Automate your strategy: The revolution of algorithmic trading explained

Why Did Algorithmic Trading Change the Game?

For years, traders have battled against their own emotions. Fear and greed make decisions that logic would never approve. This is where algorithmic trading comes in: programs that execute buys and sells automatically, without emotional influence, based on rules that you define.

A trading algorithm does not sleep, does not get distracted, and does not give in to panic. It works 24/7, analyzing market data and executing trades in milliseconds when your criteria are met. For any serious trader, understanding how this technology works is practically mandatory.

The three most used strategies in algorithmic trading

Before building your own algorithm, you must know the strategies that dominate this space:

Volume Weighted Average Price (VWAP)

This strategy divides a large order into smaller fragments, executing them over time so that the average execution price approaches as closely as possible to the volume-weighted average of the market. It is especially useful when you want to execute large positions without drastically impacting the price.

Time-Weighted Average Price (TWAP)

Similar to VWAP, but with a key difference: it distributes your orders evenly over a specific period, regardless of market volume. If what matters to you is to execute gradually without generating price movements, TWAP is your option.

Volume Percentage (POV)

The algorithm executes operations that represent a predefined percentage of the total market volume. For example, if you set POV to 10%, the algorithm will automatically adjust its execution speed based on how the market volume fluctuates in real-time.

How to Build a Trading Algorithm from Scratch

The process has five clearly defined phases:

1. Define your strategy

It all starts with a simple rule. For example: “Buy when the price drops 5% from the previous close, sell when it rises 5%.” This rule will be the foundation of everything else. It can be based on technical indicators, price patterns, support/resistance levels, or even on-chain data.

2. Program the logic

The strategy translates into code. This means creating a program that constantly monitors the market, identifies when your conditions are met, and executes orders automatically. The most popular languages for this are Python and C++, primarily for their speed and flexibility.

3. Test with historical data (Backtesting)

Before risking real money, your algorithm is tested with past data. How would it have acted in the last 6 months? And during the last correction? Backtesting shows you if your strategy is viable or if it needs adjustments. This step is critical because it prevents unpleasant surprises.

4. Calibrate and optimize

Backtesting results reveal what works and what doesn't. This is where you fine-tune the parameters: you change the percentages, adjust the time frames, add additional filters. The goal is to maximize profits while minimizing risk.

5. Connect to an exchange and monitor

Once you are sure, you connect the algorithm through an API ( programming interface) to a trading platform. The algorithm then operates in real-time. But this does not mean “disconnect and forget”. It requires constant supervision: reviewing logs, monitoring performance, and being ready to pause if something looks wrong.

Advantages that make algorithmic trading irresistible

Unprecedented speed: Machines execute in milliseconds what humans would take minutes to do. This is especially valuable in volatile markets where every millisecond counts.

Zero emotions: Algorithms do not know FOMO or greed. They follow their rules with mechanical precision, eliminating impulsive decisions that destroy accounts.

Scalability: An algorithm can monitor multiple pairs, multiple timeframes, and multiple strategies simultaneously. A human trader could never do that.

Consistent execution: If your strategy is solid, the algorithm will execute it with perfect consistency, day after day, without fatigue or deviations.

The real challenges you need to know

Technical barrier: Not everyone can write code or has the necessary programming knowledge. Developing a robust algorithm requires experience in both systems and finance.

Risk of failures: Software bugs, connectivity issues, server crashes… anything can go wrong. When it goes wrong at algorithm speed, the losses can be catastrophic in seconds.

Overfitting: It's easy to create an algorithm that works perfectly with historical data but fails in the real world. The line between optimization and deception is dangerously thin.

Market Changes: Strategies that worked a year ago may be outdated today. Markets evolve and your algorithms must do so as well.

The future of trading is already here

Algorithmic trading is not futuristic, it is present. Every day, millions of trades are executed by algorithms on exchanges around the world. The question is not whether you should learn about this, but when. For serious traders looking to automate, scale, and eliminate emotions from their decisions, mastering this technology is the difference between surviving and thriving in modern markets.