Trading With The Odds: Using the Power of Statistics to Profit in the futures Market

INTRODUCTION

I can’t believe that God plays dice with the universe.

Albert Einstein

My educational background was in engineering, while my trading background was as a corporate trader with a large oil company and then with a money center bank. Both these experiences have had a major impact on how I view the markets and how I trade. Accordingly, this book is about understanding the market from both an engineer’s and a trader’s points of view. It is about looking at the markets scientifically and accurately, without making the procedure for doing so too complex.

The book also offers views of the market from new perspectives. The reader will learn that simultaneously viewing the markets from multiple vantage points can provide profitable insights; that definitions and relationships based upon tradition are not necessarily the most accurate (15th-century mapmakers, for example, defined the world as flat); that an examination of statistically dependent and independent relationships can provide universal views of the market that are not impeded by differing units of measure in time or volume; and that, by combining statistics with common sense, aggressive stops can be placed with confidence and without fears of missed opportunities.

Where many older indicators are based strictly on empirical observations, we now have the tools to derive indicators from the natural structure of the market itself. Patterns that were difficult to observe with primitive tools now emerge for examination, and the reader is thereby led through complete and detailed step-by-step trades, utilizing his intellectual capacity and application of new tools to better understand the market.

Because I spent 10 years as a design and construction engineer and Naval Reserve engineering duty officer before I became a trader, I view the markets with an engineer’s eye. Like pure research scientists, engineers think about the world in abstract mathematical terms. Unlike them, however, engineers are paid to convert their abstract mathematical understanding into practical applications. This book adopts the engineer’s understanding of the market and applies practical and real-world terms, thus improving trading strategies and generating superior trading results.

Admittedly, this approach requires crunching lots of numbers quickly and accurately, an overwhelming obstacle in the past because the tools required for these calculations were extremely intimidating. The computational power of early computers was recognized, but getting at that power was tedious; computers were neither user-friendly nor affordable. Today, however, computer-phobia is rapidly vanishing, and many people in the vast majority of developed nations are as familiar with their computers as they are with their microwave ovens and telephone answering machines. We have powerful, affordable, and user-friendly computers. I say, let’s use them and make them work hard for us.

Once the reluctance to use new tools is overcome, all kinds of possibilities unfold. Markets can be explored in entirely new ways that can broaden our understanding by astronomical proportions. Those early mapmakers, for example, were exceedingly accurate in the things they could measure, but their perspective was limited to the use of the tools of their day. Consider the differences in their calculations and resultant maps if satellite imagery had been available to them.

One early technical indicator, developed in the late 1950s and early 1960s by Investment Educators, Inc., was the Stochastic, the most sophisticated tool extant. Though the Stochastic utilizes fairly rudimentary mathematical principles, calculating it by hand was still a tedious endeavor. During the ensuing 20 years, the programmable calculator, reverse polish notation (RPN) programming language, and the first affordable personal computer (PC) were developed. As these tools became available, traders took advantage of this increase in available computational speed, using it to perform many tasks.

In the late 1960s, Richard Donchian used the new calculators to test moving average systems (see Sidebar, Moving Averages) and, in the early 1970s, published the results. In 1978, shortly after Hewlett Packard introduced RPN, Wells Wilder published a book called New Concepts in Technical Trading, which contained the directional movement indicator (DMI), parabolic indicator, relative strength index (RSI), and other indicators still popular today. (This book included steps for programming a calculator in RPN, making, for the first time, such sophistication available to the average trader.) In the late 1970s, Gerald Appel introduced the moving average convergence divergence indicator (MACD), which is derived from exponential moving averages, again adding a layer of mathematical complexity to calculations that would have been too time consuming to perform by hand.

These indicators became popular among technicians—and remain perennial favorites today—yet they viewed the market in terms of rudimentary, programmable calculators. No matter how insightful these early developers were about the market, they were still severely limited by the analytical tools available to them.

Surprisingly, while the computing capability of computer hardware has continued to develop at an astronomical rate, the development of early PCs seemed to mark the beginning of a period of stagnation in the development of technical analysis tools. In the early days of PCs, it was thought that no one would use more than 64K of RAM, but today most computer users feel hamstrung without many megabytes of RAM, and it is no longer necessary to make the mathematical compromises mandated by older technology, yet traders are still using methods developed for the calculator.

Once PCs had been developed with graphic capabilities, traders instantly recognized charting ramifications. Developing a graphic interface capable of synthesizing raw data from various exchanges and converting it into bar and line charts was a major undertaking, but the results were enormously popular with traders and opened the field to many new players. The effort required to program indicators on the original, hard-coded charting packages was great, but the payoff was considered to be worthwhile. In a total void, automatic calculation of a simple moving average, which would be displayed in relation to price data, along with a display on a computer screen, was a major advancement.

The reason technical analysis stalled at this point was that modifying the early computer code for existing indicators or modifying the graphic interface to include new indicators involved much time and expense. In an almost classic chicken-and-egg scenario, indicators had to be in great demand in order to justify the expense of reprogramming these early charting packages, but the indicators had to be widely available to traders (i.e., already programmed) in order to gain such popularity.

