How Technology is Changing the Trading?

Technology is changing the Trading

rading in the financial markets have come a long way over the past few decades. From the pit trading where traders used to outbid each other vocally, to the new age exchanges today that has machines competing with other machines, trying to outsmart each other. This transition has been very much accompanied and enabled by technology.

How is technology changing the rules of the game in the trading space? In quite a few ways! In fact, the distinction between trading & technology has been getting vanished with every day and the same is getting increasingly acknowledged by more and more leaders from the industry. The following are few ways through which technology has changed the way financial markets trading happen now.

Faster access to information

Faster access to the news, data and other information has always been a crucial factor in financial markets. When Paul Julius Reuters started Reuters in the 1850s, the primary activity of the company was to transmit stock prices between Aachen & Brussels using a mix of telegraph and pigeons!

Today, technology has reached to another dimension. It is not just the fiber optic cables that enables fast data transfer between various data sources & consumers, in this case: investors, traders, corporates & institutions, we have faster data sources including Microwave, LASER, etc being used for transmitting data at the speed that nears the speed of light.

The transmitted data may include market data like last traded price, bid, ask, last traded quantity, etc, as well as alternative data like economic data releases, news, etc. These connections are also actively used by traders for arbitrage across the exchanges, ensuring an efficient market.

Faster Infrastructure

Using the network scalability that technology advancement has brought in, many exchanges are able to offer either co-location or proximity hosting. This has helped in bringing down the time it takes for the order to travel from trader’s end to the exchange, reducing the price risk & uncertainty faced by the trader earlier.

Faster Processing

Most of the algorithmic trading systems used for HFT are able to process the market data, run complex trading strategies and send out the order, within a few microseconds. Today, many firms across the globe have been using FPGAs, ASIC, etc to further bring it down in sub-microsecond range.

Higher computational power

In the last two decades, peak speed of the fastest supercomputer in the world has gone up by a factor of 100,000s (Source: History of supercomputing on Wikipedia). The computations which were nearly impossible to do for most machines are now possible. That is one of the reasons how traders are now able to implement machine learning techniques to make sense out of terabytes of market data that receive from the trading destinations.

Cheaper computational power

It is not just the computational power that has gone up in two decades; the costs have come down drastically as well. The cost per GFLOPS (at 2018 prices) has come down from nearly $47,000 to $0.02 (Source: FLOPS on Wikipedia). That is more than a million times reduction in cost! This means that it is not just the big firms but even an average retail trader has a much better access to the computing power than a few years ago.

Cloud servers

With the advent of cloud services, you no longer need to buy expensive hardware to test out your trading strategies. One can simply rent the required computational power for a fraction of the price to back-test & deploy even complex algorithms. These types of algorithms would have been out of reach for most of the individual traders earlier given the cost of hardware, but thanks to cloud servers, they are empowered to use them for their trading now.

Open source

The open source movement has empowered the masses with the ability to create sophisticated algorithms, which they can use in their trading or trading systems. Also, rise of relatively easier & open source languages like Python has given a massive boost to the use of algorithms & data sciences across industries, including in the financial markets. In fact, global banks now need Python on the resumes of analysts applying in the markets & securities services. (Source: Bloomberg).


While the technology is making things more efficient & accessible, it is important to know how to use & implement it as well. Thanks to technology, there are many free & paid avenues to learn through MOOCs, interactive learning portals, remote learning/certificate programmes. Global banks are now creating labs for traders to code & for coders to trade (Source: Bloomberg).

The technology has changed the face of trading. And from what I’ve learnt through the experience of leading ventures in algorithmic & quantitative trading, as well as on training, in the last decade, this change is still continuing & probably accelerating.

Source: Ciol

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