A BRIEF HISTORY OF ALGORITHMIC TRADING
By: Naman Shiroha
Tags: Trading, Data Science, Finance, Stock Market, Algorithms

A BRIEF HISTORY OF ALGORITHMIC TRADING

By Naman Shiroha on The Capital

The New York Stock Exchange, 1817

Over the last decade, there has been a dramatic shift in how securities are traded in the capital markets. Utilizing supercomputers and complex algorithms that pick up on breaking news, company/stock/economic information and price and volume movements, many institutions now make trades in a matter of microseconds, through a practice known as high-frequency trading.

Today, high-frequency traders are virtually a giant on the trader's floor and have averaged investors of the past. With the recent attempted robbery of one of these high-frequency trading platforms from Goldman Sachs, this “rise of the machines” and the growing trend of algorithmic trading has become front-page news, generating vast controversy and discourse over this largely secretive and ultra- lucrative practice.

India allowed algorithmic trading only in 2008, but algo’s control nearly a third of all trades already. In the West, where the phenomenon first surfaced nearly three decades ago, algo’s were running about 50% of US equity trading volumes by 2012. In the foreign exchange markets, which run 23 hours a day, algorithms account for 80% of the trading volumes.

High-Frequency Trading is all about increasing the speed at which information travels. An HFT trader uses cutting edge technological innovations to get information faster than anyone else and then be able to execute his trading order faster than anyone else. Interestingly, the phenomenon of ‘fast information’ delivery goes long back to the 17th century. An interesting anecdote is about Nathan Mayer Rothschild knowing about the victory of the Duke of Wellington over Napoleon at Waterloo before the government of London did.

Julius Reuter, the founder of Thomson Reuters, in the 19th century, used a combination of technology including telegraph cables and a fleet of carrier pigeons to run a news delivery system.

Financial markets with fully electronic execution and similar electronic communication networks developed in the late 1980s and 1990s. In the U.S., decimalization, which changed the minimum tick size from 1/16 of a dollar (US$0.0625) to US$0.01 per share, may have encouraged algorithmic trading as it changed the market structure by permitting smaller differences between the bid and offer prices.

Till 1998 U.S Securities and Exchange Commission (SEC) authorized electronic exchanges paving the way for computerized High-Frequency Trading. HFT was able to execute trades 1000 times faster than a human. And since that time high-frequency trading (HFT) has become widespread.

By the year 2001, HFT trades had an execution time of several seconds. By 2010 this had shrunk to milliseconds, even microseconds, and subsequently nanoseconds in 2012. In the early 2000s, high-frequency trading accounted for less than 10% of equity orders, but this has grown rapidly. Between 2005 and 2009, according to NYSE high-frequency trading volume grew by 164%.

In 2020, more than 60percent of the traders relying on algorithmic trading, the industry is set to boom with the onset of these tech-savvy gen-Z traders. The primary benefit of Algo trading is that it can execute a large number of trades within seconds with negligible human intervention. The average reaction time of a human is about 215 MS which is ¼th of a second, according to a study. Owing to this, a lot of views flow in tormenting the algo trading scenario. Algo trading is often blamed for high market volatility and market manipulation, but in fact, the reality differs.

Let me elaborate on the above points: Consider 26th March 2020, a highly volatile day when Bank Nifty had a range of almost 10 percent from day’s low, with India VIX showing a similar move.

Attached below is a 5-min chart for bank nifty futures for the same day.

Bank Nifty 5 minute Daily chart (26th March 2020)

Result of an algo-trading system, the system changed the view four times in a day, with the average trade duration being 16 minutes, while the maximum holding duration was 38 minutes and the minimum being 2 minutes.

The best trade netted 1,179 option points and lasted a mere 16 minutes. Consider a day like this and trading multiple securities. For a discretionary trader, this is virtually impossible as it means too much of information processing and decision making within an ultra-short duration of time.

Moments when algorithms shook the world!

One key event that interests me was the NANEX conspiracy.

On September 24, 2013, the Federal Reserve revealed that some traders are under investigation for possible news leaks and insider trading, all done within seconds! An anti-HFT firm called NANEX claimed that right after the Federal Reserve announced its newest decision, trades were registered in the Chicago futures market within two milliseconds. However, the news was released to the public in Washington D.C. at exactly 2:00 pm calibrated by atomic clock and takes 3.19 milliseconds to reach Chicago at the speed of light in a straight line and California. 7 milliseconds in practice. Most of the conspiracy revolved around using inappropriate time stamps using times from the SIP (consolidated quote that is necessarily slow) and the amount of “jitter” that can happen when looking at such granular timings.

One of the key HFT conspiracies in the history of trading was the Flash Crash.

56% of equity trades in the US were made by HFT until the year 2010. On May 6th, 2010, a computer-driven sale worth $4.1 billion triggered the May Flash Crash, where the Dow Jones plummeted 1000 points within a single trading day. Nearly $1 trillion was wiped off the market value, as well as a drop of 600 points within a 5-minutes time frame, before recovering moments later. The SEC and CFTC largely blamed HFT firms for the crash.

The key academic understanding right after the crash was rightfully explained by The Chief Economist of the Commodities Futures Trading Commission.

They concluded that “Based on our analysis, we believe that High-Frequency Traders exhibit trading patterns inconsistent with the traditional definition of market making. Specifically, High-Frequency Traders aggressively trade in the direction of price changes. This activity comprises a large percentage of total trading volume but does not result in a significant accumulation of inventory. As a result, whether under normal market conditions or during periods of high volatility, High-Frequency Traders are not willing to accumulate large positions or absorb large losses. Moreover, their contribution to higher trading volumes may be mistaken for liquidity by Fundamental Traders. Finally, when rebalancing their positions, High-Frequency Traders may compete for liquidity and amplify price volatility. Consequently, we believe, that irrespective of technology, markets can become fragile when imbalances arise as a result of large traders seeking to buy or sell quantities larger than intermediaries are willing to temporarily hold, and simultaneously long-term suppliers of liquidity are not forthcoming even if significant price concessions are offered.”

The future of algorithms.

If investment is a process, then automation is the natural conclusion.

“He, who controls the past controls the future,” George Orwell

Before attempting to guess and inevitably fail what the future holds in store for us, let’s take a step back and understand why they are not so prevalent today.

To me, the immediate future of algorithms starts with asset allocation. There is a growing consensus that asset allocation is the primary driver of performance. Robot-advisors are still in their infancy, but this is where the industry is heading.

Markets change over time, this means new kinds of opportunities arise. A clever quant knows how to pick this up and convert this into an algorithm. This is what drives the industry, and one of the key factors for a few institutions/individuals to evolve. Key to growth in the industry is highly dependent on first-mover advantage since most of the strategies tend to dissolve.

In conclusion, the world of trading is becoming faster and the concept of “may the best (in this case, the fastest) win, stands stronger than ever.”