Disadvantages of Algo Trading

Now let us discuss the disadvantages.

Algorithmic Trading is not for the faint-hearted. Before venturing into this field, it is key to know what holds back majority of the masses:

Knowledge of the programming language- Formulating complex algorithms requires extensive know-how of coding software such as C+, C++, Java, Python, R, etc. Finance professionals may not be familiar with technical knowledge. It is also not a feasible idea to get the code done by a third party as it may require frequent modification to suit the current market conditions. A strong programming foundation is essential.
Dependence on technology - Faulty algorithms have the potential to result in insurmountable losses for the trader. Critics blame algorithms for the flash crash of 2010 that led to the Dow Jones Industrial Average cracking 9% & wiping about $1 trillion in stock values in a matter of a few minutes. A similar incident happened in 2018 in the Indian Markets when the Nifty Index crashed around 900 points in a few seconds, only to recover fully at the end of the day. Such freak trades trigger stop losses of other open positions, which further exaggerates the selling pressure.
Regulatory hurdles- Algo Trading is subject to several regulatory restrictions in different countries. The Securities and Exchange Board of India (SEBI) has come up with a slew of measures in recent times to smoothen market volatilities & reduce systematic risks linked to algo trading. This point will be discussed separately later in the module.
Short lifespan- Not all algorithms function effectively in different market conditions. The inherent volatile nature of equity markets makes it necessary to quickly accommodate to the changing landscape. As an illustration, let us assess a simple strategy of going long-only on those stocks that close above their 50 DMA. The success of such an approach is critical to the market texture. If the markets reverse their course and go into a bearish mode, the trades will likely hit stop-losses more frequently. The formulation of algorithms & strategies is a continuous process & it consists of regular monitoring, improving & re-inventing according to the market dynamics.
Requirement of resources- A trader needs to arrange for substantial high-end resources as well as low-latency infrastructure. There are magnanimous co-location costs (upwards of ₹13 lakh an annum for a full rack as per data on the NSE website) associated with it. Such expenditure coupled with a lack of expertise in maintenance & troubleshooting makes co-location unviable for small/medium-sized broking firms. We shall cover this point in greater detail in the next slide.
The risk of over-optimization- Strategies on paper even though thoroughly backtested may fail to perform during live trading. The back-tested data may represent a specific market period or condition that is completely oblivious to the present-day scenario. There is always a running risk that the program might be over-trained to fit certain trends aka the risk of over-optimization.
Front-Running- Quant Funds make use of futuristic algorithms that have the potential to detect impending large orders by institutional investors enabling traders to profit from front-running these securities. Put another way, upon sensing an incoming order flow, the system generates buy orders & then turns around & sells them to the institution at a slightly higher price.