What to Avoid when Developing Algo Trading Solutions?

Capital Markets CIO Outlook | Wednesday, February 12, 2020

Optimized codes are the secret to why algorithmic trading holds so much sway in today’s trading ecosystem.

FREMONT, CA: As far as ease and intelligence in trading goes, algorithmic trading has been a revelation. Carrying out trade through algorithms irons out many inherent challenges associated with trading. Be it stock market trading, or trading in foreign exchange, today, a new generation of traders is entitled to the benefits of automated decision-making. The codes that run the show in the background form the core of this trend. Thus, optimized coding and zero errors are vital to the development of a dependable algorithm. The following list mentions some of the potential errors that ought to be avoided while developing algo trading programs.

• Excessive Tweaking

Once an algo trading model is prepared, it is tested by using historical data. After getting initial results, developers often try to optimize the codes. However, over-optimizing the codes depending on a specific pool of historical data does not always guarantee actual optimization. Whenever a new set of data is fed, the optimizations may not be relevant anymore. Thus, moderation is important while tweaking the codes.

• Limited Testing

Testing trading models with historical data is essential but not sufficient. Some traders only focus on tests that use recorded data. Such an approach misses out on real-time factors that impact the performance of a program. Real-life testing situations are vital to prove that an algorithm is free from all kinds of errors. Besides, any model that seems perfect should be tested with extra caution because it might be the result of a glitch in the program.

• Introducing Too Many Rules

Although trading can be very complex, algorithms that try to incorporate multiple variables and rules always end up weaker. On the other hand, a program that identifies and focuses on a particular aspect of trading without falling for thousands of interrelated factors work better and predict trends with more accuracy.

While an experimental approach to developing solutions for algo trading can be highly rewarding, guarding against the above mistakes can deliver incredibly dependable and dynamic models.

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