Let’s accept the fact; even a die-hard fan of algorithmic trading will agree that it’s not free of risks.
Due to the involvement of machines in the process, the chances of errors are even higher.
No matter how perfect and efficient the algorithm is, it can cause loss if not used properly and updated regularly.
An old saying goes, “You can’t make an omelet without breaking eggs”. The same can be said about trading. You can’t trade without defeating your emotions. If you’re worried that you won’t be able to control your emotions, the best option is to avoid trading completely. Not even algorithms can save you from what’s coming.
However, if think you are being a serious obstacle with implementing your potentially effective strategies, you will definitely want to give algorithmic trading a try. But still, there is indeed something to be said about the dangers of this process. It’s not going to be something that you can use with no bad bits in it.
- Introduction to Algo Trading and Risks
- 5 ways traders can minimize risks of algorithmic trading
- Things to avoid while trading algorithms
Introduction to Algo Trading and Risks
An algorithm is a set of rules or commands that is used to solve a problem. Some traders use their own psychology and analytical skills to come up with the best strategy by manually programming the algorithm. This way, they can guarantee maximum efficiency.
This process also takes time, so traders will have to wait for the results for a certain period of time before implementing it in their trading.
• Algorithmic trading employs various mathematical models to refine information about trading decisions and make valuable market forecasts for investors and traders alike. The mathematical models can be based on factors like past price movements and volume, trends as well as fundamental economic data. A trader should have complete knowledge of algorithmic trading before using it for investment. He should know how to design, test, and implement a trading strategy.
A. Design: A good strategy should have clear and testable rules. The rules should be based on quantitative analysis and not on gut feeling or emotion.
B. Test: The strategy should be tested on historical data before it is deployed in live trading. The testing should be done on a variety of data sets to ensure that the strategy is robust.
C. Implementation: The strategy should be implemented in a platform that is reliable and can handle the large volume of data that is generated by the strategy.
• Algorithms are designed to predict the future movement of an asset. The predictions in turn help traders make decisions and reach decisions faster than they would by manual trading. The algorithms can be programmed to follow a certain trend, focus on specific market micro-trends or sectors, focus on specific traders or investment instruments, and so on. This can lead to trading robots that are completely different from human ones when it comes time for execution. The risk is this information is sometimes outdated, hence significantly inaccurate. A trader should have a clear understanding of the market conditions that are favorable for the strategy and those that are not. The strategy should be adapted or discontinued if the market conditions change.
The other risks that are also related to algorithmic trading include having to deal with errors or bugs in the program, and its inability to properly read data. The programmer might not have correctly translated the algorithm into code which means you will be using faulty software.
5 ways traders can minimize risks of algorithmic trading
Yes, apart from the rewards, there are risks in algorithmic trading. But there are some ways to cope with risks too.
1) Generalize Across a Diverse Set of Instruments
Algorithmic trading systems should not overfit a single instrument or market. Instead, they should be designed to trade a diverse set of instruments from different asset classes. This will not only make the system more robust but will also give the trader a better understanding of how the system works and how to interpret its signals.
2) Use Multiple Timeframes
When testing and developing algorithmic trading systems, traders should use multiple timeframe data. This will help to ensure that the system is not over-fit to a particular timeframe and will give the trader a better understanding of what’s going on in the market.
3) Use an Out-of-Sample Testing Methodology
When testing algorithmic trading systems, traders should use an out-of-sample testing methodology. This means that the system is tested on data that is not used to develop the system. This is the only way to truly know how the system will perform in the future.
4) Use a Risk-Adjusted Performance Measure
When evaluating the performance of algorithmic trading systems, traders should use a risk-adjusted performance measure. This takes into account the risk of the trade, and its potential rewards, and gives a more accurate picture of its performance.
5) Have a Solid Understanding of the System
Algorithmic traders should have a solid understanding of how the system works before trading it. This includes understanding the signals that the system generates and how to interpret them.
Things to avoid while trading algorithms
These are some things to strictly avoid while using trading algorithms, in order to cut down your risks:
1) Not knowing what you’re doing:
Many people believe that trading algorithms are some sort of “black box” that can be used to make money without any understanding of what’s happening behind the scenes. However, this couldn’t be further from the truth. In order to be successful with algorithmic trading, it’s absolutely essential that you have a firm understanding of the underlying strategies and principles that are being used. Otherwise, you’re essentially just gambling.
One of the biggest dangers of algorithmic trading is over-optimization. This is the process of tweaking your trading parameters to the point where they become unrealistic and no longer reflect real-world conditions. Over-optimization can lead to disastrous results, so it’s important to resist the temptation to keep tweaking your system until it’s “perfect.”
3) Make your algorithms too complicated
This is a problem that’s similar to over-optimization, but it involves making your algorithms too complicated. The best algorithms are the ones that are easy to understand and simple to maintain. As a general rule of thumb, you should make your algorithms as simple as possible without degrading their performance. This is one of the most difficult aspects of algorithmic trading, but it’s also one of the most important.
4) Change the rules too often
This is another common mistake that traders make when they begin trading algorithms for real money. They come up with an algorithm with solid backtested results and then begin manually adjusting it based on recent market action or external events (e.g., headlines). However, doing this can lead to disaster. Your algorithms should be based on strong rules and strategies in the first place. While it’s still the key to updating the system based on what’s happening in the market at the moment, fundamental strategies are just as important.
5) Become too attached to your algorithms:
Everybody gets emotionally attached to their creations, and that’s understandable. However, you need to understand that your algorithms are only as good as the assumptions used to backtest them. Overly attaching yourself to your trading strategy can lead to emotional instabilities.
Automation is one of the major advantages of algorithmic trading. It’s a little strange that some traders choose not to automate their systems. The only real reason for this is because they want “some big role to play”, not because they’re afraid of technology. This not only limits their potential but also sets them up for failure once the market starts going against their system.
Before you ever put real money on the line, it’s crucial that you thoroughly test your trading system. Feed the algorithm with historical data. The trading algorithm will collect and analyze the data from the past, and predict the market’s future.