Algorithmic trading: trends, platforms and emerging strategies

By Aaran Fronda

March 4, 2019 | bobsguide

In the past decade, algorithmic trading has emerged as a new way for financial institutions to gain an edge over other market participants, provided they put use this powerful tool in the correct way.

Before the emergence of modern computers, traders were left to rely on rudimentary methods for creating and executing trading strategies. Traders relied on their own cunning and healthy appetite for risk to ride the peaks and troughs, analysing data and using technical indicators like Bollinger bands to make predictions on whether a stockor commodity would rise or fall in value over a given period. But as computing power has increased , it is no surprise that the rising technological tide has brought with it a revolutionary new way to trade in the form of algorithmic and quantitative trading.

Put simply, algorithmic trading is a form of automation, whereby a computer is programmed to execute a specific set of actions that can include the buying or selling of an  asset in response to changing market data. The major advantage of this form of trading is that it has the power to passively enter and exit positions at a speed and frequency that is impossible for a regular trader to do alone.  

One of the main benefits offered by algo-trading is the speed of trade execution, allowing traders to get the best possible prices by avoiding significant fluctuations in value, reduced transaction costs and a reduced risk of human error. High frequency trading (HFT) has become the most pervasive use of the technology over the past decade, especially among large financial institutions. Its favour among big banks, insurers and hedge funds is due to its ability to place large volumes of orders at speed across various markets based on numerous algorithmic trading strategies.

There are many different strategies that can be employed by traders using an algorithmic trading platform. For example, a trader may wish to buy 100 shares of a stock if its 50-day moving average goes above the 150-day moving average or even setup another simple trading strategy like this one to sell a stock if it were to retreat below 200-day moving average. This is a particularly simple example of an algorithmic trading strategy, but it is possible for traders to create highly complex iterations based on timing, price, mathematical models, or any other combination of data sets required.

Common algorithmic trading strategies

Trend following

This is one of the most widely used algorithmic trading strategies employed by traders with the aim of leveraging various stock market scenarios to turn a profit. Traders can use algorithmic trading to track anything from moving averages and channel breakouts to price movements and other technical analysis indicators like on balance volume (OBV).

Many traders use programme trend following strategies into algorithmic trading platforms as they are one of the simplest to implement. This is because traders no not need to make any predictions or price forecasts to turn a profit, with the algorithmic trading strategy relying on the occurrence of desired trends rather than predictive analysis.

To put that into context, a trader may wish to incorporate an OBV indicator into his algorithmic trading strategy. This is what is known as a momentum-based indicator that calculates trade volume flow to ascertain whether there is an upward or downward direction in a specific market, with trading volumes and rising prices being directly proportional to one another. Therefore, if a stock rises in price it corresponds with a rising OBV and vice versa. Traders can use this trend as a price confirmation tool – meaning that that trades can be executed to buy or sell automatically when a upward price move is deemed sustainable or if a trend is reversed and the price begins to fall.


Another common strategy that can be super-charged by the power of algorithmic trading platforms is arbitrage, which is the process of buying dual-listed security and buying it from one exchange and while simultaneously selling it in another at a higher price point. Savvy traders can spot these arbitrage opportunities, but by creating an algorithm that can recognise price differentials across multiple markets it can execute trades and generate profits on a much larger scale.

The dangers of algorithmic trading

Michael Lewis, the author of Flash Boys, a book about the rise of high frequency trading in the US equity market, famously said: "People no longer are responsible for what happens in the market, because computers make all the decisions."

This statement was first made when the book came out in 2014, but it rings just as true today: around 80% of the US stock market is controlled by machine-led trading. Many market commentators contend that major market sell-offs that were a common fixture throughout 2018, particularly those seen in latter part of the year, were driven by algorithmic trading platforms all overreacting to real-time data feeds and triggering massive market downturns.

