How to Avoid Common Mistakes in Algorithmic Trading?

The world of finance and trading have been forever transformed with the advent of automated trading methods. Such methods which are now more commonly known as algorithmic trading or algo trading, can enhance the trading process and lead to consistent results. 

Especially recently, it seems that all you hear are positive things from countless algo trading platforms that can bring you profitability across different financial markets. 

But the reality is different. The reality is that there are so many mistakes that you can make in algo trading. Whether it is using an algorithmic trading platform or using a platform to build your own algo trading software, you need to steer clear of the mistakes. 

It is the focus of this article to dive into the common mistakes that you traders can make in algorithmic trading and show you how you can avoid them as well. 

 

 

Understanding Algorithmic Trading

Algorithmic trading is the use of any algorithm or pre-determined, coded program to automate any and all processes in trading. 

This means, it can be simple automated technical analysis all the way to full automation of the trading process. 

There are different factors that are involved in algorithmic trading and its success, including but not limited to: 

• Optimal strategy design
• Quality data inputs
• Proper risk management
• Rigorous testing and monitoring

The success of algo trading depends on various factors and if any of these are not fine-tuned and configured as they should be, then it can put a dent in the profitability of the algorithmic trading tool. 

 

Common Mistakes and How to Avoid Them

1. Overfitting Your Strategy

This is one of the most common mistakes or perhaps disadvantages seen with algo trading. Overfitting, which is also known as curve fitting, is when an algorithmic trading tool is too closely developed for a certain market condition based on historical data. 

In this way, it is only fitted for that past noise and not the ever changing conditions in the market in real time. 

How to avoid it:

• Make sure to use high quality data when you are backtesting your trading bot for the purpose of configuration or adjusting. Using poor data will result in bad configuration. Also, another way to stay out of overfitting is to use out-of-sample testing data. This way, after you have trained and configured your tool, you can test it to make sure. 
• Whether you have built and developed your own algo trading tool or you have acquired one from a platform, make sure to update the model regularly. 

 

2. Transaction Costs and Slippage

Another mistake that traders might make, which is present in algo trading as well as manual trading, is ignoring the costs that are in the process of trading. Costs such as commissions and fees that can really eat into your profits. 

Also, there is the matter of slippage, which simply refers to the difference in price between when a signal is present for entry and the time your order is executed. This difference in price or slippage can be quite important for the outcome of trades, especially in scalp trading strategy. 

How to avoid it:

• When you are backtesting an algo trading tool, make sure to incorporate realistic costs such as commissions and fees so that you are not surprised in the real market.
• Always make sure to model in slippage based on historical movements.

 

3. Inadequate Risk Management

Another common mistake that is seen with certain algo trading tools is the lack of risk management. 

Of course, all algorithmic trading options try to factor in risk management one way or another. But if this risk management mechanism is not tightly built-in, then it can easily lead to losses. 

How to avoid it:

• Pay the utmost attention to risk management orders such as the stop loss or take profit. Especially, if you are using trading strategies such as scalp trading, then you need to use tight stop loss and take profit orders.
• Take advantage of positing sizing to properly adjust the size of each position not to exceed your risk tolerance.

 

4. Relying Solely on Historical Data

While backtesting is an extremely advantageous feature of algo trading whereby you can test and configure an automated trading tool, you need to be careful not to solely rely on historical data. 

This is because financial markets are dynamic in their entire nature and they can get influenced by a variety of factors. These factors include news events, global events, trader sentiment, etc. 

How to avoid it:

• Make sure to always monitor fundamental factors such as news and also sentiment in the market because trader sentiment can make big moves.
• At the same time, you need to always be prepared to intervene manually. While you are using an automated trading system, it doesn’t mean it should be left on its own with no monitoring or regular upkeep.

 

5. Poor Data Quality

Data plays an enormously important role in the development of algo trading tools. The entire basis of all automated trading software and algorithms is based on the data used in their training and design. 

But data also plays an important role in the later stages of algo trading fine-tuning and adjustment. As you know, the process of backtesting heavily relies on historical data. And it is the quality of that historical data that defines the quality of the outcome of backtesting. 

So, at multiple stages of algorithmic trading development and adjustment, data plays a crucial role. 

 

6. Lack of Continuous Monitoring

An important misconception among traders about algorithmic and automated trading in general is that you no longer need to do anything when you begin using them. 

But that is not true at all. Even when you use a fully automated trading tool that is able to take care of the entire trading process, you still need to remain vigilant and observant. 

The market at its core is completely unpredictable. It can turn at any moment. Because of this unpredictability of the market, you need to always monitor the algo trading tool. 

 

7. Neglecting Psychological Factors

There have been a lot of advancements in the field of algorithmic trading. Models and strategies involved have become more and more sophisticated. But the thing is that there is perhaps one thing they still have not been able to fully predict – trader sentiment. 

There are psychological factors involved in any market that can make strong and huge moves. These factors are directly related to the sentiment of traders and how they feel about the market. 

So when you are using these tools, make sure to leave room for the unpredictability of psychological factors. 

 

Conclusion

Algorithmic trading or algo trading can have numerous advantages for your trading process. They can automate the process of trading and above all, they can bring you consistent returns in any market. 

But with all the advantages, there are still mistakes that can be made about them. In this article, we went over the most common mistakes in algorithmic trading and how to avoid them.