can execute in milliseconds, far faster than any trader clicking a screen.
Think of it like a vending machine for trades: you program the conditions, insert the parameters, and the machine handles the transaction whenever the match is made.
Every algorithmic trading system consists of four components: the set of rules that defines when to trade, the continuous market monitoring that watches for those conditions, the automatic order execution that fires when conditions match, and the backtesting process that tests how the strategy performed on historical data before going live.
The strategies themselves range from simple to highly complex. A basic trend-following algorithm buys when a 50-day moving average crosses above a 200-day moving average and sells when the reverse happens. A statistical arbitrage strategy simultaneously monitors dozens of correlated assets and exploits pricing gaps that last fractions of a second.
By 2019, approximately 92% of all trading in the foreign exchange market was executed by algorithms rather than human traders. The algorithmic trading market was projected to reach approximately $24.3 billion in value in 2025, growing at a compound annual growth rate of 12.8%, driven by automation and artificial intelligence integration.
The U.S. remains the largest market for algorithmic trading. Investment banks, hedge funds, and proprietary trading firms account for most of the volume. High-frequency trading firms, which represent the most automated end of the spectrum, can execute millions of trades per day across multiple exchanges simultaneously.
These are the approaches that define how most algorithmic systems generate returns.
Platforms like TradingView and OANDA now give individual traders access to algorithmic execution without writing code from scratch. Pre-built strategy frameworks allow you to define entry and exit rules visually, backtest them on years of price data, and deploy them live with limited technical expertise.
This democratization carries risk: a strategy that performed well in backtesting may fail in live markets due to transaction costs, slippage, or market conditions that did not appear in the historical data. Overfitting to past data is the most common reason retail algorithmic strategies underperform expectations when deployed live.
The Securities and Exchange Commission and the Commodity Futures Trading Commission both have oversight over algorithmic trading in U.S. markets. Regulators require certain firms to register their algorithms and maintain audit trails of trading decisions. Circuit breakers and market-wide halt mechanisms exist specifically to prevent algorithmic feedback loops from amplifying crashes, as occurred during the Flash Crash of May 6, 2010, when the Dow Jones Industrial Average dropped 1,000 points in minutes before recovering.
Sources:
https://en.wikipedia.org/wiki/Algorithmic_trading
https://tradetron.tech/blog/the-algorithmic-trading-market-a-comprehensive-guide-for-us-investors-in-2025
https://www.wallstreetmojo.com/algorithmic-trading/
https://www.alphagamma.eu/finance/the-impact-of-algorithmic-trading-on-traditional-methods-2025-trends/
With deep roots in traditional finance and the digital asset industry, Acquire.Fi operates as a specialist M&A advisory firm. Coverage spans M&A, secondaries, OTC, and capital markets advisory across digital assets and frontier tech. Our team has decades of combined experience across every stage of the deal lifecycle.
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