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ENGL 301 – Definitions Sean Chan Student # 2937 4105 Introduction: The purpose of this assignment is to take a word that is commonly used in a particular discipline or business field and simplify its meaning so that a wider audience is able to understand the information. In reality, many such words, commonly called ‘jargons’, need to be understood by many other disciplines to allow for inter-disciplinary cooperation – whether for commercial or research and development purposes. The term which relates to my field of business and which I shall use for the purposes of this assignment is the term “trading algorithm”, as is commonly used within the investment industry of systematic funds (investment firms which use non-discretionary i.e. systematic investment strategies to manage its capital under management). Parenthetical Definition: Trading algorithms (or automated investment or trading strategies) are the modern quantitative approach to trading the stock market. Sentence Definition: A trading algorithm is an automated trading strategy for trading the stock markets. Expanded Definition: Algorithms, in general, are programmed code (such as in Python or C++) which are the mathematical and logical set of instructions that tell a “machine” what to or what not to do, in what manner, for how long a duration and control other various ‘behavioral characteristics’ of the machine. A “trading algorithm”, according to Investopedia, is a “… [computer-program that] …follow[s] a defined set of instructions for placing a trade in order to generate profits at a speed and frequency that is impossible for a human trader.” A trading algorithm acquires data from a Data Provider firstly. This algorithm then reformats the data into meaningful information and rearranges the information into mathematical relationships that form a mathematical model. Parameters of the algorithm’s model are then optimized by a ‘genetic algorithm’ (an optimization code that uses evolutionary mathematics, or mathematical principles of biological evolution, that is embedded within the trading algorithm) and releases an output of whether to buy, sell, or not to trade a security. If the trading algorithm releases a buy or sell signal, this signal is connected with the trading account and sent to a broker dealer in order to execute the trade. Kindly review the chart below in order to better understand the process of decision-making in order to understand what a trading algorithm does. Trading algorithms can be high-frequency strategies (executing trades in and holding positions for milliseconds or lesser), medium-frequency strategies (executing trades in and holding positions for a few minutes, and low-frequency strategies (executing trades in and holding positions for days(s)). Typically, the higher the frequency, the larger the returns and the lower the drawdowns (i.e. the lowest point during the holding period of a security) but the higher the operational costs (usually more than USD$200,000 annually) and the lower the trading capacity (referring to the maximum amount of capital tradable by the algorithm). As a result, the majority of investment firms choose low-frequency strategies as their operational expenses are minimal, although this comes at the costs of high drawdowns and lower returns. Ultra-high-frequency strategies are typically statistical models that profit from price inefficiencies resulting from the same security being traded in multiple exchanges. They may also include strategies that create liquidity (the availability of shares traded) by offering a greater pool of transactions for select securities. High-frequency strategies can be event-driven strategies or millisecond level patternrecognition in alternative asset classes that result trades in the primary underlying security. Medium to low frequency strategies are typically buy-and-hold that employ some version of discounted valuation modeling, comparable analysis, forecasting models, any may include technical analysis. Trading Algorithm Data Provider Investments or Financial Data items Arrangement of Data into meaningful information Broker Dealer Placed into a mathematical-financial model for analysis If buy or sell signal, trade signal gets sent to an automated broker dealer for trade execution Model Optimization Produces either a buy signal, sell signal, or no-trade signal Examples: A Data Provider can be a company such as Thomson Reuters or Bloomberg, whose primary business is in providing investment firms and financial institutions with quality machine-readable data. For example, we humans may read an article and visually understand Company A’s revenue to be $200 million and its earnings per share (EPS) of $1 per share. Let us say revenue is category 1 and earnings per share is category 2. An algorithm can only understand machine-readable information; Bloomberg has to classify Company A’s revenue of $200 million under category 1 and its EPS of $1 per share under category 2 before providing these data to investment firms and financial institutions. When a trading algorithm of an investment firm picks up these 2 pieces of data points, it has to parse the data. For example, a simple model can be such that if both actual revenue and actual EPS outperform (underperform) expected revenue and EPS, then a buy (sell) signal is released. Otherwise, a signal of no-trade is released. It then determines the weight of each of revenue and EPS in determining the strength of the signal by optimizing the parameters to fit historical data of the company. Once all these is done, a trade signal is created (if any) and gets sent to a broker dealer. A broker dealer can be either a bank like RBC bank or TD bank, or trading platforms like Interactive Brokers, which receives the trade and executes the trade by trying to find others who may be selling (buying) the same number of shares with a similar price. Works Cited: Seth, S. (2014). Basics of Algorithmic trading: Concepts and examples. In . Retrieved from http://www.investopedia.com/articles/active-trading/101014/basics-algorithmic-trading-concepts-andexamples.asp