*by Jean Folger of PowerZone Trading*

If it looks too good to be true it probably is. This statement can be applied to many things, but it is spot on when talking about trading system optimization. Optimizing a trading system involves testing the system performance on historical data and tweaking the rules to create a positive expectancy in the system.

While optimizing is quite handy, it can also be harmful if not done in an smart manner. You could throw any trading system at me and I could make it perform like a champ - on historical data. Realistic, intelligent optimizations require patience and analysis.

So where do we start? An important first step is understanding the performance metrics by which we can assess a trading system, and how they interact. Here are several metrics that I use in system evaluations:

**Maximum Drawdown**

Maximum drawdown represents the "worst case scenario" for a trading period. It measures the greatest difference, or loss, from a previous equity peak. This can help us ascertain the amount of risk we might face for a trading system, and help establish if the system is practicable relative to our trading account. A $300,000 drawdown is not compatible with a $50,000 account. Traders should determine how much they can afford to risk - before developing a system and definitely before live trading. If the maximum drawdown is greater than this dollar amount (or percentage) then the plan will need to be changed to be in line with the acceptable risk.

**Total Net Profit**The total net profit refers to the profit or loss for a specific trading period:

gross profit of all winning trades

-- gross loss of all losing trades (including commissions)

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Total net profit

It is easy to let yourself go right to this number when looking at a performance report. It can be a bit deceptive. Consider maximum drawdown again...what if our total net profit looks great - but the maximum drawdown is too large for our account size. The total net profit might never be realized since we could be out of money before the system gets a chance to reach that nice equity peak.

We need to also be mindful of how this metric changes over different trading periods. Since it is assumed that the trading system has a positive expectancy (we wouldn't trade it otherwise) then it stands to reason that the system would make more money the longer it was in the market (allowing for the ups and downs of a regular equity curve).

**Profit Factor**Profit factor is defined as the gross profit divided by the gross loss (including commissions) for the specified trading period:

profit factor = gross profit / gross loss (including commissions)

This metric relates the amount of profit per unit of risk, with values larger than 1 representing a profitable system. Theoretically, the number should be as high as possible. But in reality, most successful trading system's profit factors fall in somewhere between 1.5 and 5. Extremely high profit factors, while exciting to dream about, rarely correlate to actual trading performance. In fact, a very high profit factor can be indicative of an overly-optimized system.

**Percent Profitable**

Percent profitable is synonymous with the probability of winning. It is calculated by dividing the number of winning trades by the total number of trades for a specific trading period:

% profitable = total number of winning trades / total trades

Trading systems that are fall in the 40 - 60 percent range are common. Remember, you don't need to have a system that wins 90% of the time to have a profitable system. The key is found in the balance between the number of winning trades, and the amount of the average win, or average trade net profit (coming up next).

**Average Trade Net Profit**

The average trade net profit is the expectancy of the trading system. This metric represents the average dollar amount that was won (or lost) per trade, and is calculated as follows:

average trade net profit = total net profit / total number of trades

Please keep in mind two things: First, this metric is greatly affected by the size of the trade. The metric will change considerably as contracts / shares are added or subtracted from the trading system. Second, we have to be mindful of outliers, those trades in historical testing that fall well outside of the normal win / loss for a trade. It is useful to review each trade in the testing period and eliminate any that appear to be outliers.

I have explained just a few of many of the performance metrics that are available in system evaluation. I will note that the more time you spend analyzing these reports, the more you will see how each metric is related to the others. It is important to look at the big picture - not just the great profit factor or amazing total net profit that you can up with. Remember - I can (and so can you) make almost any trading system look like the holy grail on paper using historical data.

My next entry will describe methods of optimizing trading systems, using these performance metrics, that will increase our odds of creating a profitable system.

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