When you see the performance of a trading system, how do you know it’s good? How do you know it’s the right system for you? Many people simply look at the net profit assuming the system with the more profit must be the better system. This is often far from a good idea. When comparing trading systems during the development process or when comparing systems before making a purchase, it is nice to have a few metrics on hand that will allow you to compare the system either to a hypothetical benchmark or against another system. There is no one single score you can use that will work for everyone since we all have unique risk tolerances and definitions on what we consider tradable. Likewise, not all scoring systems are equal or perform under all circumstances. However, in this article I’m going to talk about my favorite methods used to score and rank trading systems. These are my key system performance metrics that I use during the system development process.
Any trading system should have a “significant” number of trades. What is significant? Well that varies. For a swing system that takes no more than 10 trades a year, having 100 trades is good. This represents about 10 years of historical testing. As a given trading system starts to produce more trades per year, I would expect to see more trades utilized during backtesting.
While net profit can be a factor in your decision about a particular trading system, profit factor is often even more important in my opinion. Profit factor measures the efficiency of your trading system. Profit factor is calculated by dividing the generated profit by the generated losses. A profit factor of 1.5 indicates for every two dollars lost, three dollars are gained ($3 win / $2 lost = 1.5). Obviously a number above 1.0 means you are making money. I like to see a profit factor of 1.5 or higher.
Like profit factor, the average profit per trade tells me if a system is making enough money on each trade. When designing a trading system I like to see an average profitable trade above $50 before commissions and slippage are deducted at an absolute minimum. If the average net profit is above $50 with commissions and slippage deducted, that’s even better. The higher the average profit per trade the better.
I don’t follow this too much. I make note of it but it’s not all that important to me. The percent winning trades is simply the number of trades that generated a positive net profit divided by all trades taken. This factor can be important if you don’t like to have a large string of losers. For example, often longer term trend following systems can be very profitable, but only have a win rate of 40% or less. Can you handle many losing trades? Maybe you are only comfortable with systems that tend to produce more winning trades than losing trades. If so, then a system with a win rate of 60% or higher would be better for you. Percent winning trades is a psychological tolerance indicator that will vary between people.
This describes the growth as if it were a steady, fixed rate of return. Obviously this does not happen when trading as your trading system produces a jagged equity curve over time. Yet, this is a way to smooth your return over the same trading period. Let’s say your trading system produces a 5% CAGR over a 10 year period. Over that same period you have a bank CD that also yields a 5% return over the same time frame. Does this make the CD a better investment? Maybe. One thing to keep in mind is this: the CAGR calculation does not take into account the time your money is at risk. For example, while the trading system may be retuning 5% CAGR over 10 years, your money is only actively in the market for a fraction of the time. Most of the time it’s sitting idle in your brokerage or futures account waiting for the next trading signal. CAGR does not take into account the time your money is at risk. Remember, a 5% return in the CD is realized only if your money is locked away 100% of the time. With our example trading system our cash is also freed up to be put to use in other instruments.
This calculation takes into account the time your money is at risk in the market. This is done by taking the CAGR and dividing it by exposure. Exposure is the percentage of time (over the test period) that your money was actively in the market. I like to see a value of 50% or better.
How big are those drawdowns? Can I mentally handle such a drawdown? Along these lines I also look at the shape of the equity curve. Does it climb with shallow pullbacks or does it have steep pullbacks? Are there long extended periods with no new equity highs? Ideally, the equity curve should rise as time goes by, creating new equity highs with shallow pullbacks.
This is one you don’t see much of. The t-Test is a statistical test used to gage how likely your trading system’s results occurred by chance alone. You would like to see a value greater than 1.6 which indicates the trading results are more likely to not be based on chance. Any other value below indicates the trading results might be based upon chance. The t-Test value should be calculated with no less than 30 trades. Below is the t-Test calculation.
t = square root ( number of trades ) * (average profit per trade trade / standard deviation of trades)
Expectancy is a concept that was described in Van Tharps book “Trade Your Way To Financial Freedom”. Expectancy tells you on average how much you expect to make per dollar at risk. Expectancy might also be a value that you optimize when testing different strategy input combinations. While computing the true expectancy of a trading system is beyond this article, it can be estimated with the following simple formula.
Expectancy = Average Net Profit Per Trade / | Average losing trade in dollars |
For those no too familiar with mathematics, the vertical lines around the “Average losing trade in dollars” indicates the absolute value should be used. This simply means if the number is a negative value, we drop the negative sign thus making the value positive.
This value is an annualized expectancy value which produces an objective number that can be used in comparing various trading systems. In essence the Expectancy Score factors in “opportunity” into the value by taking into account how frequently the given trading system produces trades. Thus, this score allows you to compare very different trading systems. The higher the expectancy the more profitable the system.
Expectancy Score = Expectancy * Number of Trades * 365 / Number of strategy trading days
With the above values we can get a decent picture on how the system will perform. There are, of course, other values you could evaluate and even more you can do such as passing the historical trades through a Monte Carlo simulator. But these values discussed in this article are the important values I utilize when designing a system or when evaluating a third party trading system.