<p>Algorithmic trading refers to the use of computer programs to automatically execute trading strategies based on predefined rules. It removes manual intervention, allowing traders to implement complex logic across multiple instruments and timeframes with speed and consistency.</p>

<h2>How It Works</h2>
<p>At its core, <strong>algorithmic trading</strong> relies on a set of instructions coded into a software agent. These instructions define entry and exit conditions, risk parameters, and order types. The agent continuously monitors market data, evaluates the conditions, and sends orders to the broker’s server when criteria are met.</p>
<p>On the <strong>MetaTrader 5</strong> platform, such agents are known as <strong>expert advisors</strong>. They are written in the MQL5 language and can be attached to any chart. Once activated, the expert advisor reads price ticks, calculates indicators, and executes trades without further input from the trader.</p>
<p>Before deployment, traders typically perform <strong>backtesting</strong> on historical data to assess the strategy’s performance. This process simulates how the algorithm would have behaved in past market conditions, providing metrics such as win rate, profit factor, and drawdown.</p>
<p>Execution can be market, limit, or stop orders, depending on the logic. Risk controls—such as maximum lot size, stop‑loss levels, and daily loss limits—are embedded in the code to enforce discipline.</p>

<h2>Why It Matters for Traders</h2>
<p>Algorithmic trading enhances efficiency by eliminating emotional decision‑making. Trades are executed exactly as the rules dictate, reducing hesitation or overtrading.</p>
<p>It enables the management of multiple strategies simultaneously. A trader can run several expert advisors on different symbols or timeframes, all monitored from a single <strong>MetaTrader 5</strong> terminal.</p>
<p>Speed is another advantage. Algorithms react to market changes in milliseconds, capturing opportunities that manual trading might miss, especially in fast‑moving or illiquid conditions.</p>
<p>Moreover, the approach supports rigorous risk management. By encoding stop‑loss and position‑size rules, traders ensure that each trade adheres to predefined risk thresholds, which is difficult to maintain consistently when trading manually.</p>
<p>Finally, algorithmic trading facilitates scalability. Successful strategies can be applied to larger account sizes or deployed across multiple accounts with minimal additional effort.</p>

<h2>Example</h2>
<p>Consider a simple moving‑average crossover strategy on the EUR/USD pair. The expert advisor is programmed to buy when the 10‑period moving average crosses above the 50‑period moving average and to sell when the opposite occurs.</p>
<p>Assume the following parameters:</p>
<table>
<tr><td>Instrument</td><td>EUR/USD</td></tr>
<tr><td>Timeframe</td><td>1‑hour</td></tr>
<tr><td>Lot size</td><td>0.10</td></tr>
<tr><td>Stop‑loss</td><td>30 pips</td></tr>
<tr><td>Take‑profit</td><td>60 pips</td></tr>
</table>
<p>During a backtest over six months, the algorithm generated 112 trades, with a win rate of 58 %, an average profit of 45 pips per winning trade, and an average loss of 28 pips per losing trade. The net result was a profit of approximately 1,260 pips, equivalent to about $1,260 on a 0.10‑lot position.</p>
<p>When deployed live on <strong>STB</strong>’s STP/NDD environment, the expert advisor receives raw market prices with low latency, ensuring that the crossover signals are acted upon promptly.</p>

<h2>Key Takeaways</h2>
<ul>
<li><strong>Algorithmic trading</strong> automates strategy execution, removing emotional bias and ensuring consistent rule‑following.</li>
<li>Expert advisors on <strong>MetaTrader 5</strong> allow traders to code, backtest, and deploy strategies across numerous instruments.</li>
<li>Effective use requires solid <strong>backtesting</strong>, clear risk controls, and ongoing monitoring to adapt to changing market conditions.</li>
</ul>