How Effective Are Algorithms in Trading?

The world is getting increasingly dependent on software and now even complex tasks, which were considered to be the prerogative of humans, are going to machines. Trading of financial instruments such as forex, stocks, bonds, and derivatives is now being done by machines. A class of investors have emerged who rely more and more on technology, thanks to algo trading.  

Algorithmic trading involves the use of well-defined complex mathematical models and formulae to initiate and square off trades fast, without any human intervention. With the help of algo trading, trades and strategies can now be executed much faster. Since the volatility is quite high during certain events, it results in slippage if trades are not executed at the expected price.  

Algo robots execute trades almost instantly compared to manual orders, helping you pocket some profit, which you would have certainly missed otherwise. It also eliminates the dangers of acting on human bias, which traders often commit. The scope of human error is almost zero provided the algorithm is developed correctly. 

The benefits of algorithm trading are the single most important reason behind the popularity of this phenomenon. The 25 best-paid hedge fund honchos earned $13bn in 2015 alone, thanks to their sophisticated algorithms.   

Lets Understand IBetter 

Suppose an investor trades on just a few criteria; he/she initiates a Buy Order when the price of a financial instrument goes above the 50-Day moving average line and RSI (Relative Strength Index) remains in the oversold condition. Using these two instructions, programmers write codes, which automates the buy and sell signal whenever the rules are met. So, there is no need for traders to sit before a screen to trigger an order. They can do other tasks while an algo does the work on their behalf.   

Flip Side  

At 14:00 hours, 42 minutes, and 44 seconds on May 6, 2010, something strange happened that shook the very foundation of the US financial markets. The US stock market underwent a free fall. The Dow Jones Industrial Average nosedived dramatically, and just as quickly, recovered. The event is remembered as the “FLASH CRASH”. Later, in the ensuing investigation, it was revealed that Navinder Sarao’s spoofing orders were responsible for this flash crash. Spoofing is a trading technique used to deceive the electronic market system in order to benefit traders. When he allegedly used spoofing, algorithms could not understand the signal and started behaving abnormally.  

Experts have pointed out on many occasions that algorithmic trading significantly affects market volatility and many times, markets start behaving abnormally when the volatility peaks. Even a single wrong input or bug left in a code can result in massive losses. So, investors and traders should be aware of the dangers of handing over 100% control to machines.  

What Lies Ahead? 

Algo trading is here to stay for long as it helps institutions and professional traders to act fast. Execution time is crucial for hedge funds, arbitragers, jobbers, and scalpers.  

The swelling influence of quant-powered mathematical models will only increase with time and it will eventually render manual methods almost obsolete, though in the US, 90% trading has already been automated, and other countries are catching up fast.    

Technical Requirements for Algo Trading  

You need the capacity to transform a chosen trading strategy into an integrated computerized process that can place orders to a trading account. Before finally implementing the algorithm, you have to conduct backtests i.e. checking the algorithm based on the historical stock market performance. 

Algorithm requirements are broadly segregated into 2 categories – functional and non-functional. 

To function efficiently, the software should be able to download, filter and store structured and unstructured data. Real-time market data comprises structured data while news/social media data is unstructured. 

The functionality of an algorithm comes from a decision tree based on trading rules and strategies. It has to analyze securities against trading strategies to determine which one to buy. 

As for non-functional requirements, it is a trade-off between increased performance and cost of ownership. This category of requirements includes scalability, performance, reliability, modifiability, security, auditability, interoperability, fault tolerance etc. 

When a system has been developed, it needs to be backtested before it goes live in real money trading scenarios. It requires historical data for backtesting, so you need access to all this data. 

An efficient algorithm requires live data from different markets. You may gain access to the live data through the trading platforms you want to execute the trades on.  

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