What is the main difference between traditional and algo trading?

The primary divergence is in the way of execution; in algorithmic trading, computer programs automatically trade by following the set rules, whereas traditional trading is performed by human traders, who manually issue orders. Algo trading is quicker, more effective, and it is capable of working with high volumes of information without emotion.

Do I need to be a programmer to engage in algo trading?

You do not have to be a programmer to write and run your own algorithmic strategies. There are numerous new platforms and tools today that offer no-code or low-code solutions, which make algo trading accessible to non-programmers.

What are the common risks associated with algo trading?

Examples of common risks are system failure, coding errors, unexpected market events that the algorithms are not coded to handle, and flash crashes due to high-speed, automatic selling or buying. Moreover, poor performance in real markets can also be caused by over-optimisation of strategies backtested.

What is the purpose of AI and machine learning in algorithmic trading?

AI and machine learning introduce novel features to the field of algo trading, enabling algorithms to learn based on market data, respond to various environments, and create more intelligent predictive models. This allows adaptation of strategies dynamically and allows the identification of complex patterns that could not be identified by traditional rule-based algorithms.

What is the distinction between high-frequency trading and algo trading?

High-frequency trading (HFT) is a subset of algorithmic trading with very high speeds, high volumes of orders, and short holding times, commonly in seconds or even milliseconds.