In the late 80s and early 90s, the front-end graphic interfaces had finally been developed to the point at which they are customizable by the user, and traders can now create their own formulae and indicators in English and using standard mathematical notations. While the up-front effort is still considerable, there is no comparison to the hundreds of man-hours that the programming effort previously required. Traders now enjoy an increasingly greater ability to experiment with the concepts behind new indicators without waiting for a popular mandate.

As a trader, especially a corporate trader, with an inherent need for increased accuracy, and specifically directed to trade particular markets, and as an engineer, I have also explored and experimented with the market’s inherent numerical relationships. In the process, I developed entirely new ways of understanding the markets that I turned into trading indicators which have proven to be extremely accurate and profitable. I am not concerned about the time required to perform calculations; once I theoretically determine that a concept should prove interesting, I program it into my PC and let the computer do the work for me.

Corporate traders are busy people. They are responsible for generating positive results without the benefits of diversification and with no choice as to which markets they will trade, using a conservative, highly accurate trading style. Corporate traders often have many other responsibilities and they operate under strict and particular mandates to make money under most market conditions while taking little risk.

This book is designed to explain these new state-of-the art indicators and techniques and to help traders use them for an increased understanding of the markets and to diminish risk and increase profits.

MOVING AVERAGES

The moving average is one of the simplest and most widely used indicators available for market analysis. The term moving average usually refers to a simple moving average of closing prices. It is calculated by choosing the length of the moving average one wishes to use (n bars), calculating the sum of the closing prices of those n bars, and dividing by n:

For example, to calculate an eight-day moving average, add the closing prices of the most recent eight days and divide by eight. (Note: ∑ indicates summation, or sum all variables behind the term.) Standard summation notation is expressed as follows:

This expression is read as "the sum of ak from k = 1 to k = n."

So the moving average equation reads: X= 1/8 (the sum of the closing prices of the eight days under consideration).

The most basic and traditional systems that interpret the market use a combination of a single moving average and closing price. In this type of system, closes above the moving average are assumed to indicate that the trend is up, and closes below the moving average indicate that the trend is down.

Many traders use a double moving average system, combining a fast moving average (for example, a nine-day moving average) and a slow moving average (for example, an 18-day moving average). When the fast moving average is above the slow moving average, the trend is assumed to be up. A buy signal is generated when the fast moving average crosses from below to above the slow moving average. A sell signal is generated when the fast moving average crosses from above to below the slow moving average.

There are numerous other moving average types and systems. An exponential moving average adds greater weight to the latest data in the series, thus responding to changes faster than a simple moving average. It also does not jump as sharply when an old outlier falls off the chart.

The exponential moving average is calculated as follows:

where

K = 2/(n + 1)

n = the number of days in the exponential moving average

C = today’s closing price

EMA–1 = the EMA of yesterday (or the MA of yesterday if starting at the beginning of a data series).

Hence, to calculate an exponential moving average over a five-day period, K equals 2/(5 + 1) = 2/6 = 0.333. The closing prices of the first five days are added and divided by five in order to find the moving average of those first five days. Then, on day six, the closing price is multiplied by 0.333 and yesterday’s moving average is added and multiplied by 0.667.

CHAPTER 1

Increasing the Probability of Success with Science and Statistics

The Kase methods specifically address issues of maintaining profitability while lowering risk and simplifying trading methodology, focusing on the concerns of those traders who are not in a position either professionally or economically to trade a diverse portfolio of commodities. This chapter reviews the basic philosophy and understanding that underlies the methods provided in this book.

REPLACE EMPIRICAL METHODS WITH MATHEMATICALLY DERIVED MODELS

Older empirical techniques have been replaced with mathematically sound techniques derived from the natural structure of the markets. Most of the popular technical indicators used today were developed prior to the introduction of even the most basic of personal computers (PCs).

MANIPULATE DATA TO IMPROVE PERFORMANCE

Some of the limitations of the current ways data is displayed and analyzed can be overcome by modifying and adjusting the data using the power available from computer technology.

CONDENSE INFORMATION

Traders can limit errors of judgment and free themselves to consider strategic issues by programming the computer to perform routine calculations, and the information gathered can be condensed by use of Pareto’s Law. This law of the trivial many and the critical few, or the 80/20 law, was developed by Italian-Swiss engineer and economist Vilfredo Pareto (1848–1923), who believed that income distribution is constant, historically and geographically, regardless of external economic pressures and that a small percentage of the workforce produces most of the output. For example, 20 percent of traders generate 80 percent of revenues, and 20 percent of the population holds 80 percent of the land.

To apply Pareto’s law to trading, traders should process the most useful indicator information, disregarding more trivial details. In terms of technical analysis, 20 percent of what can be programmed about an indicator or technique will capture 80 percent of the value of that technique. Therefore, to examine a single indicator, 80 percent of our effort is used to understand the last 20 percent of detail. Instead, five indicators may be programmed to capture 80 percent of the value of each. Using the same amount of effort, the scope with which the market can be viewed increases by 400 percent.

AUTOMATICALLY ADAPTIVE INDICATORS

Indicators can be designed that adapt automatically to changing market conditions, such as volatility, the variation in volatility, and cycle or trend lengths.

Studies have shown that optimization of simple indicators and systems, generally speaking, does not work. Optimization is the process of back-testing a