*How good is a trading system? How can you compare the performance of two different systems and make a judgement as to which you would rather trade? Originally published in “Traders Magazine”, in this article I explore the various different ways of assessing the merit of a trading strategy.*

**Strategy Development Process **

**Percentage Profit**

There is an equation for the average profit per trade of any trading strategy which (before costs) is:

Avg Profit Per Trade = Pwin x Wavg – Plose x Lavg

where

Pwin = percentage winners

Wavg = average win size

Plose = percentage loses

Lavg = average lose size

At break-even this average profit per trade is zero, so re-arranging this and using the fact that Plose=1-Pwin gives us:

Wavg/Lavg = (1-Pwin)/Pwin (Equation 1)

**Case Study 1**

*Recently a client asked me to look at a set of parameters for a system that he was developing. He had chosen the parameter values based on optimisation of the total profit. When I looked at the performance figures the percentage profitability was only 7%! The optimisation process had resulted in the stops being incredibly tight which meant that the position sizing algorithm selected very large sizes to trade. This meant that most of the time he was being stopped out and in fact there were only three winning trades in the whole test but they had made very large profits compared to the numerous losses. Whilst this might still be a viable system it is important to think what it’s going to be like to trade this method. Day after day of losses waiting for that occasional big win – and what happens if you happen not to take that trade? That one trade makes up for all the losses and without it you have a losing strategy. This means that the system will psychologically be difficult to trade and also the test results are not very reliable because of the small number of winning trades.*

**The Number of Trades**

**Case Study 2**

*A client had done an optimisation and wanted to know why the optimal parameters that he was getting were different from the ones that I suggested. I looked at his optimisation results from which he had simply picked the best Net Profit and noticed that his parameter set had only traded once during the entire test period and had held the position open for many months on what was supposed to be a short-term swing trading model. It is always a good idea to check that the trade results are sensible and what you expect from your model.*

**Average Profit Per Trade**

**Maximum Drawdown**

**Annualised Return**

**Trade Distribution**

**Sharpe Ratio**

Sharpe Ratio = Rann / SDann

where

Rann = Adjusted Annualised Return

SDann = Annualised Standard Deviation of returns

**Month Year 1 Year 2**

Jan 2.45% -0.91%

Feb 0.84% 1.23%

Mar 1.20% -0.24%

Apr -1.34% 0.89%

May 0.67% -0.28%

Jun 0.80% 0.44%

Jul -0.20% -0.61%

Aug 3.20% 1.16%

Sep -0.59% 1.74%

Oct 0.65% -0.41%

Nov 1.18% -0.68%

Dec 1.35% 3.10%

SR = squareroot( Scaling Factor ) * Ravg / SDret

Where

Ravg = average of returns

SDret = standard deviation of returns.

**K Ratio**

K-Ratio = Seqc / ( Eslp * Square Root (Np) )

Seqc = Slope of Equity Curve

Eslp = standard error in slope from straight-line fit

Np = number of point in equity curve.

**Month Year1 Year2**

Jan 102,450.00 109,595.18

Feb 103,306.46 110,942.01

Mar 104,548.96 110,671.60

Apr 103,149.93 111,656.57

May 103,842.94 111,344.48

Jun 104,669.91 111,834.40

Jul 104,456.87 111,148.68

Aug 107,799.49 112,438.00

Sep 107,159.13 114,394.42

Oct 107,859.80 113,929.28

Nov 109,130.45 113,154.18

Dec 110,599.76 116,661.96

K-Ratio = 0.002221 / ( 0.000118 x sqrt(24) ) = 3.84

This is another indicator which is a good general performance figure and again which can be used for optimisations.

**Visual Inspection**

**Spreadsheet and Equity Curve Model**

Current Return = Previous Return * ( 1 + Trend + Noise * (0.5-RAND))