Algorithmic Trading: Automate your trades and eliminate emotions

Tired of letting FOMO and greed ruin your decisions? Algorithmic trading is the solution that many traders are looking for to eliminate the emotional factor from the market.

What is algorithmic trading really?

In simple terms, algorithmic trading uses computer programs to automatically execute buys and sells in financial markets. Instead of being glued to the screen, your algorithms work 24/7 according to rules that you set.

The concept is straightforward: you define a strategy ( for example, “buy when the price drops 5%, sell when it rises 5%” ), you translate it into code, and let the machine do the work. It sounds easy, right? But here comes the complicated part.

How a trading algorithm is born

The process follows several steps that you cannot skip:

First, define your strategy. This could be based on price movements, technical patterns, or specific indicators. Some traders use simple machines, while others create complex systems that analyze multiple variables simultaneously.

Then, you program the algorithm. This is where you need technical experience. You must code your rules in a language that the machine understands. Python is popular for this because it has powerful libraries designed for financial data analysis.

Then comes backtesting. Before risking real money, you test your algorithm with historical data. How would it have performed a year ago? In 2021? During crashes? This step is critical to know if your strategy really works or if you just got lucky.

Finally, you execute. Once confident in your algorithm, you connect it to a trading platform via API (Application Programming Interfaces). Your program continuously monitors the market and automatically executes orders when conditions are met.

The most used strategies in algorithmic trading

There are several proven strategies that many traders apply:

VWAP (Volume Weighted Average Price): Divide your large order into smaller chunks and execute them distributed over time, aiming to match the average market volume price. It works well to minimize the impact of large orders.

TWAP (Time-Weighted Average Price): Similar to VWAP, but distributes trades evenly over time without weighting by volume. It is simpler to implement and useful when you want to be discreet in the market.

POV (Volume Percentage): You execute trades representing a specific percentage of the total market volume (for example, 10%). The algorithm automatically adjusts its speed according to market activity.

The great advantages of algorithmic trading

Brutal speed: Algorithms execute orders in milliseconds. In fast markets, this is the difference between profits and losses.

Without emotions: Machines do not have fear, do not have greed, do not panic. They follow the rules. Period.

Efficiency: You can operate 24/7 without being present. Your algorithm works while you sleep.

Data Analysis: Algorithms can process millions of data points simultaneously, something impossible for a human.

The traps you should not ignore

Technical complexity: Developing algorithms requires mastering programming and financial markets. It's not for everyone. If you don't know what you're doing, it's easy to lose money quickly.

System Errors: Software bugs, internet connection drops, hardware failures… in trading, these “small” problems can cost you thousands. You need backup systems and constant monitoring.

Overfitting: When you backtest too much by fitting your algorithm to specific historical data, it works perfectly in the past but fails in the future. The market always changes.

Systemic risk: If many algorithms react the same way to the same conditions, the market can become unstable. We have seen crashes because of this.

The reality of algorithmic trading today

Algorithmic trading dominates much of the modern markets. Institutions, funds, and professional traders routinely use it. But it is not an automatic money-making machine. It is a powerful tool that requires expertise, constant monitoring, and adjustments according to changing market conditions.

If you are considering getting into this, be clear: you need real technical knowledge, sufficient capital to absorb losses, and the discipline to continuously monitor your system. It's not “set and forget.” It's constant responsibility.

The difference between successful traders using algorithms and those who quickly lose money is precisely this: a real understanding of what your code is doing and constant adaptation to new market conditions.