"80% of daily volume in the US is done by machines, so what you get is a lack of focus on earnings, a lack of focus on outlooks and you just get short-term movements based on very specific data that is released every day and that creates noise," Fund manager at Jupiter Asset Management Guy De Blonay told CNBC's Squawk Box Europe in an interview in December last year.

There is certainly no doubt that the stock market required a correction. with many analysts believing that stocks were overvalued. But the speed and severity of the December sell-off, which saw the FTSE 100, Nasdaq, Dow and S&P all record their worst month since the in a decade has led many to cast the blame on pre-set sell orders programmed into algorithmic trading platforms.

Another major criticism of HFT is that despite champions of the trading method rightly claiming that machine-powered trading provides additional liquidity to financial markets, during financial downturns liquidity can evaporate as financial institutions pull the plug.

High frequency trading regulation

The pervasiveness HFT and its potential to create market instability has caught the attention of regulators, particularly those in the EU and US markets. The implementation of the Markets in Financial Instruments Directive (Mifid II) in Europe and FINRA’s rules on algorithmic trading in the US have elements within them that aim to curtail the harmful impact of HFT share of the equities market.

But despite the many strengths of such regulations there are several weaknesses with the current regulatory framework, with it imposing greater transparency requirements on algorithmic trading but doing little to improve market resilience to shocks created by HFT techniques.

Consolidation of high-frequency traders

One by-product of increased regulation on high-frequency trading is the increased cost of compliance which has led to a high degree of market consolidation, especially among the larger publicly listed companies, such as Flow Traders and Virtu Financial.

The latter of the three went public back in 2015 and just two years later acquired its rival KCG Holdings in a deal valued at around $1.4bn. The newly merged company is now responsible for one fifth of all trade volumes on the US stock market and has set its sights on grabbing an even greater share of total trades, with the company showing interest in acquiring another rival in the form Investment Technology Group in October last year.

Other notable deals include DRW Holdings acquisition of RGM advisers and the merger between Hudson Trading and Sun Trading which both occurred in 2017, with deals driven by shrinking margins and the consolidated companies suffering from reduced speeds that saw them lose an edge over their rivals in the market.

But it is not essential to be a big player to turn a profit using algorithmic trading. In fact, there are numerous private outfits and even individuals using the technology to beat the market and it has never been easier to get hold of ready-made algo-trading software, with many vendors offering trial versions of their platforms.

It is worth pointing out that venturing into the world of algorithmic trading is not for the casual trader, with the software being expensive to purchase and difficult to build from scratch without a significant degree of programming and financial market experience.

For those looking to build their own algo-trading platform from scratch Quantopian is worth a look. The free online platform is perfect for traders that want to explore and develop new highly customised quantitative trading strategies, with it possible to run tests against real-time market data before deploying it in the actual market.  



Regulatory reporting: 7 Questions with Philip Flood, Gresham Technologies

Other | Behavior detection & predictive analytics Regulatory reporting: 7 Questions with Philip Flood, Gresham Technologies

Gresham Technologies

Regulatory reporting: 7 Questions with Philip Flood, Gresham Technologies

Philip Flood, Business Development Director, Regulatory and STP Services, recently joined the ‘7 questions with…’ podcast with Gert Raeves of… Continue Reading

View resource
Real-time payments tech put pressure on banks

Best Practice | Behavior detection & predictive analytics Real-time payments tech put pressure on banks


Real-time payments tech put pressure on banks

The transformation to real-time has seen the market modernise, but there is a further need for banks to have the… Continue Reading

View resource
TransferGo Case Study - payments industry

Case Study | Behavior detection & predictive analytics TransferGo Case Study - payments industry


TransferGo Case Study - payments industry

Bank statement and Account Payables reconciliation. Seamless integration with NetSuite. TransferGo outlined two major product requirements. First – full… Continue Reading

View resource

New GFT podcast on AI

In the latest episode of our new podcast series on AI entitled ‘Artificial Intelligence, Intelligently Applied’, our host Simon Thompson… Continue Reading

View resource