Financial markets witnessed a seismic change when algorithmic trading got introduced in global exchanges. It began with the authorization granted from Securities and Exchange Commission, U.S. for automated electronic exchanges in 1998. Very soon hundreds of quantitative trading firms were mushrooming across the globe; with every trading firm seeking to adapt and implement the new way of trading markets profitably.
Implementing this tectonic shift meant that these firms needed the right people, the right infrastructure, the platforms and access to data & markets. Millions and millions of dollars were put on stake by big investment banks and trading firms vying to convert millions into billions for their investors.
As the firms got super busy in their quest for the elusive alpha, behind the scenes an entirely new set of businesses were getting established to cater to their ever-changing trading needs. Businesses that would assist, support, and enhance the functionality of algorithmic trading. This article explores on the different allied businesses that came to fore on account of algorithmic trading. Listed below are some of the key businesses and needs that are complementary to algorithmic trading.
- Analytics & Trading Platforms
- High-performance Computing Infrastructure
- Financial Data Providers
- Educational Institutes and Programs
- Algorithmic Consultants and Investment Firms
Analytics and Trading Platforms
Algorithmic trading needs extensive analytical and technological support. With the advent of algorithmic trading, the old basic trading platforms offered by Brokers and third-party software providers had little use for traders seeking to trade algorithmically.
Thus, a whole new business opportunity of providing feature-rich advanced analytics and trading platforms emerged. Many brokers have started offered trading APIs to their clients along with feature-rich platforms. Third-party trading software providers have come up with analytics platforms which can be connected to select brokers for smooth execution of trades.
InteractiveBrokers is an example of how traditional trading has merged with technology to give form to smart interactive dashboards. The InteractiveBrokers portal is compatible with desktop, mobile, iBOT atmospheres. The trader has the choice to trade in stocks, options, futures, forex, CFDs, warrants, combinations, bonds, mutual funds, structured products, physical metals, Inter Commodity Spreads, all from one dashboard. Their API allows algorithmic traders to execute trading orders. The users are at liberty to invest and trade from the same portal. The portals offer a wide variety of algorithms to choose from and also provides for paper trading of algorithm strategies.
Advanced Technical Analysis, Scanners, Advanced Fixed Income Scanners, Streaming News Services, IB Market Signals are a couple of other high-end features introduced by the trading platforms. Features that showcase usage of data pooling are listed as Third Party Research, Dividend Schedules, Alerts, Options Analysis.
High-performance Computing Infrastructure Business
Unlike discretionary trading, automated trading especially, high-frequency trading and low-latency trading requires order execution within a fraction of seconds. Seeking low latencies in trading demands high-performance computing networks to be quick and efficient. A good configuration will make sure that you keep receiving real-time updates and have up-to-the-second information. A good system means faster execution with no lag and no matter how many tools or browsers you shuffle with, the system will take care of it.
This demand for high-performance computing from HFT firms led to the emergence of specialized firms like Solarflare, Arista, Exablaze, Nallatec, Mellanox etc. which offer solutions like low-latency trading servers, switches, network interface cards and Ethernet solutions. Thus, an entirely new business evolved around this need of the HFT firms to stay ahead of the competition.
Financial Data Providers
Financial data forms an important element in an algorithmic trading system. Without the relevant financial data, a trading idea cannot be validated nor be implemented live in financial markets. Algorithmic system designers require historical data for backtesting purposes. Implementing a trading idea live would require access to highly accurate real-time data matching with the exchange. High-frequency trading strategies require tick-by-tick data.
This need led to the emergence of financial data providers who provide different types of market data across financial instruments and geographies. The data provided by some vendors is also compatible with charting platforms like AmiBroker, MetaStock, NinjaTrader 6.5 and NinjaTrader 7.
One example being GlobalDataFeeds, It offers real-time, tick-by-tick data of NSE CM (Cash), NSE F&O Segment (Futures and Option), NSE Currency Derivatives (NSE CDS) and MCX in multiple charting platforms.
Dedicated Educational Institutes and Programs for Quantitative and Algorithmic trading
Algorithmic trading is a specialized domain which revolves around subjects like financial markets & products, Math & Statistics, Computer architecture, and programming languages like C++/Java/Python/R. Quantitative trading firms seek to hire the best talent from the universities and the industry.
Many Universities and independent Institutes recognized this industry demand for highly skilled professionals and devised dedicated programs for individuals seeking to make a career in quantitative trading. Apart from such dedicated programs, some online education portals have launched short-term courses on different subjects related to quantitative trading.
We at QuantInsti®, offer a comprehensive hands-on course called Executive Programme in Algorithmic Trading (EPAT™). The salient features of the course can be found here. The objective of the course is to make students market ready upon successful completion of the coursework. We have also launched Quantra, an online education portal where you will find many excellent courses on quantitative trading.
Algorithmic Consultants and Investment Partners
In the course of designing an algorithmic trading system, novice traders can encounter issues during strategy development, backtesting, strategy performance, live implementation etc. Then there are traders who have the required capital to invest in markets but do not have any know-how of designing algorithmic trading systems. To cater to such needs, independent algorithmic consultancies have come up which comprises of experts from financial markets, Applied Mathematics, and the software business.
Investment Partners stand to fund promising quantitative funds and even individual algorithmic system developers. Firms like Quantiacs and Quantopian regularly host algorithmic trading competitions where the trading systems of the top performers receive investments running in millions of dollars.
As can be seen, a number of businesses have emerged around algorithmic trading. This has further led to its adoption among the vast trader community. If you want to learn various aspects of Algorithmic trading then check out the Executive Programme in Algorithmic Trading (EPAT™). The course covers training modules like Statistics & Econometrics, Financial Computing & Technology, and Algorithmic & Quantitative Trading. EPAT™ equips you with the required skill sets to be a successful trader. Enroll now!