The New Finance Era: From HFT to Big Data

High-frequency trading (HFT), the practice of using automated financial tools to rapidly trade large volumes of securities, is one of the buzzwords that has dominated financial media and has led to a stigma against quantitative finance as a whole. It’s time for investors, quants and the media to start focusing instead on the incredible possibilities for creativity, innovation and breakthrough strategies made possible by financial data science and algorithmic trading.

Based on market arbitrage and capitalizing on latency, HFT has given rise to a rapidly escalating speed war between companies seeking any marginal advantage – no matter how small and seemingly no matter what the cost. These advantages are gained by companies spending tens of millions on cutting-edge fiber optical technology that can make sub-second improvements to their transmission speed. To the untrained eye, a millisecond decrease in a trade delay may seem like nothing; however, it can actually boost a high-speed firm’s earnings by as much as much as $100 million per year.

There are multiple sides to the story: Some experts claim that it can work to improve market liquidity, while others argue that it brings an unnecessary risk to financial markets. Regardless of which side you stand on, it should be apparent that the speed war is likely nearing its final days – and with it, hopefully, the all-consuming focus on HFT. Despite the massive investments in infrastructure, speed traders are trading fewer and fewer shares, down to 1.6 billion in 2012 from a high of 3.25 billion in 2009. And it’s not only the total shares traded that is down; it’s profits too. The entire HFT industry made about $4 billion less in 2012 than in 2013.

The profitability of HFT is declining – and that is a good thing. The time has come for the financial industry – organizations, individual traders and the media alike – to pay attention to the quantitative finance beyond the realm of HFT. New tools are opening an entirely new era in algorithmic trading in which data and algorithms can be put to use for incredibly powerful insight-based trading. Beyond the overly-flogged speed capability, there are four significant benefits that trading with algorithms can bring:

1. Reproducibility: Algorithms let traders backtest against real historical data, giving the opportunity to weed out many losing strategies before committing capital. They also let traders put historically published strategies to the test themselves to verify performance before risking their money on the market. It really is about insight – not just executing blindly on an untested strategy.

2. Removing bias: Algorithms remove human emotion and bias from trading decisions. Financial history is filled with the mistakes that humans make when they get attached to a position, company or trade; an algorithm never falls prey to sentimental pitfalls. Embracing algorithms empowers traders to rely on their financial models executing properly without interference from their emotions.

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3. Finding the signals through the noise: Just as algorithms never get nervous or overly confident and change their performance, they also don’t fall prey to influence from media coverage or social speculation. Whatever the hype in the human sphere, algorithmic trading will continue to operate on the hard data of the market.

4. Gigantic increase in scope: Algorithms can evaluate thousands of securities with complex mathematical tools, far beyond human capacity. They also can combine and analyze data to reveal insights not readily apparent to the human eye. This is where true innovation can happen – there is a seemingly endless amount of data available to us today, and with the right tools financial modeling becomes limited only by the brain power and imagination of the quant at work. The future of finance is algorithms looking beyond the order book to the rapidly expanding universe of time-series data, opening the market to the power of quant creativity.

Of course, although HFT may be declining rapidly, the scale of the initial infrastructure investment means it won’t go away entirely. But it’s not the only game in town when it comes to quantitative finance. With the increasing accessibility of big data, quantitative finance is moving beyond Wall Street to the homes and offices of entrepreneurial spirits looking to seize upon its advantages.

John “Fawce” Fawcett is founder and CEO of Quantopian.