T2W Trading Community

• Increase the profit from the winning trades or reduce the losses from the losing trades – which is what we were trying to achieve through the use of stops and targets in the previous sections.
  
• Decrease the number of losing trades through the use of filters – which is what we will examine in this section.

Three ideas for a filter system could be:

• Seasonal factors – does the system perform better or worse on a particular day of the week?
  
• Markets will often consolidate the day after a large range expansion, do we want to avoid these days?
  
• Should we only take signals in the direction of the current trend?

Firstly, let’s look at the results that we get by the day of the week:

Each day is reasonably consistent, except Thursday. Thursday has the lowest percentage of winners (at 32%), the lowest average win (26 points), the highest average loss (32) and actually makes a loss per trade. It has to be pointed out that our sample sizes for the individual days is quite low at around 20, but Thursday is overwhelmingly poor.

By not trading on Thursday we would raise our overall expectancy per trade to 11.41 from 6.42.

Secondly, when the market makes a relatively large move it will tend to pause and consolidate. Our breakout system will want to avoid days where the market is likely to consolidate. Let’s say we won’t trade when the actual trading range the day before was more than x times the average actual trading range for the previous 5 days. The actual trading range is defined as the difference between the high (or the previous close if it is higher) and the low (or the previous close if it is lower). We will test various values of x:

We can see that if the previous day’s actual trading range is 1.6 or more times the average for the previous 5 days then by not trading we will increase the winning percentage from 49% to 51%, increase the average win from 40 points to 42 points and cut the average losing trade from 26 to 25 points – increasing the expectancy per trade to 9.17 points.

Thirdly, another popular filter is to only take trades in the direction of the current trend. We could define the current trend, quite simply, as taking the difference between the latest closing price and the closing price from x days ago. If the latest close is higher then the trend is up and we will only take long trades, if it is lower then the trend is down and we will only take short trades. Let’s test for various values of x, i.e. taking the close from x days ago.

There are two problems with these results:

• If we take our directional indicator as 1 day, 3 days or 4 days we improve our expectancy per trade, but if we take 2 days or 5 days we reduce it substantially. This inconsistency suggests the filter may not be too reliable for out of sample data.
  
• The number of trades taken is halved for a relatively small increase in expectancy, if we take 1 day as being the best value. Trade frequency is important and if we half the number of trades we would want to more than double the expectancy to compensate.

For these reasons I would not include a directional filter as defined above in our system.

Overall we have now added two filters to our system:

• We will not take any trades on a Thursday.
  
• We will not take a trade if yesterday’s average trading range is more then 1.6 x the average of the previous 5 day’s average trading range.

The overall effect is:

By using the two filters we cut out 29 trades which helps to increase our win percentage from 49% to 54%, average win to 43 points from 40 points and reduce our average loss from 26 points to 23 points. Overall our expectancy per trade doubles from 6.34 points to 12.64.