R Multiples

Analyzing And Improving Your Trading Performance: Part 1.

By Chris Anderson, Ph.D

This is the first article of a two part series that explores how we can improve our trading results by carefully observing the previous trading performance of a trader, a trading system, or a trading newsletter. This first article addresses how to use those results and then predict the range of possible future outcomes. Next week, I address how to use these analytical results to set position sizing so that a balance can be maintained between the risk that we are comfortable with and the rewards that we hope to achieve.

Suppose we take a string of trades that have been profitable. These trades may originate from actual transactions, or were simulated using backtesting software, or were generated by paper trading without risking actual money. In addition, these trades may have originated from a discretionary approach, or an approach that follows precise entry and exit rules, or a middle ground between the two. Furthermore, these trades may have originated from recommendations from a newsletter, stock advisory service, your neighbor, or your own results. When observing a string of historical trades, regardless of their origin or their style, each trader will end up with two basic questions:

Do I trade this approach since it made money in the past? And,

How do I incorporate this approach into my trading business?

To further entice you, suppose that the previous data shows that you would have made $12,000, on a $50,000 account, in only 12 months. Is that a good return? Of course you would take that return if somebody guaranteed those results but the markets do not operate in that manner. Thus saying I will make another $12,000 in the coming 12 months, if I just do the same thing, is a bit of a stretch. As an alternative, many trading professionals ask the question if the system performs the same statistically in the future, can I accept the wide array of outcomes that are possible? Suppose we analyze the trading system above and determine the following:

You have a 20% chance of getting 8 losses in a row;

Your equity curve could realistically be lower than your starting equity 18 months after you start;

The drawdowns in a year may get to 20 times the amount you risk (20R) per trade;

Your gains in a year are expected to be around 40 times the amount you risk (40R);

Would you trade this profitable system? If we did a survey, some would say yes while others would say no. We can conclude two things from that answer: 1) Traders goals and risk tolerances dramatically affect what should and should not be traded and 2) If we know a little more about the range of possible future outcomes, we can make educated decisions about our trading.

How do we know what to expect as possible outcomes for the future? If you have reason to believe that future results might be similar to past results, we could use the historical data and ask what happens if I get the same outcomes statistically. To accomplish this, you would take your trade results, convert them to a representative marble bag of trades (R multiple distribution), and then pick marbles at random to simulate future trading results. Suppose you wanted to know the range of outcomes on the next 50 trades. You would start picking marbles at random and record the win or loss amount, relative to the amount you risked, until you picked 50 marbles. Then you could pick another 50 marbles to simulate another potential different outcome. If you played this marble game about 10,000 times, then you would see a wide range of outcomes and this could give you a good feel for the range of expected outcomes. Anybody that has been in one of Dr. Van Tharp’s marble game demonstrations on position sizing knows that you may not like all the outcomes. However, in trading, ignorance is not bliss and we MUST know in advance what might happen even if the trading performs SIMILARLY to previous results. This whole area falls under the topic of Monte Carlo simulation and is used by financial professions to understand what could happen. Many professionals develop or buy software to help them with this understanding but for many individual traders, this is beyond their scope. Since Van believes that it is critical to match your trading system with your goals and objectives, IITM will introduce a new Comprehensive Trading Analysis & Risk Report service next week that provides this analysis and then coaches you through the many resulting implications and position sizing issues.

So how would you perform this analysis starting with a list of profitable trading results?

Ideally, if you knew the amount risked on each trade, then your R multiples are produced by dividing your trade profit/loss by the initial risk amount. If you did this in a spreadsheet, you would have a column of R multiple results. If you don’t know the original risk amount, you could take all your losing trades and find the average loss as an approximation. Let’s consider a string of 10 trades (much too small normally but for example purposes) that had:

5 -1R losers

1 –3R loser

3 2R winners

1 6R winner

The total for these 10 trades (5+1+3+1) is positive 4R and thus our expectancy so far is 0.4. In addition, suppose this system trades once per month. Would you trade this system if you believed that future performance would be similar? We found that typical drawdowns for this system might be –12R, you could expect about 7 losses in a row at some point over the next 5 years, you may be in drawdowns lasting 5 years or longer, and the average yearly gain, relative to a large drawdown was about 0.4. Again would you trade this profitable system?

So what does this mean to you as a trader? First, you need to understand what you want from your trading results and you need to understand the negatives that you are willing to endure to achieve them. Next, you must have a method to take your, or someone else’s (like a newsletter’s) results (paper or actual) and determine if it is likely to fit your future requirements.

Next week, I will continue this discussion by using these types of quantitative results to determine 1) how we should set position sizing once we chose to trade, 2) how we might start trading a system conservatively and then scale up with market winnings, and 3) what happens if the system quits working and how will we know?

About the Author:
Chris Anderson, Ph.D
President/Founder 99% Trading Corporation

Chris Anderson has been trading since 1986 where he first got involved with purchasing options on impending acquisitions and leveraged buyouts. Since that time, his trading has transitioned to using a portfolio of automated systems that handle all aspects of his day or swing trading positions. Dr. Anderson has also founded 99% Trading Corporation upon the principle that computer testing and analysis can significantly improve the performance of many investors and traders. He has applied a unique background in engineering, statistics, computer software, and trading to develop the Know Your System^TM software system to give traders a unique approach to improving their performance

Taken from the IITM website.