Say, you are interested in learning about the various ways people trade financial instruments. The first logical step would be to check out some good books on the topic. A quick search through best-sellers and highest-rated books on “trading” in Amazon returns a mountain of technical jargon, and we haven’t even gotten past the titles. Some key words that jump out are:
- Algorithmic trading
- Quantitative trading
- Automated trading
- Day trading
- Swing trading
- Intraday trading
- High-frequency trading
- Discretionary trading
- Insider trading (hopefully not in a how-to book)
While surely something can be gleaned from a look at each of these types of trading, it is important to understand how these styles are related to each other. Many of the styles listed above are counterparts and subsets of each other. We will discuss the characteristics of each of the styles.
Schools of Thought
The terms discretionary and algorithmic are used to describe the way a trader approaches a problem. Although they will have some an effect on how the trader executes his decisions, the terms are normally invoked to describe a way of thinking. Discretionary trading implies use of instinct and intuition to filter out important information and arrive at a decision. Algorithmic trading implies strict adherence to a highly optimized set of rules with the goal to succeed through consistency.
Conventional wisdom is to embrace a single school of thought. We too often hear of the failures of fund managers who ignored their intuition and day traders who let their emotions influence their decisions.
Length of Time
The terms day, swing, intraday, and high-frequency characterize the length of time a typical trade takes.
- Swing: 1-3 days
- Intraday: few minutes to a few hours
- Day: same as intraday
- High-frequency: few seconds to a few nanoseconds
Method of Execution
The term automated describes the manner of execution. The natural counterpart to automated trading is manual trading. While there are no trading books with “manual” in title, I believe “manual trading” should be part of this generation’s financial vernacular. Manual trading is what we traditionally think of as calling your broker to place a trade, sending a trade through your broker’s platform, or cutting a deal with your buddy who owns paper stock. Automated trading involves programming and authorizing computers to trade on your behalf, typically using advanced encrypted messaging protocol to communicate with the brokerage.
Types of Information
The terms quantitative and qualitative describe the types of information used to make trading decisions. Quantitative information is plain and simply data. Data can include anything from historical prices to fundamental figures to transcripts of interviews. In general, traders seem to be comfortable with what quantitative analysis is and how it is used.
There is much less documentation on the role qualitative analysis plays in the lives of traders. Qualitative analysis relies on often subtle interpersonal and behavioral clues that have a distinct but not easily quantifiable effect on assets. A famous example would be David Einhorn’s pitch to short Green Mountain Coffee Roasters (GMCR). A major component and source of validation for his short recommendation was anecdotal evidence of accounting malpractice from GMCR employees he interviewed. He himself had no proof of the existence or impact of the malpractice, but the abundance of evidence played a large role in his decision to short the company.
A World of Opportunities
In theory, all manners of trading can be arbitrarily combined to best serve your purpose. In reality, not all forms of trading can be combined with all others due to natural limits of human ability. Most notable are time constraints on human decision-making and button-pushing. Qualitative research, discretionary trading, and manual execution can only be achieved in a time frame where a human can assess new information, exercise discretion, and push buttons, respectively. Equally, it is difficult to engage in high-frequency trading without utilizing algorithmic decision-making and automated execution.
Don’t Judge a Book by Its Cover
While the descriptors discussed in this article have very distinct and important meanings in financial trading, do not count on a book titled “Quantitative Trading”, “Algorithmic Trading”, or similar to cover the topic as it is defined here. In literature and dialogue about trading, certain terms have a strong association with others and tend to polarize around a specific style of trading. Below are rough word clouds listing terms that tend to revolve around certain styles of trading. These three categories easily characterize three trading archetypes: “Day Trader”, “Quant Trader”, and “Long-Term Investor”.
Some Categorical Recommendations
Below is a list of recommendations framed by our discussion of trading styles:
This book cleanly fits into the “Day Trader” generalization of the above word cloud. It discusses an algorithmic system based on quantitative research. Traders are meant to use this system to execute trades manually for mid-term, swing, or intraday trades.
This book fits neatly into the “Quant Trader” category of the above word cloud. It is a topic survey for algorithmic, quantitative, automated, high-frequency trading. This book touches on a larger breadth of concepts in its these style of trading than any other book I have read. It lacks detail in many places, but is a powerful overview with hundreds of useful citations.
This book squarely fits into the “Long-Term Investor” archetype. It discusses discretionary, manual, quantitative/qualitative, long-term trading with an emphasis on fundamental analysis and business cycles. It is important for all types of traders to understand this type of thinking regardless of the way they trade.
This book does not fit nicely into an archetype we discussed, rather it is a valuable high-level discussion of the guiding principles of automated trading systems. Trading styles associated with this book would be algorithmic and quantitative with no real specification given to execution style (manual vs. automated) or time frame (intraday, high-frequency, etc.).
Written by yours truly, this book takes the reader step-by-step through construction of an automated, algorithmic, quantitative trading platform in the R language. The system run on a wide variety of trade frequencies, including mid-term, swing, and intraday. It is unique in its emphasis on computer automation to trade human-achievable frequencies.