What is algorithmic trading?

TL;DR

Algorithmic trading uses computer programs that follow logical rules (algorithms) to open, manage, and close trades automatically. Unlike discretionary trading, where you're the one clicking each button, here, you delegate decision-making to code, gaining speed and discipline, but also facing new technical risks.

Introduction

Think of the market as a massive multiplayer video game. Manual traders sit at the controls, rapidly hitting buttons as enemies appear on screen. Algorithms, on the other hand, flip the switch to auto mode: they detect patterns in milliseconds and execute combos no human could match.

If you want to play in this mode, first you need to understand what makes algorithmic trading unique, its pros and cons, and the tools that power it.

In this article, you’ll see how algorithmic trading differs from discretionary trading, its advantages and risks, and an overview of three strategies: Momentum / Trend Following, Mean Reversion, and Machine-Learning Driven. With that roadmap, you’ll be ready to decide whether to keep mashing buttons or let code take the controller.

1. Definition of algorithmic trading

Algorithmic trading is the automated execution of buy and sell orders based on a set of coded rules. These rules can be as simple as “buy when the price crosses above the 50-day moving average,” or as complex as a machine learning model that processes real-time news using NLP.

💡 The algorithm decides what, when, how much, and how to trade without your direct input. You design the logic; the machine executes it.

2. Algorithmic vs. discretionary trading

When you trade discretionarily, you're the one analyzing charts, reading news, and ultimately clicking buy or sell. Your decision depends on your judgment, and often your emotions. That means human speed (seconds or minutes) and vulnerability to biases like fear.

With algorithmic trading, rules are hard-coded. The system scans data, applies objective filters, and sends orders in milliseconds. Thanks to this robotic precision, you get total discipline and massive scalability: a single bot can monitor hundreds of assets simultaneously without blinking.

The tradeoff? Technical complexity. Where manual traders battle psychological pressure, algorithmic traders must keep servers low-latency, debug code, and protect against data outages. A poorly designed system or overfitted model can lose money as fast as it makes it, so constant supervision and thorough backtesting are critical.

3. What makes a system “algorithmic”?

For your strategy to officially qualify as algorithmic, it must meet these criteria:

1. Coded, reproducible rules. Every element (entry, exit, stop-loss, position sizing) lives in code, not intuition.

2. Automated execution. The software sends orders to the exchange or broker without human confirmation.

3. Structured data. It uses data feeds, prices, indicators, news, in a format the machine can process.

4. Backtesting. Before risking real capital, you evaluate the strategy on historical data to estimate metrics like CAGR, drawdown, and Sharpe ratio.

5. Real-time monitoring. Even if the bot runs on its own, you monitor latency, slippage, and system health.

4. Pros and cons of algorithmic trading

5. Popular algorithmic strategies

Momentum / Trend Following

These strategies detect and ride well-defined trends in price or volume. The algorithm spots when an asset shows relative strength (e.g., moving average crossovers, breakouts, high RSI readings) and enters in the direction of the trend. They often use trailing stops to let profits run and exit when momentum fades.

Mean Reversion

Based on the idea that prices tend to oscillate around a statistical average. The system targets extreme deviations (e.g., price two standard deviations above the mean) and trades toward the mean. To avoid “catching a falling knife,” it uses volatility filters or multi-timeframe confirmation. It’s well-suited for sideways markets or assets with noisy intraday action.

Machine-Learning Driven

Uses supervised or unsupervised models (Random Forests, LSTMs, Transformers, etc.) to process thousands of variables like price data, order books, social sentiment, or news headlines. It requires robust data pipelines and careful overfitting control using time-series cross-validation.

Conclusion

Algorithmic trading isn’t magic, it’s a logical evolution in an increasingly fast and competitive market. If you're drawn to the idea of letting code handle execution while you focus on researching strategies, this path can offer more discipline, scale, and stronger metrics.

🧠 But remember: mistakes scale just as fast as opportunities. Start small, learn from your backtests, and always keep human oversight in the loop.