Algorithmic Trading – Why it might be a good idea

In this article, we will cover the advantages of using automated trading robots for Algorithmic Traders, advise on how to begin if you are a beginner and discuss common programming languages such as Python, MQL4 and MQL5 and which one you should use.

#1 No Emotions, just Logic

Robots have no feelings and they do what you tell them to do. This way, you can be 100% sure that your robots will follow your trading plan under any circumstances. Robots do not revenge trade, they obey/follow the given risk management plan and they do not try to be creative.

Eliminate the following common amongst many traders emotions:

  • Fear of missing out (causes premature or late entries which are suboptimal in trading)
  • Fear of loss (causes suboptimal exits where traders do not fully capitalize on an opportunity and try to avoid the risk by closing their trades)
  • Revenge Trading (A trader’s urge to make up for losses by placing more and bigger trades that violates their risk management plan)
  • Overconfidence  (Similar to revenge trading, a trader during a winning period may place more and bigger trades, disregarding the risk management plan)

If you believe you often deviate from your trading plan, algo trading might be a solution for you.

#2 Backtest your Strategies

Another advantage of being an Algo Trader is that you can see how your strategy would perform in the past. The simple fact that a strategy has been profitable in the past is a factor that contributes to the trader’s confidence. How often do traders interfere with their trading plan because they are on a losing streak? By having objective data of past performance, you can assess whether a drawdown (which every strategy has) is within your tolerance.

As a result, a trader is less inclined to interfere with their trading plan.

Past performance is not necessarily an indicator of future results, but knowing that a strategy made money in the past is more reassuring than using the one that didn’t.

#3 More time for Analysis

When you don’t have to spend countless hours in front of the screen, you can invest your time in more meaningful things, such as developing more trading models, evaluating your current trading models and backtesting.

It is said that 80% of success in trading is in preparation.

#4 Multitask & Diversify

Humans are bad at multitasking. A single human has a limited amount of attention and consequently, it is not possible to manage 1,000 charts and 10,000 trades at the same time. Furthermore, the more active you are in trading the higher the risk of making errors when trading.

As an Algo-Trader you are not limited by this as the trading robots do the work for you. You can trade any symbol, on any timeframe, using any strategy and your only job as an algo-trader is to evaluate the results, not do the trading.

Also, having more strategies on multiple assets and different timeframes diversifies your portfolio. Having a diversified portfolio adds more consistency to your trading.

Python vs MQL

Python is a universal programming language that can be deployed on any platform whereas MQL4/5 and is bound within the MetaTrader ecosystem.

The advantage of MQL4/5 is that it comes with ready-made tools to quickly code and deploy trading robots on your trading platform. This makes it easy for beginners to create trading robots as MQL4 and MQL5 have been specifically designed to develop trading related codes. Furthermore, the MetaTrader platform comes with a built-in backtester where you can quickly run a simulation of your trading strategy on historical data to evaluate its performance.

On the other hand, Python provides more flexibility and freedom. Python offers a wide range of OpenSource Libraries used for Data Science, Machine Learning. Backtesting and many more. Furthermore, it is also possible to connect your Python scripts to almost all modern trading platforms (including MetaTrader4 and MetaTrader5).

Generally, it is recommended to start with the MQL4 programming language as it is very easy. Over time, if you feel limited by the MQL language, then you might consider adding Python as another programming language.

https://docs.mql4.com/