sarwesh

The securities markets are made up of millions of investors, traders and speculators making emotional decisions to buy and sell various securities at various times for various reasons. Because over time people and their emotions are the same, our research has found that if you use sufficiently rigorous methods to avoid hindsight, you can test a system and see how it would have done in the past and get a fairly good idea of how that system will perform in the future. Therefore building objective linear models of security and market behavior gives us our edge.

An interesting study was reported about in the book Decision Traps. This book is about the process of decision-making. It puts forth the notion that objective decisions (i.e., rule based strategy trading) produce far superior results than other non-objective forms of decision making.


This is exactly what a strategy trader using computer models for systematic rule based trading does. He runs historical tests and then uses that data to take a position in the market. He uses objective, quantifiable data tested historically to make his trading decisions. The table below shows the results of the nine different types of decisions that were tested using each of the three different decision methods.

of
Types of Judgments Intuitive Prediction Subjective Linear Objective Linear
Academic Performance of Graduate Students .19 .25 .54
Life Expectancy of Cancer Patients -.01 .13 .35
Changes In Stock Prices .23 .29 .80
Mental Illness using Personality Tests .28 .31 .46
Grades and Attitudes in Psychology Course .48 .56 .62
Business Failures using Financial Ratios .50 .53 .67
Student's Rating of Teacher's Effectiveness .35 .56 .91
Performance of Life Insurance Salesmen .13 .14 .43
IQ Scores using Rorsach Tests .47 .51 .54
Mean (Across all Studies) .33 .39 .64


Successful investment is really a matter of odds, and if you can compute the odds, you can find and test methods that could beat the market. Because we know that we don’t know. No matter what information you have, no matter what you are doing, you can be wrong. Therefore a good system will incorporate the following rules. First, never bet your lifestyle, from a trading standpoint. Second, if you know what the worst possible outcome is, it gives you tremendous freedom. The truth is that, while you can’t quantify reward, you can quantify risk.

Business Cycle

In order to understand macro stock movements, the reader must first be aware of business cycles. There are generally four stages to a business cycle: expansion, prosperity, contraction, and recession. The stage in which an economy is operating will have a significant impact on a company’s profitability.

Beginning with a low point (recession), business activity increases until a period of prosperity is reached. Eventually the economy becomes over heated (inflation) and business activity begins to decline until a low point is reached again



Time Frame


The time frame in which you view your investments will also have an impact on your overall profitability, comfort level and expectations.

Below are two charts, both of the same security in different time frames.

As you can see, the shorter the time frame, the more the noise in the security movement and the harder it is to determine the true direction of price movement.

4. Building a Stable


The first step I use in implementing an automated system trading campaign, is to identify the traits of a model security I wish to trade, using the selected system. Once I have defined the ideal candidate, I then use it as a template, eliminating all securities that do not fit the template.

This creates a stable of securities that the system will trade. Be careful not to over optimize the selection criteria so there is a large enough population to choose from. The larger the stable, the more robust the system results will be.



5. Positive Expectancy and Risk Control

Key ingredients of a successful trading system include the following:

1. Entry signal or “ When do I buy?” (The smallest factor in a successful system.)

2. Exit signal or “ When do I sell?” (Accounts for approximately 30% of overall profit.)

3. Risk or position sizing, capital preservation, and capital at risk (The largest factor in a successful system.)

4. The profit expectancy of the system (If we know over a sample of 30 trades, the system will make an average profit of 7% per trade, then we can determine there is positive expectancy of $7 for every $100 in our portfolio.)

Using the above information, Next design a system with a positive expectancy and the risk parameters are comfortable with.

Only trade systems with positive expectancies.







6. Scoring and Ranking potential buy candidates

Now we have a group of stocks to choose from. We have developed a system that has a positive expectancy with good risk management.

The next question, as is often the case, “What do we do if the overall market is going up and we find ourselves with several times more buy signals than our system will allow us to trade?”

This is where we use a benchmark and security scoring.

A benchmark (can be an index or security etc.), that all of the securities in the stable are measured against can be useful in this case. Do this in real time, scoring and ranking them on a continual basis.

When the position sizing equation tells us to buy five new securities and we have buy signals for 30 securities, buy the five securities with the highest score. This ensures always buying the best possible candidates at any given time.





7. Putting it all Together


Apply the trading rules as follows:

1. Screen stocks and build a stable.

2. Apply system “Buy,” signals.

3. Apply system “Sell,” signals for profit control.

4. Apply system “Sell,” signals for risk control.

5. Apply “Position Sizing Rules,” for risk control.

6. Apply “Sorting and Ranking Rules,” for a substantial increase in overall profits.


8. Comparison: Two Automated Systems vs. S&P 500



On the following pages are the statistics from two of my systems: “A1” and ” Trident”. A comparison of each of system vs. buying an S&P 500 index Fund. Most Recent 2 years. Most Recent 9 years.
Trident
The “Trident” system is a medium term investment strategy with an average holding time of approximately 4 months. In the test period of 1/1/2003 - 9/23/2004 this system achieved an annual return of 89.28% and a total return of 197.15%. The average profit was 45.38%, with 58.97% of trades profitable. There were 46 winning trades and 32 losers.