In an era of globalization, nothing is more crucial to business than communication. There’s no doubt that learning other languages can broaden one’s business horizons, but chances are if you operate within the financial technology and derivatives trading industry, your day-to-day transactions are already facilitated by a spectrum of languages that you may or may not speak, both human and inhuman.
OptionsCity’s management team is a veritable tapestry of global heritage; Our CEO Hazem Dawani hails from Jordan, CTO Victor Glava from Romania, CIO Rudy Fasouliotis is from Cyprus, CFO Terry Gray is from the U.S., and COO Timo Pentner is a native of Germany. While the English vernacular is the common bond of the team and the company, it is underscored by the operational use of other types of language: computer languages.
Computer languages such as Java provide the building blocks of electronic trading platforms such as OptionsCity Metro. But there are a multitude of programming languages in use; some are even used in tandem as a hybrid (like a machine version of Spanglish or creole). These languages are what Dean Wampler, data scientist and author, describes as “polyglot and poly-paradigm programming” where combinations of different programming languages are used in application development.
Computer and application programming languages, in turn, operate on behalf of yet another universal language output: math and numbers.
The financial industry has been successful in pulling through to this modern age the use of the ancient Greek symbols. Born of math and set in a series of formulas that virtually only quantitative analysts can decipher, Greeks communicate financial information in a way foreign to most outside the derivatives industry. The math formulas themselves are a language of principles that self-organize, invented to represent concepts more effectively than a spoken language could.
In terms of trading these numbers have a connection to yet a whole other form of communication.
“And at some point in the late 1870s, rudimentary hand signals were developed for the trading pit…whose exact origin your humble author does not know” writes Ryan Carlson in his book Trading Pit Hand Signals.
Trading pit hand signals, interestingly enough have developed their own slang over the years. While indigenous languages die out all the time and we witness the migration from the floor to electronic trading, the use of open outcry buy and sell hand signals risks extinction after nearly 150 years.
Gerald Weinberg, author of An Introduction to General Systems Thinking says, “Newton was a genius, but not because of the superior computational power of his brain. Newton’s genius was, on the contrary, his ability to simplify, idealize, and streamline the world so that it became, in some measure, tractable to the brains of perfectly ordinary men.”
Human communication began in symbols and the trajectory of language often weaves back into the use of symbols old and new to automate and reduce the latency of personal communication. Take, for example, the added time-saving emoticons on iPhones and widespread use of internet slang like LOL to convey wordless expressions like laughter. With instant chat functionality available on trading platforms, traders can quickly include shortcut communications beyond market symbols. Adding smart shortcut symbols in an already instant medium allows for faster market opportunities to be discovered.
In the financial world, all these “interspecies” languages come together like a highly functional Tower of Babel, creating and informing one another in the world of derivatives. Each with their own unique syntax – whether Cyrillic or Romantic, Java, HTML, or C++, spoken by key players from Chicago to Hong Kong and everywhere in between – an entire portfolio of languages (both human and machine) talking to each other to make the financial markets work.
The financial industry truly exemplifies and excels at global communication. And hence, the overarching language of business is created.
An idea I find almost too cool for words. Almost.