Introduction to Kirkpatrick's Investment and Trading Strategies: Tools and Techniques for Profitable Trend Following
- Discretionary Versus Algorithmic Trading or Investing
- Why This Book?
- Investing Is a Business
- Strategies
- Backtesting—Standard and Walk-Forward Optimization
- Book Organization
This chapter is from the book
This chapter is from the book
This chapter is from the book
I write this book to describe and test the concepts I use in my favorite investment and trading strategies. These concepts are not new by themselves but are newly applied to these strategies and should become the basis for the honest study of all stock-picking methods. I use the walk-forward method of optimizing all strategies as this is to me the best and most realistic means of testing and analyzing an algorithmic system. More about the specifics of these concepts and how they work follows in the succeeding chapters as I progress through the particulars of these systems. I explain what I believe to be the only way that the uninformed, disconnected, otherwise busy, time-limited, poorly financed, stock market outsider can still compete with the “big boys.”
Discretionary Versus Algorithmic Trading or Investing
Discretionary investing occurs when all decisions are made by the investor. Success is rare and depends on knowledge, expertise, quick decision making, and the ability to master emotions, biases, and mood. Most people lose money in investing because they act on rumor, advice, intuition, hunch, incorrect information, poor judgment, or any number of other inputs into their investment decisions. There are very successful discretionary investors, such as Warren Buffett, George Soros, Paul Tudor Jones, and T. Boone Pickens. You and I are not in this class. We need help and discipline as well as the ability to determine when to buy and sell. The psyche of these billionaires is hard-wired to select and to time investments. Most people are lacking in this ability; that is why they consistently lose money and are forced out of the markets with losses. Even many so-called professionals are unable to invest successfully. The poor performance of most professionally run mutual funds and pension funds demonstrates this universal shortcoming. So how do you and I survive in the trading markets when we don’t have intrinsic discretionary investment ability?
The answer has been around for many years but only recently available in an easily applicable form. Robert Pardo (2008)1 argues, and I tend to agree, that two events have occurred in markets in the past 20 years that can help the outsider. One is the expansion of markets to include financial derivatives. Financial derivatives, those financial instruments that derive their value from another security, provide hedging and speculation in large, liquid markets that only recently have become available to the average investor. Second, Pardo argues, is the availability of cheap, fast computing power. None of the data testing done in this book would have been available to you or me 20 years ago. The ability to rapidly calculate large amounts of data has made it possible to test theories of investment that had long been simply passed down from trader to trader, investor to investor. I add a third event to the list: the ability to execute a buy or sell order inexpensively, efficiently, and rapidly. Electronic exchanges have changed the entire investment field: The old, stodgy investment manager calling his broker to execute an order is an event of the distant past.
These character changes in the markets and speedier ancillary facilities have made it possible to develop investment systems that can be proven statistically and operated almost mechanically. These systems are called algorithmic systems because they are nondiscretionary and operate solely on an algorithm or series of algorithms invented for the specific purpose of profiting in the marketplace. By using proven mechanical strategies that don’t require the expertise and knowledge of successful discretionary investors, algorithms circumvent the intrinsic human inability to profit in the markets.
However, algorithmic trading requires strict sets of well-defined rules. Even when a system is tested and perfected, the investor can still become discouraged, bored, unhappy with the immediate results, or bothered by drawdowns to such a point that he will abandon the successful system and thus lose whatever advantage he had derived from it. This is human nature, a nature not compatible with trading markets. Therefore, even though an algorithmic system can be developed, optimized, and tested in multiple ways, the user still has the impulse to abandon it. So, to be successful in algorithmic systems, not only must the investor or trader spend time and mental power to develop and test the system, but he must also have the willpower, discipline, and patience not to waver from it.
This book demonstrates and explains algorithmic systems for both investment and for trading. It uses specific rules to enter and exit individual stocks. These rules are derived from statistical methods of optimization that give a better-than-even chance of success, a definite edge, in the marketplace. No system is perfect or without losses, of course, but these limitations are understood and have been taken into consideration and study. The final algorithms in this book are as accurate and profitable as possible under present methods of backtesting. Ask yourself if your personal method has produced the same performance results as these systems. If it has, then you are in the same exalted league as the masters I mentioned previously, and you can throw this book away.