Download ENGL 301 Definitions rewritten

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