Algorithmic trading: how it works, who uses it, and what it takes

What algorithmic trading is

Algorithmic trading is the use of computer programs to execute a trading strategy: the algorithm decides when to buy, when to sell, and in what size, based on rules or models — not on a human's gut feel. "Trading bot", "algo" and "automated strategy" all describe the same idea at different levels of polish.

Who uses algorithmic trading

• Banks and hedge funds — the original users, running execution algos and high-frequency strategies on institutional infrastructure.
• Market makers — quoting bid/ask prices algorithmically, thousands of times per second.
• Prop trading firms — systematic strategies across multiple global markets.
• Retail traders — the newest group, running algorithms through managed platforms, broker APIs, or self-built Expert Advisors.

The same discipline applies at every level: a defined edge, strict risk controls, and an audit trail. What changes between institutional and retail is who runs the infrastructure.

Common algorithmic trading strategies

• Trend following — enter when a trend confirms, exit when it fails.
• Mean reversion — fade extremes back toward an average.
• Breakout systems — trade expansions out of ranges.
• Machine-learning models — pattern recognition trained on historical data, often ensembles that only act when several models agree.

Step 1 — Data ingestion

The algorithm consumes market data: prices, volume, spreads, and sometimes derived series like volatility, correlation, or order-flow imbalance. The quality and frequency of this data caps the quality of every decision that follows. Algorithms that look at one-minute bars cannot see what a tick-level algorithm sees, and vice versa.

Step 2 — Signal generation

Logic is applied to the incoming data to produce a signal. A simple system might say "go long when the 50-period moving average crosses above the 200-period." A more advanced system runs an ensemble of models — each trained on a different market regime — and only acts when several models agree.

Step 3 — Risk and position sizing

Before the algorithm sends an order, a risk module decides whether the trade is even allowed and how big it should be. Good risk modules consider per-trade risk, correlation across open positions, daily loss limits, and account-level drawdown. Skipping this layer is how most amateur bots blow up.

Step 4 — Order routing

The approved order is sent to the broker over an API. The API only has trading permission — never withdrawal permission. The broker matches the order against the live market and returns a fill confirmation.

Step 5 — Position management

Once the position is open, the algorithm keeps watching: trailing stops adjust, partial profits get taken, and exits trigger when the original thesis fails or the target is hit. This is the part people underestimate — a great entry with poor management is a losing trade.

Step 6 — Logging and auditability

Every signal, every order, every fill is timestamped and stored. This is what lets you independently verify performance later. If a system has no audit trail, you have no way to know whether the screenshots are real.

Where the platform fits in

Most retail traders never see steps 3, 4, and 6 done well, because they're handed a raw bot file and told to "install it on a VPS." A proper platform — like The Automated Trader, with 19 strategies on one subscription — runs the execution, risk, and monitoring layers for you, so you pick your strategies and connect your broker, not wire together infrastructure.

Next steps

See what a trading bot is for the practical buying checklist, then are trading bots actually profitable for the questions that separate real systems from the rest.

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