What "algorithmic trading" actually means
At its core, algorithmic trading is simply executing trades based on a predefined, rules-based strategy — a moving-average crossover, a volatility threshold, a sentiment score from news headlines — instead of a human manually deciding in the moment. Institutional desks have used this for decades to execute large orders efficiently and reduce emotional decision-making. What's changed is that platforms aimed at individual retail investors now offer no-code strategy builders, backtesting tools, and AI-generated trade signals that used to require a quant team.
Why access to these tools has expanded
Cloud computing made backtesting large datasets cheap; commission-free trading removed a major cost barrier to frequent execution; and large language models made it possible to build a "describe your strategy in plain English" interface on top of what used to require a programmer. Together, these lowered the technical floor for running a systematic strategy from "hire a quant" to "sign up for an app."
The backtesting trap
The single most common mistake in retail algorithmic trading is overfitting: tuning a strategy's parameters until it looks spectacular on historical data, without realizing you've essentially memorized the noise in that specific dataset rather than found a real, repeatable edge. A strategy that returned 40% annually in a backtest can lose money in live trading if its apparent edge was really just a coincidence in the historical window it was tested on. Any strategy should be tested on data it has never seen (out-of-sample testing) before real money touches it.
AI trade signals aren't the same as AI judgment
An AI model that flags "unusual buying pressure" or summarizes sentiment from thousands of news articles is doing pattern recognition at a scale no human could match manually — genuinely useful as an input. It is not the same thing as understanding why a pattern exists or whether it will continue, and models trained on historical market data carry no guarantee that future markets will behave the same way, especially around genuinely novel events.
Risk management doesn't get automated away
Position sizing, stop-losses, and diversification matter exactly as much in an algorithmic strategy as in a manual one — arguably more, since an automated system will execute a flawed strategy with perfect, tireless consistency, including straight through a drawdown a human might have paused to reconsider.